CHARACTERIZATION OF THE BOVINE GUT MICROBIOME, RESISTOME, AND METABOLOME ASSOCIATED WITH SHIGA TOXIN-PRODUCING ESCHERICHIA COLI SHEDDING AND THIRD-GENERATION CEPHALOSPORIN APPLICATION By Karla Vasco A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Microbiology and Molecular Genetics – Doctor of Philosophy 2022 ABSTRACT Bovines are the most important source of protein, and their production is expected to intensify with the increased human population. Despite the benefits of cattle, their intensification can result in increased negative footprints such as the transmission of foodborne pathogens and antibiotic-resistant (AR) bacteria. Cattle are the primary carriers of Shiga toxin-producing Escherichia coli (STEC) which causes over 250,000 human infections in the U.S. every year. Furthermore, about half of the mass of antibiotics sold in the U.S. are used in bovines. Third- generation cephalosporins are commonly used in cattle (i.e., ceftiofur); however, these antibiotics are considered of last resort. Globally, Enterobacteriaceae resistant to third-generation cephalosporins are the main cause of death by AR infections. In this dissertation three aims were addressed: 1) identifying microbiome diversity and composition that may favor the colonization of STEC in the hindgut of cattle; 2) determining the impacts of intramammary (IMM) ceftiofur in dairy cattle in the abundance of AR bacteria in feces; and 3) characterizing changes in the functional microbiome and metabolome associated with the IMM ceftiofur application. In Chapter 2, we analyzed 660 fecal samples from beef and dairy cattle from 5 farms with varying prevalences of STEC. The microbiome composition analyzed with 16S rRNA gene sequencing revealed that the microbiota of animals from farms with a high-STEC prevalence (HSP) had greater richness compared to those of farms with a low-STEC prevalence (LSP). Higher microbiome diversity was also identified in STEC-shedders from LSP farms but not in animals from HSP farms. Finally, we evidenced that bacterial taxa associated with STEC shedding in dairy farms were also correlated with differences in the diet and risk factors of STEC carriage such as days in milk, number of lactations, and warm temperatures. In Chapter 3, we evaluated the effects of intramammary (IMM) ceftiofur application on the abundance of resistant bacteria in cattle feces. Twenty dairy cows were treated with IMM ceftiofur and a non-antibiotic internal teat sealant and another group of 20 cows (controls) received only the non-antibiotic sealant. Feces were collected the day before the treatment and in weeks 1, 2, 3, 5, 7, and 9 after the treatment (n = 278). Through culture-based methods, no differences were observed in the number of β-lactam resistant bacteria (i.e., ampicillin and ceftiofur) between treatment groups. However, metagenomic sequencing revealed a greater abundance of genes encoding extended-spectrum β-lactamases (ESBL) that confer resistance to third-generation cephalosporins. Furthermore, an increased number of correlations between β- lactam resistance genes, mobile genetic elements, and bacterial genera were observed a week after IMM ceftiofur treatment. Finally, in Chapter 4 we analyzed the effects of IMM ceftiofur in the functional microbiome and metabolome of feces collected in the prior chapter. The IMM antibiotic treatment had minor effects on the functional microbiome, while no differences were observed in the metabolome between treatment groups. Multi-omics analyses identified correlations between natural antimicrobial compounds, pesticides, bacteriophages from enterobacteria, and ESBL genes. This suggests the role of natural bacterial stressors in the abundance of MGEs and ARGs. This dissertation aims to provide information for reducing the prevalence of STEC and antibiotic resistance in cattle farms, which is critical to prevent infections in humans and ensure the effectiveness of last-resort treatments. To my sweet daughter Jazmin Hamel, for showing me that everything is possible with love and motivation. iv ACKNOWLEDGMENTS This dissertation is the result of astounding collaborations in multidisciplinary fields including microbiology, veterinary medicine, bioinformatics, epidemiology, and biochemistry. Therefore, I want to thank all the individuals that participated directly and indirectly in the development of the studies herein presented. First, I want to thank my mentors Dr. Shannon Manning and Dr. Lixin Zhang for guiding me while pursuing my dream of becoming an independent researcher. Dr. Shannon Manning not only made sure that I was developing my skills as an analytical scientist but also that I had support in all aspects of my life. Dr. Manning gave me the courage and strength when I needed and became my referent as a woman and mother in STEM. Dr. Lixin Zhang was crucial to achieving this degree since he always trusted and motivated me to pursue my goals. Dr. Zhang allowed me to collaborate and lead multiple projects which helped me to become an independent investigator. I feel really lucky for having two incredible mentors that truly care for my well-being and professional development. I thank our collaborators who participated in the Grant proposal elaboration and contributed with their ideas while analyzing the results of the studies, including Dr. Pamela Ruegg, Dr. Bo Norby, Dr. Ronald Erskine, and Dr. Lorraine Sordillo-Gandy (†). I especially want to acknowledge Dr. Pamela Ruegg, who was also part of my committee, for supervising and organizing the fieldwork in two of our studies. Her expertise in veterinary medicine was critical for the design of the study approaches and data interpretation. Dr. Ruegg's passion for her work and love for dairy cattle were admirable. I am also thankful for the motivation Dr. Ruegg gave as a woman in science. I thank my committee members Dr. Pamela Ruegg, Dr. Linda Mansfield, and Dr. Edward Walker that shared their time and provided relevant insights into the analyses and interpretation v of our data. I also appreciate the enormous help from the personnel at the MSU metabolomics core, particularly Dr. Antony Schilmiller who was so generous in sharing their knowledge and time with us. Dr. Schilmiller helped design and carry out the protocols for metabolomics. Furthermore, Dr. Robert Quinn and his laboratory members Dr. Christian Martin and Doug Guzior were fundamental to analyze the metabolomics data. I also thank the MSU Dairy Cattle Teaching & Research Center and the personnel who participated in the field and laboratory work associated with this project, including Carmen Garcia, Zoe Hansen, Jose Rodrigues, Jaimie Strickland, Jennifer Brown, Robert West, and Aspen Robak. Importantly, I acknowledge diverse sources of funding that allowed me to complete my studies and carry out the projects presented in this document, including the MSU Biomolecular Science Gateway, the MSU College of the Natural Sciences, and the U.S. Department of Agriculture. I also thank the different awards I received from the MSU Microbiology and Molecular Genetics Department including the Dr. Thomas S. Whittam Graduate Student Award and Rudolph Hugh Scholarship Award which allowed me to share the results of our projects at several conferences. I also want to give a special acknowledgment to the P.E.O. International Sisterhood that not only conferred me a fellowship but also provided their constant support through letters and e-mails that enlightened my days. I also want to thank members of Dr. Manning's lab who always gave me support in a healthy, inclusive, and enjoyable environment. I want to give a special acknowledgment to Samantha Carbonell and Rebekah Sloup who were fundamental in the field and laboratory analyses of this dissertation, and whose experience, and skills made the results of our projects reliable. Likewise, I want to thank Bailey Bowcutt who worked in part under my mentorship; I was really pleased to have her help along the projects. Bailey always amazed us with her vi knowledge and skills in the wet and dry lab, and it was admirable how she was always willing to learn new things. Moreover, my friends and lab mates were always supportive, and I learned and laugh a lot with them, including Dr. Zoe Hansen, Macy Pell, Dr. Jose Rodrigues, Dr. Sanjana Mukherjee, and Dr. Cole McCutcheon. Likewise, I thank my friends Viviana Ortiz, Marcela Tabarez, Marco Lopez, and Paulo Izquierdo for becoming my family in Michigan. I feel blessed for sharing wonderful experiences and having support from all of them. Finally, I thank my family in Ecuador that always trusted on me and gave me their love. I thank my mother Mireya Aguas, my father Guillermo Vasco, and my sisters Ximena, Isabel, Maria Dolores, and Gabriela for taking care of me and for being the best role models to follow. I also thank my family in Michigan including my significant other Justin Hamel and our daughter Jazmin Hamel. My life has been amazing and full of joy since the moment I got to share our moments together, Justin and Jazmin are my motivation and my entire life. vii TABLE OF CONTENTS LIST OF ABBREVIATIONS ........................................................................................................ ix CHAPTER 1 ................................................................................................................................... 1 Literature review: Shiga toxin-producing Escherichia coli (STEC) and antibiotic resistance in bovines; insights into their epidemiology, molecular mechanisms, and ecological associations with the gut microbiome and resistome. ......................................................................................... 1 REFERENCES ......................................................................................................................... 31 CHAPTER 2 ................................................................................................................................. 53 Characterizing the cattle gut microbiome in farms with a high and low prevalence of Shiga toxin- producing Escherichia coli. .......................................................................................................... 53 REFERENCES ......................................................................................................................... 77 APPENDIX ............................................................................................................................... 85 CHAPTER 3 ............................................................................................................................... 101 Perturbations in intestinal antibiotic-resistant bacterial populations in dairy cattle following intramammary treatment with a third-generation cephalosporin. ............................................... 101 REFERENCES ....................................................................................................................... 132 APPENDIX ............................................................................................................................. 137 CHAPTER 4 ............................................................................................................................... 147 Untargeted metabolomics and microbial-functional profiling of the cattle hindgut associated with antibiotic dry-cow therapy and lactation phase. ......................................................................... 147 REFERENCES ....................................................................................................................... 183 APPENDIX ............................................................................................................................. 189 CHAPTER 5 ............................................................................................................................... 199 Conclusions and Future Directions ............................................................................................. 199 REFERENCES ....................................................................................................................... 207 viii LIST OF ABBREVIATIONS A/E Attaching and effacing ACC Antibiotic-resistant gene carrying contig ADAP Automated Data Analysis Pipeline AHLs Acyl-homoserine lactones AmpR Ampicillin resistant AmpS Ampicillin susceptible ANCOM-BC Analysis of compositions of microbiomes with bias correction AR Antibiotic resistance ARG Antibiotic resistance gene ATCC American Type Culture Collection BHT Butylated Hydroxytoluene BLASTP Protein Basic Local Alignment Search Tool BLV Bovine leukemia virus BMR Brown midrib BVD Bovine viral diarrhea CAT Contig Annotation Tool CCFA Ceftiofur crystalline-free acid CDC Centers for Disease Control and Prevention CefR Ceftiofur resistant CefS Ceftiofur susceptible CFB Cytophaga, Fusobacterium, and Bacteroides CFU Colony forming unit ix CHCL Ceftiofur hydrochloride CLSI Clinical & Laboratory Standards Institute CNA Columbia Nalidixic Acid Agar CP Crude Protein CS Corn silage DALYs Healthy life years DFMs Direct-fed microbes DHIA Dairy Herd Improvement Association test DIM Days in milk DM Dry matter DNA Deoxyribonucleic acid EHEC Enterohemorrhagic E. coli ELISA Enzyme-linked immunosorbent assays ESBL Extended spectrum β-lactamase FAO Food and Agriculture Organization of the United Nations FBMN Feature-Based Molecular Networking FC Fold-Change FG Fecal grab GALT Gut-associated lymphoid tissue Gb3 Glycolipid globotriaosylceramide receptor GC/MS Gas Chromatography/Mass Spectrometry GDP Gross domestic product GIT Gastrointestinal tract x GNPS Global Natural Product Social Molecular Networking HGT Horizontal Gene Transfer HILIC Hydrophilic interaction liquid chromatography HPLC High-Performance Liquid Chromatography HSP High-STEC prevalence HUMAnN HMP Unified Metabolic Analysis Network HUS Hemolytic Uremic Syndrome ICE Integrative and Conjugative Element IMM Intramammary JD Johne’s disease LC–MS Liquid chromatography–mass spectrometry LDA Linear Discriminant Analysis LEE Locus of enterocyte effacement LEfSe Linear Discriminant Analysis Effect Size LNDF Lignin as a percent of the NDF LPS Lipopolysaccharide LSP Low-STEC prevalence MAP Mycobacterium avium subsp. paratuberculosis MDA Mean decrease accuracy MDR Multidrug Resistance MGE Mobile Genetic Element MLS Macrolide, Lincosamide, Streptogramin MRGs Metal resistance genes xi MS/MS Tandem mass spectrometry fragmentation MS2 Tandem Mass Spectrometry NARMS National Antibiotic Resistance Monitoring System for Enteric Bacteria NASS National Agricultural Statistics Service NDF Neutral Detergent Fiber OIE World Organization for Animal Health OOB Out-of-bag error OTUs Operational taxonomic units PAI Pathogenicity Island PCoA Principal coordinate analysis PERMANOVA Permutational multivariate analysis of variance PWY Pathway QS Quorum-sensing RAJ Recto-anal junction RF Random Forest RPK Reads-per-kilobase RT Retention time RWA Raised without antibiotic SARA Subacute ruminal acidosis SC Subcutaneous SCC Somatic cell count SCFA Short-Chain Fatty Acid STEC Shiga toxin-producing Escherichia coli xii T3SS Type III secretion system TLRs Toll-like receptors UHPLC Ultra High-Performance Liquid Chromatography USDA U.S. Department of Agriculture VCPR Veterinarian-client-patient relationship VFAs Volatile fatty acids WHO World Health Organization xiii CHAPTER 1 Literature review: Shiga toxin-producing Escherichia coli (STEC) and antibiotic resistance in bovines; insights into their epidemiology, molecular mechanisms, and ecological associations with the gut microbiome and resistome. 1 CATTLE'S IMPORTANCE AS A PROTEIN SUPPLY The United Nations projects a world population of 9.7 billion in 2050 [1]. Hence, the demand for meat and dairy products is estimated to rise by 73% and 58%, respectively, by that year as compared to the global production in 2010 [2]. To fulfill the increased nutritional demands of the continued growing population, industrial farming will dominate dairy and meat production [2]. Indeed, the adoption of intensive production systems in low- and middle-income countries has increased meat production by 68% in Africa, 64% in Asia, and 40% in South America since 2000 [3]. Industrial practices to improve growth and feed efficiency include the use of feeds with high energy content, high-density farms, and the use of growth promoters [4,5]. Nevertheless, increased densities can intensify the spread of foodborne pathogens in farm environments [4]. Cattle have been the most stable supply of protein for human societies since their domestication approximately 10,000 years ago [6]. The supply of cattle meat is positively associated with the average gross domestic product (GDP) per capita [7]. Cattle are highly efficient in protein production and are about 3.2 times greater than monogastric animals [8]. About 45% of the global animal protein comes from ruminants, while 31% is derived from poultry and 20% from pigs [9]. Globally, there are an estimated 3.9 billion ruminants. In the United States, however, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) reported over 98.8 million head of cattle, including 30.35 million beef, 9.45 million dairy, and 59 million other cattle and calves in 2022 [10]. Despite the benefits of livestock production, there are increasing concerns about negative environmental footprints such as greenhouse gas emissions [11], manure management [12], judicious use of antibiotics, and zoonotic disease outbreaks that have been linked to cattle [13–15]. 2 THE GASTROINTESTINAL MICROBIOME OF CATTLE Obligatory symbionts Ruminants depend on their gut microbiota for productivity and health. Complex microbial communities residing in the gastrointestinal tract (GIT) are fundamental to produce vitamins, 80% of protein [16], and nearly three-quarters of the energy necessary for the host [17]. Furthermore, the gut microbiota is critical to keep intestinal homeostasis, stimulating the immune responses, preventing pathogen colonization, and for mucosal and lymphoid structure development [18]. Given this obligatory symbiotic relationship between ruminants and their gut microbiota, several studies have explored associations between microbial communities with feed efficiency [19–21]. The implementation of high-throughput sequencing enabled the study of the gut microbiome, which has been largely studied in the rumen where most nutrient components are synthesized [22–24]. However, little research has been conducted to define the role that the microbiota residing in the lower GIT plays in production, health, or pathogen carriage [25,26]. Elucidating the interactions between the gut microbiota, host, and environment is key to increasing feed efficiency and preventing negative impacts associated with cattle. Role of the gastrointestinal microbiota of cattle The cattle GIT includes mouth, esophagus, a four-compartment stomach (rumen, reticulum, omasum, and abomasum), small intestine (duodenum, jejunum, and ileum), and large intestine (cecum, colon, and rectum). During the first two weeks of life, calves are in a monogastric stage where the abomasum is the only stomach compartment used to digest milk. The first bacterial colonizers of the calf GIT are facultative anaerobes, such as Lactobacillus, E. coli, and other Enterobacteriaceae [27–32]. A few days after birth strictly anaerobic bacteria colonize the GIT, with Clostridium, Bifidobacterium, Eubacterium, and Bacteroides 3 predominating [27–32] followed by an increase in microbiome diversity and composition with age [27–30,32]. In adult cattle, transitioning microbial communities dominated by Firmicutes, Bacteroidetes, and Proteobacteria are observed in each GIT compartment, which varies in functionality and environment [33]. The rumen microbiota, dominated by Firmicutes and Bacteroidetes [33], degrades and ferments diet compounds for the biosynthesis of volatile fatty acids (VFAs), amino acids, and vitamins [34]. The characteristic papillae present in the rumen and high vascularity allow efficient absorption of these nutrients by the host [35]. The reticulum allows particle separation to selectively pass small particles to the abomasum [35]. Water and other nutrients are absorbed in the omasum; while the abomasum has a similar role as nonruminant stomachs where hydrochloric acid and digestive enzymes are released to break down nutrients [35]. The low pH in the abomasum (pH < 4) as compared to the other GIT regions (pH 6 to 8), significantly decreases the number of bacteria which gradually increases in the hindgut [33]. While along the large and small intestines Firmicutes and Bacteroidetes increase, Proteobacteria decrease their abundance [33]. The small intestine receives compounds from the pancreas and gallbladder for further digestion and nutrient absorption [35]. Finally, resident microbiota of the large intestine digest undigested feed such as cellulose and hemicellulose [36,37], and biosynthesize VFAs, vitamins, and amino acids [42]. However, the central function of the last section of the GIT is water absorption. The hindgut microbiota is crucial for gut homeostasis and immune system stimulation [18,38]. Immune mechanisms induced by the microbiota residing in the lower GIT include the generation of antibiotic peptides, mucus production, and the stimulation of immune cells for the production of immunoglobulin A and pattern-recognition receptors such as toll-like receptors 4 (TLRs) [18,39]. In fact, the development of innate and adaptative immune responses in calves depends on Lactobacillus-dominant bacteria that trigger the expression of genes involved in the chemotaxis of leucocytes and lymphocytes and cytokine signaling pathways [38]. Microbiome dysbiosis in cattle Dysbiosis is generally outlined as an imbalance in the gut microbial communities associated with disease [40]. This imbalance is characterized by changes in the abundance of some community members reflected in the microbiome diversity and composition [40]. Bovines facing dysbiosis can decrease their productivity and present multiple disorders [41]. Such is the case of the microbiome imbalance caused by high-grain feed occasioning subacute ruminal acidosis (SARA) that has been broadly studied [42–44]. The rapid fermentation of carbohydrate feeds by the rumen microbiota can result in the excessive production and accumulation of acids [55]. During SARA, the ruminal microbiome is characterized by an increase in the Firmicutes to Bacteroidetes ratio and a reduction in microbial richness [42]. High concentrations of SCFA, ethanol, biogenic amines, and endotoxins affect the barrier function of the rumen epithelium resulting in the leakage of endotoxins and bacteria into the portal circulation causing systemic inflammation and in some cases liver abscesses [45–47]. In addition, increased amounts of histamine, LPS, and lactic acid produced during SARA can predispose the animals to lameness [48]. SARA-induced dysbiosis activates the gut-associated lymphoid tissue (GALT) [49] which includes the intestinal Peyer's patches and leukocytes present in reticulo-ruminal digesta [50,51]. Because GALT prevents pathogen colonization, dysbiosis of the GIT caused by SARA may reduce the cow's ability to fight pathogen invasion and regulate homeostasis. Other causes of GIT dysbiosis in cattle include infectious illnesses such as Johne's disease (JD), which is comparable to Crohn’s disease in humans [52]. JD is a chronic intestinal 5 infection caused by Mycobacterium avium subsp. paratuberculosis (MAP) that can be fatal for bovines [52]. During JD, the intestinal GIT has a lower abundance of Bacteroidetes and Firmicutes and higher levels of Actinobacteria and Proteobacteria [52]. Bovine Viral Diarrhea (BVD) and acute Salmonellosis are also implicated in enteric and systemic infections in cattle [53], though no studies have analyzed their role in GIT dysbiosis. FOODBORNE INFECTIONS LINKED TO LIVESTOCK The World Health Organization estimates that almost 600 million cases of foodborne illnesses occur every year contributing to ~420,000 deaths and the loss of 33 million healthy life years (DALYs) [15]. Roughly 350 million foodborne infections are attributed to pathogenic bacteria [15], with Campylobacter spp., Salmonella enterica, Listeria monocytogenes, and diarrheagenic Escherichia coli (e.g., Shiga toxin-producing E. coli (STEC)), predominating in advanced and developing countries [15]. Livestock, including cattle, poultry, and pigs, are important carriers of these pathogenic bacteria [15]. Cattle, for instance, have a prevalence of 39- 99% of Campylobacter jejuni in dairy farms [54], 6-54% of Shiga-toxin-producing Escherichia coli in beef and dairy farms [55], and 7-11% of Salmonella enterica in beef, dairy, and mixed farms [56]. Considerable variation, however, has been observed across geographic locations. Factors that influence the prevalence of foodborne pathogens in cattle farms include high-grain diets, housing, seasonal variations, breed, housing, and production intensification [57]. The USDA estimated that 10–83 billion USD is spent on all aspects of foodborne- associated disease each year, including pathogen identification, outpatient and inpatient expenditure, lost wages, and disease prevention. Bacterial enteric pathogens causing 9.4 million cases, 55,961 hospitalizations, and 1,351 deaths annually [13]. Nevertheless, the actual incidence of foodborne diseases was estimated to be at least 10 times higher given the number of 6 underreported cases [13]. The underreporting of foodborne diseases is associated with the lack of appropriate testing, no access to medical care, or failure to seek medical care given that these infections are self-limiting. STEC INSIGHTS IN HUMANS AND CATTLE STEC infection in humans One of the most dangerous foodborne pathogens is STEC, which causes approximately 2.8 million cases of acute enteric disease and 230 deaths annually worldwide [58]. The primary reservoir of STEC is bovines [59,60], although other ruminants including sheep, goats, and deer have been shown to carry STEC. Pigs, pigeons, cats, dogs, rats, and rabbits are among the other species [61] that have also been found to carry this pathogen. Sources for human infections of STEC include undercooked ground beef, unpasteurized milk, and juices, as well as contaminated fruits and vegetables [62]. STEC has a low infectious dose that ranges from 10 to 100 cells [63] with an enhanced ability to survive the acidity of the stomach [64]. In humans, it can cause mild to severe symptoms including gastroenteritis, hemorrhagic colitis, hemolytic uremic syndrome (HUS), acute renal failure, microangiopathic hemolytic anemia, and thrombocytopenia in 5 to 10% of the cases [60]. Infections can affect multiple organs including kidneys, heart, lungs, pancreas, and the central nervous system [60], and children and the elderly are the most susceptible to infection. Particular attention has been paid to STEC serotype O157:H7, which causes the highest morbidity and mortality rates in the U.S. [13]. In 2017, the Centers for Disease Control and Prevention (CDC) reported 6,034 cases of STEC including 2,363 O157 and 3,646 non-O157. However, it is estimated that for every STEC O157:H7 case reported 26 cases are undiagnosed, while that number reaches 106.8 cases for non-O157 STEC [13]. 7 Despite the high frequency of STEC infections, antibiotic treatment is not recommended because they have been linked to enhanced Shiga toxin (Stx) production, which can facilitate the development of hemolytic uremic syndrome (HUS) and fatalities in some [65]. Because cattle are important reservoirs of STEC, reducing the carriage of this pathogen in livestock and preventing dissemination through food and the environment are priorities for preventing human infections [66]. In fact, model predictions determined that a 50% reduction in STEC shedding by cattle could diminish 80% of human cases [67]. STEC virulence factors There are more than 470 STEC serotypes, which are classified based on the combination of their O- and H- antigens; over half have been linked to serious enteric disease [60,68,69]. The most studied serogroup is O157 [70,71], though ~50 % of infections are caused by other STEC serogroups, such as O26, O111, O103, O121, O45, and O145 [72,73] worldwide. These six serogroups vary in frequency across geographic locations and have been linked to varying degrees of illness, with strains ranging from avirulent to hypervirulent [74,75]. Because STEC is defined by the presence of the Stx genes, however, all STEC can be pathogenic to humans [76,77]. STEC carry genes encoding several virulence factors, which are located on multiple mobile genetic elements (MGEs) such as prophages, plasmids, and pathogenicity islands (PAIs); thus, different strains can possess different virulence genes [78]. STEC are defined by their ability to produce the Shiga toxin (Stx), which is encoded by genes (stx) found in lambdoid bacteriophages [79]. There are two antigenic forms of Stx, Stx1 and Stx2, with multiple variants of each including four Stx1 (Stx1a, Stx1c, Stx1d, and Stx1e) and twelve Stx2 (Stx2a–Stx2l) subtypes [77,80]. While Stx1 can contribute to disease in humans, STEC strains that produce 8 Stx2 have enhanced pathogenicity and are associated with HUS [77,81]. Stx production is induced upon entry into the lytic phase of the bacteriophages [81]. Once released, the Stx binds to the glycolipid globotriaosylceramide (Gb3) receptor, which is present in enterocytes, glomerular endothelial cells, and other cells. This binding induces changes in protein expression and cell death [79]. Shiga toxin-encoding phages are highly mobile and are involved in the HGT of stx genes between E. coli resulting in the emergence of new strain types [82]. Furthermore, transduction of Stx-phages has been also identified between Shigella and E. coli rough types [83]. A group of STEC, which are also classified as enterohemorrhagic E. coli (EHEC), possesses unique virulence genes found on the locus of enterocyte effacement (LEE) pathogenicity island (PAI) [84]. The LEE has genes encoding the Type III secretion system (T3SS), the intimin (eae) and its translocated intimin receptor (Tir), and effector molecules. These virulence factors induce attaching and effacing (A/E) lesions that promote bacterial attachment to the intestinal epithelium [76,77,85]. Other virulence agents include the non-locus of enterocyte effacement (Nle) effectors [86], which are important for immunomodulation, bacterial colonization, and prolonging infection. Cattle are asymptomatic carriers of STEC Adult cattle are generally asymptomatic carriers that can shed STEC in their feces [58]. The lack of symptoms is due to the absence of Gb3 in ruminants which represent the vascular receptors for the Shiga toxins [87]. It has also been suggested that some STEC virulence factors are necessary to promote a symbiotic relationship in the GIT of bovines [88,89]. Some hypotheses, for instance, suggest that STEC can induce immunomodulation and immunotolerance because the Stx can alter the T and B lymphocytes [90] and some STEC 9 antigens cannot reach their target cells [91]. Prior studies, however, have demonstrated that cattle have partially protective innate and acquired immune responses against STEC [92,93]. Furthermore, grazing protozoa present in the bovine GIT are believed to increase the rate of survival of STEC O157:H7; nevertheless, contradictory findings compel further investigation [94,95]. Interestingly, quorum-sensing (QS) signals from the native microbiota such as acyl- homoserine lactones (AHLs) were shown to activate the acid-resistant mechanisms of STEC through the SdiA protein, which is a QS transcription factor of AHLs [96]. This activation inhibits the expression of the LEE PAI genes [96] that are important for A/E lesions and other key symptoms. Despite the lack of symptoms observed in adult bovines, infected calves have been shown to be susceptible to A/E lesion formation on the apical epithelial surfaces of the rectoanal junction (RAJ) where the bacteria colonize [97]. STEC survival in cattle GIT STEC strains are able to survive the acid stress of the foregut environment [98,99] because of a specialized glutamate-dependent acid resistance system encoded by gadABC; other systems like the Arg have also been implicated, though these require further study [100,101]. In addition, some STEC strains such as those belonging to O157:H7, can resist the bile in the small intestine [102]. STEC colonizes and multiplies in the hindgut [103–105], particularly the RAJ mucosa [105]. Although it is rare, microcolonies of STEC O157:H7 have been observed in the RAJ, where they induce A/E lesions through the expression of multiple virulence factors encoded by the LEE operon [106,107]. Similarly, A/E lesions have been observed in the mucosa of the ileum and colon suggesting the expression of the injectosome in these regions, though no symptoms of infection have been reported [108,109]. 10 Certain types of flagella, which are often referred to as the H-antigen, are able to promote adherence better than others. Flagellar types H7 and H6, for instance, could initiate bacterial adhesion to the intestinal epithelium [110] and bind to bovine mucins I and II [111]. Once the bacteria adhere to the epithelium, the expression of flagella decreases, and increased expression of other factors occurs to promote attachment and facilitate colonization of STEC [110]. Different variants of pili [112–115] and monomeric adhesins [106,112,116] can also be involved in STEC adherence in the hindgut, though few studies have been conducted to elucidate their role in the colonization of the bovine reservoir. Prevalence of STEC in cattle E. coli populations, including STEC, are a minority in the GIT of bovines, which corresponds to approximately 0.1-1% in feces [117] and about 10X less in the rumen [118]. Transmission routes for the acquisition of STEC in cattle include direct animal contact, consumption of contaminated feed or water, and flies [119]. STEC can be transmitted by flies belonging to Muscidae and Calliphoridae, which are important sources of crop contamination [120] and can cause human infections [119]. Fomites can also be an important transmission source in farm environments since viable STEC were found to persist for up to 4 months in manure and pen floors [121–123]. Furthermore, the persistence of STEC in cattle farms has been attributable to its ability to grow well in open ecosystems [95,124] and its potential for forming biofilms [125]. It is estimated that the global prevalence of STEC O157:H7 in cattle is 5.68%, with the USA among the countries reporting the highest prevalence [126]. Weather conditions have been suggested to play a crucial role in the prevalence of diverse STEC serotypes. Whereas STEC O157:H7 prevalence increases in the summer and early fall [127–129], other serogroups such as 11 O111 and O145, were found to peak during the winter [129]. Similarly, temperatures higher than 28.9ºC have been associated with increased STEC prevalence in dairy farms [55], which is not surprising in that higher temperatures support bacterial growth. Diet was also shown to impact STEC colonization in cattle, as increased STEC shedding was observed with high-grain feeds [88,130] or fasting [118], which contribute to alterations in the ruminal pH. Although beef cattle shed more STEC than dairy breeds, considerable variation has been identified among farms [55]. Geographic locations also play a role in the prevalence and distribution of strain serogroups [131] demonstrating the presence of environmental factors affecting the agroecosystems, though more studies are needed to confirm this hypothesis. Another study of dairy cattle also showed that cows during the first lactation and those with less than 30 days in milk (DIM) have a higher prevalence of the pathogen [55]. Finally, farm practices such as anthelmintic treatment [55] was associated with a lower prevalence of STEC. STEC shedding levels are highly variable not only between animals but within the same individual. Prior studies have classified some bovines as super-shedders when the number of STEC colony-forming units (CFU) per gram of feces is greater than 104 [59,134]. Within a farm, the number of super-shedders ranges from about 3.5% to 5% of the animals [135]. Identifying STEC super-shedders was suggested to be more important than the farm prevalence since models indicate that 20% of the animals shed about 80% of the bacteria [136]. However, other studies have shown that animals have brief periods of increased intensity of STEC shedding, resulting in higher variations within than between individuals [137]. Consequently, targeting super-shedders could be challenging and does not account for differences over time within the same animal [137]. 12 Several strategies have been proposed to decrease the shedding of STEC in cattle farms including vaccination, phage therapy, feed additives, and probiotics or direct-fed microbes (DFMs) [138,139]. DFMs offer multiple benefits in the cattle industry by preventing STEC colonization [138,139], controlling ruminal acidosis, and activating the immune system [140,141]. Nevertheless, identifying efficient DFMs to reduce the carriage of STEC is still in the early stages of study [136], and little is known about their impact on the gut microbiome. Cattle GIT microbiota associations with STEC Cattle have a diverse GIT symbiotic microbiota that can interact directly or indirectly with STEC to impact its ability to colonize and survive. Commensal indigenous microbes have been shown to mitigate the proliferation of invading pathogens through predation, nutrient competition, and the excretion of antibiotic compounds [143–145]. Inhibition of STEC has been induced by lactic acid bacteria [139]. Such is the case of lactobacilli and streptococci whose abundance in the rumen is higher in calves and animals with high-grain diets [146,147] as compared to forage-fed animals [146]. Another group of microorganisms that can alter the presence of STEC in the rumen is ciliate protozoa such as Epidinium, Polyplastron, and Entodinium [148], which help stabilize the ruminal pH and have predatory effects on STEC [149,150]. Bacteriophages can also regulate bacterial populations in the GIT and promote the horizontal transfer of genes between bacteria [151]. Associations between bacteriophages from the order Caudovirales and dominant bacteria such as phylum Proteobacteria, which includes STEC, have been identified in the rumen [148,152]. Bacteriophage families representing Myoviridae and Siphoviridae have also been identified in STEC where they can induce bacterial cell lysis [152]. 13 Factors that have been implicated in STEC shedding in cattle farms can also have profound effects on the GIT microbiota; however, no prior studies have analyzed how changes in the microbiome diversity attributable to these factors are associated with STEC prevalence. Indeed, one of the most important factors known to alter the GIT microbiome of cattle is the diet [24,46,98,118,153]. Forage-fed bovines, for example, were found to have a higher abundance of Bacteroidales, Clostridiales, Ruminococcacea, and Fibrobacter [22]. By contrast, cattle fed high- grain diets have been linked to a greater abundance of lactic acid bacteria, lactic acid-utilizing taxa like Megasphaera elsdenii and Selenomonas [140,154], and the protozoa Entodinium, which has been linked to lower levels of STEC shedding [155]. The associations between diet and STEC shedding were also suggested to be related to the ability of E. coli to survive low pH levels in the GIT hindgut [156]. ANTIBIOTIC RESISTANCE EPIDEMIOLOGY Antibiotic-resistant (AR) bacteria are major threats to human and animal health. It was estimated that infections caused by multidrug-resistant (MDR) bacteria caused 4.95 (3.62–6.57) million human deaths in 2019 alone [157]. The CDC reported that approximately 2.8 million AR infections occur in the U.S. every year, resulting in 35 thousand deaths [158]. MDR E. coli is the leading cause of global deaths by AR pathogens, particularly those strains resistant to third- generation cephalosporins and fluoroquinolones [157]. Enterobacteriaceae with resistance to third-generation cephalosporins can carry genes encoding extended-spectrum β-lactamases (ESBL) that also provide resistance to penicillins and monobactams [158]. Among all the AR threats, ESBL-producing Enterobacteriaceae are the primary cause of hospitalizations and the most economically impactful group of pathogens in the U.S. [158]. In 2017, a total of 197,400 14 hospitalizations, 9,100 deaths, and $1.2 billion in costs were attributed to infections caused by ESBL-producing Enterobacteriaceae in the U.S. [158]. Globally, it was estimated that 70% of the total mass of antibiotics is used to treat and prevent infections in animals [3]. This extensive use of antibiotics in both animals and humans has contributed to the emergence of AR threats. The transfer of AR bacteria between humans, animals, and the environment can occur through several routes [159] including water sanitation systems, use of manure on crops, ingestion through the food chain, wildlife, and insects. Moreover, tourism, migrations, and trade are important factors that impact the rapid dissemination of resistant bacteria across borders [160]. The World Health Organization (WHO) classified medically important antibiotics into three categories: critically important, highly important, and important antibiotics [161]. Critically important antibiotics are those that are used to treat infections caused by bacteria transmitted from non-human sources and for which limited therapies are available [161]. This category of antibiotics addresses the concern of increased severity of infections caused by resistant zoonotic bacteria. Among these critically important antibiotics, the third-generation cephalosporins, aminoglycosides, macrolides, sulfonamides, fluoroquinolones, and aminopenicillins are approved for use in cattle farms in the U.S. [162,163]. In this group, third-generation cephalosporins are classified as the drugs with “highest priority” [161]. Ceftiofur is a third- generation cephalosporin approved for use in bovines to treat diverse infections [164–166]. Parenteral application of ceftiofur, for instance, is applied to treat respiratory disease, foot rot, and metritis in beef and dairy cattle [164]. While intramammary ceftiofur is used to treat clinical and subclinical mastitis in dairy breeds caused by Streptococcus spp., Staphylococcus aureus, and E. coli due to its broad-spectrum effects [165]. 15 Preventing the dissemination of antibiotic resistance among animals, humans and the environment compels One Health approaches. Examples include disease prevention in humans and animals, animal husbandry, antibiotic use regulation and policy, stewardship, surveillance, sanitation, and the discovery of alternatives to antibiotics [167]. Globally, the Food and Agriculture Organization of the United Nations (FAO), the World Organization for Animal Health (OIE), and the WHO combine efforts to promote best practices for the use of antibiotics in humans and animals. In the U.S., the National Antibiotic Resistance Monitoring System for Enteric Bacteria (NARMS) integrates data from humans, retail food products, and animals through a collaboration among state and local public health departments, the CDC, the U.S. Food and Drug Administration (FDA), and the USDA [163]. Antibiotic usage in animals Globally, it is estimated that livestock consumed about 63,000 tons of antibiotics in 2010, which is expected to increase by 67% by 2030 [3]. The FDA reported in 2020 that 4 of every 5 pounds of antibiotics sold in the U.S. (81%) are used in animals and the rest are used in humans [162]. The highest mass of antibiotics used in food-producing animals in the U.S. is attributed to tetracyclines and ionophores which are mainly given by feed (Table 1.1) [162]. Even though antibiotics have been used in livestock since the 1950s, the FDA did not begin monitoring use until 2008 given the global concerns about the increase in AR infections in humans. In fact, in 2017, the WHO claimed the need to reduce the use of veterinary antibiotics used for growth promotion and disease prevention [161,168]. Remarkably, the highest mass of antibiotics is sold for use by cattle, which accounts for 51% of the total kilograms of antibiotics used in animals in the U.S. [162]. The FDA estimates that about 5.2 million kilograms of antibiotics are intended for use in bovines (Table 1.2) [162]. 16 The most common antibiotics used in cattle farms are ionophores (53%), tetracyclines (32.7%), macrolides (4.8%), sulfonamides (3.1%), aminoglycosides (3.3%), β-lactams (2%), and in lower abundance amphenicols, fluoroquinolones, and other drug classes (Table 1.2) [162]. However, beef breeds and non-lactating cattle use a greater mass of antibiotics as compared to lactating cows where only certain antibiotic classes and routes of administrations are approved (Table 1.2). Table 1.1. Antimicrobial drugs approved for use in food-producing animals in 2020 in the U.S. reported by route of administration [162]. Route Drug Class Annual total (kg) % total Feed Sulfonamides 20,915 0.20% Tetracyclines 3,256,519 31.16% Ionophores 4,447,420 42.56% Other drugs 458,831 4.39% Water Aminoglycosides 254,448 2.44% Lincosamides 62,297 0.60% Penicillins 655,060 6.27% Sulfonamides 176,062 1.68% Tetracyclines 591,722 5.66% Other drugs 90,307 0.86% Other routes Amphenicols 48,626 0.47% Cephalosporins 26,262 0.25% Tetracyclines 100,505 0.96% Other drugs 260,502 2.49% Total 10,449,476 100.00% Table 1.2. Amount of antibiotics by drug class sold for use in cattle in 2020 and approved administration routes in dairy cattle [162]. Estimated total Approved routes of administration in cattle Antibiotic class Kilograms Percentage Non-lactating cattle** Lactating cows Lincosamides* 11,165 0.2 None Intramammary Fluoroquinolones 12,446 0.2 Injection None Cephalosporins 21,007 0.4 Injection, intramammary Injection, intramammary Amphenicols 47,609 0.9 Injection None Penicillins 82,008 1.6 Injection, intramammary Injection, intramammary Sulfonamides 161,220 3.1 Injection, oral Injection, oral Aminoglycosides 174,132 3.3 Injection None Macrolides 247,581 4.8 Injection None Tetracyclines 1,703,391 32.7 Injection, oral, topical, feed additive Injection, topical Ionophore 2,758,786 53.0 Oral, feed additive Feed additive Total 5,208,227 100 *Reported for cattle and other food-producing animals **The term non-lactating cattle is defined as dairy bulls, dairy calves, and replacement heifers. 17 The amount of antibiotics reported by the FDA, however, should be carefully considered given the differences in how antibiotics are used in human and veterinary medicine. Some considerations include: 1) larger animal vs. human populations; 2) some animal species, such as cattle, require higher volumes of antibiotics given their live weight; 3) differences in physiology require adjustments in duration and dosage of the antibiotics; 4) the amounts of antibiotics sold do not show indication use, and 5) some antibiotics used in humans are prescribed to companion animals making a portion of them underrepresented in the FDA report [162]. Despite these concerns, a group of medically important antibiotics, which are readily used in human medicine, have also been approved for use in animals. Examples include tetracyclines, penicillins, macrolides, sulfonamides, aminoglycosides, lincosamides, cephalosporins, and fluoroquinolones [162,163]. These antibiotic classes are mainly intended for use in cattle (41%) and pigs (41%), but they are also commonly used in turkeys (12%), chickens (2%), and other animal species (4%) [162]. Despite the high percentage of medically important antibiotics used in cattle, only few classes of antibiotics are approved for use in lactating cattle (Table 1.3). Hence, it is necessary to study the impact that medically important antibiotics used in livestock play in the emergence of resistant bacteria of public health interest. Table 1.3. FDA approved antibiotics for use in lactating cattle and importance in human health by drug class. WHO importance Administration Class Antibiotic (antibiotic class) Injectable Cephalosporin (third generation) Ceftiofur crystalline free acid Critically important of Cephalosporin (third generation) Ceftiofur hydrochloride highest priority Cephalosporin (third generation) Ceftiofur sodium Critically important of Penicillin (aminopenicillins) Ampicillin trihydrate high priority Penicillin (narrow spectrum) Penicillin G (procaine) Highly important Sulfonamide Sulfadimethoxine Highly important Tetracycline Oxytetracycline Highly important Intramammary Cephalosporin (first generation) Cephapirin (sodium) Highly important 18 Table 1.3 (cont’d) WHO importance Administration Class Antibiotic (antibiotic class) Critically important of Intramammary Cephalosporin (third generation) Ceftiofur hydrochloride highest priority Lincosamide Pirlimycin Highly important Critically important of Penicillin (aminopenicillins) Amoxicillin trihydrate high priority Penicillin (anti-staphylococcal) Cloxacillin (sodium) Highly important Penicillin (aminopenicillins) Hetacillin (potassium) Highly important Penicillin (narrow spectrum) Penicillin G (procaine) Highly important Oral Sulfonamide Sulfadimethoxine Highly important Feed additive Ionophore Monensin (sodium) Not medically important Oxytetracycline Highly/critically Topical Tetracycline/polymyxin hydrochloride/Polymyxin B important of highest sulfate priority Role of antibiotics used in livestock in the emergence of AR bacteria Antibiotics have different mechanisms of action against bacteria including the disruption of the bacterial synthesis of proteins (tetracyclines, aminoglycosides, macrolides), nucleic acids (quinolones, sulfonamides), or the cell wall (β-lactams, vancomycin, polymyxins) [169]. However, bacteria can evade the effect of antibiotics through several mechanisms such as efflux pumps to remove the antibiotic, modifying the cell membrane porin channels to reduce the uptake of certain drugs, modifying target sites to avoid recognition, or through the presence of inactivating enzymes [169–171]. Numerous genetic events in bacteria allow the development of resistance, such as the overexpression or duplication of existing genes, point mutations, or the acquisition of antibiotic resistance genes (ARGs) through horizontal gene transfer (HGT) [172,173]. The use of antibiotics in animals could result in the selection of resistant pathogens colonizing the GIT that can disseminate into the environment [3,159]. Mathematical modeling identified that the use of antibiotics in food-producing animals can increase the emergence of resistant bacteria in humans [174]. Zoonotic transmission of resistant bacteria has been 19 evidenced through whole-genome sequencing of Salmonella [175], Campylobacter [176], and Staphylococcus aureus MRSA [177]. Furthermore, active metabolites of antibiotics excreted by urine and feces play a role in the selection and subsequent evolution of bacterial resistance in the environment. Such is the case of hospital wastewater where MDR E. coli is strongly selected even when no treatment is applied [178]. Similarly, manure from animals treated with antibiotics carries a higher number of ARGs and MGEs when compared to manure from untreated animals [12,179–181]. The administration of antibiotics can also enhance the spread of drug resistance in the GIT microbiota through the HGT of ARGs [182]. Plasmids are one of the most important mechanisms of spreading ARGs between bacteria [183], though other MGEs can allow the movement of ARGs within bacterial genomes via transposons, integrons, or gene cassettes [172,173]. Thus, within a community or farm, a common pool of ARGs could co-circulate on different plasmids among diverse strains [184] complicating epidemiological efforts to track sources of antibiotic resistance [185,186]. Abundance and composition of antibiotic-resistant determinants in cattle feces Methods used to analyze the resistome in cattle feces Traditionally, the evaluation of antibiotic resistance in feces has been performed using culture-based techniques that require bacterial isolation followed by antibiotic susceptibility testing via disk diffusion or minimum inhibitory concentration tests [187]. E. coli is the most common bacterial indicator of resistance levels in cattle given that: 1) it is used as a fecal marker in environmental and food samples; 2) it is easy to isolate in laboratory settings; 3) it has an enhanced ability to colonize other animal species and humans; and 4) its gene plasticity promotes the acquisition of ARGs [188]. However, E. coli is a minority population in cattle feces and does 20 not represent the complexity of the resistome, which is defined as the group of genes encoding antibiotic resistance within a microbial community. The use of culture-independent techniques such as RT-PCR, 16S rRNA sequencing and shot-gun metagenomic sequencing have enabled a better understanding of the ecology and abundance of microbial populations and ARGs in cattle feces [171]. Metagenomic sequencing provides a broad identification of ARGs compared to RT-PCR and facilitates the discovery of potentially novel ARGs [171]. Nevertheless, in-silico detection of antibiotic resistance has several limitations since the detection of a gene is not necessarily indicative of phenotypic resistance. Although a prior study of STEC genomes demonstrated concordance between gene presence and resistance phenotypes, many more ARGs were identified that could not be validated via susceptibility testing assays [238]. Furthermore, current databases often lack unknown antibiotic resistance determinants or intrinsic resistances [171], and hence, a broad understanding of the resistome of cattle feces should combine culture-based and molecular techniques [189]. Recent studies suggest antibiotic enrichment of environmental samples to increase the number of resistant bacteria before sequencing to increase sensitivity and resolution of the molecular analysis, particularly long-read sequencing [190,191]. This method, however, will only select for specific resistant bacterial populations and their ARGs. Because HGT can occur between different bacterial species, including commensal and pathogenic bacteria, classifying ARGs and identifying ARG mobility and frequencies within members of a microbiome is key to identifying risks in animal and public health [191]. Different bioinformatic and statistical approaches allow for the identification of bacterial taxa and MGEs associated with their carriage of ARGs, including correlation networks and contig analysis [192]. The analysis of long-read sequences also enables the identification of complete ARGs and their 21 localization in the bacterial genome, providing information on bacterial hosts and linkage with MGEs or other ARGs that can be co-selected [191]. Resistome composition of cattle feces The fecal resistome composition of cattle shows a similar pattern across studies, with tetracycline as the dominant drug resistance class [193–202]. Genes with resistance to macrolide, lincosamide, streptogramin (MLS), aminoglycosides, and β-lactams were also commonly detected in the bovine resistome. Despite the extensive use of antibiotics in livestock, relatively few studies have assessed their effect on the fecal resistome composition in cattle [203]. While some antibiotics do not affect the resistome composition, others selectively enrich some ARGs by altering the bacterial populations directly. For instance, the use of the feed additives monensin and tylosin did not affect the functional microbiome or resistome [193]. Moreover, no difference was observed in the profiles of fecal resistome of cattle injected with or without tulathromycin, a macrolide used to prevent and treat bovine respiratory disease, but the application of this antibiotic changed the composition of ICE and plasmids [204,205]. The use of third-generation cephalosporins to treat respiratory disease and foot rot [166], augmented the abundance of β- lactam ARGs [206,207]. Similarly, cattle fed chlortetracycline exhibited an enrichment of tetracycline ARGs in feces [207]. Furthermore, beef and dairy breeds face different antibiotic selective pressures since varying bacterial infections in both breeds demand certain antibiotic classes [208]. For instance, β-lactams such as penicillins and cephalosporins, are commonly used in dairy cows to control mastitis [208–210]. Beef cattle farms commonly administer tylosin, chlortetracycline, oxytetracycline, and virginiamycin to prevent liver abscesses, as well as macrolides and tetracyclines to treat bovine respiratory disease [211]. Comparison of the resistome composition 22 between cattle breeds shows consistent trends across studies. For instance, the fecal resistome of beef breeds has been linked with greater levels of tetracycline resistance compared to dairy cows [195,198]. Whereas dairy cattle carry higher levels of β-lactam ARGs than feedlot animals [195,198]. Other classes of ARGs such as aminoglycoside and MLS, however, showed inconsistent results across studies between both breeds [195,198]. Additionally, variations in diets impact the GIT microbiome composition; therefore, changes in resistome associated with different community members have been observed in the rumen [212,213]. Selection of ARGs can also occur with the ingestion of heavy metals and minerals present in the diet such as zinc, copper, chromium, arsenic, cadmium, and lead [212,213]. For instance, in swine and poultry, zinc is supplemented in diets to improve the immune response and feed efficiency [214], but they have been linked with the selection of ARGs [215,216]. Since ARGs and metal resistance genes (MRGs) can be collocated in MGEs, such as plasmids, the use of metals can co-select both kinds of resistances [217,218]. In beef cattle, zinc supplementation increased the abundance of enterococci resistant to macrolides [219]. Similarly, copper also has shown associations with increased levels of macrolide ARGs [220]. Finally, the exposure to antibiotics at the farm level has been analyzed through a comparison of conventional and raised without antibiotic (RWA) farms. Some studies identified that RWA farms showed a lower abundance of tetracycline, macrolide, and aminoglycoside ARGs as compared to conventional farms [195,198,201,221]. Similarly, through RT-PCR and culture-based methods, feces from animals at conventional farms carried a higher number of E. coli resistant to tetracycline and third-generation cephalosporins, as well as a higher abundance of the erm(B) gene (MLS class) as compared to RWA cattle [221,222]. However, other studies 23 did not identify differences in the fecal resistome profile in feedlot cattle under conventional vs. RWA production settings [223]. Based on these findings, withdrawing the administration of antibiotics may not necessarily impact the profiles of resistome in the fecal microbiota of ruminants. Thus, the complexity of the resistome in bovines is multifactorial and includes not only antibiotic exposure but also farm practices, physiology, breed, diet, transmission between animals, and other factors that need further study. Insights into the limitation of antibiotic use in bovines Antibiotics are fundamental to treating infections in cattle farms. Particularly, when infections are caused by bacterial pathogens for which there are no vaccines or alternative therapies available. However, the use of non-therapeutic antibiotics in livestock is controversial given the global concern of antibiotic resistance [3,224]. A meta-analysis identified that restrictions on the use of antibiotics in food-producing animals could reduce their levels of resistance by up to 39% [168]. Based on that study, the WHO recommended stopping the use of antibiotics used to prevent diseases and growth promotion in 2017 [161], while enacting judicious use of medically important antibiotics in food-producing animals to maximize therapeutic efficacy in animals and humans. The American Veterinary Medical Association and the FDA include prophylaxis and metaphylaxis in the category of therapeutic uses of antibiotics [162,225]. However, these practices are considered ‘sub-therapeutic’, ‘non-therapeutic’, or ‘production usage’ in other countries. Despite concerns related to the use of growth promoters, studies showed the lack of effect of major feed additives used in cattle (i.e., tylosin and monensin) in the fecal resistome [5,226]. Similarly, cattle from RWA farms have a high diversity of ARGs whose composition has little or no difference when compared to cattle feces from conventional farms [195,198]. 24 However, studies focused on the effect of β-lactams and tetracyclines have shown their effect on the selection of resistant bacteria in cattle feces [227,228]. Undoubtedly, the misuse and overuse of antibiotics in humans and animals contribute to the emergence of antibiotic-resistant threats [229]. Limiting the use of medically important antibiotics in food-producing animals can potentially decrease the risk of resistance to last-resort drugs, also classified as critically important antibiotics of highest priority [161,229]. For instance, colistin is the last option to treat MDR Gram-negative such as Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii [230]. Nevertheless, the broad use of colistin in livestock threatens the spread of resistance to this drug class. Pigs, for instance, are considered amplifiers of the plasmid-associated mcr-1 gene providing resistance to colistin [231]. Moreover, colocalization of the mcr-1 gene and ESBL genes identified in plasmids increases the risk of co-selection of important resistant threats [230]. Authorities worldwide banned the use of colistin in livestock; nevertheless, several developing countries are still using this antibiotic in food production [232]. In the U.S., three classes of antibiotics of highest priority are intended to use in cattle farms, including third-generation cephalosporins, macrolides, and quinolones [162]. Third- generation cephalosporins are broad-spectrum antibiotics used in a high number of people, particularly children and pregnant women given their low toxicity [161]. However, the transmission of Enterobacteriaceae resistant to third-generation cephalosporins through the production of ESBLs has been linked to non-human sources [161]. ESBL Enterobacteriaceae of zoonotic origin include E. coli and Salmonella spp. [158]. In the U.S. 47% of human infections with ESBL Enterobacteriaceae are community-associated [158]. An increased number of infections in the community rises the risk of dispersion of this threat complicating their control 25 and containment [158]. There is no clear evidence linking the use of cephalosporins in cattle with the rising human cases of ESBL Enterobacteriaceae. Nevertheless, the number of hospitalizations caused by infections with ESBL Enterobacteriaceae increased by 52% between 2012-2017 [158]; whereas the amount of cephalosporins used in livestock increased by 56% between 2009 and 2018 [162]. Macrolides used in beef and non-lactating cattle (i.e., tulathromycin and tylosin) are of highest priority given their frequent use in human medicine and reports of transmission of macrolide-resistant Campylobacter spp. from non-human sources [162]. While, quinolones, including danofloxacin and enrofloxacin, are approved to treat respiratory infections in beef and non-lactating cattle. The identification of fluoroquinolone-resistant Campylobacter spp. and Enterobacteriaceae, including E. coli and Salmonella spp., from non-human sources, is an emergent problem in human populations worldwide [158,162,233]. Restrictions of these therapeutic options in cattle are controversial given the limited number of antibiotics approved to use in bovines in the U.S. [163]. The FDA controls the use of drugs following the approved labeling that includes species for which they are approved, indications (disease or other conditions), dosage levels, frequencies, and routes of administration. Extra labeling uses of an antibiotic are possible under a valid veterinarian-client-patient relationship (VCPR), and when the drug is neither administered via feed nor results in a residue that constitutes a risk to public health. However, extra labeled uses are prohibited for fluoroquinolones and cephalosporins to control the misuse of antibiotics of highest priority [234]. In the U.S., cephapirin and ceftiofur are the only cephalosporins currently approved for use in food-producing animals including cattle, swine, sheep, goats, chicken, and turkeys [234]. 26 Approved use of ceftiofur products in cattle includes their parenteral application for the treatment and control of respiratory disease, acute bovine interdigital necrobacillosis (foot rot) and acute bovine metritis [164]. Furthermore, intramammary infusions of ceftiofur are approved to treat clinical mastitis in lactating cows and subclinical mastitis in dairy cattle at the time of dry-off [165]. Cephapirin is a first-generation cephalosporin only approved as an intramammary infusion for the treatment of mastitis in lactating cows [234]. Using ceftiofur for disease prevention is prohibited by the FDA’s Center for Veterinary Medicine [234]. Despite the regulations, ceftiofur is one of the most used antibiotics in dairy farms [209,210]. However, prudent use of this and other antibiotic therapies should include testing to determine the most effective treatment. Enforcing the use of other antibiotic classes of lower importance to human health rather than choosing the last line of antibiotics could prevent the emergence of resistance threats in farm environments. Yet, more research is needed to ensure that other drug classes can lower the risk of selecting resistant threats such as ESBL Enterobacteriaceae since co-selection of ARGs has been observed priorly [218,228]. Given the increasing global population and the concomitant growth of intensive livestock production, it is expected that the demand for antibiotics will rise [3]. Consumer requests for products from “antibiotic-free” farms will also increase. Adopting regulations and incentives to reduce the use of antibiotics in food-producing animals is fundamental to fighting antibiotic resistance under a One Health approach. However, more scientific evidence is needed to guide effective policies while ensuring animal welfare. Alternative strategies to replace antibiotics include bacteriophages and antibiotic peptides [235,236]. Nonetheless, reducing the risk of infections in cattle farms through improving hygiene, vaccination, appropriate housing and husbandry practices are the best options to avoid the use of antibiotics. 27 Several countries already banned the use of certain antibiotics in food-producing animals. In 2005, FDA banned fluoroquinolone use in poultry, and in 2017 the use of antibiotics for growth promotion [162]. In 2006, the European Union prohibited the use of antibiotics for ‘non- therapeutic’ purposes [237]. While China withdrew the use of medicated feed additives through their National Action Plan to Combat Antibiotic Resistance from Animal Resources (2017– 2020). The banned use of colistin in feed demonstrated a significant effect in reducing mcr-1- mediated resistance in humans and animals in China [230], proving the effectiveness of these policies in reducing the levels of antibiotic resistance in animal and human populations. SUMMARY Bovines have been one the most important animal species for the development of human societies [2]. Their complex GIT microbiome makes them the most efficient protein producers among food-producing animals [8]. As the demand for meat and dairy products is expected to increase with the human population, the adoption of intensive production systems will also escalate [2]. Despite the benefits of bovines, their expansion can result in increased negative footprints such as the transmission of foodborne pathogens and AR bacteria [2]. Cattle are the primary carriers of STEC and the main consumers of antibiotics worldwide. Preventing the transmission of STEC to humans is critical since no antibiotic therapies are recommended given that they increase toxicity with fatal consequences [65]. Understanding the role of the microbiome in STEC colonization can help identify alternative methods to decrease the prevalence of this pathogen in cattle farms. Notably, medically important antibiotics for humans are also used in cattle farms in the U.S. [162,163]. Third-generation cephalosporins, for instance, are classified as critically important antibiotics of highest priority [161]. The overuse of third-generation cephalosporins is 28 associated with rising infections with ESBL Enterobacteriaceae which are the main cause of deaths attributed to MDR bacteria worldwide. Ceftiofur is a third-generation widely used in dairy farms to treat multiple infections including mastitis [164–166]. To prevent the misuse of this drug, the FDA prohibits the application of ceftiofur for extra labeling uses [234]. Nevertheless, policies are needed to prevent the overuse of ceftiofur in bovines, such as encouraging the use of other therapies or drugs with lower implications in the emergence of AR threats and the implementation of antibiotic resistance tests. Identifying the effects of ceftiofur application in the fecal resistome of cattle can help to enforce the judicious use of this last resort drug. Studies demonstrated the increase of ESBLs in the resistome of cattle after its parenteral application [207,227]. However, there is no evidence of the effect of intramammary ceftiofur application in the resistome of cattle feces. Reducing the prevalence of STEC and antibiotic resistance in cattle farms is critical to prevent infections in humans and ensuring the effectiveness of last-resort treatments. Studying the ecological interactions between the GIT microbiome, STEC, and AR through culture-depend and independent methods can provide valuable information to reduce the dispersion of bacterial threats in cattle farms. STUDY AIMS Despite the high prevalence of STEC and the use of third-generation cephalosporins in cattle farms, little is known about the ecological interactions of this pathogen and the effect of antibiotics on the cattle microbiome. Characterizing the GIT microbiome of cattle carrying STEC and the effects of antibiotic therapies in the abundance of resistant bacteria can help identify strategies to decrease the prevalence of resistant foodborne pathogens. To address this research gap culture-based methods, 16S rRNA sequencing, metagenomics, and metabolomics were applied to study the fecal microbiome, metabolome, and resistome of cattle. 29 Three main aims were developed in this study: 1) identify microbiome diversity and composition that may favor the colonization of STEC in the hindgut of cattle; 2) determine the impacts of IMM ceftiofur in dairy cattle at the time of dry-off in the abundance of resistant bacteria; and 3) characterize changes in the functional microbiome and metabolome associated with the IMM application of ceftiofur. This study seeks to understand better the impacts of pathogens and antibiotic treatment on the cattle microbiome ecology. These findings could guide new prevention and control strategies to limit the spread of resistant bacteria and STEC in cattle farms. 30 REFERENCES 1. United Nations Department of Economic and Social Affairs, Population Division World Population Prospects 2022: Summary of Results 2022. 2. McLeod, A. World Livestock 2011-Livestock in Food Security.; Food and agriculture organization of the United Nations (FAO), 2011; ISBN 92-5-107013-X. 3. Boeckel, T.P.V.; Brower, C.; Gilbert, M.; Grenfell, B.T.; Levin, S.A.; Robinson, T.P.; Teillant, A.; Laxminarayan, R. Global Trends in Antimicrobial Use in Food Animals. PNAS 2015, 112, 5649–5654, doi:10.1073/pnas.1503141112. 4. Gilchrist, M.J.; Greko, C.; Wallinga, D.B.; Beran, G.W.; Riley, D.G.; Thorne, P.S. The Potential Role of Concentrated Animal Feeding Operations in Infectious Disease Epidemics and Antibiotic Resistance. Environmental Health Perspectives 2007, 115, 313– 316, doi:10.1289/ehp.8837. 5. Hales, K.E.; Wells, J.E.; Berry, E.D.; Kalchayanand, N.; Bono, J.L.; Kim, M. The Effects of Monensin in Diets Fed to Finishing Beef Steers and Heifers on Growth Performance and Fecal Shedding of Escherichia Coli O157:H71. Journal of Animal Science 2017, 95, 3738–3744, doi:10.2527/jas.2017.1528. 6. Diamond, J. Evolution, Consequences and Future of Plant and Animal Domestication. Nature 2002, 418, 700–707, doi:10.1038/nature01019. 7. Whitton, C.; Bogueva, D.; Marinova, D.; Phillips, C.J.C. Are We Approaching Peak Meat Consumption? Analysis of Meat Consumption from 2000 to 2019 in 35 Countries and Its Relationship to Gross Domestic Product. Animals (Basel) 2021, 11, 3466, doi:10.3390/ani11123466. 8. Mottet, A.; de Haan, C.; Falcucci, A.; Tempio, G.; Opio, C.; Gerber, P. Livestock: On Our Plates or Eating at Our Table? A New Analysis of the Feed/Food Debate. Global Food Security 2017, 14, 1–8, doi:10.1016/j.gfs.2017.01.001. 9. Ritchie, H.; Rosado, P.; Roser, M. Meat and Dairy Production. Our World in Data 2017. 10. U.S. Department of Agriculture, National Agricultural Statistics Service. Cattle bi-annual report July 2022, ISSN: 1948-9099 (2022). Available online: https://downloads.usda.library.cornell.edu/usda- esmis/files/h702q636h/tq57pz24k/cz30r103f/catl0722.pdf (accessed on 22 November 2022). 11. Garnett, T. Livestock-Related Greenhouse Gas Emissions: Impacts and Options for Policy Makers. Environmental science & policy 2009, 12, 491–503. 12. Manyi-Loh, C.E.; Mamphweli, S.N.; Meyer, E.L.; Makaka, G.; Simon, M.; Okoh, A.I. An Overview of the Control of Bacterial Pathogens in Cattle Manure. International journal of environmental research and public health 2016, 13, 843. 31 13. Scallan, E.; Hoekstra, R.M.; Angulo, F.J.; Tauxe, R.V.; Widdowson, M.-A.; Roy, S.L.; Jones, J.L.; Griffin, P.M. Foodborne Illness Acquired in the United States—Major Pathogens. Emerging infectious diseases 2011, 17, 7. 14. Nichols, M.; Gollarza, L.; Sockett, D.; Aulik, N.; Patton, E.; Francois Watkins, L.K.; Gambino-Shirley, K.J.; Folster, J.P.; Chen, J.C.; Tagg, K.A. Outbreak of Multidrug- Resistant Salmonella Heidelberg Infections Linked to Dairy Calf Exposure, United States, 2015–2018. Foodborne Pathogens and Disease 2022, 19, 199–208. 15. World Health Organization WHO Estimates of the Global Burden of Foodborne Diseases: Foodborne Disease Burden Epidemiology Reference Group 2007-2015; World Health Organization, 2015; ISBN 92-4-156516-0. 16. Bach, A.; Calsamiglia, S.; Stern, M.D. Nitrogen Metabolism in the Rumen*. Journal of Dairy Science 2005, 88, E9–E21, doi:10.3168/jds.S0022-0302(05)73133-7. 17. Mackie, R.I. Mutualistic Fermentative Digestion in the Gastrointestinal Tract: Diversity and Evolution. Integrative and Comparative Biology 2002, 42, 319–326. 18. Malmuthuge, N.; Guan, L.L. Understanding Host-Microbial Interactions in Rumen: Searching the Best Opportunity for Microbiota Manipulation. J Animal Sci Biotechnol 2017, 8, 8, doi:10.1186/s40104-016-0135-3. 19. Jami, E.; White, B.A.; Mizrahi, I. Potential Role of the Bovine Rumen Microbiome in Modulating Milk Composition and Feed Efficiency. PloS one 2014, 9, e85423. 20. Li, F.; Guan, L.L. Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle. Applied and environmental microbiology 2017, 83, e00061-17. 21. Xue, M.-Y.; Xie, Y.-Y.; Zhong, Y.; Ma, X.-J.; Sun, H.-Z.; Liu, J.-X. Integrated Meta- Omics Reveals New Ruminal Microbial Features Associated with Feed Efficiency in Dairy Cattle. Microbiome 2022, 10, 32, doi:10.1186/s40168-022-01228-9. 22. Henderson, G.; Cox, F.; Ganesh, S.; Jonker, A.; Young, W.; Janssen, P.H. Rumen Microbial Community Composition Varies with Diet and Host, but a Core Microbiome Is Found across a Wide Geographical Range. Scientific reports 2015, 5, 1–15. 23. Zhou, M.; Peng, Y.-J.; Chen, Y.; Klinger, C.M.; Oba, M.; Liu, J.-X.; Guan, L.L. Assessment of Microbiome Changes after Rumen Transfaunation: Implications on Improving Feed Efficiency in Beef Cattle. Microbiome 2018, 6, 1–14. 24. Deusch, S.; Camarinha-Silva, A.; Conrad, J.; Beifuss, U.; Rodehutscord, M.; Seifert, J. A Structural and Functional Elucidation of the Rumen Microbiome Influenced by Various Diets and Microenvironments. Frontiers in microbiology 2017, 8, 1605. 32 25. Myer, P.R.; Wells, J.E.; Smith, T.P.; Kuehn, L.A.; Freetly, H.C. Microbial Community Profiles of the Colon from Steers Differing in Feed Efficiency. Springerplus 2015, 4, 1– 13. 26. O’Hara, E.; Kelly, A.; McCabe, M.S.; Kenny, D.A.; Guan, L.L.; Waters, S.M. Effect of a Butyrate-Fortified Milk Replacer on Gastrointestinal Microbiota and Products of Fermentation in Artificially Reared Dairy Calves at Weaning. Scientific reports 2018, 8, 1–11. 27. Klein-Jöbstl, D.; Schornsteiner, E.; Mann, E.; Wagner, M.; Drillich, M.; Schmitz-Esser, S. Pyrosequencing Reveals Diverse Fecal Microbiota in Simmental Calves during Early Development. Frontiers in microbiology 2014, 5, 622. 28. Oikonomou, G.; Bicalho, M.L.; Meira, E.; Rossi, R.E.; Foditsch, C.; Machado, V.S.; Teixeira, A.G.V.; Santisteban, C.; Schukken, Y.H.; Bicalho, R.C. Microbiota of Cow’s Milk; Distinguishing Healthy, Sub-Clinically and Clinically Diseased Quarters. PloS one 2014, 9, e85904. 29. Alipour, M.J.; Jalanka, J.; Pessa-Morikawa, T.; Kokkonen, T.; Satokari, R.; Hynönen, U.; Iivanainen, A.; Niku, M. The Composition of the Perinatal Intestinal Microbiota in Cattle. Scientific reports 2018, 8, 1–14. 30. Dias, J.; Marcondes, M.I.; Motta de Souza, S.; Cardoso da Mata e Silva, B.; Fontes Noronha, M.; Tassinari Resende, R.; Machado, F.S.; Cuquetto Mantovani, H.; Dill- McFarland, K.A.; Suen, G. Bacterial Community Dynamics across the Gastrointestinal Tracts of Dairy Calves during Preweaning Development. Applied and environmental microbiology 2018, 84, e02675-17. 31. Jost, T.; Lacroix, C.; Braegger, C.P.; Chassard, C. New Insights in Gut Microbiota Establishment in Healthy Breast Fed Neonates. 2012. 32. Lukáš, F.; Koppová, I.; Kudrna, V.; Kopečný, J. Postnatal Development of Bacterial Population in the Gastrointestinal Tract of Calves. Folia microbiologica 2007, 52, 99–104. 33. Mao, S.; Zhang, M.; Liu, J.; Zhu, W. Characterising the Bacterial Microbiota across the Gastrointestinal Tracts of Dairy Cattle: Membership and Potential Function. Sci Rep 2015, 5, 16116, doi:10.1038/srep16116. 34. O’Hara, E.; Neves, A.L.A.; Song, Y.; Guan, L.L. The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger? Annual Review of Animal Biosciences 2020, 8, 199–220, doi:10.1146/annurev-animal-021419-083952. 35. Dehority, B.A. Gastrointestinal Tracts of Herbivores, Particularly the Ruminant: Anatomy, Physiology and Microbial Digestion of Plants. Journal of applied animal research 2002, 21, 145–160. 36. Hoover, W.H. Digestion and Absorption in the Hindgut of Ruminants. journal of Animal Science 1978, 46, 1789–1799. 33 37. Gressley, T.; Hall, M.; Armentano, L. Ruminant Nutrition Symposium: Productivity, Digestion, and Health Responses to Hindgut Acidosis in Ruminants. Journal of animal science 2011, 89, 1120–1130. 38. Malmuthuge, N.; Griebel, P.J.; Guan, L.L. Taxonomic Identification of Commensal Bacteria Associated with the Mucosa and Digesta throughout the Gastrointestinal Tracts of Preweaned Calves. Appl Environ Microbiol 2014, 80, 2021–2028, doi:10.1128/AEM.03864-13. 39. Hooper, L.V.; Littman, D.R.; Macpherson, A.J. Interactions between the Microbiota and the Immune System. science 2012, 336, 1268–1273. 40. Carding, S.; Verbeke, K.; Vipond, D.T.; Corfe, B.M.; Owen, L.J. Dysbiosis of the Gut Microbiota in Disease. Microbial ecology in health and disease 2015, 26, 26191. 41. Khalil, A.; Batool, A.; Arif, S. Healthy Cattle Microbiome and Dysbiosis in Diseased Phenotypes. Ruminants 2022, 2, 134–156. 42. Mao, S.; Zhang, R.; Wang, D.; Zhu, W. Impact of Subacute Ruminal Acidosis (SARA) Adaptation on Rumen Microbiota in Dairy Cattle Using Pyrosequencing. Anaerobe 2013, 24, 12–19. 43. Khafipour, E.; Li, S.; Plaizier, J.C.; Krause, D.O. Rumen Microbiome Composition Determined Using Two Nutritional Models of Subacute Ruminal Acidosis. Applied and environmental microbiology 2009, 75, 7115–7124. 44. McCann, J.C.; Luan, S.; Cardoso, F.C.; Derakhshani, H.; Khafipour, E.; Loor, J.J. Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and Epithelium. Frontiers in microbiology 2016, 7, 701. 45. Emmanuel, D.G.V.; Madsen, K.L.; Churchill, T.A.; Dunn, S.M.; Ametaj, B.N. Acidosis and Lipopolysaccharide from Escherichia Coli B:055 Cause Hyperpermeability of Rumen and Colon Tissues. Journal of Dairy Science 2007, 90, 5552–5557, doi:10.3168/jds.2007- 0257. 46. Zebeli, Q.; Metzler-Zebeli, B.U. Interplay between Rumen Digestive Disorders and Diet- Induced Inflammation in Dairy Cattle. Research in Veterinary Science 2012, 93, 1099– 1108, doi:10.1016/j.rvsc.2012.02.004. 47. Aschenbach, J.R.; Zebeli, Q.; Patra, A.K.; Greco, G.; Amasheh, S.; Penner, G.B. Symposium Review: The Importance of the Ruminal Epithelial Barrier for a Healthy and Productive Cow. Journal of Dairy Science 2019, 102, 1866–1882, doi:10.3168/jds.2018- 15243. 48. Abdela, N. Sub-Acute Ruminal Acidosis (SARA) and Its Consequence in Dairy Cattle: A Review of Past and Recent Research at Global Prospective. Achievements in the Life Sciences 2016, 10, 187–196, doi:10.1016/j.als.2016.11.006. 34 49. Parsons, K.R.; Howard, C.J.; Jones, B.V.; Sopp, P. Investigation of Bovine Gut Associated Lymphoid Tissue (GALT) Using Monoclonal Antibodies against Bovine Lymphocytes. Vet Pathol 1989, 26, 396–408, doi:10.1177/030098588902600505. 50. Arroyo, J.; Hosseini, A.; Zhou, Z.; Alharthi, A.; Trevisi, E.; Osorio, J.; Loor, J. Reticulo- Rumen Mass, Epithelium Gene Expression, and Systemic Biomarkers of Metabolism and Inflammation in Holstein Dairy Cows Fed a High-Energy Diet. Journal of dairy science 2017, 100, 9352–9360. 51. Trevisi, E.; Riva, F.; Filipe, J.F.S.; Massara, M.; Minuti, A.; Bani, P.; Amadori, M. Innate Immune Responses to Metabolic Stress Can Be Detected in Rumen Fluids. Research in veterinary science 2018, 117, 65–73. 52. Fecteau, M.-E.; Pitta, D.W.; Vecchiarelli, B.; Indugu, N.; Kumar, S.; Gallagher, S.C.; Fyock, T.L.; Sweeney, R.W. Dysbiosis of the Fecal Microbiota in Cattle Infected with Mycobacterium Avium Subsp. Paratuberculosis. PLoS One 2016, 11, e0160353, doi:10.1371/journal.pone.0160353. 53. Cho, Y.-I.; Yoon, K.-J. An Overview of Calf Diarrhea - Infectious Etiology, Diagnosis, and Intervention. J Vet Sci 2014, 15, 1–17, doi:10.4142/jvs.2014.15.1.1. 54. Englen, M.; Hill, A.E.; Dargatz, D.; Ladely, S.; Fedorka‐Cray, P. Prevalence and Antimicrobial Resistance of Campylobacter in US Dairy Cattle. Journal of applied microbiology 2007, 102, 1570–1577. 55. Venegas-Vargas, C.; Henderson, S.; Khare, A.; Mosci, R.E.; Lehnert, J.D.; Singh, P.; Ouellette, L.M.; Norby, B.; Funk, J.A.; Rust, S.; et al. Factors Associated with Shiga Toxin-Producing Escherichia Coli Shedding by Dairy and Beef Cattle. Applied and Environmental Microbiology 2016, 82, 5049–5056, doi:10.1128/AEM.00829-16. 56. Gutema, F.D.; Agga, G.E.; Abdi, R.D.; De Zutter, L.; Duchateau, L.; Gabriël, S. Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and Meta-Analysis of Published Studies, 2000-2017. Front Vet Sci 2019, 6, 102, doi:10.3389/fvets.2019.00102. 57. Newell, D.G.; Koopmans, M.; Verhoef, L.; Duizer, E.; Aidara-Kane, A.; Sprong, H.; Opsteegh, M.; Langelaar, M.; Threfall, J.; Scheutz, F.; et al. Food-Borne Diseases — The Challenges of 20years Ago Still Persist While New Ones Continue to Emerge. International Journal of Food Microbiology 2010, 139, S3–S15, doi:10.1016/j.ijfoodmicro.2010.01.021. 58. Majowicz, S.E.; Scallan, E.; Jones-Bitton, A.; Sargeant, J.M.; Stapleton, J.; Angulo, F.J.; Yeung, D.H.; Kirk, M.D. Global Incidence of Human Shiga Toxin–Producing Escherichia Coli Infections and Deaths: A Systematic Review and Knowledge Synthesis. Foodborne Pathogens and Disease 2014, 11, 447–455, doi:10.1089/fpd.2013.1704. 35 59. Duffy, G.; McCabe, E. Veterinary Public Health Approach to Managing Pathogenic Verocytotoxigenic Escherichia Coli in the Agri-Food Chain. Microbiol Spectr 2014, 2, 2.5.21, doi:10.1128/microbiolspec.EHEC-0023-2013. 60. Gyles, C.L. Shiga Toxin-Producing Escherichia Coli: An Overview. Journal of Animal Science 2007, 85, E45–E62, doi:10.2527/jas.2006-508. 61. Persad, A.K.; LeJeune, J.T. Animal Reservoirs of Shiga Toxin-Producing Escherichia Coli. Microbiol Spectr 2014, 2, EHEC-0027-2014, doi:10.1128/microbiolspec.EHEC- 0027-2014. 62. Pires, S.M.; Majowicz, S.; Gill, A.; Devleesschauwer, B. Global and Regional Source Attribution of Shiga Toxin-Producing Escherichia Coli Infections Using Analysis of Outbreak Surveillance Data. Epidemiol Infect 2019, 147, e236, doi:10.1017/S095026881900116X. 63. Tilden Jr, J.; Young, W.; McNamara, A.-M.; Custer, C.; Boesel, B.; Lambert-Fair, M.A.; Majkowski, J.; Vugia, D.; Werner, S.; Hollingsworth, J. A New Route of Transmission for Escherichia Coli: Infection from Dry Fermented Salami. American journal of public health 1996, 86, 1142–1145. 64. Tree, J.J.; Roe, A.J.; Flockhart, A.; McAteer, S.P.; Xu, X.; Shaw, D.; Mahajan, A.; Beatson, S.A.; Best, A.; Lotz, S.; et al. Transcriptional Regulators of the GAD Acid Stress Island Are Carried by Effector Protein-Encoding Prophages and Indirectly Control Type III Secretion in Enterohemorrhagic Escherichia Coli O157:H7. Molecular Microbiology 2011, 80, 1349–1365, doi:10.1111/j.1365-2958.2011.07650.x. 65. Freedman, S.B.; Xie, J.; Neufeld, M.S.; Hamilton, W.L.; Hartling, L.; Tarr, P.I.; Alberta Provincial Pediatric Enteric Infection Team (APPETITE); Nettel-Aguirre, A.; Chuck, A.; Lee, B. Shiga Toxin–Producing Escherichia Coli Infection, Antibiotics, and Risk of Developing Hemolytic Uremic Syndrome: A Meta-Analysis. Clinical Infectious Diseases 2016, 62, 1251–1258. 66. Thomas, D.E.; Elliott, E.J. Interventions for Preventing Diarrhea-Associated Hemolytic Uremic Syndrome: Systematic Review. BMC Public Health 2013, 13, 799, doi:10.1186/1471-2458-13-799. 67. Matthews, L.; Reeve, R.; Gally, D.L.; Low, J.C.; Woolhouse, M.E.J.; McAteer, S.P.; Locking, M.E.; Chase-Topping, M.E.; Haydon, D.T.; Allison, L.J.; et al. Predicting the Public Health Benefit of Vaccinating Cattle against Escherichia Coli O157. Proceedings of the National Academy of Sciences 2013, 110, 16265–16270, doi:10.1073/pnas.1304978110. 68. Mora, A.; Herrera, A. Characteristics of the Shiga-Toxin-Producing Enteroaggregative Escherichia Coli O104:H4 German Outbreak Strain and of STEC Strains Isolated in Spain. International Microbiology 2011, 121–141, doi:10.2436/20.1501.01.142. 36 69. Ludwig, J.B.; Shi, X.; Shridhar, P.B.; Roberts, E.L.; DebRoy, C.; Phebus, R.K.; Bai, J.; Nagaraja, T.G. Multiplex PCR Assays for the Detection of One Hundred and Thirty Seven Serogroups of Shiga Toxin-Producing Escherichia Coli Associated With Cattle. Front. Cell. Infect. Microbiol. 2020, 10, 378, doi:10.3389/fcimb.2020.00378. 70. Boyce, T.G.; Swerdlow, D.L.; Griffin, P.M. Escherichia Coli O157:H7 and the Hemolytic–Uremic Syndrome. New England Journal of Medicine 1995, 333, 364–368, doi:10.1056/NEJM199508103330608. 71. Rangel, J.M.; Sparling, P.H.; Crowe, C.; Griffin, P.M.; Swerdlow, D.L. Epidemiology of Escherichia Coli O157:H7 Outbreaks, United States, 1982–2002 - Volume 11, Number 4—April 2005 - Emerging Infectious Diseases Journal - CDC., doi:10.3201/eid1104.040739. 72. Hughes, J.M.; Wilson, M.E.; Johnson, K.E.; Thorpe, C.M.; Sears, C.L. The Emerging Clinical Importance of Non-O157 Shiga Toxin—Producing Escherichia Coli. Clinical infectious diseases 2006, 43, 1587–1595. 73. Brooks, J.T.; Sowers, E.G.; Wells, J.G.; Greene, K.D.; Griffin, P.M.; Hoekstra, R.M.; Strockbine, N.A. Non-O157 Shiga Toxin–Producing Escherichia Coli Infections in the United States, 1983–2002. The Journal of infectious diseases 2005, 192, 1422–1429. 74. Karmali, M.A.; Mascarenhas, M.; Shen, S.; Ziebell, K.; Johnson, S.; Reid-Smith, R.; Isaac-Renton, J.; Clark, C.; Rahn, K.; Kaper, J.B. Association of Genomic O Island 122 of Escherichia Coli EDL 933 with Verocytotoxin-Producing Escherichia Coli Seropathotypes That Are Linked to Epidemic and/or Serious Disease. J Clin Microbiol 2003, 41, 4930–4940, doi:10.1128/JCM.41.11.4930-4940.2003. 75. Laing, C.R.; Buchanan, C.; Taboada, E.N.; Zhang, Y.; Karmali, M.A.; Thomas, J.E.; Gannon, V.P. In Silico Genomic Analyses Reveal Three Distinct Lineages of Escherichia Coli O157:H7, One of Which Is Associated with Hyper-Virulence. BMC Genomics 2009, 10, 287, doi:10.1186/1471-2164-10-287. 76. O’Loughlin, E.V.; Robins-Browne, R.M. Effect of Shiga Toxin and Shiga-like Toxins on Eukaryotic Cells. Microbes and Infection 2001, 3, 493–507, doi:10.1016/S1286- 4579(01)01405-8. 77. EFSA BIOHAZ Panel; Koutsoumanis, K.; Allende, A.; Alvarez‐Ordóñez, A.; Bover‐Cid, S.; Chemaly, M.; Davies, R.; De Cesare, A.; Herman, L.; Hilbert, F.; et al. Pathogenicity Assessment of Shiga Toxin‐producing Escherichia Coli (STEC) and the Public Health Risk Posed by Contamination of Food with STEC. EFS2 2020, 18, doi:10.2903/j.efsa.2020.5967. 78. Kaper, J.B.; Mellies, J.L.; Nataro, J.P. Pathogenicity Islands and Other Mobile Genetic Elements of Diarrheagenic Escherichia Coli. Pathogenicity islands and other mobile virulence elements 1999, 33–58. 37 79. Melton-Celsa, A.; Mohawk, K.; Teel, L.; O’Brien, A. Pathogenesis of Shiga-Toxin Producing Escherichia Coli. In Ricin and Shiga Toxins; Mantis, N., Ed.; Current Topics in Microbiology and Immunology; Springer Berlin Heidelberg: Berlin, Heidelberg, 2011; Vol. 357, pp. 67–103 ISBN 978-3-642-27469-5. 80. Scheutz, F.; Teel, L.D.; Beutin, L.; Piérard, D.; Buvens, G.; Karch, H.; Mellmann, A.; Caprioli, A.; Tozzoli, R.; Morabito, S.; et al. Multicenter Evaluation of a Sequence-Based Protocol for Subtyping Shiga Toxins and Standardizing Stx Nomenclature. J Clin Microbiol 2012, 50, 2951–2963, doi:10.1128/JCM.00860-12. 81. Pacheco, A.R.; Sperandio, V. Shiga Toxin in Enterohemorrhagic E.Coli: Regulation and Novel Anti-Virulence Strategies. Front. Cell. Inf. Microbio. 2012, 2, doi:10.3389/fcimb.2012.00081. 82. Herold, S.; Karch, H.; Schmidt, H. Shiga Toxin-Encoding Bacteriophages – Genomes in Motion. International Journal of Medical Microbiology 2004, 294, 115–121, doi:10.1016/j.ijmm.2004.06.023. 83. Strauch, E.; Lurz, R.; Beutin, L. Characterization of a Shiga Toxin-Encoding Temperate Bacteriophage of Shigella Sonnei. Infection and immunity 2001, 69, 7588–7595. 84. Morabito, S.; Tozzoli, R.; Oswald, E.; Caprioli, A. A Mosaic Pathogenicity Island Made up of the Locus of Enterocyte Effacement and a Pathogenicity Island of Escherichia Coli O157: H7 Is Frequently Present in Attaching and Effacing E. coli. Infection and Immunity 2003, 71, 3343–3348. 85. Kaper, J.B.; Nataro, J.P.; Mobley, H.L.T. Pathogenic Escherichia Coli. Nat Rev Microbiol 2004, 2, 123–140, doi:10.1038/nrmicro818. 86. Coombes, B.K.; Wickham, M.E.; Mascarenhas, M.; Gruenheid, S.; Finlay, B.B.; Karmali, M.A. Molecular Analysis as an Aid To Assess the Public Health Risk of Non-O157 Shiga Toxin-Producing Escherichia Coli Strains. Appl Environ Microbiol 2008, 74, 2153–2160, doi:10.1128/AEM.02566-07. 87. Pruimboom-Brees, I.M.; Morgan, T.W.; Ackermann, M.R.; Nystrom, E.D.; Samuel, J.E.; Cornick, N.A.; Moon, H.W. Cattle Lack Vascular Receptors for Escherichia Coli O157: H7 Shiga Toxins. Proceedings of the National Academy of Sciences 2000, 97, 10325– 10329. 88. Diard, M.; Garry, L.; Selva, M.; Mosser, T.; Denamur, E.; Matic, I. Pathogenicity- Associated Islands in Extraintestinal Pathogenic Escherichia Coli Are Fitness Elements Involved in Intestinal Colonization. J Bacteriol 2010, 192, 4885–4893, doi:10.1128/JB.00804-10. 89. Dean-Nystrom, E.A.; Bosworth, B.T.; Moon, H.W.; O’Brien, A.D. Escherichia Coli O157:H7 Requires Intimin for Enteropathogenicity in Calves. Infect Immun 1998, 66, 4560–4563, doi:10.1128/IAI.66.9.4560-4563.1998. 38 90. Smith, D.G.E.; Naylor, S.W.; Gally, D.L. Consequences of EHEC Colonisation in Humans and Cattle. International Journal of Medical Microbiology 2002, 292, 169–183, doi:10.1078/1438-4221-00202. 91. Fröhlich, J.; Baljer, G.; Menge, C. Maternally and Naturally Acquired Antibodies to Shiga Toxins in a Cohort of Calves Shedding Shiga-Toxigenic Escherichia Coli. Appl Environ Microbiol 2009, 75, 3695–3704, doi:10.1128/AEM.02869-08. 92. Snedeker, K.G.; Campbell, M.; Sargeant, J.M. A Systematic Review of Vaccinations to Reduce the Shedding of Escherichia Coli O157 in the Faeces of Domestic Ruminants. Zoonoses and Public Health 2012, 59, 126–138, doi:10.1111/j.1863-2378.2011.01426.x. 93. Mir, R.A.; Schaut, R.G.; Allen, H.K.; Looft, T.; Loving, C.L.; Kudva, I.T.; Sharma, V.K. Cattle Intestinal Microbiota Shifts Following Escherichia Coli O157:H7 Vaccination and Colonization. PLOS ONE 2019, 14, e0226099, doi:10.1371/journal.pone.0226099. 94. Besser, T.E.; Richards, B.L.; Rice, D.H.; Hancock, D.D. Escherichia Coli O157[Ratio ]H7 Infection of Calves: Infectious Dose and Direct Contact Transmission. Epidemiol. Infect. 2001, 127, 555–560, doi:10.1017/S095026880100615X. 95. Semenov, A.M.; Kuprianov, A.A.; van Bruggen, A.H.C. Transfer of Enteric Pathogens to Successive Habitats as Part of Microbial Cycles. Microb Ecol 2010, 60, 239–249, doi:10.1007/s00248-010-9663-0. 96. Sperandio, V. SdiA Sensing of Acyl-Homoserine Lactones by Enterohemorrhagic E. coli (EHEC) Serotype O157: H7 in the Bovine Rumen. Gut Microbes 2010, 1, 432–435, doi:10.4161/gmic.1.6.14177. 97. Naylor, S.W.; Low, J.C.; Besser, T.E.; Mahajan, A.; Gunn, G.J.; Pearce, M.C.; McKendrick, I.J.; Smith, D.G.E.; Gally, D.L. Lymphoid Follicle-Dense Mucosa at the Terminal Rectum Is the Principal Site of Colonization of Enterohemorrhagic Escherichia Coli O157:H7 in the Bovine Host. Infection and Immunity 2003, 71, 1505–1512, doi:10.1128/IAI.71.3.1505-1512.2003. 98. Boukhors, K.; Pradel, N.; Girardeau, J.-P.; Livrelli, V.; Ou Sa d, A.M.; Contrepois, M.; Martin, C. Effect of Diet on Shiga Toxin-Producing Escherichia Coli (STEC) Growth Andsurvival in Rumen and Abomasum Fluids. Vet. Res. 2002, 33, 405–412, doi:10.1051/vetres:2002026. 99. Kudva, I.T.; Stanton, T.B.; Lippolis, J.D. The Escherichia Coli O157:H7 Bovine Rumen Fluid Proteome Reflects Adaptive Bacterial Responses. BMC Microbiol 2014, 14, 48, doi:10.1186/1471-2180-14-48. 100. Lim, J.Y.; Sheng, H.; Seo, K.S.; Park, Y.H.; Hovde, C.J. Characterization of an Escherichia Coli O157:H7 Plasmid O157 Deletion Mutant and Its Survival and Persistence in Cattle. Appl Environ Microbiol 2007, 73, 2037–2047, doi:10.1128/AEM.02643-06. 39 101. Price, S.B.; Wright, J.C.; DeGraves, F.J.; Castanie-Cornet, M.-P.; Foster, J.W. Acid Resistance Systems Required for Survival of Escherichia Coli O157:H7 in the Bovine Gastrointestinal Tract and in Apple Cider Are Different. Appl Environ Microbiol 2004, 70, 4792–4799, doi:10.1128/AEM.70.8.4792-4799.2004. 102. Moran, J. Tropical Dairy Farming: Feeding Management for Small Holder Dairy Farmers in the Humid Tropics; Csiro publishing, 2005; ISBN 0-643-09123-8. 103. Grauke, L.J.; Kudva, I.T.; Yoon, J.W.; Hunt, C.W.; Williams, C.J.; Hovde, C.J. Gastrointestinal Tract Location of Escherichia Coli O157:H7 in Ruminants. Appl Environ Microbiol 2002, 68, 2269–2277, doi:10.1128/AEM.68.5.2269-2277.2002. 104. Keen, J.E.; Laegreid, W.W.; Chitko-McKown, C.G.; Durso, L.M.; Bono, J.L. Distribution of Shiga-Toxigenic Escherichia Coli O157 in the Gastrointestinal Tract of Naturally O157-Shedding Cattle at Necropsy. Appl Environ Microbiol 2010, 76, 5278–5281, doi:10.1128/AEM.00400-10. 105. Naylor, S.W.; Low, J.C.; Besser, T.E.; Mahajan, A.; Gunn, G.J.; Pearce, M.C.; McKendrick, I.J.; Smith, D.G.E.; Gally, D.L. Lymphoid Follicle-Dense Mucosa at the Terminal Rectum Is the Principal Site of Colonization of Enterohemorrhagic Escherichia Coli O157:H7 in the Bovine Host. Infect Immun 2003, 71, 1505–1512, doi:10.1128/IAI.71.3.1505-1512.2003. 106. Dziva, F.; van Diemen, P.M.; Stevens, M.P.; Smith, A.J.; Wallis, T.S. Identification of Escherichia Coli O157 : H7 Genes Influencing Colonization of the Bovine Gastrointestinal Tract Using Signature-Tagged Mutagenesis. Microbiology 2004, 150, 3631–3645, doi:10.1099/mic.0.27448-0. 107. Naylor, S.W.; Roe, A.J.; Nart, P.; Spears, K.; Smith, David.G.E.; Low, J.C.; Gally, D.L. Escherichia Coli O157 : H7 Forms Attaching and Effacing Lesions at the Terminal Rectum of Cattle and Colonization Requires the LEE4 Operon. Microbiology 2005, 151, 2773–2781, doi:10.1099/mic.0.28060-0. 108. Baehler, A.A.; Moxley, R.A. Escherichia Coli O157:H7 Induces Attaching-Effacing Lesions in Large Intestinal Mucosal Explants from Adult Cattle. FEMS Microbiology Letters 2000, 185, 239–242, doi:10.1111/j.1574-6968.2000.tb09068.x. 109. Phillips, A.D. Enterohaemorrhagic Escherichia Coli O157:H7 Target Peyer’s Patches in Humans and Cause Attaching/Effacing Lesions in Both Human and Bovine Intestine. Gut 2000, 47, 377–381, doi:10.1136/gut.47.3.377. 110. Mahajan, A.; Currie, C.G.; Mackie, S.; Tree, J.; McAteer, S.; McKendrick, I.; McNeilly, T.N.; Roe, A.; La Ragione, R.M.; Woodward, M.J.; et al. An Investigation of the Expression and Adhesin Function of H7 Flagella in the Interaction of Escherichia Coli O157 : H7 with Bovine Intestinal Epithelium. Cellular Microbiology 2009, 11, 121–137, doi:10.1111/j.1462-5822.2008.01244.x. 40 111. Erdem, A.L.; Avelino, F.; Xicohtencatl-Cortes, J.; Girón, J.A. Host Protein Binding and Adhesive Properties of H6 and H7 Flagella of Attaching and Effacing Escherichia Coli. J Bacteriol 2007, 189, 7426–7435, doi:10.1128/JB.00464-07. 112. Monteiro, R.; Ageorges, V.; Rojas-Lopez, M.; Schmidt, H.; Weiss, A.; Bertin, Y.; Forano, E.; Jubelin, G.; Henderson, I.R.; Livrelli, V.; et al. A Secretome View of Colonisation Factors in Shiga Toxin-Encoding Escherichia Coli (STEC): From Enterohaemorrhagic E. coli (EHEC) to Related Enteropathotypes. FEMS Microbiology Letters 2016, 363, fnw179, doi:10.1093/femsle/fnw179. 113. Low, A.S.; Dziva, F.; Torres, A.G.; Martinez, J.L.; Rosser, T.; Naylor, S.; Spears, K.; Holden, N.; Mahajan, A.; Findlay, J.; et al. Cloning, Expression, and Characterization of Fimbrial Operon F9 from Enterohemorrhagic Escherichia Coli O157:H7. Infect Immun 2006, 74, 2233–2244, doi:10.1128/IAI.74.4.2233-2244.2006. 114. Kudva, I.T.; Griffin, R.W.; Krastins, B.; Sarracino, D.A.; Calderwood, S.B.; John, M. Proteins Other than the Locus of Enterocyte Effacement-Encoded Proteins Contribute to Escherichia Coli O157:H7 Adherence to Bovine Rectoanal Junction Stratified Squamous Epithelial Cells. BMC Microbiol 2012, 12, 103, doi:10.1186/1471-2180-12-103. 115. Xicohtencatl-Cortes, J.; Monteiro-Neto, V.; Ledesma, M.A.; Jordan, D.M.; Francetic, O.; Kaper, J.B.; Puente, J.L.; Girón, J.A. Intestinal Adherence Associated with Type IV Pili of Enterohemorrhagic Escherichia Coli O157:H7. J. Clin. Invest. 2007, 117, 3519–3529, doi:10.1172/JCI30727. 116. Ageorges, V.; Monteiro, R.; Leroy, S.; Burgess, C.M.; Pizza, M.; Chaucheyras-durand, F.; Desvaux, M. Molecular Determinants of Surface Colonisation in Diarrhoeagenic Escherichia Coli (DEC): From Bacterial Adhesion to Biofilm Formation. FEMS Microbiology Reviews 2020, 44, 314–350, doi:10.1093/femsre/fuaa008. 117. Wells, J.E.; Kim, M.; Bono, J.L.; Kuehn, L.A.; Benson, A.K. MEAT SCIENCE AND MUSCLE BIOLOGY SYMPOSIUM:Escherichia Coli O157:H7, Diet, and Fecal Microbiome in Beef Cattle12. Journal of Animal Science 2014, 92, 1345–1355, doi:10.2527/jas.2013-7282. 118. Callaway, T.R.; Carr, M.A.; Edrington, T.S.; Anderson, R.C.; Nisbet, D.J. Diet, Escherichia Coli O157:H7, and Cattle: A Review after 10 Years. Curr Issues Mol Biol 2009, 11, 67–79. 119. Berry, E.D.; Wells, J.E. Escherichia Coli O157:H7: Recent Advances in Research on Occurrence, Transmission, and Control in Cattle and the Production Environment. Adv Food Nutr Res 2010, 60, 67–117, doi:10.1016/S1043-4526(10)60004-6. 120. Talley, J.L.; Wayadande, A.C.; Wasala, L.P.; Gerry, A.C.; Fletcher, J.; DeSILVA, U.; Gilliland, S.E. Association of Escherichia Coli O157:H7 with Filth Flies (Muscidae and Calliphoridae) Captured in Leafy Greens Fields and Experimental Transmission of E. coli O157:H7 to Spinach Leaves by House Flies (Diptera: Muscidae). Journal of Food Protection 2009, 72, 1547–1552, doi:10.4315/0362-028X-72.7.1547. 41 121. Wang, G.; Zhao, T.; Doyle, M.P. Fate of Enterohemorrhagic Escherichia Coli O157:H7 in Bovine Feces. Appl Environ Microbiol 1996, 62, 2567–2570, doi:10.1128/aem.62.7.2567- 2570.1996. 122. Fukushima, H.; Hoshina, K.; Gomyoda, M. Long-Term Survival of Shiga Toxin- Producing Escherichia Coli O26, O111, and O157 in Bovine Feces. Appl Environ Microbiol 1999, 65, 5177–5181, doi:10.1128/AEM.65.11.5177-5181.1999. 123. Fremaux, B.; Prigent-Combaret, C.; Vernozy-Rozand, C. Long-Term Survival of Shiga Toxin-Producing Escherichia Coli in Cattle Effluents and Environment: An Updated Review. Veterinary Microbiology 2008, 132, 1–18, doi:10.1016/j.vetmic.2008.05.015. 124. van Elsas, J.D.; Semenov, A.V.; Costa, R.; Trevors, J.T. Survival of Escherichia Coli in the Environment: Fundamental and Public Health Aspects. ISME J 2011, 5, 173–183, doi:10.1038/ismej.2010.80. 125. Blankenship, H.M.; Carbonell, S.; Mosci, R.E.; McWilliams, K.; Pietrzen, K.; Benko, S.; Gatesy, T.; Grooms, D.; Manning, S.D. Genetic and Phenotypic Factors Associated with Persistent Shedding of Shiga Toxin-Producing Escherichia Coli by Beef Cattle. Appl Environ Microbiol 2020, 86, e01292-20, doi:10.1128/AEM.01292-20. 126. Islam, Md.Z.; Musekiwa, A.; Islam, K.; Ahmed, S.; Chowdhury, S.; Ahad, A.; Biswas, P.K. Regional Variation in the Prevalence of E. coli O157 in Cattle: A Meta-Analysis and Meta-Regression. PLoS ONE 2014, 9, e93299, doi:10.1371/journal.pone.0093299. 127. Ferens, W.A.; Hovde, C.J. Escherichia Coli O157:H7: Animal Reservoir and Sources of Human Infection. Foodborne Pathogens and Disease 2011, 8, 465–487, doi:10.1089/fpd.2010.0673. 128. Ekong, P.S.; Sanderson, M.W.; Cernicchiaro, N. Prevalence and Concentration of Escherichia Coli O157 in Different Seasons and Cattle Types Processed in North America: A Systematic Review and Meta-Analysis of Published Research. Preventive Veterinary Medicine 2015, 121, 74–85, doi:10.1016/j.prevetmed.2015.06.019. 129. Stanford, K.; Johnson, R.P.; Alexander, T.W.; McAllister, T.A.; Reuter, T. Influence of Season and Feedlot Location on Prevalence and Virulence Factors of Seven Serogroups of Escherichia Coli in Feces of Western-Canadian Slaughter Cattle. PLoS ONE 2016, 11, e0159866, doi:10.1371/journal.pone.0159866. 130. Paddock, Z.D.; Renter, D.G.; Shi, X.; Krehbiel, C.R.; DeBey, B.; Nagaraja, T.G. Effects of Feeding Dried Distillers Grains with Supplemental Starch on Fecal Shedding of Escherichia Coli O157:H7 in Experimentally Inoculated Steers1. Journal of Animal Science 2013, 91, 1362–1370, doi:10.2527/jas.2012-5618. 131. Blankenship, H.M.; Mosci, R.E.; Dietrich, S.; Burgess, E.; Wholehan, J.; McWilliams, K.; Pietrzen, K.; Benko, S.; Gatesy, T.; Rudrik, J.T.; et al. Population Structure and Genetic Diversity of Non-O157 Shiga Toxin-Producing Escherichia Coli (STEC) Clinical Isolates from Michigan. Sci Rep 2021, 11, 4461, doi:10.1038/s41598-021-83775-z. 42 132. McALLISTER, T.A.; Bach, S.J.; Stanford, K.; Callaway, T.R. Shedding of Escherichia Coli O157:H7 by Cattle Fed Diets Containing Monensin or Tylosin†. Journal of Food Protection 2006, 69, 2075–2083, doi:10.4315/0362-028X-69.9.2075. 133. Paddock, Z.D.; Walker, C.E.; Drouillard, J.S.; Nagaraja, T.G. Dietary Monensin Level, Supplemental Urea, and Ractopamine on Fecal Shedding of Escherichia Coli O157:H7 in Feedlot Cattle1. Journal of Animal Science 2011, 89, 2829–2835, doi:10.2527/jas.2010- 3793. 134. Chase-Topping, M.; Gally, D.; Low, C.; Matthews, L.; Woolhouse, M. Super-Shedding and the Link between Human Infection and Livestock Carriage of Escherichia Coli O157. Nat Rev Microbiol 2008, 6, 904–912, doi:10.1038/nrmicro2029. 135. Murphy, B.P.; McCabe, E.; Murphy, M.; Buckley, J.F.; Crowley, D.; Fanning, S.; Duffy, G. Longitudinal Study of Two Irish Dairy Herds: Low Numbers of Shiga Toxin-Producing Escherichia Coli O157 and O26 Super-Shedders Identified. Front. Microbiol. 2016, 7, doi:10.3389/fmicb.2016.01850. 136. Omisakin, F.; MacRae, M.; Ogden, I.D.; Strachan, N.J.C. Concentration and Prevalence of Escherichia Coli O157 in Cattle Feces at Slaughter. Applied and Environmental Microbiology 2003, 69, 2444–2447, doi:10.1128/AEM.69.5.2444-2447.2003. 137. Robinson, S. e; Brown, P. e; Wright, E. j; Hart, C. a; French, N. p Quantifying Within- and between-Animal Variation and Uncertainty Associated with Counts of Escherichia Coli O157 Occurring in Naturally Infected Cattle Faeces. Journal of The Royal Society Interface 2009, 6, 169–177, doi:10.1098/rsif.2008.0183. 138. Luedtke, B.E.; Bosilevac, J.M.; Harhay, D.M.; Arthur, T.M. Effect of Direct-Fed Microbial Dosage on the Fecal Concentrations of Enterohemorrhagic Escherichia Coli in Feedlot Cattle. Foodborne pathogens and disease 2016, 13, 190–195. 139. Bertin, Y.; Habouzit, C.; Dunière, L.; Laurier, M.; Durand, A.; Duchez, D.; Segura, A.; Thévenot-Sergentet, D.; Baruzzi, F.; Chaucheyras-Durand, F. Lactobacillus Reuteri Suppresses E. coli O157: H7 in Bovine Ruminal Fluid: Toward a Pre-Slaughter Strategy to Improve Food Safety? PLoS One 2017, 12, e0187229. 140. McAllister, T.A.; Beauchemin, K.A.; Alazzeh, A.Y.; Baah, J.; Teather, R.M.; Stanford, K. Review: The Use of Direct Fed Microbials to Mitigate Pathogens and Enhance Production in Cattle. Can. J. Anim. Sci. 2011, 91, 193–211, doi:10.4141/cjas10047. 141. Uyeno, Y.; Shigemori, S.; Shimosato, T. Effect of Probiotics/Prebiotics on Cattle Health and Productivity. Microbes and environments 2015, 30, 126–132, doi:10.1264/jsme2.ME14176. 142. Wisener, L.V.; Sargeant, J.M.; O’Connor, A.M.; Faires, M.C.; Glass-Kaastra, S.K. The Use of Direct-Fed Microbials to Reduce Shedding of Escherichia Coli O157 in Beef Cattle: A Systematic Review and Meta-Analysis. Zoonoses Public Health 2015, 62, 75– 89, doi:10.1111/zph.12112. 43 143. Buffie, C.G.; Pamer, E.G. Microbiota-Mediated Colonization Resistance against Intestinal Pathogens. Nat Rev Immunol 2013, 13, 790–801, doi:10.1038/nri3535. 144. Hibbing, M.E.; Fuqua, C.; Parsek, M.R.; Peterson, S.B. Bacterial Competition: Surviving and Thriving in the Microbial Jungle. Nat Rev Microbiol 2010, 8, 15–25, doi:10.1038/nrmicro2259. 145. Correlation Analysis of Shiga Toxin–Producing Escherichia Coli Shedding and Faecal Bacterial Composition in Beef Cattle - Zhao - 2013 - Journal of Applied Microbiology - Wiley Online Library Available online: https://sfamjournals.onlinelibrary.wiley.com/doi/full/10.1111/jam.12250 (accessed on 23 October 2022). 146. Wood, B.J. The Lactic Acid Bacteria: Volume 1: The Lactic Acid Bacteria in Health and Disease; Springer Science & Business Media, 2012; ISBN 1-4615-3522-0. 147. Yang, H.E.; Zotti, C.A.; McKinnon, J.J.; McAllister, T.A. Lactobacilli Are Prominent Members of the Microbiota Involved in the Ruminal Digestion of Barley and Corn. Front. Microbiol. 2018, 9, 718, doi:10.3389/fmicb.2018.00718. 148. Huws, S.A.; Creevey, C.J.; Oyama, L.B.; Mizrahi, I.; Denman, S.E.; Popova, M.; Muñoz- Tamayo, R.; Forano, E.; Waters, S.M.; Hess, M.; et al. Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future. Front. Microbiol. 2018, 9, 2161, doi:10.3389/fmicb.2018.02161. 149. Plaizier, J.C.; Danesh Mesgaran, M.; Derakhshani, H.; Golder, H.; Khafipour, E.; Kleen, J.L.; Lean, I.; Loor, J.; Penner, G.; Zebeli, Q. Review: Enhancing Gastrointestinal Health in Dairy Cows. Animal 2018, 12, s399–s418, doi:10.1017/S1751731118001921. 150. Kameshwar, A.K.S.; Ramos, L.P.; Qin, W. Metadata Analysis Approaches for Understanding and Improving the Functional Involvement of Rumen Microbial Consortium in Digestion and Metabolism of Plant Biomass. J. Genomics 2019, 7, 31–45, doi:10.7150/jgen.32164. 151. Gilbert, R.A.; Townsend, E.M.; Crew, K.S.; Hitch, T.C.A.; Friedersdorff, J.C.A.; Creevey, C.J.; Pope, P.B.; Ouwerkerk, D.; Jameson, E. Rumen Virus Populations: Technological Advances Enhancing Current Understanding. Front. Microbiol. 2020, 11, 450, doi:10.3389/fmicb.2020.00450. 152. Pinto, G.; Sampaio, M.; Dias, O.; Almeida, C.; Azeredo, J.; Oliveira, H. Insights into the Genome Architecture and Evolution of Shiga Toxin Encoding Bacteriophages of Escherichia Coli. BMC Genomics 2021, 22, 366, doi:10.1186/s12864-021-07685-0. 153. Liu, J.; Liu, F.; Cai, W.; Jia, C.; Bai, Y.; He, Y.; Zhu, W.; Li, R.W.; Song, J. Diet-Induced Changes in Bacterial Communities in the Jejunum and Their Associations with Bile Acids in Angus Beef Cattle. Animal Microbiome 2020, 2, 33, doi:10.1186/s42523-020-00051-7. 44 154. Wood, B.J. The Lactic Acid Bacteria: Volume 1: The Lactic Acid Bacteria in Health and Disease; Springer Science & Business Media, 2012; ISBN 1-4615-3522-0. 155. Matthews, C.; Crispie, F.; Lewis, E.; Reid, M.; O’Toole, P.W.; Cotter, P.D. The Rumen Microbiome: A Crucial Consideration When Optimising Milk and Meat Production and Nitrogen Utilisation Efficiency. Gut Microbes 2019, 10, 115–132, doi:10.1080/19490976.2018.1505176. 156. Stein, R.A.; Katz, D.E. Escherichia Coli, Cattle and the Propagation of Disease. FEMS Microbiology Letters 2017, 364, fnx050, doi:10.1093/femsle/fnx050. 157. Murray, C.J.; Ikuta, K.S.; Sharara, F.; Swetschinski, L.; Aguilar, G.R.; Gray, A.; Han, C.; Bisignano, C.; Rao, P.; Wool, E.; et al. Global Burden of Bacterial Antimicrobial Resistance in 2019: A Systematic Analysis. The Lancet 2022, 399, 629–655, doi:10.1016/S0140-6736(21)02724-0. 158. Centers for Disease Control and Prevention (U.S.) Antibiotic Resistance Threats in the United States, 2019; Centers for Disease Control and Prevention (U.S.), 2019; 159. Godijk, N.G.; Bootsma, M.C.J.; Bonten, M.J.M. Transmission Routes of Antibiotic Resistant Bacteria: A Systematic Review. BMC Infectious Diseases 2022, 22, 482, doi:10.1186/s12879-022-07360-z. 160. Findlater, A.; Bogoch, I.I. Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel. Trends Parasitol 2018, 34, 772–783, doi:10.1016/j.pt.2018.07.004. 161. World Health Organization Critically Important Antimicrobials for Human Medicine; 6th rev.; World Health Organization: Geneva, 2019; ISBN 978-92-4-151552-8. 162. U.S. Food & Drug Administration (FDA) Summary Report on Antimicrobials Sold or Distributed for Use in Food-Producing Animals; FDA: USA, 2021; pp. 1–49. 163. U.S. Food & Drug Administration (FDA) Antimicrobial Use and Resistance in Animal Agriculture the United States. Executive Summary. Center for Veterinary Medicine. 2022. 164. U.S. Food & Drug Administration (FDA) Code of Federal Regulations, Title 21, Volume 6, CITE: 21CFR522.313a. Implantation or Injectable Dosage Form New Animal Drugs. Ceftiofur Crystalline Free Acid. Available online: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=522.313a (accessed on 23 October 2022). 165. U.S. Food & Drug Administration (FDA) Code of Federal Regulations, Title 21, Volume 6, CITE: 21CFR526.313. Intramammary Dosage Form New Animal Drugs. Ceftiofur. Available online: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=526.313 (accessed on 23 October 2022). 45 166. Hornish, R.; Katarski, S. Cephalosporins in Veterinary Medicine - Ceftiofur Use in Food Animals. CTMC 2002, 2, 717–731, doi:10.2174/1568026023393679. 167. McEwen, S.A.; Collignon, P.J. Antimicrobial Resistance: A One Health Perspective. Microbiol Spectr 2018, 6, doi:10.1128/microbiolspec.ARBA-0009-2017. 168. Tang, K.L.; Caffrey, N.P.; Nóbrega, D.B.; Cork, S.C.; Ronksley, P.E.; Barkema, H.W.; Polachek, A.J.; Ganshorn, H.; Sharma, N.; Kellner, J.D.; et al. Restricting the Use of Antibiotics in Food-Producing Animals and Its Associations with Antibiotic Resistance in Food-Producing Animals and Human Beings: A Systematic Review and Meta-Analysis. The Lancet Planetary Health 2017, 1, e316–e327, doi:10.1016/S2542-5196(17)30141-9. 169. Blair, J.M.A.; Webber, M.A.; Baylay, A.J.; Ogbolu, D.O.; Piddock, L.J.V. Molecular Mechanisms of Antibiotic Resistance. Nat Rev Microbiol 2015, 13, 42–51, doi:10.1038/nrmicro3380. 170. Kapoor, G.; Saigal, S.; Elongavan, A. Action and Resistance Mechanisms of Antibiotics: A Guide for Clinicians. J Anaesthesiol Clin Pharmacol 2017, 33, 300–305, doi:10.4103/joacp.JOACP_349_15. 171. Boolchandani, M.; D’Souza, A.W.; Dantas, G. Sequencing-Based Methods and Resources to Study Antimicrobial Resistance. Nat Rev Genet 2019, doi:10.1038/s41576-019-0108-4. 172. Stokes, H.W.; Gillings, M.R. Gene Flow, Mobile Genetic Elements and the Recruitment of Antibiotic Resistance Genes into Gram-Negative Pathogens. FEMS microbiology reviews 2011, 35, 790–819. 173. Partridge, S.R.; Kwong, S.M.; Firth, N.; Jensen, S.O. Mobile Genetic Elements Associated with Antimicrobial Resistance. Clinical microbiology reviews 2018, 31, e00088-17. 174. Smith, D.L.; Harris, A.D.; Johnson, J. a; Silbergeld, E.K.; Morris, J.G. Animal Antibiotic Use Has an Early but Important Impact on the Emergence of Antibiotic Resistance in Human Commensal Bacteria. Proceedings of the National Academy of Sciences of the United States of America 2002, 99, 6434–6439, doi:10.1073/pnas.082188899. 175. Campioni, F.; Vilela, F.P.; Cao, G.; Kastanis, G.; dos Prazeres Rodrigues, D.; Costa, R.G.; Tiba-Casas, M.R.; Yin, L.; Allard, M.; Falcão, J.P. Whole Genome Sequencing Analyses Revealed That Salmonella Enterica Serovar Dublin Strains from Brazil Belonged to Two Predominant Clades. Sci Rep 2022, 12, 10555, doi:10.1038/s41598-022-14492-4. 176. Epping, L.; Walther, B.; Piro, R.M.; Knüver, M.-T.; Huber, C.; Thürmer, A.; Flieger, A.; Fruth, A.; Janecko, N.; Wieler, L.H.; et al. Genome-Wide Insights into Population Structure and Host Specificity of Campylobacter Jejuni. Sci Rep 2021, 11, 10358, doi:10.1038/s41598-021-89683-6. 177. Harrison, E.M.; Paterson, G.K.; Holden, M.T.G.; Larsen, J.; Stegger, M.; Larsen, A.R.; Petersen, A.; Skov, R.L.; Christensen, J.M.; Bak Zeuthen, A.; et al. Whole Genome 46 Sequencing Identifies Zoonotic Transmission of MRSA Isolates with the Novel MecA Homologue MecC. EMBO Mol Med 2013, 5, 509–515, doi:10.1002/emmm.201202413. 178. Kraupner, N.; Hutinel, M.; Schumacher, K.; Gray, D.A.; Genheden, M.; Fick, J.; Flach, C.-F.; Larsson, D.G.J. Evidence for Selection of Multi-Resistant E. coli by Hospital Effluent. Environment International 2021, 150, 106436, doi:10.1016/j.envint.2021.106436. 179. Chee‐Sanford, J.C.; Mackie, R.I.; Koike, S.; Krapac, I.G.; Lin, Y.; Yannarell, A.C.; Maxwell, S.; Aminov, R.I. Fate and Transport of Antibiotic Residues and Antibiotic Resistance Genes Following Land Application of Manure Waste. Journal of environmental quality 2009, 38, 1086–1108. 180. Chen, C.; Pankow, C.A.; Oh, M.; Heath, L.S.; Zhang, L.; Du, P.; Xia, K.; Pruden, A. Effect of Antibiotic Use and Composting on Antibiotic Resistance Gene Abundance and Resistome Risks of Soils Receiving Manure-Derived Amendments. Environment International 2019, 128, 233–243, doi:10.1016/j.envint.2019.04.043. 181. Heuer, H.; Schmitt, H.; Smalla, K. Antibiotic Resistance Gene Spread Due to Manure Application on Agricultural Fields. Current opinion in microbiology 2011, 14, 236–243. 182. Dzidic, S.; Bedeković, V. Horizontal Gene Transfer-Emerging Multidrug Resistance in Hospital Bacteria. Acta Pharmacologica Sinica 2003, 24, 519–526. 183. Bennett, P. Plasmid Encoded Antibiotic Resistance: Acquisition and Transfer of Antibiotic Resistance Genes in Bacteria. British journal of pharmacology 2008, 153, S347–S357. 184. Salinas, L.; Cárdenas, P.; Johnson, T.J.; Vasco, K.; Graham, J.; Trueba, G. Diverse Commensal Escherichia Coli Clones and Plasmids Disseminate Antimicrobial Resistance Genes in Domestic Animals and Children in a Semirural Community in Ecuador. mSphere 2019, 4, e00316-19, doi:10.1128/mSphere.00316-19. 185. Evans, D.R.; Griffith, M.P.; Sundermann, A.J.; Shutt, K.A.; Saul, M.I.; Mustapha, M.M.; Marsh, J.W.; Cooper, V.S.; Harrison, L.H.; Van Tyne, D. Systematic Detection of Horizontal Gene Transfer across Genera among Multidrug-Resistant Bacteria in a Single Hospital. eLife 2020, 9, e53886, doi:10.7554/eLife.53886. 186. von Wintersdorff, C.J.H.; Penders, J.; van Niekerk, J.M.; Mills, N.D.; Majumder, S.; van Alphen, L.B.; Savelkoul, P.H.M.; Wolffs, P.F.G. Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer. Frontiers in Microbiology 2016, 7. 187. Reller, L.B.; Weinstein, M.; Jorgensen, J.H.; Ferraro, M.J. Antimicrobial Susceptibility Testing: A Review of General Principles and Contemporary Practices. Clinical Infectious Diseases 2009, 49, 1749–1755, doi:10.1086/647952. 47 188. Nyirabahizi, E.; Tyson, G.H.; Dessai, U.; Zhao, S.; Kabera, C.; Crarey, E.; Womack, N.; Crews, M.K.; Strain, E.; Tate, H. Evaluation of Escherichia Coli as an Indicator for Antimicrobial Resistance in Salmonella Recovered from the Same Food or Animal Ceca Samples. Food Control 2020, 115, 107280. 189. Wright, G.D. The Antibiotic Resistome: The Nexus of Chemical and Genetic Diversity. Nat Rev Microbiol 2007, 5, 175–186, doi:10.1038/nrmicro1614. 190. Marano, R.B.M.; Gupta, C.L.; Cozer, T.; Jurkevitch, E.; Cytryn, E. Hidden Resistome: Enrichment Reveals the Presence of Clinically Relevant Antibiotic Resistance Determinants in Treated Wastewater-Irrigated Soils. Environ. Sci. Technol. 2021, 55, 6814–6827, doi:10.1021/acs.est.1c00612. 191. Qian, X.; Gunturu, S.; Sun, W.; Cole, J.R.; Norby, B.; Gu, J.; Tiedje, J.M. Long-Read Sequencing Revealed Cooccurrence, Host Range, and Potential Mobility of Antibiotic Resistome in Cow Feces. PNAS 2021, 118, doi:10.1073/pnas.2024464118. 192. Qiu, T.; Huo, L.; Guo, Y.; Gao, M.; Wang, G.; Hu, D.; Li, C.; Wang, Z.; Liu, G.; Wang, X. Metagenomic Assembly Reveals Hosts and Mobility of Common Antibiotic Resistome in Animal Manure and Commercial Compost. Environ Microbiome 2022, 17, 42, doi:10.1186/s40793-022-00437-x. 193. Thomas, M.; Webb, M.; Ghimire, S.; Blair, A.; Olson, K.; Fenske, G.J.; Fonder, A.T.; Christopher-Hennings, J.; Brake, D.; Scaria, J. Metagenomic Characterization of the Effect of Feed Additives on the Gut Microbiome and Antibiotic Resistome of Feedlot Cattle. Sci Rep 2017, 7, 12257, doi:10.1038/s41598-017-12481-6. 194. Noyes, N.R.; Yang, X.; Linke, L.M.; Magnuson, R.J.; Dettenwanger, A.; Cook, S.; Geornaras, I.; Woerner, D.E.; Gow, S.P.; McAllister, T.A.; et al. Resistome Diversity in Cattle and the Environment Decreases during Beef Production. eLife 2016, 5, e13195, doi:10.7554/eLife.13195. 195. Noyes, N.R.; Yang, X.; Linke, L.M.; Magnuson, R.J.; Cook, S.R.; Zaheer, R.; Yang, H.; Woerner, D.R.; Geornaras, I.; McArt, J.A.; et al. Characterization of the Resistome in Manure, Soil and Wastewater from Dairy and Beef Production Systems. Sci Rep 2016, 6, 24645, doi:10.1038/srep24645. 196. Doster, E.; Rovira, P.; Noyes, N.R.; Burgess, B.A.; Yang, X.; Weinroth, M.D.; Lakin, S.M.; Dean, C.J.; Linke, L.; Magnuson, R.; et al. Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period. Front. Microbiol. 2018, 9, 1715, doi:10.3389/fmicb.2018.01715. 197. Bickhart, D.M.; Watson, M.; Koren, S.; Panke-Buisse, K.; Cersosimo, L.M.; Press, M.O.; Van Tassell, C.P.; Van Kessel, J.A.S.; Haley, B.J.; Kim, S.W.; et al. Assignment of Virus and Antimicrobial Resistance Genes to Microbial Hosts in a Complex Microbial Community by Combined Long-Read Assembly and Proximity Ligation. Genome Biol 2019, 20, 153, doi:10.1186/s13059-019-1760-x. 48 198. Rovira, P.; McAllister, T.; Lakin, S.M.; Cook, S.R.; Doster, E.; Noyes, N.R.; Weinroth, M.D.; Yang, X.; Parker, J.K.; Boucher, C.; et al. Characterization of the Microbial Resistome in Conventional and “Raised Without Antibiotics” Beef and Dairy Production Systems. Frontiers in Microbiology 2019, 10. 199. Haley, B.J.; Kim, S.-W.; Salaheen, S.; Hovingh, E.; Van Kessel, J.A.S. Differences in the Microbial Community and Resistome Structures of Feces from Preweaned Calves and Lactating Dairy Cows in Commercial Dairy Herds. Foodborne Pathogens and Disease 2020, 17, 494–503, doi:10.1089/fpd.2019.2768. 200. Lim, S.-K.; Kim, D.; Moon, D.-C.; Cho, Y.; Rho, M. Antibiotic Resistomes Discovered in the Gut Microbiomes of Korean Swine and Cattle. GigaScience 2020, 9, giaa043, doi:10.1093/gigascience/giaa043. 201. Pitta, D.W.; Indugu, N.; Toth, J.D.; Bender, J.S.; Baker, L.D.; Hennessy, M.L.; Vecchiarelli, B.; Aceto, H.; Dou, Z. The Distribution of Microbiomes and Resistomes across Farm Environments in Conventional and Organic Dairy Herds in Pennsylvania. Environmental Microbiome 2020, 15, 21, doi:10.1186/s40793-020-00368-5. 202. Xue, M.-Y.; Xie, Y.-Y.; Zhong, Y.-F.; Liu, J.-X.; Guan, L.L.; Sun, H.-Z. Ruminal Resistome of Dairy Cattle Is Individualized and the Resistotypes Are Associated with Milking Traits. anim microbiome 2021, 3, 18, doi:10.1186/s42523-021-00081-9. 203. Haley, B.J.; Van Kessel, J.A.S. The Resistome of the Bovine Gastrointestinal Tract. Current Opinion in Biotechnology 2022, 73, 213–219, doi:10.1016/j.copbio.2021.07.025. 204. O’Connor, A.M.; Yuan, C.; Cullen, J.N.; Coetzee, J.F.; da Silva, N.; Wang, C. A Mixed Treatment Meta-Analysis of Antibiotic Treatment Options for Bovine Respiratory Disease – An Update. Preventive Veterinary Medicine 2016, 132, 130–139, doi:10.1016/j.prevetmed.2016.07.003. 205. Abell, K.M.; Theurer, M.E.; Larson, R.L.; White, B.J.; Apley, M. A Mixed Treatment Comparison Meta-Analysis of Metaphylaxis Treatments for Bovine Respiratory Disease in Beef Cattle. J Anim Sci 2017, 95, 626–635, doi:10.2527/jas.2016.1062. 206. Chambers, L.; Yang, Y.; Littier, H.; Ray, P.; Zhang, T.; Pruden, A.; Strickland, M.; Knowlton, K. Metagenomic Analysis of Antibiotic Resistance Genes in Dairy Cow Feces Following Therapeutic Administration of Third Generation Cephalosporin. PLOS ONE 2015, 10, e0133764, doi:10.1371/journal.pone.0133764. 207. Weinroth, M.D.; Scott, H.M.; Norby, B.; Loneragan, G.H.; Noyes, N.R.; Rovira, P.; Doster, E.; Yang, X.; Woerner, D.R.; Morley, P.S.; et al. Effects of Ceftiofur and Chlortetracycline on the Resistomes of Feedlot Cattle. Applied and Environmental Microbiology 2018, 84, e00610-18, doi:10.1128/AEM.00610-18. 208. Ferroni, L.; Lovito, C.; Scoccia, E.; Dalmonte, G.; Sargenti, M.; Pezzotti, G.; Maresca, C.; Forte, C.; Magistrali, C.F. Antibiotic Consumption on Dairy and Beef Cattle Farms of 49 Central Italy Based on Paper Registers. Antibiotics 2020, 9, 273, doi:10.3390/antibiotics9050273. 209. Campos, J.L. de; Kates, A.; Steinberger, A.; Sethi, A.; Suen, G.; Shutske, J.; Safdar, N.; Goldberg, T.; Ruegg, P.L. Quantification of Antimicrobial Usage in Adult Cows and Preweaned Calves on 40 Large Wisconsin Dairy Farms Using Dose-Based and Mass- Based Metrics. Journal of Dairy Science 2021, 104, 4727–4745, doi:10.3168/jds.2020- 19315. 210. Pol, M.; Ruegg, P.L. Treatment Practices and Quantification of Antimicrobial Drug Usage in Conventional and Organic Dairy Farms in Wisconsin. J Dairy Sci 2007, 90, 249–261, doi:10.3168/jds.S0022-0302(07)72626-7. 211. Cameron, A.; McAllister, T.A. Antimicrobial Usage and Resistance in Beef Production. Journal of Animal Science and Biotechnology 2016, 7, 68, doi:10.1186/s40104-016-0127- 3. 212. Auffret, M.D.; Dewhurst, R.J.; Duthie, C.-A.; Rooke, J.A.; John Wallace, R.; Freeman, T.C.; Stewart, R.; Watson, M.; Roehe, R. The Rumen Microbiome as a Reservoir of Antimicrobial Resistance and Pathogenicity Genes Is Directly Affected by Diet in Beef Cattle. Microbiome 2017, 5, 159, doi:10.1186/s40168-017-0378-z. 213. Zhang, F.; Li, Y.; Yang, M.; Li, W. Content of Heavy Metals in Animal Feeds and Manures from Farms of Different Scales in Northeast China. IJERPH 2012, 9, 2658– 2668, doi:10.3390/ijerph9082658. 214. Cope, C.M.; Mackenzie, A.M.; Wilde, D.; Sinclair, L.A. Effects of Level and Form of Dietary Zinc on Dairy Cow Performance and Health. Journal of Dairy Science 2009, 92, 2128–2135, doi:10.3168/jds.2008-1232. 215. Bednorz, C.; Oelgeschläger, K.; Kinnemann, B.; Hartmann, S.; Neumann, K.; Pieper, R.; Bethe, A.; Semmler, T.; Tedin, K.; Schierack, P.; et al. The Broader Context of Antibiotic Resistance: Zinc Feed Supplementation of Piglets Increases the Proportion of Multi- Resistant Escherichia Coli in Vivo. International Journal of Medical Microbiology 2013, 303, 396–403, doi:10.1016/j.ijmm.2013.06.004. 216. Ciesinski, L.; Guenther, S.; Pieper, R.; Kalisch, M.; Bednorz, C.; Wieler, L.H. High Dietary Zinc Feeding Promotes Persistence of Multi-Resistant E. coli in the Swine Gut. PLoS ONE 2018, 13, e0191660, doi:10.1371/journal.pone.0191660. 217. Baker-Austin, C.; Wright, M.S.; Stepanauskas, R.; McArthur, J.V. Co-Selection of Antibiotic and Metal Resistance. Trends in Microbiology 2006, 14, 176–182, doi:10.1016/j.tim.2006.02.006. 218. Zhou, B.; Wang, C.; Zhao, Q.; Wang, Y.; Huo, M.; Wang, J.; Wang, S. Prevalence and Dissemination of Antibiotic Resistance Genes and Coselection of Heavy Metals in Chinese Dairy Farms. Journal of Hazardous Materials 2016, 320, 10–17, doi:10.1016/j.jhazmat.2016.08.007. 50 219. Murray, S.A.; Amachawadi, R.G.; Norman, K.N.; Lawhon, S.D.; Nagaraja, T.G.; Drouillard, J.S.; Scott, H.M. Effects of Zinc and Menthol-Based Diets on Co-Selection of Antibiotic Resistance among E. coli and Enterococcus Spp. in Beef Cattle. Animals 2021, 11, 259, doi:10.3390/ani11020259. 220. Amachawadi, R.G.; Scott, H.M.; Aperce, C.; Vinasco, J.; Drouillard, J.S.; Nagaraja, T.G. Effects of In-Feed Copper and Tylosin Supplementations on Copper and Antimicrobial Resistance in Faecal Enterococci of Feedlot Cattle. J Appl Microbiol 2015, 118, 1287– 1297, doi:10.1111/jam.12790. 221. Vikram, A.; Rovira, P.; Agga, G.E.; Arthur, T.M.; Bosilevac, J.M.; Wheeler, T.L.; Morley, P.S.; Belk, K.E.; Schmidt, J.W. Impact of “Raised without Antibiotics” Beef Cattle Production Practices on Occurrences of Antimicrobial Resistance. Appl Environ Microbiol 2017, 83, e01682-17, doi:10.1128/AEM.01682-17. 222. Morley, P.S.; Dargatz, D.A.; Hyatt, D.R.; Dewell, G.A.; Patterson, J.G.; Burgess, B.A.; Wittum, T.E. Effects of Restricted Antimicrobial Exposure on Antimicrobial Resistance in Fecal Escherichia Coli from Feedlot Cattle. Foodborne Pathogens and Disease 2011, 8, 87–98, doi:10.1089/fpd.2010.0632. 223. Schmidt, J.W.; Vikram, A.; Arthur, T.M.; Belk, K.E.; Morley, P.S.; Weinroth, M.D.; Wheeler, T.L. Antimicrobial Resistance at Two U.S. Cull Cow Processing Establishments. Journal of Food Protection 2020, 83, 2216–2228, doi:10.4315/JFP-20-201. 224. Milanov, D.; Ljubojević, D.; Čabarkapa, I.; Aleksić, N. Impact of Antibiotics Used as Growth Promoters on Bacterial Resistance. Food and Feed Research 2016, 43, 83–92, doi:10.5937/FFR1602083M. 225. Judicious Therapeutic Use of Antimicrobials in Aquatic Animal Medicine Available online: https://www.avma.org/resources-tools/avma-policies/judicious-therapeutic-use- antimicrobials-aquatic-animal-medicine (accessed on 23 October 2022). 226. Weinroth, M.D.; Martin, J.N.; Doster, E.; Geornaras, I.; Parker, J.K.; Carlson, C.R.; Metcalf, J.L.; Morley, P.S.; Belk, K.E. Investigation of Tylosin in Feed of Feedlot Cattle and Effects on Liver Abscess Prevalence, and Fecal and Soil Microbiomes and Resistomes1. Journal of Animal Science 2019, 97, 4567–4578, doi:10.1093/jas/skz306. 227. Alali, W. q.; Scott, H. m.; Norby, B.; Gebreyes, W.; Loneragan, G. h. Quantification of the BlaCMY-2 in Feces from Beef Feedlot Cattle Administered Three Different Doses of Ceftiofur in a Longitudinal Controlled Field Trial. Foodborne Pathogens and Disease 2009, 6, 917–924, doi:10.1089/fpd.2009.0271. 228. Kanwar, N.; Scott, H.M.; Norby, B.; Loneragan, G.H.; Vinasco, J.; McGowan, M.; Cottell, J.L.; Chengappa, M.M.; Bai, J.; Boerlin, P. Effects of Ceftiofur and Chlortetracycline Treatment Strategies on Antimicrobial Susceptibility and on Tet(A), Tet(B), and BlaCMY-2 Resistance Genes among E. coli Isolated from the Feces of Feedlot Cattle. PLOS ONE 2013, 8, e80575, doi:10.1371/journal.pone.0080575. 51 229. Van Boeckel, T.P.; Glennon, E.E.; Chen, D.; Gilbert, M.; Robinson, T.P.; Grenfell, B.T.; Levin, S.A.; Bonhoeffer, S.; Laxminarayan, R. Reducing Antimicrobial Use in Food Animals. Science 2017, 357, 1350–1352, doi:10.1126/science.aao1495. 230. Wang, Y.; Xu, C.; Zhang, R.; Chen, Y.; Shen, Y.; Hu, F.; Liu, D.; Lu, J.; Guo, Y.; Xia, X.; et al. Changes in Colistin Resistance and Mcr-1 Abundance in Escherichia Coli of Animal and Human Origins Following the Ban of Colistin-Positive Additives in China: An Epidemiological Comparative Study. The Lancet Infectious Diseases 2020, 20, 1161– 1171, doi:10.1016/S1473-3099(20)30149-3. 231. Liu, Y.-Y.; Wang, Y.; Walsh, T.R.; Yi, L.-X.; Zhang, R.; Spencer, J.; Doi, Y.; Tian, G.; Dong, B.; Huang, X.; et al. Emergence of Plasmid-Mediated Colistin Resistance Mechanism MCR-1 in Animals and Human Beings in China: A Microbiological and Molecular Biological Study. The Lancet Infectious Diseases 2016, 16, 161–168, doi:10.1016/S1473-3099(15)00424-7. 232. Kumar, H.; Chen, B.-H.; Kuca, K.; Nepovimova, E.; Kaushal, A.; Nagraik, R.; Bhatia, S.K.; Dhanjal, D.S.; Kumar, V.; Kumar, A.; et al. Understanding of Colistin Usage in Food Animals and Available Detection Techniques: A Review. Animals (Basel) 2020, 10, 1892, doi:10.3390/ani10101892. 233. Centers for Disease Control and Prevention (U.S.). Drug-Resistant Salmonella Serotype Typhi. 2. Antibiotic resistance threats in the United States, 2019. DOI: 10.15620/cdc:82532 234. Center for Veterinary Medicine Extralabel Use and Antimicrobials. FDA. FDA 2022. 235. Marquardt, R.R.; Li, S. Antimicrobial Resistance in Livestock: Advances and Alternatives to Antibiotics. Animal Frontiers 2018, 8, 30–37, doi:10.1093/af/vfy001. 236. Nowakiewicz, A.; Zięba, P.; Gnat, S.; Matuszewski, Ł. Last Call for Replacement of Antimicrobials in Animal Production: Modern Challenges, Opportunities, and Potential Solutions. Antibiotics (Basel) 2020, 9, E883, doi:10.3390/antibiotics9120883. 237. Anadón, A. WS14 The EU Ban of Antibiotics as Feed Additives (2006): Alternatives and Consumer Safety. Journal of Veterinary Pharmacology and Therapeutics 2006, 29, 41– 44, doi:10.1111/j.1365-2885.2006.00775_2.x. 238. Mukherjee, S.; Blankenship, H.M.; Rodrigues, J.A.; Mosci, R.E.; Rudrik, J.T.; Manning, S.D. Antibiotic Susceptibility Profiles and Frequency of Resistance Genes in Clinical Shiga Toxin-Producing Escherichia Coli Isolates from Michigan over a 14-Year Period. Antimicrobial Agents and Chemotherapy 2021, 65, e01189-21, doi:10.1128/AAC.01189- 21. 52 CHAPTER 2 Characterizing the cattle gut microbiome in farms with a high and low prevalence of Shiga toxin- producing Escherichia coli. This chapter is from an open access published manuscript: Karla Vasco, Brian Nohomovich, Pallavi Singh, Cristina Venegas-Vargas, Rebekah E. Mosci, Steven Rust, Paul Bartlett, Bo Norby, Daniel Grooms, Lixin Zhang & Shannon D. Manning. "Characterizing the cattle gut microbiome in farms with a high and low prevalence of Shiga toxin-producing Escherichia coli." Microorganisms 9, no. 8 (2021): 1737. DOI: 10.3390/microorganisms9081737 53 ABSTRACT Cattle are the main reservoirs of Shiga-toxin-producing Escherichia coli (STEC), a major foodborne pathogen associated with acute enteric disease and hemolytic-uremic syndrome in humans. A total of 397 beef and dairy cattle from 5 farms were included in this study, of which 660 samples were collected for 16S rRNA sequencing. The microbiota of farms with a high- STEC prevalence (HSP) had greater richness compared to farms with a low-STEC prevalence (LSP). Longitudinal analyses showed STEC-shedders from LSP farms had higher microbiome diversity; meanwhile, changes in the microbiome composition in HSP farms were independent of the STEC shedding status. Most of the bacterial genera associated with STEC shedding in dairy farms were also correlated with differences in the percentage of forage in diet and risk factors of STEC carriage such as days in milk, number of lactations, and warm temperatures. Identifying factors that alter the gut microbiota and enable STEC colonization in livestock could lead to novel strategies to prevent fecal shedding and the subsequent transmission to humans. INTRODUCTION Shiga-toxin producing Escherichia coli (STEC) is a foodborne pathogen causing 2.8 million cases of acute enteric disease and 230 deaths annually [1]. STEC infections are associated with the consumption of contaminated food and water or result from direct contact with cattle feces since cattle represent an important reservoir for this pathogen [2]. While livestock carriers of STEC are asymptomatic, humans can develop bloody diarrhea, hemolytic- uremic-syndrome, thrombotic thrombocytopenic purpura, or end-stage renal disease [1]. STEC virulence is caused by bacteriophage-encoded Shiga toxins (Stx1 and Stx2) that induce cellular apoptosis of endothelial cells in the gut, kidney and brain of humans [3–6]. Cattle are more tolerant to STEC due to the lack of Stx receptors (glycolipid globotriaosylceramide, Gb3) in the 54 intestinal tract as well as a lower receptivity of Gb3 receptors present in the kidney and brain [7]. Some STEC strains, classified as enterohemorrhagic E. coli (EHEC), possess the locus of enterocyte effacement (LEE) pathogenicity island that encodes for a type III secretion system and effectors, such as the intimin protein (eae), which are responsible for attaching and effacing (AE) lesion formation [8]. Adult cattle carrying EHEC are typically unaffected, though infected calves develop AE lesions on the apical epithelial surfaces of the recto-anal junction where the bacteria colonize [9]. Because cattle are important reservoirs of STEC, reducing carriage of this pathogen in livestock and preventing dissemination in food and the environment are priorities for preventing human infections [10]. Our previous study identified risk factors associated with high STEC prevalence in dairy farms including first lactation, less than 30 days in milk, and warm temperatures [11]. Meanwhile, protective factors identified in farms with low STEC prevalence included access to pasture, anthelmintic treatment, and antibiotic treatment for respiratory infections [11]. Nonetheless, it is not clear how factors associated with STEC prevalence influence the microbiota composition and potentially favor STEC colonization. The gut microbiome is critical for the activation and regulation of the immune response and preventing pathogen colonization [12]. Some studies have analyzed the association between the gut microbiome and STEC in both humans and cattle. In humans, the gut microbiome of infected patients had a lower abundance of dominant taxa from Bifidobacteriales and Clostridiales [13]. We also previously showed that microbial communities from patients with acute enteric infections caused by STEC and other pathogens, had a lower bacterial richness with an increased abundance of Proteobacteria (genus Escherichia) and decreased abundance of Bacteroidetes compared to healthy communities [14,15]. With regard to cattle, varying results 55 have been observed in the richness and composition of the fecal microbiome between STEC shedders and non-shedders [16–24]. Within a specific farm, for example, some studies observed no difference in diversity among STEC shedders and non-shedders [19,21], whereas other studies have detected significantly higher [22] and lower [23] diversity in STEC shedders despite controlling for age, farm, and diet. This lack of consensus among previous reports compels further investigation. Herein, we sought to compare the microbiota structure and function of cattle among farms with a high versus low STEC prevalence. Additionally, we aimed to determine whether STEC carriage is associated with changes in the microbiota composition over time. Characterizing a healthy cattle microbiome that does not support pathogen colonization and identifying key beneficial microorganisms (probiotics) can guide the development of new prevention protocols in an effort to eradicate STEC colonization in animal reservoirs. MATERIALS AND METHODS Sample collection An initial study was carried out in the spring and summer of 2011 and 2012 in Michigan in which 1,096 samples were collected in eleven cattle farms including six dairy herds and five beef herds to determine STEC prevalence and risk factors [11]. Here, samples from five of those eleven cattle herds were selected for microbiome analysis, which was based on the varying prevalence of STEC in each herd. Specifically, a low STEC prevalence (LSP) was observed in one feedlot, 1B (8.2%), and two dairy farms, 2D (8.7%) and 4D (13.8%). Comparatively, the prevalence was considerably higher in feedlot 8B and dairy farm 9D (53.7 and 28.0%, respectively), which were considered high STEC prevalence (HSP) herds. The three dairy herds had Holstein cows (farms 2D, 4D, 9D), while the other two were beef feedlots with Crossbreed 56 (farm 1B) and Angus (farm 8B) breeds. Epidemiological information obtained from each herd included demographics, geographic location, husbandry practices, health management, and diet. Additional information including number of lactations, days in milk, and dry status was collected at the dairy farms. Fecal grabs (FG) were collected by rectal palpation using obstetric sleeves (n = 308), while recto-anal junction (RAJ) samples were collected by swabbing the RAJ with a sterile cotton swab (n = 352) as described [11]. Roughly 256 pairs of fecal grabs (FG) and rectal-anal junction (RAJ) swabs were collected simultaneously from the same animal for microbiome comparison. A subset of cattle was also sampled over time at an interval of 2 to 3 weeks between each sampling point to examine microbiome changes and STEC shedding over time (Table A.2). In addition, blood samples were collected from the coccygeal or jugular vein of each animal for serology [25]. Pathogen identification STEC was detected using CHROMOagar STEC and sorbitol MacConkey agar followed by PCR confirmation targeting key virulence genes [11]. Suspect isolates were classified as STEC if they were positive for any Shiga toxin gene (stx) subtypes with or without the intimin gene (eae). In addition, exposure to pathogens that can alter the gut microbiota was evaluated to account for confounding effects between STEC shedders and non-shedders. These pathogens included bovine leukemia virus (BLV), bovine viral diarrhea (BVD), and Mycobacterium avium subsp. paratuberculosis (MAP), which were identified by serology using enzyme-linked immunosor-bent assays (ELISA) that detects antibodies specific for these pathogens as described [24]. 57 Amplicon library processing DNA was extracted from 660 samples recovered from cattle at each of the five farms using the QIAamp DNA Stool Mini Kit (QIAGEN; Valencia, CA). DNA was extracted from 250 mg of feces, or the RAJ swabs stored at -80oC. A fragment of approximately 569 bp from the V3–V5 hypervariable region of the bacterial 16S rRNA gene was amplified using the linker primer 357F (5′-CCGTCAATTCMTTTRAGT-3′) and the reverse primer 926R (5′- CCTACGGGAGGCAGC AG -3′). A sample-specific barcode of 6-8 nucleotides was used to sequence samples in parallel on a single 454-sequencing plate. The amplification and pyrosequencing methods were described in our prior study [14]. The raw pyrosequencing reads were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software v.1.9.1 workflow for 454 data [26]. First, the sequences were demultiplexed based on the nucleotide barcode and quality filtered using split_libraries.py. Then, de novo operational taxonomic units (OTUs) were identified with pick_de_novo_otus.py and the taxonomy was assigned with the SILVA database (v132) [27]; OTUs were clustered at 97% similarity. All samples were denoised with denoise_wrapper.py to reduce the amount of erroneous OTUs [28] and OTU chimera detection and filtering was done with VSEARCH [29]. Lastly, the sequences were aligned with align_seqs.py [30] and were converted into a phylogenetic tree using the QIIME 2 plugin ‘qiime fragment-insertion sepp’ [31–33]. Microbiome analyses The OTU table, taxonomy, metadata, and phylogenetic tree were imported into the R package Phyloseq v.1.24.2 [34]. Mitochondria and chloroplast OTUs were removed. Library rarefaction was applied to calculate alpha and beta diversities among samples. Alpha diversity was estimated to determine richness and evenness of OTUs with the Shannon index, and richness 58 based on the presence of rare OTUs (singletons and doubletons) was estimated with Chao1. The Wilcoxon and Kruskal-Wallis non-parametric tests were used to compare the alpha diversity estimates among LSP and HSP farms and STEC shedders and non-shedders. Beta diversity was also analyzed to compare the microbiome composition among groups using Bray-Curtis dissimilarity and Weighted UniFrac distances [35]. The ordination was calculated by principal coordinate analysis (PCoA), which was plotted with two axes. The difference between categorical variables and the microbial profiles were calculated with permutational multivariate analysis of variance (PERMANOVA) with 999 permutations using the Vegan package v.2.5-6 [36]. The differentially abundant taxa analysis was performed using differential expression analysis based on the negative binomial distribution DESeq2 (v.1.30.1) with default settings [37]. The R package metacoder v.0.3.3 [38] was used to visualize the taxa abundance as “heat trees” with the proportion of bacterial families. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUST2) [39] was also used to predict metabolic pathways and enzymes based on 16S rRNA sequences. Linear Discriminant Analysis (LDA) Effect Size (LEfSe) v.1.0 [40] was used to identify differentially abundant pathways. RESULTS Farm characteristics Three dairy and two beef herds were included in this study based on their STEC prevalence [11]. One beef and two dairy farms (1B, 2D, and 4D) were classified as low STEC prevalence (LSP) farms, which were compared with farms with high STEC prevalence (HSP) represented by one beef and one dairy farm (8B and 9D). The five farms represented varying breeds and sizes with different healthcare and management practices (Table 2.1). Notably, the 59 LSP farms fed animals a lower percentage of forage in their diet (15-65%) and used anthelmintics, while HSP farms used a diet almost exclusively based on forage (80-100%) and did not provide anthelmintic treatments. Specific characteristics unique to the dairy farms, such as number of milkings per day, days in milk (DIM), and number of lactations are shown in Table A.1. Table 2.1. Characteristics of each cattle farm examined. Farm Feature 2D 4D 9D 1B 8B Breed Holstein Holstein Holstein Crossbreed Angus Herd Dairy Dairy Dairy Beef Beef Herd size 320 3000 243 136 54 STEC prevalence (%) 8.7 13.8 28.0 8.2 53.7 STEC prev. classification LSP LSP HSP LSP HSP No. samplesa 213 81 77 206 83 Fecal grab 48 40 77 60 83 Recto anal junction 165 41 0 146 0 Mean age days (SD) 1382 (476) NR 1362 (522) 372 (19) 442 (17) Access to pasture/dry Housing Free stall; tie stall Free stall Feedlot Loose house lot; Free stall Diet % (SD) 40.62 Forage 65.01 (18.76) 80 (0) 15 (0) 100 (0) (9.47) 59.38 Concentrate 34.99 (18.76) 20 (0) 85 (0) 0 (9.47) Corn silage 29.06 (8.82) 41.7 (3.74) 0 15 (0) 0 Cotton seed 1.60 (2.60) 0 0 0 0 Rumensin No Yes No Yes No Roughage, Protein No No Yes No No Season b Summer Summer Summer Spring Summer Temperature (°C) 25 25 36 4.4 36 Humidity (g/m3) 66 68 31 75 42 Temp. max. 5 days c 23.44 29.89 37.11 20.33 29.33 Temp. avg 5 days c 19.22 16.89 30.11 13.89 22.78 Treatment Anthelmintic Yes Yes No Yes No Ceftiofur, Ceftiofur, Respiratory Ceftiofur None Florfenicol Florfenicol Tulathromycin Copper sulfate, Copper sulfate, Copper Oxytetracyclin Foot infection Oxytetracycline, Ceftiofur Penicillin sulfate e Ceftiofur NR=Not reported; SD=Standard deviation b During sample collection; c Temperature five days prior to sample collection Sequencing results Twenty-eight 454-sequencing plates containing 660 samples yielded 1,937,794 reads of 569 bp paired-end fragments of the 16S rRNA gene. After trimming and quality filtering the sequences, the library size varied from 650 to 16,786 with a median library size of 2,332 60 sequences per sample. Following the de novo clustering, denoising, filtering chimera, and removal of chloroplast and mitochondria OTUs, 15,158 OTUs were detected. Hindgut microbiota composition The microbiota profiles for the 256 paired fecal grab (FG) and rectal-anal junction (RAJ) samples were similar (p > 0.05) and hence, these samples were combined into a single group representing the hindgut for downstream analyses (Figures A.1 and A.2). Overall, the hindgut microbiota was dominated by two phyla, Firmicutes (54.6%) and Bacteroidetes (38.9%), although varying proportions of other phyla were detected across farms with different forage percentages (Figure 2.1A). Indeed, the percentage of forage in the diet significantly influenced the microbial composition. Farms with low forage diets, for instance, had a lower abundance of Firmicutes and a higher abundance of Bacteroidetes (p < 0.0001). Classifying by family identified similar differences across farms with Ruminococceaceae predominating but increasing with the forage percentage (p < 0.001) (Figure 2.1B). Several additional bacterial families were significantly correlated with the percentage of forage in the diet (Table A.3). STEC prev. % 8 14 9 28 54 STEC prev. % 8 14 9 28 54 Forage in diet % 15 40 65 80 100 Forage in diet % 15 40 65 80 100 A 1.00 B 1.00 Family Ruminococcaceae Rikenellaceae 0.75 Phylum 0.75 Prevotellaceae Relative abundance Relative abundance Firmicutes Lachnospiraceae Bacteroidetes Bacteroidaceae 0.50 Proteobacteria 0.50 Muribaculaceae Spirochaetes Christensenellaceae Tenericutes Spirochaetaceae 0.25 Minorities 0.25 Bacteroidales RF16 group Bacteroidales uncultured Mollicutes RF39 uncultured 0.00 0.00 Minorities 1B 4D 2D 9D 8B 1B 4D 2D 9D 8B Farm Farm Figure 2.1. Hindgut C microbiota Crossbreed 1B composition Holstein 4D of cattle Holstein 2Dfrom five farms Holstein 9D with varying Angus 8B percentages of forage 1.00 in the diet. Stacked bar charts show the relative abundance of bacterial A) phyla and B) families per farm. Less abundant taxa were grouped together and named “Minorities”. 0.75 Relative abundance 0.50 61 0.25 HSP farms characterized by forage-dominant diets exhibited higher alpha diversity and a distinct microbiota structure To assess the association between STEC prevalence and the hindgut microbiota among farms, we analyzed the Shannon and Chao1 indices for alpha diversity and the Bray-Curtis dissimilarity and Weighted Unifrac for beta diversity. HSP farms exhibited greater richness than LSP farms, although no significant difference was observed using the Shannon index (p = 0.67) (Figure 2.2A). The Chao1 index, however, detected significantly greater diversity in HSP farms (p = 1e-09), indicating that a high number of OTUs were present in low proportions (singletons and doubletons) in the two HSP farms (Figure 2.2B). Notably, when comparing the alpha diversity indices between herds, the lowest and the highest OTU richness corresponded to farms 1B and 8B, respectively, which also had the lowest and the highest STEC prevalence (Figure 2.3). When the farms were plotted separately to evaluate beta diversity, the PCoA plot of Weighted Unifrac distances showed that the microbial communities from LSP farm 1B were the most divergent relative to the other four farms (Figure 2.2C). Comparatively, the farms were classified by STEC prevalence and a Bray-Curtis dissimilarity PCoA was generated (Figure 2.2D). This plot shows that cattle from the two HSP farms had a more similar microbiota structure that was significantly different than the microbiota profiles observed in the three LSP farms (PERMANOVA, p < 0.0001). HSP microbiota clustering was strongly associated with dominant forage diets (Figure A.4). 62 A Wilcoxon, p = 0.67 C Control STEC Farm 1B 2D 4D 8B 9D 5 0.10 PERMANOVA, R2= 0.17, p=0.001 Shannon PCoA 2 (6.1%) 0.05 4 0.00 −0.05 3 −0.1 0.0 0.1 Low High PCoA 1 (16.8%) B Wilcoxon, p = 1e−09 D 1000 STEC prevalence High Low STEC Control STEC 750 0.2 Chao1 0.1 PCoA 2 (4.3%) 500 0.0 −0.1 250 −0.2 PERMANOVA, R2= 0.06, p=0.001 −0.3 −0.50 −0.25 0.00 0.25 Low High PCoA 1 (17.3%) Figure 2.2. Cattle-hindgut microbiota alpha and beta diversities among farms with a low STEC prevalence (LSP) and high STEC prevalence (HSP). LSP Farms (1B, 2D, and 4D) were combined as were the two HSP farms (8B and 9D) to evaluate the alpha diversity using the A) Shannon index and B) Chao1 index. Beta diversity was evaluated by performing a C) Principal Coordinate Analysis (PCoA) of Weighted-Unifrac distances or and a D) PCoA of Bray-Curtis dissimilarity. The former plotted each farm separately along with the STEC shedders (+) and non-shedders (control, black circle), while the latter compared farms with LSP versus HSP. Hindgut-microbiota diversity comparisons between STEC positive samples and controls The microbiota profiles of STEC-shedders and non-shedders were examined in more detail regardless of farm. To do this, the animals were split into two comparison groups based on the recovery of isolates positive for stx and/or eae for comparison to cattle with isolates that were 63 negative for both virulence factors; 3-4 control animals were randomly selected from each of five farms for each case included in the analysis. The first comparison group examined 31 STEC positive (stx only) cattle from HSP farms and 85 STEC/EHEC negative (control) cattle including 20 from HSP and 65 from LSP farms. For the second comparison group, EHEC positive (stx and eae only) cattle (n = 52) were compared to a larger set of STEC/EHEC negative control animals (n = 205). EHEC comparison included cattle from HSP farms (cases = 34, controls = 20) and LSP herds (cases = 18, controls = 185). These groups excluded animals positive for BLV and MAP because they had significantly different microbiota profiles based on the alpha and beta diversity metrics (p < 0.05) (data not shown). Indeed, the exclusion of samples from MAP- and BLV-positive cattle was necessary given that prior studies showed that these pathogens were associated with important gut microbiome changes [41,42]. Notably, the STEC shedders possessing stx only had higher microbiota richness than the non-shedders (Shannon, p = 0.19; Chao1, p = 0.008), though only the Chao1 metric was significant (Figures 2.3A & 2.3B). By contrast, no difference in alpha diversity was observed when the EHEC shedders (stx-positive and eae-positive) were compared to the STEC/EHEC-negative controls (Shannon, p = 0.27; Chao1, p = 0.41) (Figures 2.3C & 2.3D). The microbiota structure of the STEC and EHEC shedders, however, was significantly different from the controls in both comparison groups (PERMANOVA, p < 0.001) (Figures 2.3E & 2.3F). 64 A Wilcoxon, p = 0.19 B Wilcoxon, p = 0.0083 C Wilcoxon, p = 0.27 D Wilcoxon, p = 0.41 750 5 750 5 Shannon Chao1 Shannon Chao1 500 500 4 4 250 250 3 3 Control STEC+EPEC− Control STEC+EPEC− Control STEC+EPEC+ Control STEC+EPEC+ E F 0.10 Control STEC+EPEC− Control STEC+EPEC+ 0.05 PERMANOVA, R2= 0.06, p=0.001 PCoA 2 (8.1%) PCoA 2 (7.2%) 0.05 0.00 0.00 −0.05 PERMANOVA, R2= 0.014, p=0.001 −0.05 −0.10 0.0 0.1 −0.15 −0.10 −0.05 0.00 0.05 PCoA 1 (23.6%) PCoA 1 (19.4%) Figure 2.3. Cattle-hindgut microbiota diversity comparisons between animals shedding STEC or EHEC and non-shedders (controls). The alpha diversity was evaluated for 31 STEC- positive (stx-positive, eae-negative) cattle (red dots) for comparison to 85 STEC/EHEC-negative control cattle (blue dots) using the A) Shannon and B) Chao1 indices, while E) beta diversity was examined using a Principal Coordinate Analysis (PCoA) of Weighted Unifrac distances. The C) Shannon and D) Chao1 alpha diversity indices as well as a F) PCoA for beta diversity were also evaluated for the 52 EHEC shedders (stx-positive, eae-positive; light blue dots) for comparison to a larger sample of 205 randomly selected non-shedders (yellow dots) from the five herds. STEC carriers from farms with LSP but not HSP showed changes in microbiota diversity over time Among the 59 cattle evaluated longitudinally from five farms (Table A.2), we sought to determine how STEC shedding impacted the microbiota composition in the hindgut. Despite finding no significant modifications due to diet, management, and environmental conditions (Table A.4) were reported within the farms over time, all cattle had significant differences in the alpha and beta diversities across the four samplings or phases. In general, the STEC positive samples exhibited higher alpha diversity within each phase, particularly among the three LSP farms (Figures 2.4A & 2.4B), however, none of the animals from the LSP farms carried STEC 65 in more than one phase. By contrast, the cohorts from the HSP farms, 8B and 9D, had a high proportion of cattle shedding STEC in all four phases. Each animal from farm 8B (n = 13) shed STEC in two or more phases. Indeed, the biggest difference in alpha diversity over time was observed in the HSP 8B farm as both the Shannon and Chao1 indices were significantly different between STEC shedders and non-shedders over time. In farm 9D, 25% of animals shed the pathogen in two phases, while the remaining cattle shed in just one phase, and the alpha diversity was steady. Differences in the microbiota composition or beta diversity, as determined by the PCoA, were also observed across samplings at each of the five farms evaluated (Figure 2.4C). Curiously, the Angus farm (8B) had two microbiota profiles that were not associated with STEC shedding status. In farm 8B, the microbiota profiles in phases 1 and 2 were similar, highly diverse, and dominated by Bacteroidetes (log2 fold change = 0.22; p = 0.0009). The microbiota profiles in phases 3 and 4, however, were distinct from those observed in phases 1 and 2, which coincided with a decreased alpha diversity and an increased abundance of Firmicutes (log2 fold change = 0.15; p = 0.05). Despite these differences, the proportion of STEC positive animals in farm 8B was steady across the four phases. 66 STEC Negative Positive A 1B p=0.66 2D p = 0.20 4D p=0.002 8B p=1.65e−05 9D p=0.30 5.1 5.0 5.1 5.0 4.8 Shannon 5.0 4.5 4.8 4.5 4.0 4.5 4.5 4.5 4.2 3.5 4.0 4.0 3.9 1 2 3 4 1 2 3 4 2 3 4 1 2 3 4 1 2 3 4 B 1B p=0.018 2D p = 0.006 4D p=0.35 8B p=1.049e−07 9D p=0.13 500 700 600 400 500 600 500 600 Chao1 300 500 400 400 400 300 400 200 300 200 300 100 200 200 1 2 3 4 1 2 3 4 2 3 4 1 2 3 4 1 2 3 4 STEC Negative Positive Phase 1 2 3 4 C 1B R^2=0.1, p=0.001 2D R^2=0.08, p=0.001 4D R^2=0.045, p=0.001 8B R^2=0.15, p=0.001 9D R^2=0.105, p=0.013 0.05 0.05 0.05 0.05 0.02 PCoA 2 (5.1%) PCoA 2 (5.9%) PCoA 2 (6.6%) PCoA 2 (9.1%) PCoA 2 (8.5%) 0.00 0.00 0.00 0.00 −0.05 −0.05 0.00 −0.05 −0.02 −0.10 −0.10 −0.10 −0.05 −0.04 −0.10 −0.05 0.00 0.05 −0.05 0.00 0.05 −0.05 0.00 0.05 −0.2 −0.1 0.0 −0.05 0.00 0.05 PCoA 1 (16.5%) PCoA 1 (6.8%) PCoA 1 (11.1%) PCoA 1 (10.1%) PCoA 1 (13.6%) Figure 2.4. Temporal dynamics in microbiota diversity among 59 cattle from five farms. Alpha diversity was compared using the A) Shannon and B) Chao1 indices by farm and sampling period. Each box represents a different farm with the numbers on the x-axis representing the four sampling visits; significant differences were detected using the Kruskal- Wallis test. C) Beta diversity was also examined using a Principal Coordinate Analysis (PCoA) of the Weighted Unifrac distances; PERMANOVA results (R2 and p-value) were calculated. STEC shedders (+) and non-shedders (circles) were plotted by phase, which is represented by four different colors. Differentially abundant taxa among the STEC shedders from dairy farms Among the three dairy farms, STEC shedders had a significantly greater abundance of Firmicutes and a lower abundance of Proteobacteria than the non-shedders (p < 0.01) (Figure 2.5). No taxa were identified when comparing between STEC-shedders and non-shedders among the dairy farms when controlling for farm. A total of 30 genera were found to be differentially abundant between STEC carriers and non-carriers (Table A.5). 67 STEC shedder Non-shedder Figure 2.5. Heat tree showing the differentially abundant taxa found in the hindgut microbiota among STEC shedders (red) and non-shedders (blue) at three dairy farms. Only those taxa with a p-value lower than 0.01 were included. Node size and color correspond to the relative abundance at each taxonomic level. Taxa correlated with factors associated with STEC carriage Next, we analyzed how the microbiota composition can be disturbed by previously identified risk factors of STEC shedding in cattle [11] including maximum temperature 5 days prior to sampling, days in milk (DIM), and the number of lactations (Figure 2.6, Table A.6). Notably, temperature increases were associated with a differential abundance of 189 taxa including 42 observed among the STEC shedders (Table A.5). Similarly, the number of DIM was significantly correlated with 24 differentially abundant genera including those associated with STEC carriage such as Actinobacteria, Anaeroporobacter, Kingella, Ruminococcaceae 68 UCG-005, Tenericutes, Veillonellaceae, and Eubacterium ruminantium group. Finally, seven taxa were correlated with the number of lactations, including an increase of Kingella and Neisseriaceae and decrease of Lentisphaerae and Ruminococcaceae UCG-011 as observed in non-STEC shedders. Forage percentage in diet across farms was associated with changes in 211 taxa of which 48 were associated with STEC shedding (Figure 2.6). Bacilli, Betaproteobacteriales, Chloroflexi, Clostridia, Cyanobacteria, Dependentiae, DIM STEC Elusimicrobia, EMP-G18, Euryarchaeota, Actinobacteria, Tenericutes, Veillonellaceae Flavobacteriaceae, Fusobacteria, Gammaproteobacteria, Izimaplasmatales, Lachnospiraceae uncultured, Patescibacteria, Clostridiales vadinBB60 Peptostreptococcaceae, Planctomycetes, group, Epsilonbacteraeota, 1 18 Synergistetes Kiritimatiellaeota, p-251-o5, (0.4%) (7.6%) Rhodospirillales uncultured 0 2 (0.0%) 1 (0.8%) (0.4%) Romboustia 19 4 (8.0%) (1.7%) 3 1 Acidaminococcaceae, Alloprevotella, Anaerovorax, Bacteroidales RF16 (1.3%) (0.4%) group, Bacteroidetes, Butyrivibrio, Clostridiales vadinBB60 group, dgA 11 gut group, Erysipelotrichaceae UCG- 004, F082, Family XIII AD3011 group, Fibrobacteres, Firmicutes, 3 Gastranaerophilales uncultured, Eubacterium ruminantium group Izimaplasmatales, Lentisphaerae, (1.3%) Neisseriaceae, Oscillibacter, 5 1 p-251-o5 uncultured, (2.1%) (0.4%) Phascolarctobacterium, 37 13 Proteobacteria, Rhodospirillales uncultured, Rikenellaceae RC9 gut (15.6%) (5.5%) group, Roseburia, Ruminobacter, Ruminococcaceae UCG-010, Forage 129 Temperature Ruminococcaceae UCG-011, Ruminococcus 1, Ruminococcus 2, (54.4%) Spirochaetes, Tyzzerella 4, Anaerosporobacter, Kingella, Verrucomicrobia, Eubacterium Ruminococcaceae UCG 005 coprostanoligenes group Figure 2.6. Venn diagram showing the number of differentially abundant genera associated with STEC shedding (STEC), percentage of forage in diet and risk factors of STEC in dairy farms (temperature and days in milk). Percentages represent the proportion of taxa intersected between different variables. DISCUSSION Preventing STEC shedding in livestock could significantly reduce the number of human infections. In this study, we sought to determine differences in the gut microbiota of bovines from farms with a low versus high STEC prevalence. In addition, we explored factors that could affect the microbial composition and contribute to STEC shedding. The diversity and composition of 16S rRNA sequences of 660 hindgut samples from five cattle farms (beef and 69 dairy) were analyzed in this study. Cattle from HSP farms, characterized by being fed a high percentage of forage in diet (80-100%), had a significantly higher richness of OTUs than LSP farms, which had a lower proportion of forage in diet (15-65%). Longitudinal analysis showed that most STEC-shedders from LSP farms had a greater microbial diversity than non-shedders; however, cattle from HSP farms showed changes in the microbial diversity that were not linked to the STEC carriage. Furthermore, bacterial taxa associated with STEC shedding was also correlated with diet and previously described risk factors of STEC. Meanwhile, significant differences in predicted metabolic pathways in animals from LSP and HSP farms reflect functional differences of the microbiota between herds that could affect STEC colonization. The overall bacterial composition of the hindgut microbiota was similar to prior studies where Firmicutes and Bacteroidetes were the dominant taxa [43,44]. Though farm-specific composition was identified as previously observed in ten dairy farms with different housing, diet, and husbandry [43]. Notably, unlike LSP farms, HSP farms had in common a high-forage diet and did not administer anthelmintic treatment, which could affect the microbiome composition. The effect of diet in the gut microbiota was previously studied in cattle, where different ratios of forage:concentrate impacted changes in the microbiota [45–47]. As previously observed, a grain- based diet was associated with a higher abundance of Proteobacteria and lower abundance of Bacteroidetes [45,47]. Meanwhile, forage dominant diets were associated with a higher abundance of Firmicutes, Ruminococcaceae and Paludibacter, which have a critical function degrading forage [45,47]. Dietary interventions in beef cattle have been suggested to reduce the prevalence of STEC O157 as a preharvest intervention [48,49]. However, without knowledge of the microbiome and ecological interactions, those studies had conflicting results [48]. Furthermore, the effect of helminths in the cattle’s microbiome has not yet been studied. 70 Anthelmintic treatment in dogs and horses, for instance, was not associated with shifts in the microbiome composition [50,51]. Humans treated with Albendazole, however, had a higher abundance of Clostridiales and a lower abundance of Enterobacteriales [52]. Meanwhile, helminthic infections were associated with a lower abundance of Lachnospiraceae in the human gut microbiome [53]. Higher alpha diversity identified among some STEC shedders has been observed in previous studies in both beef [54,55] and dairy cattle [22]. By contrast, some reports have found that STEC carriage in individual cattle was associated with lower alpha diversity [17,20,23,56]. Two of these reports were carried out in beef herds, where correlations between bacterial richness, STEC enumeration, and age (weaning to one year) were compared. While the authors found that older animals had higher microbial diversity and that younger animals (1-6 months) shed a higher number of STEC, the correlation between microbiome diversity and STEC shedding reflects factors related to age and dietary changes [17,20]. Other reports in dairy cattle that identified a lower alpha diversity in the gut microbiome were focused on shedding of STEC O157:H7 (O157), the serotype associated with a higher number of hospitalizations in humans. Stemkamp-Strahm et. al ( 2017) detected a weak association after removing outliers [23], while Mir et. al (2020) identified lower alpha diversity in STEC carriers only after vaccination and oral challenge with O157. Hence, these findings indicate that O157 carriage did not directly affect the microbiota, but that vaccination for O157 can alter the microbiota diversity [56]. Indeed, the longitudinal analysis of HSP farms examined herein and in our prior study [57], showed similar microbiota shifts as those observed in O157 vaccinated cattle, suggesting that STEC re-infection in cattle can be followed by a lower alpha diversity. 71 The microbiota composition of STEC-positive samples mostly overlapped with negative samples in the PCoA. Nevertheless, differentially abundant taxa have been documented among STEC shedders and non-shedders [17,19,23,24,55]. Zhao et. al (2013), for instance, found that butyrate-producing species were more abundant in low-STEC shedding cattle and were critical in avoiding RAJ lesions [17], suggesting the role of certain taxa as ‘inhibitors’ or ‘promoters’ [17]. Contrasting results have been observed among studies, but in general, there is a consensus that STEC shedders have a higher proportion of members from the order Clostridiales, the dominant order found in the bovine gut microbiome [18,22–24,55,56,58,59]. Consistent with other reports, a lower abundance of Proteobacteria were observed in STEC shedders [22,56]. Varying results in differentially abundant taxa among STEC shedders and non-shedders denote a high variability between species and strains within taxa, as well as differences between study approaches and farms. In this study, the main microbial biomarkers of STEC shedders were Romboustia and Alloprevotella, implicated in the production of C12-C19 fatty acids [60] and succinic acid [61], respectively. Other genera significantly higher in STEC shedders were associated with sugar fermentation and the production of acetic, formic, propionic, and succinic acids [62–64]. By contrast, the main biomarkers of non-shedders were Kingella, Bacteroidales p-251-o5 and Anaerosporobacter. In humans, Kingella is implicated in invasive infections due to its cytotoxicity [65]. Butyrate-producing bacteria including Butyrivibrio, Oscillibacter, Roseburia, and Ruminobacter, were also found to be associated with non-shedders [66,67]. These families have previously been linked to a healthy human gut microbiota and were suggested to play a role in preventing chronic intestinal inflammation [68]. The functional role of these taxa in the bovine 72 microbiome or in immunomodulation as well as the correlation with pathogen colonization, however, requires further investigation. Differences in predicted metabolic pathways observed between LSP and HSP farms suggest that distinct functional microbiomes could favor STEC carriage. For instance, metabolic- pathway prediction showed important differences between HSP and LSP farms associated with diet, where HSP farms had higher oxidation, production of short-chain fatty acids, degradation, and fermentation than LSP farms, which had higher biosynthesis of amino-acids and sugar degradation (Figures 2.5 & 2.6). Enhanced fermentation and fatty acid production in HSP farms could be influenced by forage dominant diets. Comparatively, the LSP farms had higher amino acid biosynthesis, suggesting differing amino acid availability in diets within the LSP and HSP farms. Higher inositol degradation in LSP farms shows enhanced cleav[39–42]age of phospholipid membranes that generate cell signaling molecules (i.e., inositol phosphate and diacylglycerol) important for microbial-host interactions [69]. Indeed, distinct metabolic profiles were suggested to be influenced by the diet, as a higher grain diet lowered the ruminal pH and altered the abundance of several metabolites including short-chain fatty acids, amino acids, ethanol, endotoxins, and biogenic amines [70]. Increasing amounts of grain in diets are also correlated with increasing concentrations of ethanolamine [70], the main product of enterocyte membranes, which is degraded to ethanol and acetate. Studies have shown that both Salmonella spp. and STEC O157 can use ethanolamine as a nitrogen source to outcompete commensal bacteria [71,72]. These studies, however, were carried out in-vitro under aerobic conditions, unlike the intestinal environment. Microbiota diversity and STEC shedding are dynamic over time as different patterns were observed between farms with a low and high STEC prevalence. Longitudinal studies in 73 cattle found that the stability of the gut microbiota diversity and composition depends on the diet [73]. Once the animals are adapted to a specific diet, the microbial communities are relatively stable [73]. Unlike farms with low STEC prevalence, animals from farms with high STEC prevalence had access to pasture and a diet primarily based on forage. The grazing behavior and differences in forage composition in farms with high STEC prevalence could explain the high variability over time in their microbiota and a higher STEC detection. We identified that cattle from farms with low-STEC prevalence only shed the pathogen once in an 8-12-week period. Meanwhile, most of the cattle from farms with a HSP prevalence shed the bacteria more than once. We also identified STEC super shedders only in the HSP farms (data not shown). A longitudinal study carried in dairy cattle for a 12-month period identified a very low number of STEC super-shedders in farms with a low STEC prevalence (3.5-5%), and those animals only shed the bacteria once a year [74]. Other studies have reported that the within-farm proportion of super-shedders ranges from 3.8% to 25%, highlighting the importance of farm-specific differences on STEC prevalence and shedding levels [75–77]. The bioinformatics pipeline used in this study was designed to improve the quality of the pyrosequencing results. We used SATé-Enabled Phylogenetic Placement (SEPP) trees to more accurately identify the phylogenetic relationships between OTUs by including sequences of known species [33]. SEPP trees are strongly recommended to avoid incorrect results driven by erroneous phylogenetic placements as observed in de-novo trees [31]. Using SEPP trees was critical to account for differences in the beta diversity using Weighted UniFrac metrics and to predict metabolic pathways with PICRUSt2. In addition, we used non-linear approaches to identify differentially abundant taxa. Linear discriminant analyses which assume normality, showed similar results than DESeq though fewer taxa were identified as significantly different. 74 Prior studies looking for associations between the bacterial composition and STEC shedding used different techniques to identify taxa including denaturing gradient gel electrophoresis (DGGE) [17], pyrosequencing [16,20,24,55,78], and Illumina dye sequencing [19,22,23,56,58,59]. Despite differences in the pipeline, commonalities in the microbiota composition and differentially abundant taxa present in STEC shedders were observed across studies. As the sequencing techniques and bioinformatic tools evolve rapidly, high-resolution results will help to better understand more complex relationships within the microbiome. Along with defining the taxonomic composition, it is important to characterize molecular interactions between microorganisms and hosts by identifying KEGG pathways and metabolites that are common among STEC positive cattle. Nonetheless, this study has several limitations and hence, the data should be interpreted carefully. For instance, we compared animals from cattle farms with different genetic backgrounds, diet, housing, locations, and husbandry practices, which could be confounding factors that also influence microbiota diversity and composition between farms. Furthermore, using pyrosequencing we were able to detect differences in numerically dominant taxa, limiting the identification of low abundant taxa that could also play a key role in defining the composition of the microbiota. For instance, the proportion of Escherichia was very low and absent in a large proportion of samples through pyrosequencing analysis. Metabolic pathways predicted from 16S rRNA showed significant differences between HSP and LSP farms (Figure 2.5) and between STEC shedders and non-shedders (Figure 2.6). These predictions, however, are not entirely accurate as they are based on metabolic reconstruction of a few representative species and do not account for genome differences between closely related strains. Despite this limitation, they provide clues that can be used to guide future studies aimed at defining the function of the cattle 75 gut microbiome in the presence and absence of STEC. Future studies should also use a longitudinal approach and consider the within-farm STEC prevalence to better identify changes in the microbiome among shedders. Understanding the role that anthelmintics play in STEC shedding should also be addressed, while metagenomic and metabolomic data should be evaluated to identify key metabolites, genes, and bacterial species that could inhibit STEC colonization and boost the gut immune response. CONCLUSIONS This study suggests that STEC carriage in cattle is favored by highly diverse microbiota profiles, which are associated with forage-dominant diets. In addition, multiple factors affect the abundance of taxa associated with STEC-shedding in dairy farms, including diet, number or lactations, DIM, and warm temperatures. Identifying healthy microbiomes could guide novel husbandry decisions that aim to decrease levels of pathogen shedding. 76 REFERENCES 1. Majowicz, S.E.; Scallan, E.; Jones-Bitton, A.; Sargeant, J.M.; Stapleton, J.; Angulo, F.J.; Yeung, D.H.; Kirk, M.D. Global Incidence of Human Shiga Toxin–Producing Escherichia coli Infections and Deaths: A Systematic Review and Knowledge Synthesis. Foodborne Pathogens and Disease 2014, 11, 447–455, doi:10.1089/fpd.2013.1704. 2. Sapountzis, P.; Segura, A.; Desvaux, M.; Forano, E. An Overview of the Elusive Passenger in the Gastrointestinal Tract of Cattle: The Shiga Toxin Producing Escherichia coli. Microorganisms 2020, 8, 877, doi:10.3390/microorganisms8060877. 3. Oppenheim, A.B.; Kobiler, O.; Stavans, J.; Court, D.L.; Adhya, S. Switches in Bacteriophage Lambda Development. Annual Review of Genetics 2005, 39, 409–429, doi:10.1146/annurev.genet.39.073003.113656. 4. Scheutz, F.; Teel, L.D.; Beutin, L.; Pierard, D.; Buvens, G.; Karch, H.; Mellmann, A.; Caprioli, A.; Tozzoli, R.; Morabito, S.; et al. Multicenter Evaluation of a Sequence-Based Protocol for Subtyping Shiga Toxins and Standardizing Stx Nomenclature. Journal of Clinical Microbiology 2012, 50, 2951–2963, doi:10.1128/JCM.00860-12. 5. O’Brien, A.D.; Holmes, R.K. Shiga and Shiga-like Toxins. Microbiological reviews 1987, 51, 206–20. 6. O’Brien, A.; Newland, J.; Miller, S.; Holmes, R.; Smith, H.; Formal, S. Shiga-like Toxin- Converting Phages from Escherichia coli Strains That Cause Hemorrhagic Colitis or Infantile Diarrhea. Science 1984, 226, 694–696, doi:10.1126/science.6387911. 7. Pruimboom-Brees, I.M.; Morgan, T.W.; Ackermann, M.R.; Nystrom, E.D.; Samuel, J.E.; Cornick, N.A.; Moon, H.W. Cattle Lack Vascular Receptors for Escherichia coli O157:H7 Shiga Toxins. Proceedings of the National Academy of Sciences 2000, 97, 10325–10329, doi:10.1073/pnas.190329997. 8. McDaniel, T.K.; Kaper, J.B. A Cloned Pathogenicity Island from Enteropathogenic Escherichia coli Confers the Attaching and Effacing Phenotype on E. coli K-12. Molecular Microbiology 1997, 23, 399–407, doi:https://doi.org/10.1046/j.1365- 2958.1997.2311591.x. 9. Naylor, S.W.; Low, J.C.; Besser, T.E.; Mahajan, A.; Gunn, G.J.; Pearce, M.C.; McKendrick, I.J.; Smith, D.G.E.; Gally, D.L. Lymphoid Follicle-Dense Mucosa at the Terminal Rectum Is the Principal Site of Colonization of Enterohemorrhagic Escherichia coli O157:H7 in the Bovine Host. Infection and Immunity 2003, 71, 1505–1512, doi:10.1128/IAI.71.3.1505-1512.2003. 10. Thomas, D.E.; Elliott, E.J. Interventions for Preventing Diarrhea-Associated Hemolytic Uremic Syndrome: Systematic Review. BMC Public Health 2013, 13, 799, doi:10.1186/1471-2458-13-799. 77 11. Venegas-Vargas, C.; Henderson, S.; Khare, A.; Mosci, R.E.; Lehnert, J.D.; Singh, P.; Ouellette, L.M.; Norby, B.; Funk, J.A.; Rust, S.; et al. Factors Associated with Shiga Toxin-Producing Escherichia coli Shedding by Dairy and Beef Cattle. Applied and Environmental Microbiology 2016, 82, 5049–5056, doi:10.1128/AEM.00829-16. 12. Kamada, N.; Chen, G.Y.; Inohara, N.; Núñez, G. Control of Pathogens and Pathobionts by the Gut Microbiota. Nature Immunology 2013, 14, 685–690, doi:10.1038/ni.2608. 13. Gigliucci, F.; von Meijenfeldt, F.A.B.; Knijn, A.; Michelacci, V.; Scavia, G.; Minelli, F.; Dutilh, B.E.; Ahmad, H.M.; Raangs, G.C.; Friedrich, A.W.; et al. Metagenomic Characterization of the Human Intestinal Microbiota in Fecal Samples from STEC- Infected Patients. Frontiers in Cellular and Infection Microbiology 2018, 8, doi:10.3389/fcimb.2018.00025. 14. Singh, P.; Teal, T.K.; Marsh, T.L.; Tiedje, J.M.; Mosci, R.; Jernigan, K.; Zell, A.; Newton, D.W.; Salimnia, H.; Lephart, P.; et al. Intestinal Microbial Communities Associated with Acute Enteric Infections and Disease Recovery. Microbiome 2015, 3, 45, doi:10.1186/s40168-015-0109-2. 15. Lee, K.-S.; Jeong, Y.-J.; Lee, M.-S. Escherichia coli Shiga Toxins and Gut Microbiota Interactions. Toxins 2021, 13, 416, doi:10.3390/toxins13060416. 16. Xu, Y.; Dugat-Bony, E.; Zaheer, R.; Selinger, L.; Barbieri, R.; Munns, K.; McAllister, T.A.; Selinger, L.B. Escherichia coli O157:H7 Super-Shedder and Non-Shedder Feedlot Steers Harbour Distinct Fecal Bacterial Communities. PLoS ONE 2014, 9, e98115, doi:10.1371/journal.pone.0098115. 17. Zhao, L.; Tyler, P.J.; Starnes, J.; Bratcher, C.L.; Rankins, D.; McCaskey, T.A.; Wang, L. Correlation Analysis of Shiga Toxin-Producing Escherichia coli Shedding and Faecal Bacterial Composition in Beef Cattle. Journal of Applied Microbiology 2013, 115, 591– 603, doi:10.1111/jam.12250. 18. Dong, H.-J.; Kim, W.; An, J.-U.; Kim, J.; Cho, S. The Fecal Microbial Communities of Dairy Cattle Shedding Shiga Toxin–Producing Escherichia coli or Campylobacter jejuni. Foodborne Pathogens and Disease 2016, 13, 502–508, doi:10.1089/fpd.2016.2121. 19. Salaheen, S.; Kim, S.W.; Karns, J.S.; Hovingh, E.; Haley, B.J.; Van Kessel, J.A.S. Metagenomic Analysis of the Fecal Microbiomes from Escherichia coli O157:H7- Shedding and Non-Shedding Cows on a Single Dairy Farm. Food Control 2019, 102, 76– 80, doi:10.1016/j.foodcont.2019.03.022. 20. Mir, R.A.; Weppelmann, T.A.; Elzo, M.; Ahn, S.; Driver, J.D.; Jeong, K.C. Colonization of Beef Cattle by Shiga Toxin-Producing Escherichia coli during the First Year of Life: A Cohort Study. PloS one 2016, 11, e0148518, doi:10.1371/journal.pone.0148518. 21. Wang, O.; McAllister, T.A.; Plastow, G.; Stanford, K.; Selinger, B.; Guan, L.L. Interactions of the Hindgut Mucosa-Associated Microbiome with Its Host Regulate 78 Shedding of Escherichia coli O157:H7 by Cattle. Applied and Environmental Microbiology 2017, 84, doi:10.1128/AEM.01738-17. 22. Mir, R.A.; Schaut, R.G.; Looft, T.; Allen, H.K.; Sharma, V.K.; Kudva, I.T. Recto-Anal Junction (RAJ) and Fecal Microbiomes of Cattle Experimentally Challenged With Escherichia coli O157:H7. Frontiers in Microbiology 2020, 11, doi:10.3389/fmicb.2020.00693. 23. Stenkamp-Strahm, C.; McConnel, C.; Magzamen, S.; Abdo, Z.; Reynolds, S. Associations between Escherichia coli O157 Shedding and the Faecal Microbiota of Dairy Cows. Journal of Applied Microbiology 2018, 124, 881–898, doi:10.1111/jam.13679. 24. Kim, M.; Kuehn, L.A.; Bono, J.L.; Berry, E.D.; Kalchayanand, N.; Freetly, H.C.; Benson, A.K.; Wells, J.E. The Impact of the Bovine Faecal Microbiome on Escherichia coli O157:H7 Prevalence and Enumeration in Naturally Infected Cattle. Journal of Applied Microbiology 2017, 123, 1027–1042, doi:10.1111/jam.13545. 25. Venegas-Vargas, C.; Manning, S.D.; Coussens, P.M.; Roussey, J.A.; Bartlett, P.; Grooms, D. Bovine Leukemia Virus and Mycobacterium avium subsp. paratuberculosis Are Not Associated with Shiga Toxin–Producing Escherichia coli Shedding in Cattle. Journal of Food Protection 2017, 80, 86–89, doi:10.4315/0362-028X.JFP-16-090. 26. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME Allows Analysis of High-Throughput Community Sequencing Data. Nature Methods 2010, 7, 335–336, doi:10.1038/nmeth.f.303. 27. Yilmaz, P.; Parfrey, L.W.; Yarza, P.; Gerken, J.; Pruesse, E.; Quast, C.; Schweer, T.; Peplies, J.; Ludwig, W.; Glöckner, F.O. The SILVA and “All-Species Living Tree Project (LTP)” Taxonomic Frameworks. Nucleic Acids Research 2013, 42, D643–D648, doi:10.1093/nar/gkt1209. 28. Reeder, J.; Knight, R. Rapidly Denoising Pyrosequencing Amplicon Reads by Exploiting Rank-Abundance Distributions. Nature Methods 2010, 7, 668–669, doi:10.1038/nmeth0910-668b. 29. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A Versatile Open Source Tool for Metagenomics. PeerJ 2016, 4, e2584, doi:10.7717/peerj.2584. 30. Caporaso, J.G.; Bittinger, K.; Bushman, F.D.; DeSantis, T.Z.; Andersen, G.L.; Knight, R. PyNAST: A Flexible Tool for Aligning Sequences to a Template Alignment. Bioinformatics 2010, 26, 266–267, doi:10.1093/bioinformatics/btp636. 31. Janssen, S.; McDonald, D.; Gonzalez, A.; Navas-Molina, J.A.; Jiang, L.; Xu, Z.Z.; Winker, K.; Kado, D.M.; Orwoll, E.; Manary, M.; et al. Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information. mSystems 2018, 3, doi:10.1128/mSystems.00021-18. 79 32. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2. Nature Biotechnology 2019, 37, 852–857, doi:10.1038/s41587-019-0209-9. 33. Mirarab, S.; Nguyen, N.; Warnow, T. SEPP: SATé-Enabled Phylogenetic Placement. In Proceedings of the Biocomputing 2012; WORLD SCIENTIFIC, December 20 2011; pp. 247–258. 34. McMurdie, P.J.; Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e61217, doi:10.1371/journal.pone.0061217. 35. Lozupone, C.; Knight, R. UniFrac: A New Phylogenetic Method for Comparing Microbial Communities. Applied and environmental microbiology 2005, 71, 8228–8235, doi:10.1128/AEM.71.12.8228-8235.2005. 36. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’hara, R.B.; Simpson, G.L.; Solymos, P.; Stevens, M.H.H.; et al. Package ‘Vegan’ Version 2.5-6. Community ecology package, version 2019, 2, 1–295. 37. Love, M.I.; Huber, W.; Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biology 2014, 15, 550, doi:10.1186/s13059- 014-0550-8. 38. Foster, Z.S.L.; Sharpton, T.J.; Grünwald, N.J. Metacoder: An R Package for Visualization and Manipulation of Community Taxonomic Diversity Data. PLOS Computational Biology 2017, 13, e1005404, doi:10.1371/journal.pcbi.1005404. 39. Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for Prediction of Metagenome Functions. Nature Biotechnology 2020, 38, 685–688, doi:10.1038/s41587-020-0548-6. 40. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery and Explanation. Genome Biol 2011, 12, R60, doi:10.1186/gb-2011-12-6-r60. 41. Uchiyama, J.; Murakami, H.; Sato, R.; Mizukami, K.; Suzuki, T.; Shima, A.; Ishihara, G.; Sogawa, K.; Sakaguchi, M. Examination of the Fecal Microbiota in Dairy Cows Infected with Bovine Leukemia Virus. Veterinary Microbiology 2020, 240, 108547, doi:10.1016/j.vetmic.2019.108547. 42. Fecteau, M.-E.; Pitta, D.W.; Vecchiarelli, B.; Indugu, N.; Kumar, S.; Gallagher, S.C.; Fyock, T.L.; Sweeney, R.W. Dysbiosis of the Fecal Microbiota in Cattle Infected with Mycobacterium avium subsp. paratuberculosis. PLOS ONE 2016, 11, e0160353, doi:10.1371/journal.pone.0160353. 80 43. Hagey, J. V.; Bhatnagar, S.; Heguy, J.M.; Karle, B.M.; Price, P.L.; Meyer, D.; Maga, E.A. Fecal Microbial Communities in a Large Representative Cohort of California Dairy Cows. Frontiers in Microbiology 2019, 10, doi:10.3389/fmicb.2019.01093. 44. Durso, L.M.; Harhay, G.P.; Smith, T.P.L.; Bono, J.L.; DeSantis, T.Z.; Harhay, D.M.; Andersen, G.L.; Keen, J.E.; Laegreid, W.W.; Clawson, M.L. Animal-to-Animal Variation in Fecal Microbial Diversity among Beef Cattle. Applied and Environmental Microbiology 2010, 76, 4858–4862, doi:10.1128/AEM.00207-10. 45. Zhang, J.; Shi, H.; Wang, Y.; Cao, Z.; Yang, H.; Li, S. Effect of Limit-Fed Diets With Different Forage to Concentrate Ratios on Fecal Bacterial and Archaeal Community Composition in Holstein Heifers. Frontiers in Microbiology 2018, 9, doi:10.3389/fmicb.2018.00976. 46. Kim, M.; Kim, J.; Kuehn, L.A.; Bono, J.L.; Berry, E.D.; Kalchayanand, N.; Freetly, H.C.; Benson, A.K.; Wells, J.E. Investigation of Bacterial Diversity in the Feces of Cattle Fed Different Diets. Journal of Animal Science 2014, 92, 683–694, doi:10.2527/jas.2013- 6841. 47. Liu, J.; Liu, F.; Cai, W.; Jia, C.; Bai, Y.; He, Y.; Zhu, W.; Li, R.W.; Song, J. Diet-Induced Changes in Bacterial Communities in the Jejunum and Their Associations with Bile Acids in Angus Beef Cattle. Animal Microbiome 2020, 2, 33, doi:10.1186/s42523-020-00051-7. 48. Jacob, M.E.; Callaway, T.R.; Nagaraja, T.G. Dietary Interactions and Interventions Affecting Escherichia coli O157 Colonization and Shedding in Cattle. Foodborne Pathogens and Disease 2009, 6, 785–792, doi:10.1089/fpd.2009.0306. 49. Callaway, T.R.; Carr, M.A.; Edrington, T.S.; Anderson, R.C.; Nisbet, D.J. Diet, Escherichia coli O157:H7, and Cattle: A Review after 10 Years. Current issues in molecular biology 2009, 11, 67–79. 50. Kunz, I.G.Z.; Reed, K.J.; Metcalf, J.L.; Hassel, D.M.; Coleman, R.J.; Hess, T.M.; Coleman, S.J. Equine Fecal Microbiota Changes Associated With Anthelmintic Administration. Journal of Equine Veterinary Science 2019, 77, 98–106, doi:10.1016/j.jevs.2019.01.018. 51. Fujishiro, M.A.; Lidbury, J.A.; Pilla, R.; Steiner, J.M.; Lappin, M.R.; Suchodolski, J.S. Evaluation of the Effects of Anthelmintic Administration on the Fecal Microbiome of Healthy Dogs with and without Subclinical Giardia spp. and Cryptosporidium canis Infections. PLOS ONE 2020, 15, e0228145, doi:10.1371/journal.pone.0228145. 52. Easton, A. V.; Quiñones, M.; Vujkovic-Cvijin, I.; Oliveira, R.G.; Kepha, S.; Odiere, M.R.; Anderson, R.M.; Belkaid, Y.; Nutman, T.B. The Impact of Anthelmintic Treatment on Human Gut Microbiota Based on Cross-Sectional and Pre- and Postdeworming Comparisons in Western Kenya. mBio 2019, 10, doi:10.1128/mBio.00519-19. 53. Rosa, B.A.; Supali, T.; Gankpala, L.; Djuardi, Y.; Sartono, E.; Zhou, Y.; Fischer, K.; Martin, J.; Tyagi, R.; Bolay, F.K.; et al. Differential Human Gut Microbiome 81 Assemblages during Soil-Transmitted Helminth Infections in Indonesia and Liberia. Microbiome 2018, 6, 33, doi:10.1186/s40168-018-0416-5. 54. Xu, Y.; Dugat-Bony, E.; Zaheer, R.; Selinger, L.; Barbieri, R.; Munns, K.; McAllister, T.A.; Selinger, L.B. Escherichia coli O157:H7 Super-Shedder and Non-Shedder Feedlot Steers Harbour Distinct Fecal Bacterial Communities. PLOS ONE 2014, 9, e98115. 55. Zaheer, R.; Dugat-Bony, E.; Holman, D.; Cousteix, E.; Xu, Y.; Munns, K.; Selinger, L.J.; Barbieri, R.; Alexander, T.; McAllister, T.A.; et al. Changes in Bacterial Community Composition of Escherichia coli O157:H7 Super-Shedder Cattle Occur in the Lower Intestine. PLOS ONE 2017, 12, e0170050, doi:10.1371/journal.pone.0170050. 56. Mir, R.A.; Schaut, R.G.; Allen, H.K.; Looft, T.; Loving, C.L.; Kudva, I.T.; Sharma, V.K. Cattle Intestinal Microbiota Shifts Following Escherichia coli O157:H7 Vaccination and Colonization. PLOS ONE 2019, 14, e0226099, doi:10.1371/journal.pone.0226099. 57. Blankenship, H.M.; Carbonell, S.; Mosci, R.; McWilliams, K.; Pietrzen, K.; Benko, S.; Gatesy, T.; Grooms, D.; Manning, S.D. Genetic and Phenotypic Factors Associated with Persistent Shiga Toxin-Producing Escherichia coli Shedding in Beef Cattle. Applied and Environmental Microbiology 2020, doi:10.1128/AEM.01292-20. 58. Chopyk, J.; Moore, R.M.; DiSpirito, Z.; Stromberg, Z.R.; Lewis, G.L.; Renter, D.G.; Cernicchiaro, N.; Moxley, R.A.; Wommack, K.E. Presence of Pathogenic Escherichia coli Is Correlated with Bacterial Community Diversity and Composition on Pre-Harvest Cattle Hides. Microbiome 2016, 4, 9, doi:10.1186/s40168-016-0155-4. 59. Wang, O.; McAllister, T.A.; Plastow, G.; Stanford, K.; Selinger, B.; Guan, L.L. Interactions of the Hindgut Mucosa-Associated Microbiome with Its Host Regulate Shedding of Escherichia coli O157:H7 by Cattle. Applied and Environmental Microbiology 2018, 84, e01738-17, doi:10.1128/AEM.01738-17. 60. Gerritsen, J.; Umanets, A.; Staneva, I.; Hornung, B.; Ritari, J.; Paulin, L.; Rijkers, G.T.; de Vos, W.M.; Smidt, H. Romboutsia hominis sp. nov., the First Human Gut-Derived Representative of the Genus Romboutsia, Isolated from Ileostoma Effluent. International Journal of Systematic and Evolutionary Microbiology 2018, 68, 3479–3486, doi:10.1099/ijsem.0.003012. 61. Downes, J.; Dewhirst, F.E.; Tanner, A.C.R.; Wade, W.G.Y. 2013 Description of Alloprevotella rava gen. nov., sp. nov., Isolated from the Human Oral Cavity, and Reclassification of Prevotella tannerae Moore et al. 1994 as Alloprevotella tannerae gen. nov., comb. nov. International Journal of Systematic and Evolutionary Microbiology 63, 1214–1218, doi:10.1099/ijs.0.041376-0. 62. Krumholz, L.R.; Bryant, M.P.; Brulla, W.J.; Vicini, J.L.; Clark, J.H.; Stahl, D.A.Y. 1993 Proposal of Quinella ovalis gen. nov., sp. nov., Based on Phylogenetic Analysis. International Journal of Systematic and Evolutionary Microbiology 43, 293–296, doi:10.1099/00207713-43-2-293. 82 63. Alou, M.T.; Ndongo, S.; Frégère, L.; Labas, N.; Andrieu, C.; Richez, M.; Couderc, C.; Baudoin, J.-P.; Abrahão, J.; Brah, S.; et al. Taxonogenomic Description of Four New Clostridium Species Isolated from Human Gut: “Clostridium amazonitimonense”, “Clostridium merdae”, “Clostridium massilidielmoense” and “Clostridium nigeriense.” New Microbes New Infect 2018, 21, 128–139, doi:10.1016/j.nmni.2017.11.003. 64. Brenner, D.J.; Krieg, N.; Staley, J.; Garrity, G. Bergey’s Manual of Systematic Bacteriology, Vol 2: The Proteobacteria. NY: Springer 2005. 65. Yagupsky, P. Kingella kingae: Carriage, Transmission, and Disease. Clin Microbiol Rev 2015, 28, 54–79, doi:10.1128/CMR.00028-14. 66. Flint, H.J.; Scott, K.P.; Duncan, S.H.; Louis, P.; Forano, E. Microbial Degradation of Complex Carbohydrates in the Gut. Gut Microbes 2012, 3, 289–306, doi:10.4161/gmic.19897. 67. Chen, T.; Long, W.; Zhang, C.; Liu, S.; Zhao, L.; Hamaker, B.R. Fiber-Utilizing Capacity Varies in Prevotella- versus Bacteroides-Dominated Gut Microbiota. Sci Rep 2017, 7, 2594, doi:10.1038/s41598-017-02995-4. 68. Lepage, P.; Häsler, R.; Spehlmann, M.E.; Rehman, A.; Zvirbliene, A.; Begun, A.; Ott, S.; Kupcinskas, L.; Doré, J.; Raedler, A.; et al. Twin Study Indicates Loss of Interaction between Microbiota and Mucosa of Patients with Ulcerative Colitis. Gastroenterology 2011, 141, 227–236, doi:10.1053/j.gastro.2011.04.011. 69. Berridge, M.J. Inositol Trisphosphate and Diacylglycerol as Intracellular Second Messengers. In Mechanisms of Receptor Regulation; Poste, G., Crooke, S.T., Eds.; New Horizons in Therapeutics; Springer US: Boston, MA, 1985; pp. 111–130 ISBN 978-1- 4613-2131-6. 70. Saleem, F.; Ametaj, B.N.; Bouatra, S.; Mandal, R.; Zebeli, Q.; Dunn, S.M.; Wishart, D.S. A Metabolomics Approach to Uncover the Effects of Grain Diets on Rumen Health in Dairy Cows. Journal of Dairy Science 2012, 95, 6606–6623, doi:10.3168/jds.2012-5403. 71. Bertin, Y.; Girardeau, J.P.; Chaucheyras-Durand, F.; Lyan, B.; Pujos-Guillot, E.; Harel, J.; Martin, C. Enterohaemorrhagic Escherichia coli Gains a Competitive Advantage by Using Ethanolamine as a Nitrogen Source in the Bovine Intestinal Content. Environmental Microbiology 2011, 13, 365–377, doi:https://doi.org/10.1111/j.1462-2920.2010.02334.x. 72. Thiennimitr, P.; Winter, S.E.; Winter, M.G.; Xavier, M.N.; Tolstikov, V.; Huseby, D.L.; Sterzenbach, T.; Tsolis, R.M.; Roth, J.R.; Bäumler, A.J. Intestinal Inflammation Allows Salmonella to Use Ethanolamine to Compete with the Microbiota. PNAS 2011, 108, 17480–17485, doi:10.1073/pnas.1107857108. 73. Snelling, T.J.; Auffret, M.D.; Duthie, C.-A.; Stewart, R.D.; Watson, M.; Dewhurst, R.J.; Roehe, R.; Walker, A.W. Temporal Stability of the Rumen Microbiota in Beef Cattle, and Response to Diet and Supplements. Animal Microbiome 2019, 1, 16, doi:10.1186/s42523- 019-0018-y. 83 74. Murphy, B.P.; McCabe, E.; Murphy, M.; Buckley, J.F.; Crowley, D.; Fanning, S.; Duffy, G. Longitudinal Study of Two Irish Dairy Herds: Low Numbers of Shiga Toxin-Producing Escherichia coli O157 and O26 Super-Shedders Identified. Frontiers in Microbiology 2016, 7, doi:10.3389/fmicb.2016.01850. 75. Cobbold, R.N.; Hancock, D.D.; Rice, D.H.; Berg, J.; Stilborn, R.; Hovde, C.J.; Besser, T.E. Rectoanal Junction Colonization of Feedlot Cattle by Escherichia coli O157:H7 and Its Association with Supershedders and Excretion Dynamics. Applied and Environmental Microbiology 2007, 73, 1563–1568, doi:10.1128/AEM.01742-06. 76. Cernicchiaro, N.; Pearl, D.L.; McEwen, S.A.; Zerby, H.N.; Fluharty, F.L.; Loerch, S.C.; Kauffman, M.D.; Bard, J.L.; LeJeune, J.T. A Randomized Controlled Trial to Assess the Impact of Dietary Energy Sources, Feed Supplements, and the Presence of Super- Shedders on the Detection of Escherichia coli O157:H7 in Feedlot Cattle Using Different Diagnostic Procedures. Foodborne Pathogens and Disease 2010, 7, 1071–1081, doi:10.1089/fpd.2009.0531. 77. Menrath, A.; Wieler, L.H.; Heidemanns, K.; Semmler, T.; Fruth, A.; Kemper, N. Shiga Toxin Producing Escherichia coli: Identification of Non-O157:H7-Super-Shedding Cows and Related Risk Factors. Gut Pathogens 2010, 2, 7, doi:10.1186/1757-4749-2-7. 78. Dong, H.-J.; Kim, W.; An, J.-U.; Kim, J.; Cho, S. The Fecal Microbial Communities of Dairy Cattle Shedding Shiga Toxin-Producing Escherichia coli or Campylobacter jejuni. Foodborne pathogens and disease 2016, 13, 502–508, doi:10.1089/fpd.2016.2121. 84 APPENDIX Table A.1. Characteristics of the three dairy farms examined in this study. Dairy farm Feature 2D 4D 9D No. milkings/day 2 times 3 times 2-3 times DIM (SD) 206.7 (134) 259 (118.5) 195.25 (136.43) No. lactations 0 (No. cows) 4 0 0 1 (No. cows) 80 36 34 ≥ 2 (No. cows) 141 86 43 Dry (No. cows) 5 9 0 Treatment Ceftiofur, Pirlimycin Penicillin G Procaine, hydrochloride, Penicillin, Ceftiofur, Pirlimycin Clinic mastitis Ampicillin, Ceftiofur hydrochloride, Oxytetracycline, Amoxicillin Sulfadimethoxine Ceftiofur, Metritis Oxytetracycline, Penicillin Ceftiofur Isoflupredone acetate Penicillin-Novobiocin, Penicillin- Dry Penicillin- dihydrostreptomycin, None dihydrostreptomycin Orbeseal No.=Number; DIM=Days in milk; SD=Standard deviation 85 Table A.2. Number of animals sampled longitudinally per farm and number of days from each phase. No. Days (No.) Farm Animals Phase 1 Phase 2 Phase 3 Phase 4 1B 10 0 14 28 35 2D 10 0 10 38 67 4D 14 NA 0 21 42 8B 13 0 13 27 43 9D 12 0 29 43 49 86 Table A.3. Bacterial families differentially abundant in the bovine-gut microbiota of animals fed with different percentages of forage in diet. Family log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Ruminococcaceae 0.016471 0.0007213 22.8351089 2.05E-115 5.45E-114 Prevotellaceae -0.0143815 0.00087637 -16.410246 1.62E-60 2.14E-59 Rikenellaceae 0.01722959 0.00105481 16.3343473 5.62E-60 6.62E-59 Lachnospiraceae -0.0032235 0.00061375 -5.2521776 1.50E-07 4.83E-07 Bacteroidaceae 0.00520128 0.00117731 4.41791991 9.97E-06 2.78E-05 Muribaculaceae -0.0407878 0.00118366 -34.459027 3.30E-260 3.50E-258 Christensenellaceae 0.02058984 0.0012825 16.0545191 5.31E-58 5.63E-57 Spirochaetaceae -0.0285462 0.00210975 -13.53062 1.03E-41 7.81E-41 Acidaminococcaceae -0.0117091 0.00101771 -11.505348 1.24E-30 6.92E-30 Succinivibrionaceae -0.0390218 0.00304894 -12.798487 1.67E-37 1.18E-36 Planococcaceae 0.04576269 0.0056949 8.03572909 9.30E-16 3.52E-15 Clostridiaceae 1 -0.0332732 0.00220972 -15.057646 3.08E-51 2.72E-50 Veillonellaceae -0.009967 0.00273246 -3.6476253 0.00026468 0.00066799 Bacteroidales RF16 group 0.02558717 0.00188224 13.5939779 4.35E-42 3.55E-41 Enterobacteriaceae -0.0429341 0.00368522 -11.650361 2.28E-31 1.42E-30 Erysipelotrichaceae -0.0057838 0.00099778 -5.7966039 6.77E-09 2.24E-08 p-2534-18B5 gut group 0.0655837 0.00367406 17.8504911 2.87E-71 5.06E-70 Gastranaerophilales uncultured 0.0086763 0.00191412 4.53278973 5.82E-06 1.67E-05 Fibrobacteraceae -0.0196056 0.0045473 -4.3114863 1.62E-05 4.30E-05 Neisseriaceae 0.01931658 0.00612223 3.1551537 0.00160414 0.00377863 Tannerellaceae -0.052279 0.0020561 -25.426363 1.29E-142 4.56E-141 Bacteroidales uncultured 0.03093304 0.00202808 15.2523773 1.59E-52 1.53E-51 Family XIII 0.01915138 0.00110293 17.3641336 1.54E-67 2.34E-66 p-251-o5 0.00772406 0.00306325 2.52152497 0.01168474 0.0269257 F082 0.07207967 0.00266119 27.0855519 1.46E-161 7.72E-160 Anaeroplasmataceae -0.0182558 0.00241217 -7.568193 3.78E-14 1.38E-13 Barnesiellaceae 0.01620163 0.00214236 7.56250378 3.95E-14 1.40E-13 Burkholderiaceae -0.0218915 0.0019579 -11.181093 5.05E-29 2.67E-28 Clostridiales vadinBB60 group 0.00752756 0.00174331 4.31797553 1.57E-05 4.28E-05 Peptococcaceae 0.01093276 0.00126473 8.64431919 5.41E-18 2.29E-17 Rhodospirillales uncultured 0.01311455 0.00191066 6.86389013 6.70E-12 2.29E-11 Paludibacteraceae 0.04345508 0.00236365 18.3847304 1.74E-75 3.69E-74 EMP G18 uncultured -0.0361119 0.00345028 -10.466371 1.23E-25 5.68E-25 Akkermansiaceae 0.02514747 0.0029462 8.53556341 1.39E-17 5.69E-17 Bifidobacteriaceae -0.0232566 0.00603712 -3.8522622 0.00011703 0.00030257 Izimaplasmatales uncultured 0.01951321 0.00220312 8.85709434 8.21E-19 3.63E-18 Bacteroidales UCG-001 0.04805996 0.00431028 11.150077 7.15E-29 3.61E-28 Victivallaceae 0.04549642 0.00394805 11.523781 1.00E-30 5.90E-30 WCHB1 41 uncultured 0.02284092 0.00464949 4.91256231 8.99E-07 2.80E-06 Eggerthellaceae 0.00785087 0.00387652 2.02523987 0.04284274 0.09461105 Desulfovibrionaceae 0.02477596 0.00300461 8.24599331 1.64E-16 6.43E-16 Campylobacteraceae 0.02246802 0.00664923 3.37904159 0.00072739 0.0017931 Marinifilaceae 0.03259325 0.00274105 11.8908113 1.32E-32 8.75E-32 Dysgonomonadaceae 0.0274436 0.00589212 4.65768126 3.20E-06 9.42E-06 Defluviitaleaceae 0.0236732 0.00218957 10.8118247 3.03E-27 1.46E-26 Eubacteriaceae 0.02711129 0.00552608 4.90606036 9.29E-07 2.81E-06 gir aah93h0 0.01833041 0.00576371 3.18031613 0.00147115 0.00354412 Pirellulaceae 0.01399368 0.00662901 2.1109765 0.03477433 0.07842722 * Coefficients and significance values were calculated with DESeq2. 87 Table A.4. Temperature (°C) and relative humidity (RH) the cattle farms during four- sampling phases. Farms 1B 2D 4D 8B 9D Phase °C RH °C RH °C RH °C RH °C RH 1 13.9 56.7 19.2 66.2 - - 22.8 71.1 30.1 46.3 2 19.7 93.7 24.8 59.8 23.6 62.1 24.2 46.9 24.9 35.3 3 23.3 54.2 25.8 84.6 19.3 69.2 24.2 50.7 18.0 53.0 4 16.6 66.2 19.7 68.8 13.7 82.9 22.1 68.5 18.0 56.3 Mean 18.4 67.7 22.4 69.8 18.9 71.4 23.3 59.3 22.8 47.7 St. dev. 4.1 18.1 3.4 10.5 5.0 10.6 1.1 12.3 5.9 9.3 88 Table A.5. Differentially abundant taxa identified between STEC shedders and non- shedders among dairy farms. Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Acidaminococcaceae -0.565 0.136 -4.144 3.42E-05 6.27E-04 Actinobacteria -0.126 0.346 -0.365 7.15E-01 9.74E-01 Alloprevotella 0.704 0.192 3.670 2.43E-04 3.05E-03 Anaerosporobacter -1.363 0.340 -4.011 6.04E-05 1.33E-03 Anaerovorax -0.598 0.252 -2.369 1.78E-02 8.26E-02 Bacilli 2.008 0.469 4.282 1.86E-05 5.38E-04 Bacteroidales RF16 group -0.382 0.161 -2.374 1.76E-02 9.64E-02 Bacteroidales RF16 group uncultured -0.368 0.159 -2.312 2.08E-02 8.30E-02 Bacteroidetes 0.086 0.068 1.265 2.06E-01 8.74E-01 Betaproteobacteriales -1.287 0.340 -3.788 1.52E-04 8.20E-03 Butyrivibrio -0.706 0.290 -2.437 1.48E-02 7.24E-02 Chloroflexi -0.138 4.294 -0.032 9.74E-01 9.74E-01 Clostridia 0.215 0.080 2.700 6.93E-03 6.70E-02 Clostridiales vadinBB60 group -0.770 0.229 -3.366 7.64E-04 6.00E-03 Clostridiales vadinBB60 group uncultured -0.779 0.225 -3.468 5.24E-04 5.50E-03 Cyanobacteria -0.250 0.253 -0.987 3.24E-01 9.25E-01 Dependentiae -0.148 4.294 -0.034 9.73E-01 9.74E-01 dgA 11 gut group -0.666 0.230 -2.891 3.84E-03 2.39E-02 Elusimicrobia -0.583 0.943 -0.618 5.37E-01 9.74E-01 EMP G18 -1.015 0.324 -3.137 1.71E-03 3.08E-02 Epsilonbacteraeota 0.387 0.650 0.596 5.51E-01 9.74E-01 Erysipelotrichaceae UCG 004 -0.768 0.236 -3.250 1.15E-03 9.23E-03 Euryarchaeota -0.215 1.175 -0.183 8.55E-01 9.74E-01 F082 -0.777 0.264 -2.947 3.20E-03 2.20E-02 F082 uncultured -0.741 0.258 -2.872 4.08E-03 2.39E-02 Family XIII AD3011 group 0.293 0.138 2.124 3.36E-02 9.87E-02 Fibrobacteres -0.601 0.537 -1.118 2.63E-01 8.78E-01 Firmicutes 0.270 0.085 3.178 1.48E-03 1.48E-02 Flavobacteriaceae -0.778 0.338 -2.300 2.15E-02 9.84E-02 Fusobacteria -0.140 3.951 -0.036 9.72E-01 9.74E-01 Gammaproteobacteria -0.970 0.298 -3.255 1.13E-03 1.64E-02 Gastranaerophilales uncultured -0.561 0.262 -2.144 3.20E-02 9.87E-02 Izimaplasmatales -0.872 0.265 -3.287 1.01E-03 2.73E-02 Izimaplasmatales uncultured -1.034 0.279 -3.699 2.16E-04 2.97E-03 Kingella -1.985 0.599 -3.315 9.15E-04 8.05E-03 Kiritimatiellaeota -0.352 0.544 -0.648 5.17E-01 9.74E-01 Lachnospiraceae uncultured -0.255 0.117 -2.171 3.00E-02 9.87E-02 Lentisphaerae -0.560 0.455 -1.231 2.18E-01 8.74E-01 Neisseriaceae -2.102 0.606 -3.468 5.24E-04 5.33E-03 Oscillibacter -0.586 0.170 -3.449 5.63E-04 5.50E-03 p 251 o5 -1.479 0.332 -4.453 8.45E-06 4.65E-04 p 251 o5 uncultured -1.473 0.327 -4.501 6.76E-06 5.95E-04 Patescibacteria -0.333 0.847 -0.393 6.94E-01 9.74E-01 Peptostreptococcaceae 1.096 0.259 4.225 2.38E-05 6.27E-04 Phascolarctobacterium -0.551 0.135 -4.072 4.66E-05 1.33E-03 Planctomycetes 0.072 0.694 0.103 9.18E-01 9.74E-01 Proteobacteria -0.772 0.209 -3.696 2.19E-04 4.38E-03 Rhodospirillales uncultured -0.599 0.254 -2.362 1.82E-02 9.64E-02 Rikenellaceae -0.232 0.099 -2.340 1.93E-02 9.64E-02 Rikenellaceae RC9 gut group -0.307 0.100 -3.063 2.19E-03 1.49E-02 Romboutsia 1.039 0.268 3.882 1.04E-04 1.83E-03 Roseburia -0.971 0.226 -4.302 1.69E-05 7.45E-04 Ruminobacter -1.070 0.434 -2.467 1.36E-02 7.05E-02 Ruminococcaceae UCG 005 0.220 0.087 2.520 1.17E-02 6.46E-02 89 Table A.5 (cont’d) Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Ruminococcaceae UCG 010 -0.366 0.172 -2.131 3.31E-02 9.87E-02 Ruminococcaceae UCG 011 0.761 0.244 3.116 1.83E-03 1.34E-02 Ruminococcus 1 0.308 0.142 2.172 2.98E-02 9.87E-02 Ruminococcus 2 -0.521 0.240 -2.174 2.97E-02 9.87E-02 Spirochaetes 0.202 0.235 0.858 3.91E-01 9.74E-01 Synergistetes -0.162 4.294 -0.038 9.70E-01 9.74E-01 Tenericutes 0.011 0.108 0.106 9.16E-01 9.74E-01 Tyzzerella 4 0.404 0.174 2.319 2.04E-02 8.30E-02 Veillonellaceae 1.191 0.346 3.440 5.82E-04 5.33E-03 Verrucomicrobia 0.691 0.314 2.203 2.76E-02 1.84E-01 Eubacterium coprostanoligenes group 0.227 0.098 2.323 2.02E-02 8.30E-02 Eubacterium ruminantium group 0.578 0.262 2.208 2.72E-02 9.87E-02 90 Table A.6. Taxa correlated with factors associated with STEC carriage. Risk factor Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj DIM Acetitomaculum 0.003 0.001 2.447 1.44E-02 9.94E-02 DIM Actinobacteria 0.004 0.001 3.432 5.99E-04 5.99E-03 DIM Anaerosporobacter 0.003 0.001 3.159 1.58E-03 4.15E-02 DIM Anaerovibrio -0.005 0.001 -4.567 4.94E-06 6.47E-04 DIM Cellulosilyticum -0.003 0.001 -2.631 8.52E-03 7.44E-02 DIM Coprococcus 3 -0.001 0.001 -2.546 1.09E-02 8.34E-02 DIM Enterobacteriaceae -0.005 0.002 -3.417 6.34E-04 3.04E-02 DIM Escherichia Shigella -0.006 0.002 -3.382 7.19E-04 2.36E-02 DIM hoa5 07d05 gut group 0.009 0.002 3.844 1.21E-04 6.85E-03 DIM Kingella 0.004 0.002 2.528 1.15E-02 8.34E-02 DIM Mollicutes RF39 uncultured -0.001 0.000 -2.792 5.23E-03 6.86E-02 DIM Prevotellaceae UCG 003 -0.001 0.000 -3.780 1.57E-04 6.85E-03 DIM Ruminiclostridium 0.002 0.001 2.667 7.65E-03 7.44E-02 DIM Ruminiclostridium 1 0.002 0.001 2.738 6.19E-03 7.37E-02 DIM Ruminiclostridium 6 -0.002 0.001 -2.540 1.11E-02 8.34E-02 DIM Ruminococcaceae 0.001 0.000 3.785 1.54E-04 1.48E-02 DIM Ruminococcaceae UCG 005 0.001 0.000 2.850 4.37E-03 6.36E-02 DIM Ruminococcaceae UCG 009 0.001 0.000 2.679 7.39E-03 7.44E-02 DIM Ruminococcaceae uncultured 0.001 0.000 2.885 3.92E-03 6.36E-02 DIM Tenericutes -0.001 0.000 -3.494 4.75E-04 5.99E-03 DIM Turicibacter -0.003 0.001 -3.039 2.37E-03 5.18E-02 DIM Veillonellaceae -0.003 0.001 -3.125 1.78E-03 5.70E-02 DIM Veillonellaceae uncultured -0.003 0.001 -2.653 7.99E-03 7.44E-02 DIM Eubacterium ruminantium group -0.002 0.001 -2.864 4.18E-03 6.36E-02 Lactations Kingella 0.610 0.167 3.644 2.69E-04 4.06E-02 Lactations Lentisphaerae -0.392 0.125 -3.135 1.72E-03 3.43E-02 Lactations Neisseriaceae 0.631 0.169 3.738 1.86E-04 1.87E-02 Lactations Ruminococcaceae UCG 004 -0.226 0.066 -3.409 6.52E-04 5.04E-02 Lactations Ruminococcaceae UCG 011 -0.247 0.076 -3.267 1.09E-03 6.56E-02 Lactations Victivallaceae uncultured -0.530 0.143 -3.706 2.11E-04 4.06E-02 Lactations Eubacterium oxidoreducens group -0.177 0.052 -3.403 6.67E-04 5.04E-02 Temp. ave. 5 days Acetitomaculum 0.060 0.026 2.342 1.92E-02 3.54E-02 Temp. ave. 5 days Acidaminococcaceae -0.073 0.007 -10.709 9.28E-27 7.66E-26 Temp. ave. 5 days Agathobacter -0.058 0.016 -3.659 2.53E-04 5.68E-04 Temp. ave. 5 days Akkermansia 0.155 0.021 7.309 2.68E-13 1.11E-12 Temp. ave. 5 days Akkermansiaceae 0.124 0.020 6.274 3.52E-10 1.01E-09 Temp. ave. 5 days Alistipes 0.190 0.013 15.161 6.45E-52 2.87E-50 Temp. ave. 5 days Alloprevotella -0.082 0.011 -7.130 1.00E-12 4.03E-12 Temp. ave. 5 days Anaerofustis 0.173 0.040 4.290 1.79E-05 4.27E-05 Temp. ave. 5 days Anaeroplasma -0.046 0.016 -2.781 5.42E-03 1.12E-02 Temp. ave. 5 days Anaeroplasmataceae -0.085 0.016 -5.210 1.89E-07 4.16E-07 Temp. ave. 5 days Anaerosporobacter -0.072 0.016 -4.544 5.51E-06 1.39E-05 Temp. ave. 5 days Anaerostipes -0.089 0.037 -2.370 1.78E-02 3.33E-02 Temp. ave. 5 days Anaerovorax 0.084 0.015 5.414 6.17E-08 1.81E-07 Temp. ave. 5 days Angelakisella 0.060 0.025 2.466 1.36E-02 2.63E-02 Temp. ave. 5 days Bacteroidaceae 0.041 0.008 5.264 1.41E-07 3.21E-07 Temp. ave. 5 days Bacteroidales RF16 group 0.108 0.013 8.197 2.47E-16 1.02E-15 Temp. ave. 5 days Bacteroidales RF16 group uncultured 0.136 0.015 9.296 1.45E-20 9.29E-20 Temp. ave. 5 days Bacteroidales UCG 001 0.263 0.033 7.860 3.85E-15 1.49E-14 Temp. ave. 5 days Bacteroidales UCG 001 uncultured 0.327 0.036 9.182 4.23E-20 2.51E-19 Temp. ave. 5 days Bacteroidales uncultured 0.137 0.015 9.228 2.76E-20 1.30E-19 Temp. ave. 5 days Bacteroidales uncultured uncultured 0.177 0.017 10.592 3.26E-26 2.70E-25 Temp. ave. 5 days Bacteroides 0.081 0.009 8.750 2.13E-18 1.10E-17 Temp. ave. 5 days Bacteroidetes -0.027 0.003 -10.258 1.09E-24 1.09E-23 Temp. ave. 5 days Barnesiellaceae 0.088 0.014 6.140 8.26E-10 2.18E-09 Temp. ave. 5 days Barnesiellaceae uncultured 0.126 0.016 7.981 1.45E-15 7.07E-15 Temp. ave. 5 days Blautia -0.054 0.012 -4.320 1.56E-05 3.77E-05 Temp. ave. 5 days Breznakia 0.057 0.022 2.573 1.01E-02 2.01E-02 Temp. ave. 5 days Burkholderiaceae -0.124 0.013 -9.305 1.34E-20 6.82E-20 Temp. ave. 5 days Butyrivibrio -0.097 0.015 -6.493 8.42E-11 3.08E-10 Temp. ave. 5 days Campylobacter 0.125 0.045 2.765 5.68E-03 1.16E-02 Temp. ave. 5 days Campylobacteraceae 0.083 0.044 1.878 6.04E-02 9.73E-02 91 Table A.6 (cont’d) Risk factor Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Temp. ave. 5 days Candidatus Soleaferrea 0.106 0.012 8.614 7.08E-18 3.60E-17 Temp. ave. 5 days Candidatus Stoquefichus 0.153 0.029 5.343 9.14E-08 2.65E-07 Temp. ave. 5 days Caproiciproducens 0.153 0.021 7.251 4.14E-13 1.69E-12 Temp. ave. 5 days Christensenellaceae 0.092 0.009 10.064 8.00E-24 4.40E-23 Temp. ave. 5 days Christensenellaceae R 7 group 0.134 0.011 12.767 2.51E-37 4.80E-36 Temp. ave. 5 days Christensenellaceae uncultured 0.161 0.021 7.670 1.72E-14 7.40E-14 Temp. ave. 5 days Clostridiaceae 1 -0.098 0.016 -6.157 7.40E-10 2.03E-09 Temp. ave. 5 days Clostridiales vadinBB60 group uncultured 0.052 0.014 3.774 1.61E-04 3.67E-04 Temp. ave. 5 days Clostridium sensu stricto 1 -0.086 0.015 -5.645 1.65E-08 5.08E-08 Temp. ave. 5 days Clostridium sensu stricto 6 0.192 0.068 2.849 4.39E-03 9.11E-03 Temp. ave. 5 days Coprobacillus 0.147 0.025 5.934 2.96E-09 9.46E-09 Temp. ave. 5 days Coprococcus 3 0.077 0.011 6.808 9.88E-12 3.67E-11 Temp. ave. 5 days Defluviitaleaceae 0.108 0.015 7.402 1.34E-13 4.91E-13 Temp. ave. 5 days Defluviitaleaceae UCG 011 0.150 0.016 9.208 3.33E-20 2.02E-19 Temp. ave. 5 days Desulfovibrionaceae 0.079 0.020 3.846 1.20E-04 2.47E-04 Temp. ave. 5 days dgA 11 gut group 0.251 0.020 12.338 5.67E-35 8.83E-34 Temp. ave. 5 days Dielma 0.207 0.018 11.579 5.26E-31 5.95E-30 Temp. ave. 5 days Dorea 0.140 0.015 9.211 3.22E-20 2.01E-19 Temp. ave. 5 days Dysgonomonadaceae 0.085 0.039 2.145 3.20E-02 5.41E-02 Temp. ave. 5 days Dysgonomonadaceae uncultured 0.129 0.041 3.151 1.62E-03 3.40E-03 Temp. ave. 5 days Eisenbergiella 0.113 0.022 5.089 3.61E-07 9.76E-07 Temp. ave. 5 days EMP G18 uncultured -0.219 0.025 -8.912 5.01E-19 2.21E-18 Temp. ave. 5 days EMP G18 uncultured uncultured -0.171 0.024 -7.026 2.13E-12 8.42E-12 Temp. ave. 5 days Enterobacteriaceae -0.278 0.026 -10.904 1.10E-27 1.04E-26 Temp. ave. 5 days Enterorhabdus 0.078 0.032 2.403 1.62E-02 3.06E-02 Temp. ave. 5 days Erysipelatoclostridium 0.134 0.019 6.993 2.68E-12 1.04E-11 Temp. ave. 5 days Erysipelotrichaceae -0.039 0.007 -5.951 2.66E-09 6.76E-09 Temp. ave. 5 days Erysipelotrichaceae UCG 004 -0.118 0.012 -9.503 2.03E-21 1.41E-20 Temp. ave. 5 days Erysipelotrichaceae uncultured 0.061 0.012 5.000 5.74E-07 1.54E-06 Temp. ave. 5 days Escherichia Shigella -0.251 0.026 -9.621 6.52E-22 4.64E-21 Temp. ave. 5 days Eubacteriaceae 0.125 0.039 3.205 1.35E-03 2.62E-03 Temp. ave. 5 days F082 0.279 0.021 13.111 2.83E-39 6.23E-38 Temp. ave. 5 days F082 uncultured 0.307 0.023 13.170 1.30E-39 4.04E-38 Temp. ave. 5 days Faecalibacterium -0.294 0.024 -12.485 9.01E-36 1.50E-34 Temp. ave. 5 days Faecalitalea 0.089 0.024 3.764 1.67E-04 3.79E-04 Temp. ave. 5 days Family XIII 0.084 0.008 10.242 1.29E-24 7.72E-24 Temp. ave. 5 days Family XIII AD3011 group 0.120 0.010 11.735 8.45E-32 1.00E-30 Temp. ave. 5 days Family XIII UCG 001 0.147 0.019 7.822 5.18E-15 2.39E-14 Temp. ave. 5 days Fibrobacter -0.065 0.032 -2.025 4.29E-02 7.68E-02 Temp. ave. 5 days Fibrobacteraceae -0.092 0.031 -3.006 2.65E-03 4.86E-03 Temp. ave. 5 days Fibrobacteres -0.113 0.030 -3.738 1.85E-04 5.30E-04 Temp. ave. 5 days Firmicutes 0.024 0.003 7.693 1.43E-14 9.56E-14 Temp. ave. 5 days Fournierella -0.183 0.042 -4.386 1.15E-05 2.82E-05 Temp. ave. 5 days Gastranaerophilales uncultured 0.025 0.013 1.936 5.28E-02 8.72E-02 Temp. ave. 5 days Gastranaerophilales uncultured uncultured 0.065 0.015 4.439 9.03E-06 2.23E-05 Temp. ave. 5 days GCA 900066225 0.057 0.022 2.597 9.40E-03 1.89E-02 Temp. ave. 5 days gir aah93h0 uncultured 0.099 0.039 2.526 1.15E-02 2.26E-02 Temp. ave. 5 days GWE2 31 10 0.152 0.036 4.258 2.06E-05 4.89E-05 Temp. ave. 5 days hoa5 07d05 gut group -0.319 0.032 -10.065 7.85E-24 5.92E-23 Temp. ave. 5 days Hydrogenoanaerobacterium 0.136 0.029 4.736 2.18E-06 5.53E-06 Temp. ave. 5 days Intestinimonas 0.117 0.050 2.351 1.87E-02 3.48E-02 Temp. ave. 5 days Izimaplasmatales uncultured 0.057 0.015 3.772 1.62E-04 3.24E-04 Temp. ave. 5 days Izimaplasmatales uncultured uncultured 0.091 0.017 5.286 1.25E-07 3.50E-07 Temp. ave. 5 days Kingella -0.156 0.041 -3.815 1.36E-04 3.14E-04 Temp. ave. 5 days Lachnoclostridium -0.069 0.014 -4.900 9.58E-07 2.48E-06 Temp. ave. 5 days Lachnoclostridium 10 0.122 0.018 6.886 5.72E-12 2.19E-11 Temp. ave. 5 days Lachnospira 0.120 0.027 4.483 7.35E-06 1.83E-05 Temp. ave. 5 days Lachnospiraceae FCS020 group 0.087 0.011 7.911 2.55E-15 1.22E-14 Temp. ave. 5 days Lachnospiraceae NC2004 group 0.100 0.039 2.546 1.09E-02 2.15E-02 Temp. ave. 5 days Lachnospiraceae NK3A20 group 0.105 0.021 4.966 6.85E-07 1.79E-06 Temp. ave. 5 days Lachnospiraceae NK4A136 group 0.084 0.008 11.208 3.72E-29 3.20E-28 92 Table A.6 (cont’d) Risk factor Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Temp. ave. 5 days Lachnospiraceae UCG 001 0.041 0.012 3.494 4.75E-04 1.03E-03 Temp. ave. 5 days Lachnospiraceae UCG 008 0.122 0.018 6.866 6.58E-12 2.48E-11 Temp. ave. 5 days Lachnospiraceae UCG 010 0.090 0.025 3.604 3.13E-04 6.84E-04 Temp. ave. 5 days Lentisphaerae 0.118 0.024 4.853 1.22E-06 4.06E-06 Temp. ave. 5 days Lysinibacillus 0.333 0.044 7.569 3.76E-14 1.59E-13 Temp. ave. 5 days Mailhella 0.119 0.022 5.303 1.14E-07 3.22E-07 Temp. ave. 5 days Marinifilaceae 0.133 0.019 7.179 7.03E-13 2.44E-12 Temp. ave. 5 days Marvinbryantia 0.035 0.018 1.913 5.57E-02 9.84E-02 Temp. ave. 5 days Mogibacterium 0.153 0.025 6.026 1.68E-09 5.67E-09 Temp. ave. 5 days Mollicutes RF39 uncultured 0.024 0.009 2.730 6.33E-03 1.13E-02 Temp. ave. 5 days Mollicutes RF39 uncultured uncultured 0.056 0.010 5.630 1.80E-08 5.48E-08 Temp. ave. 5 days Muribaculaceae -0.186 0.010 -18.328 4.94E-75 3.26E-73 Temp. ave. 5 days Muribaculaceae uncultured -0.156 0.010 -15.156 6.90E-52 2.87E-50 Temp. ave. 5 days Muribaculum -0.102 0.042 -2.423 1.54E-02 2.92E-02 Temp. ave. 5 days Negativibacillus 0.143 0.012 11.494 1.41E-30 1.53E-29 Temp. ave. 5 days Neisseriaceae -0.196 0.041 -4.768 1.86E-06 3.96E-06 Temp. ave. 5 days Odoribacter 0.198 0.021 9.368 7.39E-21 4.84E-20 Temp. ave. 5 days Oscillibacter -0.073 0.009 -7.775 7.56E-15 3.42E-14 Temp. ave. 5 days Oscillospira -0.147 0.029 -5.138 2.77E-07 7.58E-07 Temp. ave. 5 days p 1088 a5 gut group 0.102 0.045 2.299 2.15E-02 3.91E-02 Temp. ave. 5 days p 251 o5 uncultured 0.060 0.022 2.735 6.24E-03 1.26E-02 Temp. ave. 5 days p 2534 18B5 gut group 0.324 0.027 12.144 6.15E-34 6.76E-33 Temp. ave. 5 days p 2534 18B5 gut group uncultured 0.348 0.028 12.279 1.17E-34 1.71E-33 Temp. ave. 5 days Paeniclostridium 0.071 0.029 2.498 1.25E-02 2.43E-02 Temp. ave. 5 days Paludibacter 0.133 0.041 3.251 1.15E-03 2.43E-03 Temp. ave. 5 days Paludibacteraceae 0.183 0.017 10.595 3.13E-26 2.29E-25 Temp. ave. 5 days Paludibacteraceae uncultured 0.240 0.019 12.580 2.71E-36 4.81E-35 Temp. ave. 5 days Papillibacter 0.205 0.022 9.454 3.27E-21 2.20E-20 Temp. ave. 5 days Parabacteroides -0.215 0.016 -13.027 8.61E-39 2.38E-37 Temp. ave. 5 days Parasutterella 0.119 0.019 6.212 5.24E-10 1.79E-09 Temp. ave. 5 days Peptococcaceae 0.046 0.009 5.331 9.77E-08 2.30E-07 Temp. ave. 5 days Peptococcaceae uncultured 0.089 0.010 8.887 6.30E-19 3.48E-18 Temp. ave. 5 days Phascolarctobacterium -0.039 0.008 -4.863 1.16E-06 2.97E-06 Temp. ave. 5 days Planococcaceae 0.294 0.041 7.109 1.17E-12 3.86E-12 Temp. ave. 5 days Prevotella 2 -0.635 0.037 -17.189 3.23E-66 4.02E-64 Temp. ave. 5 days Prevotella 9 -0.442 0.034 -12.852 8.38E-38 1.90E-36 Temp. ave. 5 days Prevotellaceae -0.080 0.006 -12.926 3.22E-38 5.32E-37 Temp. ave. 5 days Prevotellaceae Ga6A1 group 0.107 0.028 3.869 1.09E-04 2.54E-04 Temp. ave. 5 days Prevotellaceae NK3B31 group -0.243 0.041 -5.942 2.81E-09 9.09E-09 Temp. ave. 5 days Prevotellaceae UCG 001 -0.077 0.012 -6.343 2.26E-10 7.93E-10 Temp. ave. 5 days Prevotellaceae UCG 003 0.089 0.010 8.760 1.95E-18 1.03E-17 Temp. ave. 5 days Prevotellaceae UCG 004 0.247 0.017 14.828 9.69E-50 3.45E-48 Temp. ave. 5 days Prevotellaceae uncultured -0.435 0.034 -12.972 1.77E-38 4.40E-37 Temp. ave. 5 days Proteobacteria -0.134 0.012 -11.193 4.40E-29 8.79E-28 Temp. ave. 5 days Rhodospirillales uncultured 0.040 0.013 3.071 2.13E-03 4.02E-03 Temp. ave. 5 days Rikenellaceae 0.079 0.008 10.351 4.16E-25 2.74E-24 Temp. ave. 5 days Rikenellaceae RC9 gut group 0.094 0.009 9.998 1.55E-23 1.14E-22 Temp. ave. 5 days Romboutsia 0.033 0.014 2.331 1.98E-02 3.62E-02 Temp. ave. 5 days Roseburia 0.020 0.010 1.971 4.88E-02 8.68E-02 Temp. ave. 5 days Ruminiclostridium 0.220 0.018 12.235 2.02E-34 2.80E-33 Temp. ave. 5 days Ruminiclostridium 1 0.209 0.020 10.580 3.71E-26 2.98E-25 Temp. ave. 5 days Ruminiclostridium 5 0.115 0.015 7.858 3.89E-15 1.83E-14 Temp. ave. 5 days Ruminiclostridium 6 0.069 0.019 3.619 2.95E-04 6.51E-04 Temp. ave. 5 days Ruminiclostridium 9 0.045 0.014 3.276 1.05E-03 2.24E-03 Temp. ave. 5 days Ruminobacter -0.135 0.023 -5.850 4.91E-09 1.55E-08 Temp. ave. 5 days Ruminococcaceae 0.075 0.006 12.879 5.90E-38 7.79E-37 Temp. ave. 5 days Ruminococcaceae NK4A214 group 0.213 0.012 17.462 2.80E-68 6.96E-66 Temp. ave. 5 days Ruminococcaceae UCG 002 0.125 0.014 9.070 1.19E-19 6.89E-19 Temp. ave. 5 days Ruminococcaceae UCG 004 0.158 0.014 11.283 1.59E-29 1.42E-28 Temp. ave. 5 days Ruminococcaceae UCG 005 0.148 0.009 16.907 3.98E-64 3.30E-62 Temp. ave. 5 days Ruminococcaceae UCG 008 0.104 0.019 5.479 4.27E-08 1.27E-07 93 Table A.6 (cont’d) Risk factor Taxa log2 Fold Change Lfc SE Stat (Wald) p-value p-adj Temp. ave. 5 days Ruminococcaceae UCG 009 0.151 0.013 11.823 2.97E-32 3.70E-31 Temp. ave. 5 days Ruminococcaceae UCG 010 0.150 0.013 11.455 2.21E-30 2.20E-29 Temp. ave. 5 days Ruminococcaceae UCG 011 0.110 0.017 6.343 2.25E-10 7.93E-10 Temp. ave. 5 days Ruminococcaceae UCG 013 0.061 0.010 5.952 2.64E-09 8.77E-09 Temp. ave. 5 days Ruminococcaceae UCG 014 0.048 0.010 4.969 6.73E-07 1.78E-06 Temp. ave. 5 days Ruminococcaceae uncultured 0.164 0.010 16.411 1.60E-60 9.96E-59 Temp. ave. 5 days Ruminococcus 1 0.093 0.008 11.305 1.24E-29 1.14E-28 Temp. ave. 5 days Ruminococcus 2 0.112 0.015 7.675 1.65E-14 7.23E-14 Temp. ave. 5 days Saccharofermentans 0.171 0.032 5.304 1.14E-07 3.22E-07 Temp. ave. 5 days Solibacillus 0.470 0.074 6.321 2.60E-10 9.01E-10 Temp. ave. 5 days Sphaerochaeta 0.143 0.026 5.583 2.36E-08 7.08E-08 Temp. ave. 5 days Spirochaetaceae -0.087 0.015 -5.738 9.57E-09 2.34E-08 Temp. ave. 5 days Spirochaetes -0.106 0.015 -6.891 5.55E-12 2.78E-11 Temp. ave. 5 days Subdoligranulum -0.256 0.022 -11.442 2.58E-30 2.47E-29 Temp. ave. 5 days Succinivibrio -0.132 0.025 -5.214 1.84E-07 5.10E-07 Temp. ave. 5 days Succinivibrionaceae -0.145 0.021 -6.811 9.67E-12 2.90E-11 Temp. ave. 5 days Sutterella -0.290 0.025 -11.484 1.59E-30 1.65E-29 Temp. ave. 5 days Tannerellaceae -0.245 0.016 -15.034 4.39E-51 1.45E-49 Temp. ave. 5 days Treponema 2 -0.067 0.016 -4.240 2.23E-05 5.24E-05 Temp. ave. 5 days Turicibacter -0.151 0.020 -7.720 1.17E-14 5.18E-14 Temp. ave. 5 days Tyzzerella 4 0.057 0.010 5.743 9.31E-09 2.90E-08 Temp. ave. 5 days Verrucomicrobia 0.116 0.019 6.061 1.35E-09 5.42E-09 Temp. ave. 5 days Victivallaceae 0.200 0.028 7.059 1.67E-12 5.26E-12 Temp. ave. 5 days Victivallaceae uncultured 0.297 0.033 8.977 2.79E-19 1.58E-18 Temp. ave. 5 days WCHB1 41 uncultured 0.076 0.032 2.396 1.66E-02 2.88E-02 Temp. ave. 5 days WCHB1 41 uncultured uncultured 0.123 0.034 3.628 2.86E-04 6.35E-04 Temp. ave. 5 days Anaerorhabdus furcosa group 0.162 0.013 12.808 1.49E-37 3.09E-36 Temp. ave. 5 days Eubacterium brachy group 0.138 0.017 8.144 3.83E-16 1.91E-15 Temp. ave. 5 days Eubacterium coprostanoligenes group 0.088 0.010 8.801 1.36E-18 7.35E-18 Temp. ave. 5 days Eubacterium eligens group 0.170 0.029 5.945 2.77E-09 9.07E-09 Temp. ave. 5 days Eubacterium nodatum group 0.194 0.016 11.889 1.34E-32 1.76E-31 Temp. ave. 5 days Eubacterium oxidoreducens group 0.106 0.010 10.217 1.67E-24 1.30E-23 Temp. ave. 5 days Eubacterium ruminantium group 0.060 0.018 3.370 7.53E-04 1.62E-03 Temp. ave. 5 days Eubacterium ventriosum group -0.074 0.034 -2.160 3.08E-02 5.55E-02 Temp. ave. 5 days Ruminococcus torques group -0.111 0.017 -6.423 1.34E-10 4.82E-10 Temp. ave. 5 days X28 4 0.076 0.031 2.464 1.37E-02 2.63E-02 94 Type FG RAJ A B Wilcoxon, p = 0.028 Wilcoxon, p = 0.52 * ns 6 1500 Shannon Chao1 5 1000 4 500 3 FG RAJ FG RAJ C 0.10 PCoA 2 (5.1%) 0.05 0.00 −0.05 PERMANOVA, R2= 0.005, p=0.091 −0.05 0.00 0.05 0.10 PCoA 1 (16.5%) Figure A.1. Paired comparison of fecal grab (FG) and recto-anal junction (RAJ) microbiome alpha and beta diversities. A) Shannon index boxplots. B) Chao1 index boxplots. C) Principal coordinate analysis of Weighted Unifrac ordination. The centroids of FG and RAJ distributions are shown with a bigger point size. 95 Farm 1B 2D 4D A B ns ns ns ns 800 **** ns ns * 5.5 NS. ns ns ns ns ns ns ns ns ns 600 ** ns Shannon Chao1 5.0 ns ns 400 4.5 200 4.0 1B RAJ 1 1B FG 1 1B RAJ 1 1B FG 1 1B RAJ 2 1B FG 2 1B RAJ 3 1B RAJ 2 1B FG 2 1B RAJ 3 1B FG 3 1B RAJ 4 1B FG 4 1B FG 3 1B RAJ 4 1B FG 4 2D RAJ 1 2D FG 1 2D RAJ 2 2D RAJ 1 2D FG 1 2D RAJ 2 2D FG 2 2D RAJ 3 2D FG 3 2D FG 2 2D RAJ 3 2D FG 3 2D RAJ 4 2D FG 4 4D RAJ 2 2D RAJ 4 2D FG 4 4D RAJ 2 4D FG 2 4D RAJ 3 4D FG 3 4D FG 2 4D RAJ 3 4D FG 3 4D RAJ 4 4D FG 4 4D RAJ 4 4D FG 4 Phase 1 2 3 4 Phase FG RAJ C 1B R^2=0.02, p=0.004 2D R^2=0.022, p=0.003 4D R^2=0.011,p=0.576 0.10 0.05 0.050 PCoA 2 (8.9%) PCoA 2 (5.7%) PCoA 2 (6.9%) 0.05 0.025 0.00 0.000 −0.05 0.00 −0.025 −0.10 −0.05 −0.050 −0.10 −0.05 0.00 0.05 −0.04 0.00 0.04 −0.05 0.00 0.05 0.10 PCoA 1 (12.5%) PCoA 1 (7.5) PCoA 1 (13%) Figure A.2. Paired comparison of fecal grab (FG) and recto-anal junction (RAJ) microbiome alpha and beta diversities per farm and sampling phase. The p-values in A and B were calculated with the Wilcoxon test and are represented with symbols “****” ≤ 0.0001, “*” ≤ 0.05, and “ns” not significant. A) Shannon index boxplots, B) Chao1 index boxplots. C) PCoA of Weighted Unifrac ordination; each box corresponds to a farm; the R2 and p-value are from the PERMANOVA test between RAJ and FG. 96 A B 1500 7 Kruskal−Wallis, p < 2.2e−16 Kruskal−Wallis, p < 2.2e−16 **** **** **** ns * ns 6 ** * 1000 ns **** ns Shannon Chao1 **** 5 500 4 3 1B 2D 4D 9D 8B 1B 2D 4D 9D 8B 8.2 8.7 13.8 28.0 53.7 % 8.2 8.7 13.8 28.0 53.7 % STEC prevalence STEC prevalence Figure A.3. Cattle-hindgut microbiota alpha diversity among five farms. Farms classified as low STEC prevalence (LSP) are shown in blue, and farms with a high STEC prevalence (HSP) are in red. p-value: **** ≤ 0.0001, *** ≤ 0.001, ** ≤ 0.01, ns not significant. 97 A B Forage % Forage % 20 40 60 80 100 20 40 60 80 100 PERMANOVA, R2= 0.18, p=0.001 0.10 0.2 PERMANOVA, R2= 0.13, p=0.001 PCoA 2 (7.2%) PCoA 2 (6.3%) 0.05 0.0 0.00 −0.2 −0.05 −0.50 −0.25 0.00 0.25 −0.1 0.0 0.1 PCoA 1 (18.8%) PCoA 1 (18.6%) Figure A.4. Principal coordinate analysis showing differences in the microbiome composition associated with the percentage of forage in diet. A) Bray-Curtis dissimilarity distances. B) Weighted Unifrac ordination. 98 STEC prevalence Low High octane oxidation 3−phenylpropanoate degradation pyruvate fermentation to acetone acetyl−CoA fermentation to butanoate II (S)−propane−1,2−diol degradation methylphosphonate degradation I formaldehyde assimilation I (serine pathway) toluene degradation V (aerobic) (via toluene−cis−diol) fatty acid elongation −− saturated superpathway of adenosylcobalamin salvage from cobinamide I catechol degradation to β−ketoadipate protocatechuate degradation II (ortho−cleavage pathway) Metabolic pathway superpathway of pyrimidine ribonucleosides degradation pyruvate fermentation to propanoate I acetylene degradation (anaerobic) D−galactose degradation I (Leloir pathway) L−valine biosynthesis L−isoleucine biosynthesis I (from threonine) peptidoglycan maturation (meso−diaminopimelate containing) superpathway of L−methionine biosynthesis (transsulfuration) superpathway of S−adenosyl−L−methionine biosynthesis pyruvate fermentation to isobutanol (engineered) L−methionine biosynthesis I L−isoleucine biosynthesis IV superpathway of L−aspartate and L−asparagine biosynthesis incomplete reductive TCA cycle myo−, chiro− and scyllo−inositol degradation −2 −1 0 1 2 LDA score (log10) Figure A.5. Linear discriminant effect sizes of metabolic pathways inferred from 16S rRNA sequences with PICRUSt2, between farms with low and high STEC prevalence. A total of 487 MetaCyc pathways were predicted from 16S rRNA sequences. LDA effect sizes of 5.5% of the pathways were significantly different between farms with a high and low STEC prevalence. 99 STEC shedding status non−shedder STEC shedder octane oxidation Metabolic pathway 3−phenylpropanoate degradation pyruvate fermentation to acetone acetyl−CoA fermentation to butanoate II (S)−propane−1,2−diol degradation incomplete reductive TCA cycle L−isoleucine biosynthesis IV −2 −1 0 1 2 LDA score (log10) Figure A.6. Linear discriminant effect sizes of metabolic pathways inferred from 16S rRNA sequences between STEC shedders and non-shedders. 100 CHAPTER 3 Perturbations in intestinal antibiotic-resistant bacterial populations in dairy cattle following intramammary treatment with a third-generation cephalosporin. This chapter is from a manuscript submitted for publication: Karla Vasco, Samantha Carbonell, Rebekah E. Sloup, Bailey Bowcutt, Rita R. Colwell, Karlis Graubics, Ronald Erskine, Bo Norby, Pamela L. Ruegg, Lixin Zhang, Shannon D. Manning. “Perturbations in intestinal antibiotic-resistant bacterial populations in dairy cattle following intramammary treatment with a third-generation cephalosporin”. (2022). Manuscript submitted for publication. 101 ABSTRACT Background. Intramammary (IMM) ceftiofur treatment is commonly used in dairy farms to prevent mastitis, though its impact on the cattle gut microbiome and selection of antibiotic- resistant bacteria has not been elucidated. Herein, we enrolled 40 healthy dairy cows after lactation: 20 were treated with IMM ceftiofur (Spectramast®DC) and a non-antibiotic internal teat sealant (bismuth subnitrate) and 20 (controls) received only bismuth subnitrate. Fecal samples were collected before (day -1) and after treatment (weeks 1, 2, 3, 5, 7, and 9) for quantifying resistant bacteria and metagenomic next-generation sequencing. Results. Overall, no difference was observed in the quantity of resistant bacteria or prevalence of β-lactam resistance between treatment groups, yet 90% and 24% of the 278 samples had Gram-negative bacteria with resistance to ampicillin and ceftiofur, respectively. Although the number of Gram-negative ceftiofur-resistant bacteria increased in treated cows up to 2 weeks after treatment, considerable variation was observed across animals. Indeed, only a subset (25%) of cows shed higher levels of ceftiofur-resistant bacteria post-treatment. Treated cows had lower microbiome richness during weeks 5 and 9 with a decreased abundance of Proteobacteria, Tenericutes, Verrucomicrobia, and Firmicutes and higher abundance of Bacteroidetes and Actinobacteria. A greater abundance of extended-spectrum β-lactamase (ESBL) genes (bla-CFXA, bla-ACI-1, and bla-CMY) and blc genes was also observed at weeks 1, 5 and 9 (LDA > 3.3; P < 0.03), while a network analysis detected correlations between β- lactam resistance genes and phages, mobile genetic elements, and genera 1-week post-treatment. Specific commensal bacteria possessed ESBL genes as did members of Enterobacteriaceae, though in lower proportions. 102 Conclusion. This study highlights variable, long-term effects of IMM ceftiofur treatment on the gut microbiome and resistome in dairy cattle. Indeed, treated cattle had an increased abundance of specific taxa and genes encoding ESBL production that persisted for up to 9 weeks. Fecal shedding of ESBL-producing Enterobacteriaceae, which are a serious public health threat, varied across animals and hence, additional research should focus on identifying factors associated with shedding levels as well as the dissemination and persistence of antibiotic resistance determinants on dairy farms. INTRODUCTION Approximately three quarters of the quantity of antimicrobials produced globally are employed in production of food-producing animals [1]. Thus, effects of these drugs on the emergence of antimicrobial resistant bacteria require analysis. In particular, extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae are the most concerning and economically impactful antimicrobial-resistant threats [2]. Use of third generation cephalosporins to treat humans and for livestock production may contribute to the emergence of ESBL-producing Enterobacteriaceae. Indeed, bacteria carrying ESBL genes have widespread resistance to clinically important antibiotics such as the penicillins and cephalosporins; resistance to these drugs has been linked to an increased risk of hospitalization and death in humans [3]. In the U.S., 81% of the total kilograms of cephalosporins used in livestock are consumed on cattle farms [4]. In addition, ~90% of dairy farms use intramammary (IMM) β-lactam antibiotics during the dry-off period to treat and prevent mastitis, an infection of the mammary gland [5–7]. Ceftiofur, a third-generation cephalosporin, is one of the most common β-lactam antibiotics used intramammarily as a dry-cow therapy. These broad-spectrum drugs have bactericidal activity against both Gram-negative and Gram-positive bacterial populations, low 103 toxicity potential, and excellent penetration of most body fluids. Consequently, β-lactams are used to treat a variety of pathologies in humans such as septicemia, urinary tract infections, respiratory infections, meningitis, and peritonitis. In dairy cattle, ceftiofur is recommended for the IMM treatment of mastitis as well as respiratory disease, metritis, and footrot. Although cephalosporins like ceftiofur are mainly excreted in the urine (61-77%), they have been also found in the biliary system (~30%) [8], ileum and colon (20% of plasmatic concentration) [9]. However, the effects of IMM ceftiofur treatment on the fecal microbiome and resistome have not yet been determined. A prior study using mathematical modeling predicted that parenteral ceftiofur therapy would reduce the total concentration of E. coli in cattle, but would lead to an increase in the fraction of ESBL-resistant E. coli [10]. However, there is no significant evidence that ceftiofur treatment is associated with an increase in the emergence of ESBL-producing bacterial populations [9,11,12]. One study of cows in early lactation demonstrated that systemic ceftiofur temporarily increased the abundance of resistant Enterobacteriaceae for 7-8 days but not over the long-term (29-35 days) [13]. Similarly, in feedlot cattle, the combined treatment of chlortetracycline and ceftiofur was shown to contribute to an increase in the number of resistant E. coli and ESBL genes [14], suggesting co-selection of these antibiotic resistance genes (ARGs). Because of these prior associations, we sought to determine how IMM ceftiofur treatment at dry off impacts the cattle gut microbiome and abundance of antibiotic resistant bacterial populations over a 9-week period through the dry period and early part of the subsequent lactation. 104 MATERIALS AND METHODS Animals and treatments The aim of this study was to assess the long-term effects of IMM ceftiofur hydrochloride (CHCL) treatment on the gut microbiome of dairy cows at dry-off, the last milking before the dry period. This study was conducted in 2019 (June-November) at the Dairy Cattle Teaching and Research Center at Michigan State University, which contained ~230 lactating dairy cows. Forty healthy Holstein cows were enrolled at dry-off if they met the following inclusion criteria: no antibiotic treatment during the last 90 days of lactation, and a somatic cell count (SCC) of <150,000 cells/mL using the most recent Dairy Herd Improvement Association (DHIA) test (Figure 3.1). Cows were matched based on parity and monthly milk production, while data about health status, ambient temperature, and diet were recorded. The treated group (n = 20) received 4 IMM infusions (1 per mammary gland) that each contained 500 mg ceftiofur (SpectramastDC®; Zoetis Animal Health) after the last milking and an internal IMM teat sealant containing bismuth subnitrate (Orbeseal®; Zoetis Animal Health). The second (control) group received only the internal IMM teat sealant. Cows were randomly assigned to treatment and researchers were blinded to treatment during sampling collection and laboratory analyses. Fecal grab samples were collected using clean obstetric sleeves one day before treatment (day -1) and in weeks 1, 2, 3, 5, 7, and 9 post-treatment. Each sample was homogenized by hand massage in a whirl-pak bag and immediately aliquoted for bacterial culture and DNA extraction for metagenomic next-generation sequencing (mNGS). 105 Figure 3.1. Methods used to evaluate long-term changes in the gut microbiome and resistome following dry-cow therapy with ceftiofur. Quantification of antibiotic-resistant bacteria The total bacterial counts were quantified and presented as colony-forming units (CFUs) per gram (g) of feces. The percentage of ceftiofur- and ampicillin-resistance were also quantified for Gram-positive (samples collected on day -1 and week 1) and Gram-negative (day-1 through week 9) bacterial populations. Fecal samples were diluted at a concentration of 10-1 using 1X PBS and plated in duplicate on media with and without antibiotics (ampicillin or ceftiofur) using a spiral autoplater (Neutec Group Inc.). Gram-negative bacteria were quantified on MacConkey 106 lactose agar (MAC; Criterion®), while Columbia Nalidixic Acid agar (CNA; BD Difco ®) with 5% sheep blood was used for Gram-positive bacteria. Amphotericin B (4 μg/ml) was added to the media to inhibit fungal growth. The ceftiofur concentration was 8 μg/ml for both Gram- negative and -positive bacteria [15], whereas 32 μg/ml and 25 μg/ml of ampicillin were used for Gram-negative and Gram-positive bacteria, respectively, per the Clinical and Laboratory Standards Institute (CLSI) guidelines [16]. The plates were incubated at 37oC for 24 hours under aerobic conditions (MAC) or in the presence of 5% carbon dioxide (CNA). The volumes of 10-1 dilution of fresh feces plated to quantify Gram-negative bacteria were 40 μl (C mode in spiral plater) for total count, 100 μl (C mode) for ampicillin resistant, and 400 μl (linear mode) for ceftiofur resistant CFUs. Whereas, total Gram-positive were quantified from frozen stocks preserved in a 1:1 dilution with glycerol. The 10-1 dilution of frozen feces consisted of 2 g of the preserved stock with 8 ml of 1X PBS, which was plated in the following volumes: 40 μl (C mode) for total count, 200 μl (linear mode) for ampicillin resistant, and 100 μl (C mode) for ceftiofur resistant CFUs. The volumes used for quantification of Gram-negative and Gram- positive bacteria were determined after comparing different modes with samples from this farm that enable identifying isolated CFUs and enhancing the limits of detection for resistant bacteria. Additionally, media controls were plated to test each batch of MAC for the ability to inhibit Gram-positives with Staphylococcus aureus ATCC 29213 and Enterococcus faecalis ATCC 29212. The antibiotic concentration on MAC necessary to inhibit susceptible bacteria and to enable the growth of resistant strains was tested with the following control strains: E. coli ATCC 25922 (AmpS, CefS), E. coli ATCC 35218 (AmpS, CefS), E. coli strain TW18741 (AmpR, CefS), three ESBL E. coli strains (AmpR, CefR) obtained from clinical samples in a prior study [17]. CNA media controls included ATCC 29212 (AmpS, CefR), ATCC 29213 (AmpS, CefS), 107 Listeria monocytogenes ATCC 3382 (AmpS, CefR), Listeria monocytogenes ATCC 19115 (AmpS, CefR), Streptococcus pneumoniae ATCC 49619 (AmpS, CefS), Streptococcus equi subsp. Zooepidemicus ATCC 700400 (AmpS, CefS), and Streptococcus agalactiae strain COH1 (AmpS, CefS). Inhibition of the Gram-negative bacteria was tested with E. coli ATCC 25922 and ESBL E. coli strains. Paired non-parametric tests, Wilcoxon and Friedman, were used to compare the number of CFU/g and proportion of resistant bacteria between treatment groups and time points. Metagenomic sequencing Fecal DNA of samples collected on day -1 and weeks 1, 5, and 9, were extracted with the DNeasy PowerSoil Pro Kit (Qiagen, Germantown, MD, USA). Genomic DNA (102.18 ± 24.84 ng/μl) was sent to CosmosID (Rockville, MD, USA) for mNGS. Libraries were prepared with the Nextera™ XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA) and sequenced on the Illumina HiSeq X platform 2x150 bp. Shotgun metagenomic datasets were analyzed using the CosmosID bioinformatics software package to characterize the microbiome, resistome, and virulome. The CosmosID pipeline includes a reference GeneBook® database that comprises genomes and gene sequences from multiple publicly available databases as described [18]. Reads were assigned to a species or gene with the highest aggregation statistics. Each GeneBook® database was screened and cleaned for bovine (Bos taurus) genome sequences. Metagenome characterization The CosmosID bioinformatics platform was used to compare bacteria, eukaryotes, fungi, phages, antibiotic resistance genes (ARGs), and virulence genes between the two treatment groups. Relative abundance estimates, abundance scores, total matches percentage, unique matches percentage, and read frequencies were determined. Sample richness was calculated with the Shannon and Chao1 alpha diversity indices, while beta diversity was estimated with the 108 Bray-Curtis dissimilarity ordination and depicted with principal coordinate analysis (PCoA). Normalized abundance scores were used for statistical analyses and visualizations using the following R packages as described [19]: Phyloseq v.1.34.0 [20], vegan v.2.5-7 [21], and Metacoder v.0.3.5 [22]. The Wilcoxon and Friedman paired non-parametric tests were used to compare alpha diversity estimates between treatment groups and sampling points. Meanwhile, permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was used to compare the median composition between treatment groups and sampling points in the PCoA of the Bray- Curtis dissimilarity ordination. The analysis of differentially abundant taxa was carried out with four different approaches: 1) DESeq2, which uses a negative binomial distribution [23] with three settings (Wald test with a local fit, Wald test with Gamma-Poisson Generalized Linear Model, and likelihood ratio test (LRT) with Gamma-Poisson Generalized Linear Model); 2) Linear Discriminant Analysis (LDA) Effect Size (LEfSe), which identifies the effect relevance of a differential feature based on an algorithm that includes non-parametric tests and LDA [24]; 3) Analysis of compositions of microbiomes with bias correction (ANCOM-BC), which uses linear regression models and corrects for bias induced by sample differences [25]; and 4) Microbiome Multivariable Associations with Linear Models (MaAsLin2) [26] that uses generalized linear and mixed models. A consensus approach was used to ensure robust identification of differentially abundant taxa; only differentially abundant features (P < 0.05) identified with two or more pipelines were reported as was suggested in a prior study [27]. ARG host association analyses Biochemical identification of Gram-negative ceftiofur resistant strains was done with oxidase tests (OxiStripsTM, Hardy Diagnostics) and Chromocult® Coliform agar (Merck KGaA, 109 Darmstadt, Germany) to test β-glucuronidase and β-galactosidase activity. Additionally, metagenomic contigs over 500 bp containing β-lactam resistance genes were assembled with metaSPADES [28] and identified with DeepARG [29] for those ARGs with at least 70% query coverage and 80% identity. These ARG-carrying contigs (ACCs) were extracted with seqtk and translated to amino acid sequences with Prodigal (PROkaryotic DYnamic programming Gene- finding ALgorithm) [30]. The amino acid sequences were taxonomically classified with BLASTP [31] and with the contig annotation tool (CAT) v.5.2.3 [32]. Network analysis Correlations between ARGs, mobile genetic elements (MGEs), and bacterial genera were identified by calculating Spearman’s correlation coefficients; only coefficients (ρ) greater than 0.75 and p-values <0.01 were included in the networks. Significant correlations were analyzed in R v.4.1.2. with the package Hmisc [33] and visualized with Gephi v.0.9.2 [34]. Network statistics were calculated in Gephi including the degree of centrality, weighted degree of centrality, graph density, modularity, and clustering coefficient. The comparisons of centrality measures among β-lactam ARGs were analyzed between treatment groups and time points using non-parametric statistics. RESULTS Phenotypic identification of resistant bacteria Incidence of β-lactam resistant bacteria. Gram-positive bacteria with resistance to both ampicillin and ceftiofur were recovered from all samples (100%) from both antibiotic treated and control animals. Comparatively, the percentage of samples with Gram-negative bacteria resistant to ampicillin and ceftiofur was 90% and 24%, respectively, and was not associated with the IMM treatment (P > 0.69). 110 Gram-positive bacteria quantifications. Overall, the number of total Gram-positive CFU/g was 7.96E+05 (±9.68E+05) which was similar between treatment groups (P = 0.127) (Figure 3.2B). Though, one week after IMM treatment, control animals had higher counts of Gram-positive (P = 0.029) bacteria relative to the antibiotic group. A greater proportion of Gram-positive bacteria were resistant to ceftiofur (28.16% ±21.82%) than ampicillin (4.81% ± 6.06%) (P < 2.2e-16). The proportion of ampicillin resistant Gram-positives was significantly higher in cows treated with ceftiofur (P = 0.0413) at week 1 as compared to controls (Figure B.1). However, no difference was observed in the number of Gram-positive CFUs/g resistant to ceftiofur and ampicillin between day -1 and week 1 after the treatment with ceftiofur, or between treatment groups (Figures 3.2D and 3.2F). Gram-negative bacteria quantifications. The average total Gram-negative CFU/g was 9.11E+05 (±1.16E+06), which was significantly lower in cows treated with ceftiofur (P = 0.003). The lower abundance of Gram-negative CFUs, however, was observed one day before treatment, when fecal samples from control cows had twice the number of Gram-negative bacteria than cows that were going to be treated with ceftiofur (control = 8.25x105 vs. antibiotic = 4.41x105; P = 0.027) (Figure 3.2A). Lower total Gram-negative bacteria were also observed one week after the IMM treatment (P = 0.0148), but not in further sampling points (P > 0.05). For this reason, the FC was calculated by dividing the logarithm with base 10 (log10) of the CFU/g at each time point by the log10 of the CFU/g on the day before the treatment for each animal. Using this approach, no significant differences in the fold-change (FC) were observed between treatment groups for total Gram-negatives (P = 0.215). Regardless of treatment, fecal samples had a greater proportion of Gram-negative CFUs resistant to ampicillin (2.81%±10.60%) than to ceftiofur (0.02%±0.09%) (P < 2.2e-16). 111 Considerable variation was observed in the percentage of Gram-negative bacteria resistant to ampicillin (P = 0.001) and ceftiofur (P = 0.0015) across animals at the different time points (Figure B.1). The average number of ceftiofur resistant bacteria was ~14 times higher in the antibiotic treated group during weeks 1 (197 CFU/g) and 2 (335 CFU/g) relative to the control group (week 1: 24 CFU/g; week 2: 14 CFU/g) (Figure 3.2C). This difference was not significant (P > 0.2), which is likely because only a subset (25%) of the treated cows shed more ceftiofur- resistant bacteria. By contrast, the number of ampicillin resistant Gram-negatives was significantly higher in the control group in weeks 1 (P = 0.041) and 2 (P = 0.021) compared to day -1, but no difference was observed in the ceftiofur treated group (P > 0.3) (Figure 3.2E). Intriguingly, the total number of Gram-negative bacteria and the number of ampicillin resistant Gram-negative colonies increased at week 9 during pre-calving (P = 0.033) in both the treatment groups (Figures 3.2C and 3.2E). The number of Gram-negatives resistant to ceftiofur was also significantly higher in both groups at week 9 compared to weeks 5 and 7 (P <0.006), which was also true for Gram-negatives resistant to ampicillin (P <0.01). 112 Figure 3.2. Number of bacterial colony-forming units (CFUs) per gram of feces. Total number of A) Gram-negatives and B) Gram-positives; ceftiofur resistant C) Gram-negatives and D) Gram-positives; and ampicillin-resistant E) Gram-negatives and F) Gram-positives with (orange) and without (gray) intramammary ceftiofur treatment. Numbers are plotted before (Day -1) and after treatment for Gram negatives through 9 weeks and Gram-positives after 1 week. 113 Figure 3.2 (cont’d) Line plots show means and standard error bars with sample counts represented as dots. Boxplots indicate the median, lower, and upper quartiles, and the whiskers represent extreme values in the distribution. The per animal variability over time was calculated with the Friedman test (FT), which is shown per treatment group for Gram-negatives. Significant p-values between sampling points are represented for all animals (black) as well as for control (grey) and antibiotic (orange) groups. Metagenome analysis The metagenomic composition of cattle feces was analyzed for a total of 159 samples collected one day prior to treatment (day -1) and at weeks 1, 5, and 9 post-treatment. There was no difference in the number of reads per sample (P = 0.067) between groups, resulting in an average of 5.74 (±1.1) million reads. The microbiome was dominated by bacteria (92.51%) followed by archaea (4.27%), eukaryotes (1.65%), viruses (1.54%), and fungi (0.03%). The alpha diversity of bacteria was significantly lower during dry-off and increased during pre- calving (P < 0.001) (Figures 3.3A and 3.3B). Treatment with IMM ceftiofur at dry off did not affect bacterial richness in the short-term; though a significant reduction in the Chao1 index was observed in week 5 (P = 0.014) (Figure 3.3B) and in the Shannon diversity in week 9 (P = 0.053) (Figure 3.3A) compared to the control group. Likewise, the resistome alpha diversity was similar between groups (P > 0.24), though the richness of ARGs increased during pre-calving regardless of treatment (P < 0.0001) (Figures 3.3C and 3.3D). 114 Figure 3.3. Alpha diversity in the microbiome (A and B) and resistome (C and D) of dairy cattle. Alpha diversity was determined using the Shannon and Chao1 indices over a 9-week period after dry-off in cows with (orange) and without (grey) intramammary ceftiofur treatment. P-values were calculated with the Wilcoxon test to compare treatment groups within a sampling point (black) and changes in diversity between sampling points (grey) regardless of the treatment group. Friedman test shows per animal variability in the alpha diversity over time. Each boxplot shows the median, lower, and upper quartiles with the whiskers representing extreme values in the distribution. Similarly, the Bray-Curtis dissimilarity ordination showed significant changes in the bacterial composition over the sampling period (PERMANOVA, F = 3.93, P = 0.001) (Figure 3.4A). These changes were also detectable in the phyla distribution visualized in a relative abundance plot (Figure 3.4B). Specifically, the day before the dry-off (Day -1) was characterized by a higher abundance of Actinobacteria, Firmicutes and Proteobacteria compared 115 to weeks 1, 5, and 9 regardless of treatment status. An analysis of the resistome composition also showed significant differences in the Bray-Curtis dissimilarity ordination over time (PERMANOVA, F = 4.28, P = 0.001), which is likely due to distribution of points at week 9 (Figure 3.4C). Despite this difference, the relative abundance of ARGs was highly similar over the sampling period (Figure 3.4D). Genes encoding tetracycline resistance were the most abundant followed by those for macrolides and the β-lactams. Figure 3.4. Fecal microbiome and resistome composition of dairy cows during the dry-off period by treatment group. PCoAs of the Bray-Curtis dissimilarity are shown for the A) microbiome and C) resistome clustered by sampling point (ellipses contain at least 90% of the samples). The densities on the X and Y axis show the distribution of samples per sampling point in each principal coordinate. The mean abundance score of the B) phyla and D) antibiotic resistance gene (ARG) drug classes are shown for each treatment group and sampling point. This normalized metric takes the genome size and number of reads into consideration and can be used to calculate the relative abundance (%). Additionally, three viruses affecting cattle were identified at different time points including Bos taurus polyomavirus which was identified in one control animal on week 9, Pestivirus G in one control in week 5, and Bovine papillomavirus in three animals including 2 control and 1 antibiotic-treated cows, which were identified in more than once per individual 116 including day -1, week 1, and week 5 after dry-cow therapy. Other viruses identified included Baculoviruses and Granuloviruses that affect insects, two viruses from plants, 1 virus from Penicillium, and viruses from other mammal species including Torque teno midi virus 2 (animals, n = 3), murine mastadenovirus B (n = 1), and mouse mammary tumor virus (n = 1). Differentially abundant taxa were also identified over time after stratifying by treatment group. Cows treated with IMM ceftiofur, for instance, had lower proportions of phyla Proteobacteria (P < 0.02) and Tenericutes (P < 0.007) compared to controls 1-week following treatment (Figure 3.5). By contrast, a lower abundance of Chloroflexi and Firmicutes (P < 0.0001) and a higher abundance of Actinobacteria (P < 0.0001) and Bacteroidetes (P < 0.0001) were observed at week 9 in the ceftiofur-treated cows as compared to controls. Bacteroidetes was also more abundant at week 5 in the treated animals. Further classification at the genus level identified a greater abundance of Garciella and Pseudobutyrivibrio in the treated cows as well as a decreased abundance of Clostridium, Desulfosporosinus, Neorickettsia, Paeniclostridium, Phascolarctobacterium, Romboutsia, Ruminiclostridium, Terrisporobacter and Turicibacter (Table B.1). IMM treatment with ceftiofur was also associated with a differential proportion of other genera in the long term. The abundance of Clostridium and Turicibacter, for example, was lower in weeks 1 and 9, whereas the abundance of genus Peptostreptococcaceae bacterium VA2 was higher. During weeks 5 and 9, however, a higher proportion of genus Bacillales bacterium UBA1231 and a lower proportion of Hungatella hathewayi, Methanobacteriaceae archaeon UBA237 and Ruminococcaceae bacterium UBA2851 were identified. 117 9 9 1 5 IMM Ceftiofur 1 9 9 9 1 Control 9 5 5 5 5 9 9 1 5 1 9 5 9 1 5 9 1 5 1 -1, 5 -1, 5 5, 9 -1, 5 -1, 5 9 5 -1 (antibiotic), 5 (control) 5, 9 1 1 5 1 -1 1 5 -1 (control), 9 (antibiotic) -1 9 9 5, 9 1 9 9 9 1 9 5, 9 5 1 9 Figure 3.5. Heat tree showing the differential abundance of taxa from phylum to family over the 9-week sampling period. For each taxonomic level, the size of the node represents the proportion of a taxa within the microbiome. Significantly different taxa are noted for controls (grey) and antibiotic-treated cows (orange). The red numbers at the significant nodes indicate the sampling time (e.g., day (-1) or week (1, 5, or 9)) when taxa were found to be differentially abundant. A similar analysis of the resistome composition also detected differentially abundant resistance genes in the treated and untreated animals over the sampling period (Table B.2). For instance, the untreated (control) cows had a greater abundance of genes encoding resistance to macrolides in weeks 1 and 5, and to tetracycline and multidrug resistance at weeks 1 and 9 (Figure 3.6). The ceftiofur-treated cows, however, had a greater proportion of β-lactam resistance genes at all three post-treatment samplings (weeks 1, 5, and 9). Upon further analysis, 118 the abundance of β-lactam resistance genes was significantly higher 1 week after treatment in the ceftiofur-treated cows, with similar levels persisting through week 9 (Figure 3.7A). Indeed, the treated cows had a greater abundance of bla-CFXA, bla-ACI-1, and bla-CMY, which contribute to ESBL production, as well as blc (outer membrane lipoprotein) over the 9-week period (LDA > 4.3; P < 0.03). A significant increase in the abundance of bla-CFXA and bla-ACI-1 was observed in the treated cows at 1 week following treatment (Figures 3.7B and 3.7C). The increased abundance of these genes (bla-CFXA and bla-ACI-1) was maintained through the 9-week period, though some fluctuations were observed. Increased abundance of blc was only observed at week 1 with no difference by treatment group (Figure 3.7D), whereas bla-CMY was only significantly more abundant in the treated animals at week 9 (Figure 3.7E). Unlike the Wilcoxon test results presented in Figure 3.7, use of other statistical tests including DESeq2, LEfSe, ANCOM-BC and MaAsLin2, identified significant differences for both blc and bla-CMY at weeks 1, 5, and 9 (Table B.2). Taxonomic identification of bacteria resistant to β-lactams From 882 Gram-negative bacterial isolates resistant to ceftiofur, a total of 146 Gram- negative isolates were preserved for further analyses (72 from controls and 74 from ceftiofur- treated animals). These strains were recovered at day -1 (n = 26), week 1 (n = 25), week 2 (n=17), week 3 (n = 17), week 5 (n = 5), week 7 (n = 10), week 9 (n = 44), and week 11 (n = 2). Through biochemical identification, 94 isolates were identified as E. coli, while 25 were classified as other members of Enterobacteriaceae and 27 were classified as non- Enterobacteriaceae. 119 -1 (antibiotic), 9 (Control) 1, 5, 9 1 1, 9 9 9 5, 9 IMM Ceftiofur 5 Control -1 (antibiotic), 9 (Control) 1 -1 1, 5, 9 1, 5 1 9 1 5 9 1 (control), 5 (antibiotic) 9 Figure 3.6. Heat tree showing the differential abundance of antibiotic resistance genes (ARGs) at the class, mechanism, and gene level. ARGs that were significantly different between controls (grey) and antibiotic treated cows (orange) are highlighted. For each level, the size of the node represents the proportion of each within the microbiome, while the red numbers indicate the day (-1) or week (1, 5, or 9) when the genes were differentially abundant. 120 Figure 3.7. Abundance of β-lactam resistance genes before and after intramammary ceftiofur treatment over the 9-week period. Boxplots show the abundance score for A) the β- lactam antibiotic resistance gene class; B) bla-CFXA; C) bla-ACI-1; D) blc; and E) bla-CMY. The median, lower, and upper quartiles are shown in each boxplot with the whiskers representing extreme values in the distribution. P-values were calculated with the Wilcoxon test to compare treatment groups within a sampling point (black) and changes in abundance between sampling points (grey) regardless of the treatment group. The Friedman test assesses variability in class and gene abundance per animal over the sampling period. In all, 121 contigs contained β-lactam resistance genes that could be classified with DeepARG [29] and analyzed with BLASTP [31] and CAT [32]. Of these, 77.98% of the BLASTP hits were identified as members of the Cytophaga, Fusobacterium, and Bacteroides (CFB) group, followed by Firmicutes (9.67%) and Gamma-Proteobacteria (4.65%); 7.7% were classified as “other”. At the genus level, 44.34% of the hits belonged to Bacteroides, while a lower proportion were classified as Prevotella (6.76%), Parabacteroides (4.41%), Escherichia (1.57%), Salmonella (0.31%), Campylobacter (0.04%), and other (62.05%) (Table B.3). Among these ARG-carrying contigs (ACCs), most were associated with Bacteroidetes (n = 30), Firmicutes (n = 23), and Proteobacteria (n = 6) by CAT, though 62 could not be identified and were considered unknown. At the order level, a subset of these ACCs was associated with 121 Bacteroidales (n = 30), Clostridiales (n = 8), and Aeromonadales (n = 6) using CAT, whereas only 9 contigs could be classified at the species level. These species include Bacteriodales bacterium (n = 3), Bacteroides sp. (n = 1), Ruminococcaceae bacterium (n = 4), and “uncultured prokaryote” (n = 1). Network analyses Co-occurrence networks compared the interconnections among β-lactam ARGs and other resistance genes, MGEs, and bacterial genera between controls and ceftiofur-treated cows over time (Figure B.2). At week 1, for instance, the network for ceftiofur-treated animals showed β- lactam resistance genes with a higher degree of centrality [number of connections] (P = 0.033; Figure 3.8A) and betweenness centrality [number of times a node stands between two random nodes] (P = 0.004; Figure 3.8B) than before IMM treatment. This network included a wide range of bacterial species, phages, and plasmid genes. Meanwhile, the control animals had a similar degree of centrality in the β-lactam resistance genes before and 1 week after dry-off (P = 0.79), though this metric was significantly higher than in the antibiotic-treated cows at day -1 (P = 0.043). At week 5, both groups had a similar number of connections for the β-lactam resistance genes (P = 0.86) and a similar betweenness centrality value (P =0.95), which was significantly lower than the value observed at week 1 (P < 0.02). 122 Figure 3.8. Average of centrality measures between β-lactam resistant genes and MGEs, phages and bacterial genera. A) Degree of centrality B) Betweenness centrality. Values are plotted over a 9-week period following IMM treatment. Line plots show means and standard error bars, while gene values are represented as dots. P-values indicate differences between treatment groups by time point and between sampling points despite the treatment (black bracket) or within treatment group (control = gray; antibiotic = orange brackets). Friedman test shows per gene variability in the centrality measures over time. Interestingly, during pre-calving (week 9), the networks showed a greater number of interconnections between β-lactam resistance genes, other ARG, MGEs, and taxa in both groups but there was no difference relative to week 5 (P > 0.2). This result is likely due to the heterogeneity of connections among the different genes important for β-lactam resistance. For instance, on week 9, the control group had a network with a high number of edges (density = 0.139; connected components = 95; average weighted degree = 36.32) but with few clusters (modularity = 0.074) (Figure B.2). The main cluster in the control group at week 9 contained the β-lactam resistance genes blaAmpC, blaAmpH, pbp2, and pbp4, which shared connections with the same nodes (average degree of centrality = 106) as phages (e.g., Escherichia virus phiV10, Stx2-converting phage 86, Salmonella phage vBSemP, and others), transposases, plasmids, 123 virulence genes, and bacterial taxa (Pseudoxanthomonas, Enterobacteriaceae, and Shewanella). The ESBL genes, bla-CFXA and bla-ACI, in the control group, however, were not linked with the main cluster at week 9 and had very few edges (average degree of centrality = 1.56) with commensal bacteria such as Halocynthiibacter, Holophagacae, Lachnobacterium, Lachnospira, Methylobacterium, Ureaplasma, and phages from Shigella and Salmonella. In contrast, the network for ceftiofur-treated cows at week 9 had a smaller number of edges (density = 0.032, average weighted degree = 7.336) but a greater number of connected components (n = 128) and clusters (modularity = 0.586). The ESBL genes, for instance, had a greater degree of centrality (average degree = 8.95) than in the control networks (P = 0.008) when excluding those β-lactam genes (blaAmpC, blaAmpH, pbp2, and pbp4) found in the main cluster of controls. In the ceftiofur-treated cows, the ESBL genes were linked with several taxa including Cetobacterium, Clostridiales, Coprococcus, Corynebacterium, Dorea, Gallibacterium, Gordonia, Halorubrum, Kocuria, Microbacterium, Microvirga, Paracaedibacteriacea, Rickettsiales, Saccharibacillus, Saccharomonospora, Thermoactinomyces, Thermobifida, Treponema, Turicibacter, Tyzzerella, and Yersinia. Linkages with phages from Shigella, Escherichia, and Salmonella were also identified. On average, the bla genes with the highest betweenness centrality were bla-CFXA, bla-CMY-13, bla-ACI-1, pbp2, bla-1030 Branch, pbp4, and blaAmpH. Notably, bla-CMY-13 had the highest value of betweenness centrality at week 9 in the antibiotic-treated group (n = 1202.04) when compared to the other bla genes identified. DISCUSSION It was estimated that ~90% of dairy farms use IMM β-lactam antibiotics during the dry- off period to treat and prevent mastitis [5–7] despite the possibility of selecting for resistant bacterial populations. Of great concern is the emergence and selection of ESBL-producing 124 Enterobacteriaceae, which are classified as a serious public health threat [2]. Although the effect of IMM ceftiofur treatment has been studied in the milk microbiome, including five days with IMM 125 mg/day [35,36] and a single application of 2 g of CHCL [37], the impact of this treatment on the gut microbiome has not been elucidated. Through this study, however, we have demonstrated long-term effects on the fecal microbiome due to a single 2g dose of IMM ceftiofur via culture-based analyses and metagenomics. Indeed, compared to controls, antibiotic- treated cows had reduced microbial richness over time, differentially abundant taxa, and an increased abundance and persistence of β-lactam resistance genes that were associated with Enterobacteriaceae hosts and commensal bacteria. A subset of the treated cows also had greater concentrations of ceftiofur-resistant Gram-negative bacterial populations. Following subcutaneous treatment, a prior study showed that Holstein steers had higher concentrations of CHCL in the gastrointestinal tract compared to ceftiofur crystalline-free acid (CCFA) [9], though only CCFA resulted in decreased fecal E. coli concentrations for up to two weeks. Similarly, parenteral ceftiofur treatment resulted in lower fecal E. coli concentrations for 3 days [12] and up to a month post-treatment [13] in two other studies. In the latter study of 96 dairy cows, systemic ceftiofur administration resulted in a significant increase in the level of ceftiofur-resistant Enterobacteriaceae, though the concentrations returned to baseline levels after one week [13]. Consistent with these findings, we observed a reduction in the total number of Gram-negative bacteria one week after IMM ceftiofur treatment, and an increased number of ceftiofur-resistant Gram-negative bacteria in 25% of the treated cows. Enhanced recovery of Gram-negative bacteria with resistance to ceftiofur was observed for two weeks after the treatment. Re-emergence of ceftiofur resistance was also observed in the Gram-negative bacterial populations at 9 weeks (pre-calving) in both the treated and untreated animals, which is 125 consistent with data generated in another study [13]. This increase could be linked to environmental acquisition of resistant strains, horizontal gene transfer, peri-parturient immune suppression, higher contact with personnel, or modifications in the diet formulation during pre- calving, all of which could contribute to the expansion of resistant Enterobacteriaceae populations. While these data are intriguing, it is important to note that in vitro bacterial quantifications do not distinguish between acquired and intrinsic antimicrobial resistance. Hence, future studies should involve isolating the resistant strains for further identification using biochemical tests and whole-genome sequencing, which can also be used to define the mechanisms of resistance. Similar to a prior report of six healthy 1st lactation cows given 2 subcutaneous doses of CCFA [38], we observed no difference in fecal microbiome diversity after IMM ceftiofur treatment in the short term. The abundance of some taxa belonging to Turicibacter, Peptostreptococcaceae, and Tenericutes, however, decreased a week after treatment. A decrease was also observed in Gammaproteobacteria, which are Gram-negatives of the order Enterobacterales (e.g., E. coli) that are typically susceptible to β-lactam antibiotics, and Epsilonproteobacteria comprising Campylobacter. Members of the latter group are mostly resistant to third-generation cephalosporins with the exception of Campylobacter fetus [39]. A greater abundance of Campylobacter was observed during pre-calving (9 weeks) in the control group, which was correlated with the multidrug-resistant genes cpxA, crp, gadX, and mdtK. By contrast, Erysipelotrichia, including Turicibacter and Peptostreptococcaceae, are Gram-positives susceptible to penicillin and cephalosporins [40]. Because Tenericutes typically have intrinsic resistance to β-lactams due to the lack of a cell wall, the observed decrease in abundance at week 1 was unexpected. Despite a prior report that steers treated with subcutaneous ceftiofur had a 126 lower abundance of Firmicutes and increased Bacteroidetes for two weeks [9], we only observed changes in these phyla at weeks 5 and 9 post-treatment. The effects of these changes on cattle health and production, however, could not be elucidated. A key finding of this study was the exclusive and persistent increase in the abundance of ESBL genes (bla-CFXA, bla-ACI-1, and bla-CMY) in the fecal resistome of cows given IMM ceftiofur treatment. Although increases in the abundance of ESBL genes following parenteral ceftiofur treatment have been reported [12,14,38,41,42], no prior studies have examined the effect of IMM treatment. Steers receiving subcutaneous CCFA, for example, had a higher abundance of bacterial isolates harboring bla-CMY-2 up to 4 days post-treatment, which resulted in co-selection of isolates containing tet(A) and bla-CMY-2 after a subsequent chlortetracycline treatment for up to 26 days [14]. Similarly, Holstein cows treated with systemic CCFA showed a higher abundance of genes encoding bla-CFXA β-lactamases three days after treatment [38], while other studies reported an increase of bla-CMY-2 in cattle feces for up to 10 days post- treatment when pure cultures were analyzed [12,43]. In addition, our observation of a persistent increase in the abundance of the gene encoding Blc (bacterial lipocalin), an outer membrane lipoprotein, suggests a stress response related to the use of β-lactams. A prior study demonstrated that Blc production is linked to a starvation response and β-lactam resistance in E. coli, which is activated by the sigma factor, RpoS, between exponential and stationary growth phases [44]. Consequently, the higher abundance of blc observed during weeks 1, 5 and 9 after IMM ceftiofur treatment indicates lasting effects of a single IMM application of ceftiofur on the gut microbiota. Despite observing an increased abundance of ESBL genes, no increment in resistant third-generation cephalosporin CFUs was identified in cows treated with IMM ceftiofur in this study. This inconsistency between culture-dependent and independent methods could be 127 attributed to the oxygenic environment used to identify bacteria on plates. The hindgut microbiome is composed predominantly of anaerobic bacteria; thus, aerobic and microaerophilic conditions used for the quantification of Gram-negative and Gram-positive bacteria represented a small fraction of the microbiota. For instance, Bacteroidetes (e.g., Prevotella and Bacteroides), are predominant anaerobic Gram-negative bacteria in the hindgut, priorly identified as the main carriers of the ESBL gene bla-CFXA [45], which was the most abundant β-lactamase gene identified by metagenomics. Likewise, bla-ACI1 was the second most abundant ESBL gene, priorly reported in the Gram-negative Firmicutes Acidaminococcus [46] and the Gram-positive genus Bifidobacterium [47]. Consistently, a significantly higher abundance of Bacteroidetes, Acidaminoccocales, and Bifidobacterium was identified in cows treated with IMM ceftiofur when compared with controls. These results suggest that the increased abundance of ESBLs identified after IMM ceftiofur treatment was associated with changes in the abundance of anaerobic bacteria. The identification of bacterial hosts harboring β-lactam resistance genes is also critical for developing new interventions as well as enhancing understanding of the ecology of resistance within the cattle microbiome and risks associated with carriage of specific genes. Indeed, our use of different approaches including in-vitro and in-silico analyses, allowed us to identify those bacterial hosts more accurately. While culture identification of the resistant bacteria indicated 64% of the isolates were E. coli, metagenomic analyses showed most (44%) of the β-lactamases were associated with Bacteroidetes and only 1.57% were associated with E. coli. Similarly, co- occurrence networks indicated that bacteriophages of Enterobacteriaceae and other genera were associated with ESBL genes. Network analysis also revealed changes in the interconnections of ESBL genes over the dry-off period, as a significant increase in the degree of centrality of bla 128 genes was observed in the antibiotic-treated cows one week after treatment and during pre- calving. This finding suggests that disturbances in the microbiome composition likely contributed to the increased distribution of ESBL genes across bacterial strains, potentially aided by connections with MGEs. During pre-calving, for instance, the control group had high levels of centrality among ARGs, MGEs, virulence genes, and potential pathogens compared to the ceftiofur-treated cows. However, ESBL genes that significantly increased after IMM ceftiofur treatment were not part of that cluster. Differences in networks between controls and antibiotic- treated cows before treatment (day -1) could therefore be related to the higher number of Gram- negative bacteria recovered in the control group, compromising comparisons between these two groups. To our knowledge, this is the first study to report network dynamics of ARGs during dry-off in dairy cows. An increased proportion of ESBL genes in cattle manure following IMM ceftiofur administration could contribute to enhanced dissemination and persistence of resistance in the farm environment. For example, one study demonstrated that use of manure from dairy cows treated with IMM antibiotics (pirlimycin and cephapirin) in unamended soils resulted in a significant 2.2X increase in ARG abundance [48]. Composting and a 120-day waiting period, however, reduced the total ARG relative abundance as well as the co-occurrence of ARGs, MGEs and pathogens [48]. Enterobacteriaceae one week after IMM ceftiofur treatment and during pre-calving in both the antibiotic-treated and control groups. These findings highlight the need for additional measures such as separating manure from antibiotic-treated and control animals during peripartum, to prevent the spread and persistence of resistant bacteria and resistance determinants in the farm environment. 129 Intriguingly, the abundance of Actinobacteria was significantly higher on day -1 compared to further time points. The most abundant family of phylum Actinobacteria was Bifidobacteriaceae represented mainly by the genus Bifidobacterium. Bifidobacteriaceae are implicated in the utilization of oligosaccharides in the colon resulting in the production of VFAs [49]. Differences in the composition of the fecal microbiome, primarily caused by the abundance of Actinobacteria, observed on day -1 could be associated with differences in the diet given in late lactation which has higher levels of dry matter intake and metabolizable energy and protein than during dry off (weeks 1 – 7) and fresh cows (week 9). However, further analyses of microbial metabolic pathways and metabolite composition are necessary to better explain the impacts of differentially abundant taxa on cattle’s performance. Although this study is the first to describe the impact of IMM ceftiofur treatment on the gut microbiome, it is important to highlight a few limitations. For instance, current resistome databases do not include all known ARGs from cattle samples and hence, novel resistance determinants may remain unclassified. The identification of species, ARGs, and ACCs can also be limited by a low number of metagenomic reads, as sequencing depth of ≥50 million reads is needed for complex microbial communities such as those residing in the bovine gut [50]. Since the proportion of microbial phyla and ARG classes was constant across various sequencing depths [50], we were able to detect the predominant and differential metagenomic features. Sequencing depth, assembly method, and size of DNA segments (150 bp) are also important for the identification of bacterial hosts through ACCs. Future work involving use of third-generation sequencing platforms that sequence ultralong DNA segments such as the PacBio (40-70 kbp) or Oxford Nanopore Technologies (>100 kbp), however, would be highly beneficial for confirmation and characterization of these regions [51]. Since the identification of differentially 130 abundant features, including bacterial taxa and genes, tends to vary across bioinformatic pipelines, we applied six different approaches but only reported those features with significant p- values using at least two pipelines, as suggested previously [27]. Altogether, our analyses highlight those metagenomic features that are most impacted by IMM ceftiofur treatment. CONCLUSIONS One application of IMM ceftiofur (2 g) at dry off contributed to an increase in the abundance of genes encoding ESBLs in the fecal samples of antibiotic-treated cattle that persisted for nine weeks. These genes were associated with Enterobacteriaceae hosts, which illustrates how ESBL-producing pathogens emerge and are selected for in this niche. While most of the cows given the prophylactic IMM ceftiofur treatment did not have an increase in the number of resistant bacteria, 25% had an increased level of ceftiofur-resistant Gram-negative bacteria. Indeed, the recovery of resistant isolates was 14X greater in the antibiotic-treated versus control cows for up to two weeks after treatment, highlighting significant variation in fecal shedding levels across animals. Future studies should therefore focus on understanding the association between shedding and the dissemination and persistence of antibiotic resistance determinants in dairy farm environments. 131 REFERENCES 1. Boeckel, T.P.V.; Brower, C.; Gilbert, M.; Grenfell, B.T.; Levin, S.A.; Robinson, T.P.; Teillant, A.; Laxminarayan, R. Global Trends in Antimicrobial Use in Food Animals. PNAS 2015, 112, 5649–5654, doi:10.1073/pnas.1503141112. 2. Centers for Disease Control and Prevention (U.S.) Antibiotic Resistance Threats in the United States, 2019; Centers for Disease Control and Prevention (U.S.), 2019 3. Reid, R.; Samarasinghe, S.; Varnakulasingam, A. The Distribution of ESBL-Producing Enterobacteriaceae: Leicestershire UK Compared to Worldwide. 2019. 4. FDA Summary Report on Antimicrobials Sold or Distributed for Use in Food-Producing Animals; FDA: USA, 2021; pp. 1–49. 5. Mathew, A.G.; Cissell, R.; Liamthong, S. Antibiotic Resistance in Bacteria Associated with Food Animals: A United States Perspective of Livestock Production. Foodborne pathogens and disease 2007, 4, 115–133. 6. United States Department of Agriculture–APHIS–VS–NAHMS Milk Quality, Milking Procedures, and Mastitis on U.S. Dairies, 2014. Fort Collins 2016, CO. 7. Campos, J.L. de; Kates, A.; Steinberger, A.; Sethi, A.; Suen, G.; Shutske, J.; Safdar, N.; Goldberg, T.; Ruegg, P.L. Quantification of Antimicrobial Usage in Adult Cows and Preweaned Calves on 40 Large Wisconsin Dairy Farms Using Dose-Based and Mass- Based Metrics. Journal of Dairy Science 2021, 104, 4727–4745, doi:10.3168/jds.2020- 19315. 8. Brown, S.; Jaglan, P.; Banting, A. Ceftiofur Sodium: Disposition, Protein-Binding, Metabolism, and Residue Depletion Profile in Various Species. Acta Veterinaria Scandinavica. Supplementum (Denmark) 1991. 9. Foster, D.M.; Jacob, M.E.; Farmer, K.A.; Callahan, B.J.; Theriot, C.M.; Kathariou, S.; Cernicchiaro, N.; Prange, T.; Papich, M.G. Ceftiofur Formulation Differentially Affects the Intestinal Drug Concentration, Resistance of Fecal Escherichia Coli, and the Microbiome of Steers. PLOS ONE 2019, 14, e0223378. 10. Volkova, V. V; Lanzas, C.; Lu, Z.; Gröhn, Y.T. Mathematical Model of Plasmid-Mediated Resistance to Ceftiofur in Commensal Enteric Escherichia coli of Cattle. PloS one 2012, 7, e36738–e36738, doi:10.1371/journal.pone.0036738. 11. Boyer, T.C.; Singer, R.S. Quantitative Measurement of Bla CMY-2 in a Longitudinal Observational Study of Dairy Cattle Treated with Ceftiofur. Foodborne pathogens and disease 2012, 9, 1022–1027. 12. Singer, R.S.; Patterson, S.K.; Wallace, R.L. Effects of Therapeutic Ceftiofur Administration to Dairy Cattle on Escherichia coli Dynamics in the Intestinal Tract. 132 Applied and environmental microbiology 2008, 74, 6956–6962, doi:10.1128/AEM.01241- 08. 13. Sheedy, D.B.; Okello, E.; Williams, D.R.; Precht, K.; Cella, E.; Lehenbauer, T.W.; Aly, S.S. Effect of Antimicrobial Treatment on the Dynamics of Ceftiofur Resistance in Enterobacteriaceae from Adult California Dairy Cows. Microorganisms 2021, 9, 828, doi:10.3390/microorganisms9040828. 14. Kanwar, N.; Scott, H.M.; Norby, B.; Loneragan, G.H.; Vinasco, J.; McGowan, M.; Cottell, J.L.; Chengappa, M.M.; Bai, J.; Boerlin, P. Effects of Ceftiofur and Chlortetracycline Treatment Strategies on Antimicrobial Susceptibility and on Tet(A), Tet(B), and Bla CMY-2 Resistance Genes among E. coli Isolated from the Feces of Feedlot Cattle. PloS one 2013, 8, e80575–e80575, doi:10.1371/journal.pone.0080575. 15. National Antimicrobial Resistance Monitoring System (NARMS) Breakpoints Used for Susceptibility Testing of Salmonella and E. coli. USDA 2014. 16. CLSI, C. Performance Standards for Antimicrobial Susceptibility Testing; Twenty-Fourth Informational Supplement. M100-S24 January 2014. 17. Regasa Dadi, B.; Abebe, T.; Zhang, L.; Mihret, A.; Abebe, W.; Amogne, W. Drug Resistance and Plasmid Profile of Uropathogenic Escherichia coli among Urinary Tract Infection Patients in Addis Abeba. The Journal of Infection in Developing Countries 2018, 12, 608–615, doi:10.3855/jidc.9916. 18. Connelly, S.; Bristol, J. a.; Hubert, S.; Subramanian, P.; Hasan, N. a.; Colwell, R. r.; Kaleko, M. SYN-004 (Ribaxamase), an Oral Beta-Lactamase, Mitigates Antibiotic- Mediated Dysbiosis in a Porcine Gut Microbiome Model. Journal of Applied Microbiology 2017, 123, 66–79, doi:10.1111/jam.13432. 19. Vasco, K.; Nohomovich, B.; Singh, P.; Venegas-Vargas, C.; Mosci, R.E.; Rust, S.; Bartlett, P.; Norby, B.; Grooms, D.; Zhang, L.; et al. Characterizing the Cattle Gut Microbiome in Farms with a High and Low Prevalence of Shiga Toxin Producing Escherichia coli. Microorganisms 2021, 9, 1737, doi:10.3390/microorganisms9081737. 20. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data Available online: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061217 (accessed on 13 May 2021). 21. Dixon, P. VEGAN, a Package of R Functions for Community Ecology. Journal of Vegetation Science 2003, 14, 927–930, doi:https://doi.org/10.1111/j.1654- 1103.2003.tb02228.x. 22. Foster, Z.S.L.; Sharpton, T.J.; Grünwald, N.J. Metacoder: An R Package for Visualization and Manipulation of Community Taxonomic Diversity Data. PLOS Computational Biology 2017, 13, e1005404, doi:10.1371/journal.pcbi.1005404. 133 23. Love, M.I.; Huber, W.; Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biology 2014, 15, 550, doi:10.1186/s13059- 014-0550-8. 24. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery and Explanation. Genome Biol 2011, 12, R60, doi:10.1186/gb-2011-12-6-r60. 25. Lin, H.; Peddada, S.D. Analysis of Compositions of Microbiomes with Bias Correction. Nat Commun 2020, 11, 3514, doi:10.1038/s41467-020-17041-7. 26. Mallick, H.; Rahnavard, A.; McIver, L.J.; Ma, S.; Zhang, Y.; Nguyen, L.H.; Tickle, T.L.; Weingart, G.; Ren, B.; Schwager, E.H.; et al. Multivariable Association Discovery in Population-Scale Meta-Omics Studies. PLOS Computational Biology 2021, 17, e1009442, doi:10.1371/journal.pcbi.1009442. 27. Nearing, J.T.; Douglas, G.M.; Hayes, M.G.; MacDonald, J.; Desai, D.K.; Allward, N.; Jones, C.M.A.; Wright, R.J.; Dhanani, A.S.; Comeau, A.M.; et al. Microbiome Differential Abundance Methods Produce Different Results across 38 Datasets. Nat Commun 2022, 13, 342, doi:10.1038/s41467-022-28034-z. 28. Ma, L.; Xia, Y.; Li, B.; Yang, Y.; Li, L.-G.; Tiedje, J.M.; Zhang, T. Metagenomic Assembly Reveals Hosts of Antibiotic Resistance Genes and the Shared Resistome in Pig, Chicken, and Human Feces. Environmental Science & Technology 2016, 50, 420–427, doi:10.1021/acs.est.5b03522. 29. Arango-Argoty, G.; Garner, E.; Pruden, A.; Heath, L.S.; Vikesland, P.; Zhang, L. DeepARG: A Deep Learning Approach for Predicting Antibiotic Resistance Genes from Metagenomic Data. Microbiome 2018, 6, 23, doi:10.1186/s40168-018-0401-z. 30. Hyatt, D.; Chen, G.-L.; LoCascio, P.F.; Land, M.L.; Larimer, F.W.; Hauser, L.J. Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification. BMC Bioinformatics 2010, 11, 119, doi:10.1186/1471-2105-11-119. 31. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic Local Alignment Search Tool. J Mol Biol 1990, 215, 403–410, doi:10.1016/S0022-2836(05)80360-2. 32. von Meijenfeldt, F.A.B.; Arkhipova, K.; Cambuy, D.D.; Coutinho, F.H.; Dutilh, B.E. Robust Taxonomic Classification of Uncharted Microbial Sequences and Bins with CAT and BAT. Genome Biology 2019, 20, 217, doi:10.1186/s13059-019-1817-x. 33. Harrell Jr, F.E.; Harrell Jr, M.F.E. Package ‘Hmisc.’ CRAN2018 2019, 2019, 235–236. 34. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third international AAAI conference on weblogs and social media; 2009. 134 35. Ganda, E.K.; Bisinotto, R.S.; Lima, S.F.; Kronauer, K.; Decter, D.H.; Oikonomou, G.; Schukken, Y.H.; Bicalho, R.C. Longitudinal Metagenomic Profiling of Bovine Milk to Assess the Impact of Intramammary Treatment Using a Third-Generation Cephalosporin. Sci Rep 2016, 6, 37565, doi:10.1038/srep37565. 36. Ganda, E.K.; Gaeta, N.; Sipka, A.; Pomeroy, B.; Oikonomou, G.; Schukken, Y.H.; Bicalho, R.C. Normal Milk Microbiome Is Reestablished Following Experimental Infection with Escherichia coli Independent of Intramammary Antibiotic Treatment with a Third-Generation Cephalosporin in Bovines. Microbiome 2017, 5, 74, doi:10.1186/s40168-017-0291-5. 37. Bonsaglia, E.C.R.; Gomes, M.S.; Canisso, I.F.; Zhou, Z.; Lima, S.F.; Rall, V.L.M.; Oikonomou, G.; Bicalho, R.C.; Lima, F.S. Milk Microbiome and Bacterial Load Following Dry Cow Therapy without Antibiotics in Dairy Cows with Healthy Mammary Gland. Sci Rep 2017, 7, 8067, doi:10.1038/s41598-017-08790-5. 38. Chambers, L.; Yang, Y.; Littier, H.; Ray, P.; Zhang, T.; Pruden, A.; Strickland, M.; Knowlton, K. Metagenomic Analysis of Antibiotic Resistance Genes in Dairy Cow Feces Following Therapeutic Administration of Third Generation Cephalosporin. PloS one 2015, 10, e0133764–e0133764, doi:10.1371/journal.pone.0133764. 39. Pacanowski, J.; Lalande, V.; Lacombe, K.; Boudraa, C.; Lesprit, P.; Legrand, P.; Trystram, D.; Kassis, N.; Arlet, G.; Mainardi, J.-L.; et al. Campylobacter Bacteremia: Clinical Features and Factors Associated with Fatal Outcome. Clinical Infectious Diseases 2008, 47, 790–796, doi:10.1086/591530. 40. Veraldi, S.; Girgenti, V.; Dassoni, F.; Gianotti, R. Erysipeloid: A Review. Clinical and Experimental Dermatology 2009, 34, 859–862, doi:10.1111/j.1365-2230.2009.03444.x. 41. Kanwar, N.; Scott, H.M.; Norby, B.; Loneragan, G.H.; Vinasco, J.; Cottell, J.L.; Chalmers, G.; Chengappa, M.M.; Bai, J.; Boerlin, P. Impact of Treatment Strategies on Cephalosporin and Tetracycline Resistance Gene Quantities in the Bovine Fecal Metagenome. Scientific reports 2014, 4, 5100, doi:10.1038/srep05100. 42. Sato, T.; Okubo, T.; Usui, M.; Yokota, S.; Izumiyama, S.; Tamura, Y. Association of Veterinary Third-Generation Cephalosporin Use with the Risk of Emergence of Extended- Spectrum-Cephalosporin Resistance in Escherichia coli from Dairy Cattle in Japan. PLOS ONE 2014, 9, e96101, doi:10.1371/journal.pone.0096101. 43. Alali, W. q.; Scott, H. m.; Norby, B.; Gebreyes, W.; Loneragan, G. h. Quantification of the BlaCMY-2 in Feces from Beef Feedlot Cattle Administered Three Different Doses of Ceftiofur in a Longitudinal Controlled Field Trial. Foodborne Pathogens and Disease 2009, 6, 917–924, doi:10.1089/fpd.2009.0271. 44. Bishop, R.E.; Penfold, S.S.; Frost, L.S.; Höltje, J.-V.; Weiner, J.H. Stationary Phase Expression of a Novel Escherichia coli Outer Membrane Lipoprotein and Its Relationship with Mammalian Apolipoprotein D: Implications for the Origin of Lipocalins. Journal of Biological Chemistry 1995, 270, 23097–23103, doi:10.1074/jbc.270.39.23097. 135 45. Parker, A.C.; Smith, C.J. Genetic and Biochemical Analysis of a Novel Ambler Class A Beta-Lactamase Responsible for Cefoxitin Resistance in Bacteroides Species. Antimicrobial Agents and Chemotherapy 1993, doi:10.1128/AAC.37.5.1028. 46. Rands, C.M.; Starikova, E.V.; Brüssow, H.; Kriventseva, E.V.; Govorun, V.M.; Zdobnov, E.M. ACI-1 Beta-Lactamase Is Widespread across Human Gut Microbiomes in Negativicutes Due to Transposons Harboured by Tailed Prophages. Environ Microbiol 2018, 20, 2288–2300, doi:10.1111/1462-2920.14276. 47. Taft, D.H.; Liu, J.; Maldonado-Gomez, M.X.; Akre, S.; Huda, M.N.; Ahmad, S.M.; Stephensen, C.B.; Mills, D.A. Bifidobacterial Dominance of the Gut in Early Life and Acquisition of Antimicrobial Resistance. mSphere 2018, 3, e00441-18, doi:10.1128/mSphere.00441-18. 48. Chen, C.; Pankow, C.A.; Oh, M.; Heath, L.S.; Zhang, L.; Du, P.; Xia, K.; Pruden, A. Effect of Antibiotic Use and Composting on Antibiotic Resistance Gene Abundance and Resistome Risks of Soils Receiving Manure-Derived Amendments. Environment International 2019, 128, 233–243, doi:10.1016/j.envint.2019.04.043. 49. Sela, D.A.; Chapman, J.; Adeuya, A.; Kim, J.H.; Chen, F.; Whitehead, T.R.; Lapidus, A.; Rokhsar, D.S.; Lebrilla, C.B.; German, J.B.; et al. The Genome Sequence of Bifidobacterium Longum Subsp. Infantis Reveals Adaptations for Milk Utilization within the Infant Microbiome. Proceedings of the National Academy of Sciences 2008, 105, 18964–18969, doi:10.1073/pnas.0809584105. 50. Zaheer, R.; Noyes, N.; Ortega Polo, R.; Cook, S.R.; Marinier, E.; Van Domselaar, G.; Belk, K.E.; Morley, P.S.; McAllister, T.A. Impact of Sequencing Depth on the Characterization of the Microbiome and Resistome. Scientific Reports 2018, 8, 5890, doi:10.1038/s41598-018-24280-8. 51. Amarasinghe, S.L.; Su, S.; Dong, X.; Zappia, L.; Ritchie, M.E.; Gouil, Q. Opportunities and Challenges in Long-Read Sequencing Data Analysis. Genome Biology 2020, 21, 30, doi:10.1186/s13059-020-1935-5. 136 APPENDIX Table B.1. Differentially abundant bacterial taxa identified with four approaches: LEfSe, DESeq2 (three settings), MaAsLin2, and ANCOM-BC. Taxa with a significant p-value to at least two of the six algorithms were reported in Figure 3.5. Time point Level Taxa Group # Tests TRUE Week 1 Species [Clostridium] dakarense Control 6 Week 1 Species [Clostridium] viride Control 3 Day -1 Phylum Acidobacteria Control 4 Week 5 Phylum Acidobacteria Control 2 Week 9 Class Actinobacteria Antibiotic 4 Week 9 Phylum Actinobacteria Antibiotic 4 Day -1 Class Alphaproteobacteria Control 6 Week 9 Class Alphaproteobacteria Antibiotic 3 Week 1 Family Anaplasmataceae Control 2 Day -1 Species Aspergillus_u_s Antibiotic 3 Week 9 Species Bacillales bacterium UBA1231 Antibiotic 3 Day -1 Species Bacillales bacterium UBA2753 Control 3 Week 1 Species Bacillales bacterium UBA5578 Control 5 Week 9 Family Bacteroidaceae Antibiotic 2 Week 5 Order Bacteroidales Antibiotic 5 Week 9 Order Bacteroidales Antibiotic 2 Week 5 Family Bacteroidales_u_f Antibiotic 3 Week 9 Family Bacteroidales_u_f Antibiotic 2 Week 5 Genus Bacteroidales_u_g Antibiotic 3 Week 9 Genus Bacteroidales_u_g Antibiotic 3 Week 9 Genus Bacteroides Antibiotic 3 Day -1 Species Bacteroides barnesiae Control 2 Week 5 Species Bacteroides thetaiotaomicron Control 3 Week 5 Species Bacteroides vulgatus Control 2 Week 5 Phylum Bacteroidetes Antibiotic 5 Week 9 Phylum Bacteroidetes Antibiotic 3 Week 5 Class Bacteroidia Antibiotic 5 Week 9 Family Bifidobacteriaceae Antibiotic 2 Week 9 Order Bifidobacteriales Antibiotic 2 Week 9 Genus Bifidobacterium Antibiotic 3 Day -1 Species Bifidobacterium animalis Antibiotic 4 Day -1 Species Bifidobacterium breve Antibiotic 3 Day -1 Species Bifidobacterium choerinum Antibiotic 5 Week 1 Species Bifidobacterium pseudolongum Control 2 Week 9 Species Bifidobacterium pseudolongum Antibiotic 3 Day -1 Species Bifidobacterium thermacidophilum Antibiotic 2 Week 1 Species Blautia sp UBA2945 Antibiotic 2 Week 5 Species Butyrivibrio sp. Control 2 Week 9 Order Campylobacterales Control 2 Week 5 Phylum Candidatus Saccharibacteria Control 5 Week 5 Class Candidatus Saccharibacteria_u_c Control 5 Week 5 Family Candidatus Saccharibacteria_u_f Control 5 Week 5 Genus Candidatus Saccharibacteria_u_g Control 6 Week 5 Order Candidatus Saccharibacteria_u_o Control 5 Week 5 Species Catabacter_u_s Antibiotic 3 Week 9 Genus Cellulosilyticum Control 3 Week 9 Species Cellulosilyticum lentocellum Control 3 Week 5 Phylum Chloroflexi Control 3 Week 9 Phylum Chloroflexi Control 2 137 Table B.1 (cont’d) Time point Level Taxa Group # Tests TRUE Week 5 Species Christensenella timonensis Antibiotic 2 Week 9 Class Clostridia Control 5 Week 9 Order Clostridiales Control 2 Week 1 Species Clostridiales bacterium Antibiotic 5 Day -1 Species Clostridiales bacterium UBA2907 Control 2 Week 1 Species Clostridiales bacterium UBA3705 Control 3 Week 1 Species Clostridiales bacterium UBA4638 Control 2 Week 5 Species Clostridiales bacterium UBA643 Antibiotic 3 Week 9 Species Clostridiales bacterium UBA7081 Antibiotic 3 Week 9 Species Clostridioides mangenotii Control 4 Week 1 Genus Clostridium Control 6 Week 9 Genus Clostridium Control 3 Week 9 Species Clostridium bornimense Control 4 Week 9 Species Clostridium butyricum Control 5 Week 9 Species Clostridium sp. CL-2 Control 2 Week 5 Genus Corynebacterium Control 3 Week 1 Species Corynebacterium sputi Control 2 Week 5 Genus Cytophaga Antibiotic 2 Week 5 Species Cytophaga_u_s Antibiotic 2 Week 1 Order Cytophagales Control 3 Week 1 Class Cytophagia Control 2 Week 9 Class Deltaproteobacteria Antibiotic 3 Week 9 Family Desulfomicrobiaceae Antibiotic 4 Week 9 Genus Desulfomicrobium Antibiotic 3 Week 9 Species Desulfomicrobium_u_s Antibiotic 4 Week 1 Genus Desulfosporosinus Control 2 Week 1 Species Desulfosporosinus_u_s Control 2 Week 5 Genus Desulfovibrionaceae_u_g Control 2 Day -1 Genus Diutina Control 3 Day -1 Species Diutina catenulata Control 3 Week 5 Family Eggerthellaceae Control 3 Week 5 Species Eggerthellaceae bacterium UBA3686 Control 3 Week 5 Genus Eggerthellaceae_u_g Control 3 Week 5 Order Eggerthellales Control 3 Week 5 Genus Elizabethkingia Control 3 Week 5 Species Elizabethkingia_u_s Control 3 Day -1 Order Enterobacterales Control 3 Week 1 Species Enterobacteria phage mEp460 Control 4 Week 9 Species Enterobacteria phage SfI Control 2 Day -1 Family Enterobacteriaceae Control 3 Week 1 Species Enterococcus faecium Control 2 Week 9 Class Epsilonproteobacteria Control 2 Week 9 Family Erysipelotrichaceae Control 5 Day -1 Species Erysipelotrichaceae bacterium Control 2 Week 5 Species Erysipelotrichaceae bacterium NK3D112 Control 3 Week 9 Genus Erysipelotrichaceae_u_g Control 4 Week 9 Order Erysipelotrichales Control 5 Week 9 Class Erysipelotrichia Control 6 Day -1 Genus Escherichia Control 3 Day -1 Species Escherichia coli Control 3 Week 9 Species Escherichia virus pro483 Control 2 Week 5 Family Eubacteriaceae Control 3 Week 5 Genus Eubacterium Control 3 Week 9 Phylum Firmicutes Control 2 Week 5 Species Firmicutes bacterium UBA3738 Control 6 138 Table B.1 (cont’d) Time point Level Taxa Group # Tests TRUE Week 5 Family Flavobacteriaceae Control 2 Week 1 Class Gammaproteobacteria Control 5 Day -1 Species Gammaproteobacteria bacterium UBA2804 Control 2 Week 1 Genus Garciella Antibiotic 3 Day -1 Family Holophagaceae Control 4 Week 5 Family Holophagaceae Control 2 Day -1 Genus Holophagaceae_u_g Control 4 Week 5 Genus Holophagaceae_u_g Control 2 Day -1 Species Holophagaceae_u_s Control 4 Week 5 Species Holophagaceae_u_s Control 2 Day -1 Class Holophagae Control 3 Week 5 Class Holophagae Control 2 Day -1 Order Holophagales Control 4 Week 5 Order Holophagales Control 2 Week 9 Genus Holospora Antibiotic 3 Week 9 Species Holospora_u_s Antibiotic 4 Week 9 Family Holosporaceae Antibiotic 4 Week 9 Order Holosporales Antibiotic 4 Week 5 Genus Hungatella Control 6 Week 5 Species Hungatella hathewayi Control 6 Week 9 Species Hungatella hathewayi Control 4 Day -1 Species Kandleria sp. UBA2934 Antibiotic 2 Week 9 Genus Kocuria Control 2 Week 9 Genus Lachnobacterium Control 3 Week 5 Genus Lachnoclostridium Antibiotic 3 Day -1 Species Lachnospiraceae bacterium 10-1 Antibiotic 2 Week 5 Species Lachnospiraceae bacterium UBA2825 Control 4 Day -1 Species Lachnospiraceae bacterium UBA2860 Control 5 Week 5 Species Lachnospiraceae bacterium UBA2860 Control 6 Week 5 Species Lachnospiraceae bacterium UBA2906 Antibiotic 2 Week 5 Species Lachnospiraceae bacterium UBA3632 Control 3 Week 5 Species Lachnospiraceae bacterium UBA4348 Control 5 Week 5 Species Lachnospiraceae bacterium UBA7023 Control 3 Week 5 Order Lactobacillales Control 5 Day -1 Genus Lactobacillus Antibiotic 3 Week 5 Species Lactobacillus curvatus Control 2 Day -1 Species Lactobacillus mucosae Antibiotic 5 Day -1 Species Lactobacillus phage phiAT3 Control 2 Day -1 Species Lactobacillus sp. Antibiotic 5 Week 5 Family Leuconostocaceae Control 5 Week 5 Species Methanobacteriaceae archaeon UBA237 Control 6 Week 9 Species Methanobacteriaceae archaeon UBA237 Control 2 Week 5 Species Methanobacteriaceae archaeon UBA541 Control 4 Week 5 Genus Methanobacteriaceae_u_g Control 5 Week 5 Genus Methanosphaera Control 3 Week 5 Species Methanosphaera stadtmanae Control 3 Week 1 Family Methylobacteriaceae Control 5 Week 5 Species Microbacterium_u_s Antibiotic 3 Day -1 Genus Monascus Control 3 Day -1 Species Monascus ruber Control 4 Week 1 Genus Neorickettsia Control 2 Week 1 Species Neorickettsia_u_s Control 3 Week 1 Genus Paeniclostridium Control 5 Week 1 Species Paeniclostridium sordellii Control 5 Day -1 Species Pediococcus pentosaceus Control 2 139 Table B.1 (cont’d) Time point Level Taxa Group # Tests TRUE Week 1 Family Peptococcaceae Control 6 Week 1 Family Peptostreptococcaceae Control 6 Week 1 Species Peptostreptococcaceae bacterium VA2 Control 6 Week 9 Species Peptostreptococcaceae bacterium VA2 Control 3 Week 9 Genus Peptostreptococcaceae_u_g Control 5 Week 1 Species Peptostreptococcaceae_u_s Antibiotic 3 Week 1 Genus Phascolarctobacterium Control 3 Day -1 Genus Porphyromonas Control 2 Week 1 Species Prevotella stercorea Control 3 Week 1 Family Propionibacteriaceae Control 3 Week 1 Order Propionibacteriales Control 3 Week 1 Phylum Proteobacteria Control 5 Week 1 Genus Pseudobutyrivibrio Antibiotic 3 Day -1 Genus Punavirus Antibiotic 3 Week 1 Order Rickettsiales Control 3 Day -1 Genus Robinsoniella Antibiotic 3 Week 1 Genus Romboutsia Control 6 Week 1 Genus Ruminiclostridium Control 3 Week 9 Genus Ruminobacter Control 3 Week 9 Species Ruminobacter sp. RM87 Control 4 Week 9 Family Ruminococcaceae Control 4 Week 5 Species Ruminococcaceae bacterium UBA2851 Control 3 Week 9 Species Ruminococcaceae bacterium UBA2851 Control 4 Week 5 Species Ruminococcaceae bacterium UBA2854 Control 3 Day -1 Species Ruminococcaceae bacterium UBA642 Control 2 Week 1 Species Ruminococcaceae bacterium UBA642 Antibiotic 5 Week 9 Genus Ruminococcaceae_u_g Control 6 Week 5 Species Ruminococcaceae_u_s Control 4 Day -1 Genus Ruminococcus Control 2 Week 1 Species Ruminococcus flavefaciens Control 5 Week 1 Species Ruminococcus sp. Antibiotic 5 Day -1 Family Saccharomycetales_u_f Control 3 Week 9 Order Selenomonadales Antibiotic 2 Week 5 Genus Selenomonas Antibiotic 2 Day -1 Species Shigella virus Sf6 Control 3 Week 1 Family Siphoviridae Control 5 Week 1 Genus Siphoviridae_u_g Control 6 Day -1 Genus Solobacterium Control 2 Week 5 Genus Stomatobaculum Antibiotic 2 Week 5 Species Stomatobaculum longum Antibiotic 2 Day -1 Family Streptococcaceae Antibiotic 3 Week 5 Family Streptococcaceae Control 2 Day -1 Genus Streptococcus Antibiotic 2 Week 9 Species Succinivibrionaceae_u_s Antibiotic 6 Week 1 Phylum Tenericutes Control 5 Week 1 Class Tenericutes_u_c Control 6 Week 1 Family Tenericutes_u_f Control 6 Week 1 Genus Tenericutes_u_g Control 6 Week 1 Order Tenericutes_u_o Control 6 Week 1 Genus Terrisporobacter Control 6 Week 9 Species Terrisporobacter othiniensis Control 5 Week 1 Species Terrisporobacter_u_s Control 6 Week 9 Species Terrisporobacter_u_s Antibiotic 2 Week 9 Genus Turicibacter Control 6 Week 1 Genus Turicibacter Control 4 140 Table B.1 (cont’d) Time point Level Taxa Group # Tests TRUE Week 9 Species Turicibacter sanguinis Control 3 Week 9 Species uncultured Bacteroides sp Antibiotic 2 Week 1 Species uncultured Clostridium sp Control 3 Week 9 Species uncultured Desulfovibrio sp. Antibiotic 3 Day -1 Species uncultured Dialister sp Antibiotic 2 Week 1 Species uncultured Garciella sp Antibiotic 2 Week 1 Species uncultured Prevotella sp Control 2 Week 1 Species uncultured Prevotellaceae bacterium Control 3 Day -1 Species uncultured Robinsoniella sp Antibiotic 2 Week 5 Species uncultured Ruminococcaceae bacterium Antibiotic 2 Week 1 Species uncultured Tenericutes bacterium Control 6 Week 5 Phylum Verrucomicrobia Control 3 Week 1 Species Verrucomicrobia bacterium UBA2808 Antibiotic 5 Week 5 Species Verrucomicrobia bacterium UBA3761 Control 2 Week 1 Species Verrucomicrobia bacterium UBA3841 Control 3 Week 5 Genus Weissella Control 5 Week 5 Species Weissella cibaria Control 3 141 Table B.2. Differentially abundant ARGs identified with LEfSe, DESeq2 (three settings), MaAsLin2, and ANCOM-BC. Genes with a significant p-value to at least two of the six methods were included in the Heat-Tree of Figure 3.6. Time point Level Gene Group # Tests TRUE Week 5 Gene baeR Control 2 Week 1 Class Beta-lactam Antibiotic 5 Week 5 Class Beta-lactam Antibiotic 2 Week 9 Class Beta-lactam Antibiotic 5 Week 5 Gene blaACI 1 Antibiotic 2 Week 1 Gene blaCFXA Antibiotic 4 Week 9 Gene blaCFXA Antibiotic 5 Week 5 Gene blaCMY Antibiotic 2 Week 9 Gene blaCMY Antibiotic 5 Week 1 Gene blc Antibiotic 2 Week 5 Gene blc Antibiotic 5 Week 9 Gene blc Antibiotic 2 Week 1 Gene emrB Control 2 Week 5 Gene emrB Antibiotic 2 Week 9 Gene emrK Control 2 Week 9 Gene emrR Control 5 Week 9 Gene H NS Control 2 Week 1 Class Macrolide Control 3 Week 5 Class Macrolide Control 3 Day -1 Gene Macrolide 2021 Branch Antibiotic 3 Week 9 Gene Macrolide 2021 Branch Control 2 Week 1 Gene mdtL Control 5 Week 1 Gene mefA Control 2 Week 9 Gene phoP Control 2 Day -1 Gene tet32 Antibiotic 2 Week 9 Gene tet32 Control 2 Week 1 Gene tetBP Control 2 Day -1 Gene tetO Antibiotic 2 Week 1 Gene tetQ Control 2 142 Table B.3. Taxonomic classification of contigs carrying β-lactam resistant genes analyzed with the Contig Annotation Tool (CAT). Taxa Count of ACCs Firmicutes 20 Clostridia 8 Clostridiales 8 Ruminococcaceae 4 Ruminococcaceae bacterium 4 Bacteroidetes 27 Bacteroidia 27 Bacteroidales 27 Bacteroidales bacterium 3 Bacteroidaceae 1 Bacteroides 1 Bacteroides sp. 1 Muribaculaceae 16 Proteobacteria 3 Gammaproteobacteria 3 Aeromonadales 3 Succinivibrionaceae 1 uncultured prokaryote 1 143 Figure B.1. Percentage of resistant bacteria to ceftiofur (A) Gram-negative and (B) Gram- positive; and resistant to ampicillin (C) Gram-negative and (D) Gram-positive. The percentages were calculated based on the total number of CFU/g of feces. 144 Day -1 Control Saccharibacillus Intramammary Ceftiofur Escherichia phage slur01 blc pfoA Methanosarcina Halocynthiibacter blc 3334641 Collinsella Salmonella virus SP31 Thermoanaerobacterales_u_ggadX Alkaliphilus Methylobacterium transposon 46242811 pmrE Salmonella virus PsP3 Laceyella int Gordonia mdtF Staphylococcus Propionibacteriaceae_u_g Enterobacteria phage VT2phi_272 mdtP mdtE Caloramator Firmicutes_u_g pbp4 colA Nitrospiraceae_u_g Atopobium Candidatus Paracaedibacteraceae_u_g Escherichia virus Seurat Holophagaceae_u_g Sporolactobacillus mdtG Myxococcaceae_u_g Enterobacteria phage mEp235 Gordonia Olsenella tolC mdtC Kocuria Leclercia Escherichia virus HK022 Escherichia virus mEpX1 Opitutales_u_g Enterobacteria phage P4 Aminobacter Escherichia virus CC31 Acidipropionibacterium Hungateiclostridiaceae_u_g Escherichia virus Cajan yddA Treponema Shigella phage SfII Cronobacter phage vB_CsaP_GAP52 Plasmid 38606113 Flavobacteriales_u_g Cytophaga tetL cpxA Desulfobulbus acrD Enterobacteria phage Sf101 bla 1030 Branch Ethanoligenens Candidatus Phytoplasma Bacillus Selenomonadaceae_u_g ant6 Ib Karamvirus_u_s Escherichia virus 186 Rickettsiales_u_g Mycobacterium phage Enkosi Cutibacterium Salmonella virus HK620 transposon 46242811 Microbacterium tetL-Ef Punavirus_u_s Megasphaera Thalassospira mdtH Staphylococcus Nitrospiraceae_u_g marA mdtD traA Lactobacillus phage phiAT3 Slackia Deinococcus-Thermus_u_g Salmonella virus FSLSP004 ybtP Escherichia virus HK544 Micromonospora blaCMY 27 Anaeromyxobacter Transposon 807609 rteC repB mdtA Saccharopolyspora transposon 46242809 Enterobacteria phage Sf101 Helcococcus Kocuria Atopobium Selenomonadaceae_u_g Escherichia virus HK106 Collinsella Bacteroidales_u_g Sphaerobacteraceae_u_g Holospora baeS ompA Rikenellaceae_u_g transposon 46242809 Salmonella phage g341c Corynebacterium Escherichia phage TL-2011b Sporolactobacillus pmrF Cetobacterium Enterobacteria phage SfV emrB Microbacterium cpxR Clavibacter Olsenella Anaeromyxobacter Streptococcus phage 20617 mdtE Opitutales_u_g emrY cfxA 2366 evgA Kandleria pmrB Succinatimonas emrE mdtO Desulfotomaculum pmrF pbp2 evgA rteC Gallibacterium Thermobifida Escherichia virus FFH2 Escherichia phage TL-2011b Desulfomicrobium Hippea Megamonas blc Paeniclostridium acrE aadE MobA 32395281 Helcococcus Gemella astA Seunavirus_u_s Microvirga Enterobacteria phage cdtI Peptococcaceae_u_g Stx2-converting phage 86 Peptostreptococcaceae_u_g Thermaerobacter blaCMY 111 Enterobacteria phage mEp460 Imtechella Enterobacteria phage cdtI Escherichia virus HK97 Escherichia virus fiAA91ss Vequintavirus_u_s Enterobacteria phage IME10 pmrA Campylobacter sugE Salmonella phage RE-2010 Ferrimonas traF Salmonella phoP Escherichia phage slur01 Xylella Shigella phage SfII emrK Glycomyces Escherichia bacA Escherichia virus CC31 Catabacter Hungateiclostridiaceae_u_g Parvimonas Stx2-converting phage 1717 Megamonas Salmonella phage vB_SemP_Emek Myxococcaceae_u_g Micrococcaceae_u_g emrA Escherichia virus 24B Geobacillus pmrB phoP marA Escherichia Enterobacteria phage mEp237 88 Branch cpxA Pseudoxanthomonas tnp Enterobacteria phage SfI mdtC Escherichia virus If1 Punavirus_u_s Shewanella Acidipropionibacterium Pseudobutyrivibrio phoQ Ureibacillus Enterobacteria phage P4 Cronobacter phage vB_CsaP_GAP52 Escherichia virus Min27 Ureaplasma ermC Thermoactinomyces Caloramator Holospora H NS Tyzzerella Pelosinus Pseudoxanthomonas blaACI 1 Escherichia virus Seurat Shigella virus SfMu Thermaerobacter pbp4 Porphyromonas cfxA3 mdtA Pseudonocardia Rikenellaceae_u_g Brachyspira tetBP Shewanella lpfA Bacteroidales_u_g mdtL Shigella Brachyspira Cetobacterium emrK ompA Escherichia virus Cajan Escherichia virus If1 pmrC Shigella phage SfIV yddA Erysipelotrichaceae_u_g 2024 Branch Roseburia Microvirga Enterobacteria phage mEp460 Succinivibrio Pseudobutyrivibrio Enterobacteria phageEscherichia HK140 virus HK633 tetQ Ochrobactrum blaAmpC mdtG Pelosinus Intestinibacter Lactococcus phage Tuc2009 Porphyromonas Veillonellaceae_u_g Salmonella phage RE-2010 Enterobacterial phage mEp213 Campylobacter Shuttleworthia Shigella emrY Actinobacteria_u_g cfxA 2367 Yersinia Peptostreptococcaceae_u_g mdtP Saccharomonospora Succinatimonas Escherichia phage D108 Coprococcus gadX Lachnobacterium Shigella virus Sf6 Salmonella virus SPN3US Plasmid 38606113 Turicibacter Shigella virus SfMu acrE cfxA 2366 Thermobispora sugE Candidatus Phytoplasma Escherichia virus P2 Stx2-converting phage 1717 cfxA 32395282 baeR mdtH Escherichia virus pro483 Escherichia virus mEpX1 acrD cmy-13 Halorubrum Escherichia virus P2 Escherichia virus Wphi Escherichia virus HK629 phoQ Desulfotomaculum blaAmpH cpxR mdtN emrB Shigella phage SfIV Escherichia virus pro147 Enterobacteria phage BP-4795 Escherichia virus phiV10 acrF Escherichia virus K1E acrS Erysipelotrichaceae_u_g int H NS Escherichia virus fiAA91ss Enterobacteria phage SfI cfxA 33413459 Enterobacteria phage SfV baeR Kandleria Firmicutes_u_g ant6 Ib Clostridiales Family XIII. Incertae Sedis_u_g Holophagaceae_u_g mdtK bacA tetA Escherichia virus phiV10 Actinobacteria_u_g Dorea Enterobacteria phage BP-4795 Mogibacterium Lactobacillus phage phiAT3 Enterobacteria phage mEp235 cfxA 33413459 Stx2-converting phage 86 Alphaproteobacteria_u_g Ureaplasma Thermoanaerobacterales_u_g Escherichia virus HK97 Shuttleworthia pbp2 Succiniclasticum tolC evgS Lactobacillus phage Escherichia Lrm1 virus mEpX2 Parvimonas pmrC mdtL mdtN MobA 32395281 Peptostreptococcus Salmonella virus FSLSP004 Taylorella Phyllobacteriaceae_u_g Lactobacillus phage LfeSau Treponema Plasmid 4775492 Turicibacter Mogibacterium arnA Escherichia phage D108 Desulfomicrobium mdtB crp Succinivibrio mdtK Salmonella phage SEN1 Anaerotruncus traE Slackia Enterobacteria phage IME10 mdtM Escherichia virus pro483 mdtD Catabacter Escherichia virus Wphi Enterobacteria phage vB_EcoP_ACG-C91 Seunavirus_u_s mdfA Halorubrum mdtO mdtF cfxA 2367 arnA Salmonella virusEnterobacteria SJ46 phage YYZ-2008 a 2394 Branch Salmonella virus SJ46 bla 1029 Branch lacY Runella Ochrobactrum Salmonella phage BP12B pmrA mdtB crp tetBP Bacillus Lachnobacterium Dorea Shigella virus Sf6 Veillonellaceae_u_g Escherichia virus K1-5 Butyricicoccus blaACI 1 cfxA2 acrF Tyzzerella Megasphaera emrA Anaerococcus Enterobacteria phage YYZ-2008 Acidithiobacillus tnp Intestinibacter Thermoactinomyces Roseburia Coprococcus Lactobacillus phage iA2 Rom traA Lachnospira Clostridiales Family XIII. Incertae Sedis_u_g blaCMY 32 Sphingobacteriaceae_u_g β-lactam resistance gene Corynebacterium Saccharomonospora Escherichia virus pro147 cfxA6 cfxA 32395282 Thermobifida Butyricicoccus exc1 Ethanoligenens Peptostreptococcus Lachnoclostridium Lachnospira Ureibacillus ARG, MGEs, Species fyuA Enterobacteria phage mEp043 c-1 Escherichia virus HK106 Lachnoclostridium Micrococcaceae_u_g mdtM Saccharopolyspora Paeniclostridium Mycobacterium phage Enkosi Enterobacteria phage HK140 Transposon 807609 Cardiobacterium Week 1 Control Intramammary Ceftiofur Atopobium Coprococcus baeS Peptostreptococcaceae_u_g Collinsella Megamonas Enterobacteria phage HK140 Desulfomicrobium Micrococcaceae_u_g Thermoanaerobacterales_u_g sugE Ochrobactrum Tyzzerella cfxA 2367 Lachnobacterium Roseburia pmrF Stx2-converting phage 1717 Enterobacteria phage YYZ-2008 Dorea Ferrimonas astA Sporolactobacillus Escherichia phage D108 Stx2-converting phage 86 Microvirga cpxA baeR cfxA 32395282 Lactococcus phage Tuc2009 Shigella phage SfII Thermaerobacter Anaerococcus Escherichia phage D108 Caloramator Salmonella virus SPN3US Holospora Saccharomonospora pbp2 Bacillus Salmonella virus SJ46 blc Erysipelotrichaceae_u_g Escherichia virus P2 Pseudoxanthomonas Saccharibacillus cfxA 2366 Escherichia virus fiAA91ss cfxA6 cpxR Enterobacteria phage SfV Thalassospira Slackia Escherichia virus pro147 MobA 32395281 Escherichia virus phiV10 Microbacterium Myxococcaceae_u_g Salmonella phage vB_SemP_Emek Porphyromonas lacY Imtechella Butyricicoccus Halorubrum Hippea Escherichia virus mEpX2 Collinsella Cetobacterium 2024 Branch Anaeromyxobacter Clostridiales Family XIII. Incertae Sedis_u_g cfxA 33413459 Intestinibacter Escherichia virus HK633 Enterobacteria phage P4 acrE Enterobacteria phage mEp460 Actinobacteria_u_g Porphyromonas acrD Pelosinus Desulfomicrobium Helcococcus ermC Thermobifida Salmonella virus HK620 mdtE Anaeromyxobacter bacA blc 3334641 Peptococcaceae_u_g Mogibacterium Escherichia virus fiAA91ss Pseudobutyrivibrio Ureibacillus rteC Shigella bla 1030 Branch Enterobacteria phage mEp043 c-1 Ureaplasma Saccharopolyspora Escherichia virus Wphi emrA Shigella phage SfIV Methanosarcina Peptostreptococcaceae_u_g Geobacillus Turicibacter emrB Enterobacteria phage mEp460 Thermobispora Enterobacteria phage IME10 Paeniclostridium Shuttleworthia Holophagaceae_u_g cfxA 2366 transposon 46242809 Thermoactinomyces mdtP Enterobacteria phage SfI mdtD Escherichia Gordonia Treponema Corynebacterium Parvimonas ybtP Propionibacteriaceae_u_g Halocynthiibacter acrS Seunavirus_u_s Halorubrum Escherichia virus P2 Megasphaera Enterobacteria phage SfV Shigella phage SfIV emrY int Methanosarcina mdtO Cardiobacterium mdfA Micromonospora Microbacterium Desulfobulbus Deinococcus-Thermus_u_g Salmonella virus SJ46 baeR Opitutales_u_g Caloramator Corynebacterium Campylobacter Enterobacteria phage SfI Salmonella virus FSLSP004 crp Lachnospira Salmonella phage RE-2010 Shigella H NS Cutibacterium Escherichia virus Wphi Candidatus Phytoplasma Micrococcaceae_u_g Lactobacillus phage phiAT3 acrE Erysipelotrichaceae_u_g Escherichia phage TL-2011b Escherichia virus pro147 Shigella phage SfII Escherichia virus phiV10 Lactobacillus phage phiAT3 Escherichia phage TL-2011b Halocynthiibacter Alphaproteobacteria_u_g pmrA Shigella virus Sf6 bacA lacY Slackia Cetobacterium Butyricicoccus emrK Escherichia virus HK629 Mycobacterium phage Enkosi transposon 46242811 Lachnobacterium Kandleria mdtB mdtF repB emrK Lachnoclostridium Olsenella cmy-13 Anaerotruncus mdtK marA mdtP phoQ Escherichia virus HK97 Salmonella phage vB_SemP_Emek Saccharomonospora Sporolactobacillus baeS Escherichia virus pro483 mdtD Plasmid 38606113 blc 3334641 Roseburia pmrE pmrC Acidipropionibacterium tolC emrA int Treponema Catabacter Methylobacterium Rikenellaceae_u_g bla 1030 Branch emrE tetBP Bacillus Alphaproteobacteria_u_g tetQ pmrC mdtE Shigella virus Sf6 tetQ pbp2 blc mdtH MobA 32395281 fyuA sugE Parvimonas bla 1029 Branch Ureibacillus Shewanella mdtL Enterobacteria phage cdtI cfxA3 Enterobacteria phage Sf101 Succiniclasticum Methylobacterium blaACI 1 Escherichia virus If1 Firmicutes_u_g Shigella virus SfMu cpxA Imtechella Succinatimonas Lachnospira cfxA6 Bacteroidales_u_g Stx2-converting phage 1717 Alkaliphilus Cytophaga Catabacter blaCMY 27 Vequintavirus_u_s Peptostreptococcus mdtN Candidatus Paracaedibacteraceae_u_g Ureaplasma marA Enterobacteria phage BP-4795 mdtA Holospora Salmonella phage RE-2010 Enterobacteria phage P4 Mogibacterium Pseudoxanthomonas Escherichia virus HK022 Thermobispora Firmicutes_u_g mdfA Salmonella virus FSLSP004 Clostridiales Family XIII. Incertae Sedis_u_g Thermobifida Enterobacteria phage cdtI tetL-Ef cfxA 33413459 Megasphaera bla 1029 Branch Helcococcus Veillonellaceae_u_g tetBP Cytophaga cpxR Gordonia Succinatimonas mdtC emrY evgA Runella pmrA tetL mdtG Bacteroidales_u_g Phyllobacteriaceae_u_g Kocuria Escherichia Glycomyces Ochrobactrum Desulfotomaculum tnp Escherichia virus pro483 Enterobacteria phage YYZ-2008 transposon 46242811 gadX cfxA 32395282 Clavibacter Enterobacteria phage IME10 Turicibacter Staphylococcus Megamonas tetL-Ef Saccharopolyspora Rikenellaceae_u_g aadE repB Pelosinus crp Thermoanaerobacterales_u_g a 2394 Branch Escherichia virus HK633 Lachnoclostridium Coprococcus Candidatus Phytoplasma Micromonospora Escherichia virus HK97 Kocuria Pseudobutyrivibrio Shigella virus SfMu Seunavirus_u_s Peptostreptococcus blaCMY 32 Alkaliphilus cfxA 2367 Flavobacteriales_u_g Escherichia virus mEpX1 blaCMY 111 Thermoactinomyces Olsenella Enterobacteria phage BP-4795 Succinivibrio Tyzzerella Hippea Atopobium Succinivibrio ant6 Ib Paeniclostridium Holophagaceae_u_g β-lactam resistance gene Brachyspira Geobacillus Veillonellaceae_u_g ARG, MGEs, Species Figure B.2. Correlation networks between β-lactam resistant genes, MGEs, virulence genes, and bacteria genera shown by each sampling point and treatment group. Direct connections between a feature and a β-lactam resistance gene are highlighted in pink, while other correlations are shown in grey. Only significant correlations (P < 0.01) with a coefficient >0.75 were included in the network. The size of the edges represents the strength of the of correlation. The size of the nodes corresponds to the degree of centrality. 145 Figure B.2 (cont’d) Week 5 Control Intramammary Ceftiofur Acidithiobacillus Bacteroidales_u_g Actinobacteria_u_g Brachyspira Lachnospira cfxA 2367 cfxA 32395282 int Lactobacillus phage iA2 Alkaliphilus Pseudobutyrivibrio Erysipelotrichaceae_u_g cfxA 33413459 sugE Corynebacterium Plasmid 38606113 Turicibacter tetBP Rickettsiales_u_g MobA 32395281 mdtE Ureaplasma Desulfobulbus cfxA6 Escherichia virus pro147 Shigella virus SfMu Yersinia cfxA6 Nitrospiraceae_u_g Enterobacteria phage BP-4795 Parvimonas Micrococcaceae_u_g Cytophaga blaCMY 27 Salmonella phage RE-2010 Ureibacillus Stx2-converting phage 1717 Enterobacteria phage SfV Salmonella virus SJ46 int Imtechella Escherichia virus HK544 Thermobispora cmy-13 pmrB Acidipropionibacterium Saccharopolyspora baeS Bacillus mdtA emrK Enterobacteria phage HK140 Desulfotomaculum Gordonia tetQ Thermoactinomyces transposon 46242809 tetL-Ef emrA tetBP Enterobacteria phage YYZ-2008 Megasphaera Methylobacterium Peptococcaceae_u_g Microbacterium Holophagaceae_u_g Megamonas Collinsella Clavibacter Escherichia virus Wphi Enterobacteria phage P4 Myxococcaceae_u_g Imtechella Kocuria Geobacillus Firmicutes_u_g Enterobacteria phage SfI tetQ Thermobifida Paeniclostridium mdtL Treponema Pelosinus Deinococcus-Thermus_u_g Salmonella phage vB_SemP_Emek Enterobacteria phage mEp460 Escherichia virus Wphi Micromonospora Succinivibrio Brachyspira bacA ant6 Ib Halocynthiibacter Thermoanaerobacterales_u_g Atopobium blaACI 1 baeR Pseudobutyrivibrio Ureibacillus Acidithiobacillus Saccharomonospora Punavirus_u_s Thermobispora Cetobacterium Lachnoclostridium Escherichia phage D108 Holospora Methylobacterium Caloramator Clostridiales Family XIII. Incertae Sedis_u_g Porphyromonas Pelosinus Thermaerobacter blaACI 1 Escherichia virus fiAA91ss Catabacter crp Halorubrum Roseburia Clostridiales Family XIII. Incertae Sedis_u_g Lachnobacterium Bacillus Porphyromonas Methanosarcina Shigella Thermobifida Tyzzerella Propionibacteriaceae_u_g Escherichia virus pro483 cfxA 32395282 cfxA 2367 Cutibacterium marA Cetobacterium a 2394 Branch MobA 32395281 Mogibacterium Escherichia virus phiV10 ompA Micromonospora Anaerotruncus Staphylococcus Butyricicoccus Caloramator repB Tyzzerella Helcococcus traF Bacteroidales_u_g Enterobacteria phage SfI Staphylococcus Seunavirus_u_s acrE Shigella phage SfII Microvirga Acidipropionibacterium Turicibacter tetL-Ef Cytophaga Pseudoxanthomonas Plasmid 38606113 Escherichia virus pro147 Cardiobacterium Collinsella Microvirga Laceyella transposon 46242811 Helcococcus mdtP Parvimonas Micrococcaceae_u_g Kocuria Escherichia phage TL-2011b cfxA 2366 Escherichia virus P2 Microbacterium Shuttleworthia Thermoanaerobacterales_u_g Anaerococcus cfxA 33413459 Slackia cpxA Escherichia Alphaproteobacteria_u_g Sphaerobacteraceae_u_g Escherichia virus If1 mdtH Enterobacteria phage P4 blaAmpC Deinococcus-Thermus_u_g tnp Peptostreptococcus pmrF Aminobacter crp Salmonella virus FSLSP004 Desulfobulbus Megamonas Peptococcaceae_u_g Ureaplasma Paeniclostridium Salmonella virus SP31 Sporolactobacillus tolC Peptostreptococcus Saccharopolyspora Coprococcus Escherichia Succinatimonas mdtO mdtF Holospora Thermoactinomyces Kandleria pbp4 Salmonella virus HK620 traA Desulfomicrobium Enterobacteria phage Sf101 Ochrobactrum Megasphaera Olsenella Salmonella virus HK620 Mogibacterium Gordonia Salmonella phage RE-2010 baeS Pseudoxanthomonas Geobacillus emrB Enterobacteria phage SfV Escherichia virus P2 Butyricicoccus Saccharomonospora Escherichia virus fiAA91ss Enterobacteria phage cdtI sugE Lachnoclostridium Stx2-converting phage 1717 Anaeromyxobacter Saccharibacillus Firmicutes_u_g Atopobium astA Transposon 807609 Corynebacterium mdtP Enterobacteria phage IME10 blc Shigella virus Sf6 Intestinibacter Anaeromyxobacter Shigella virus Sf6 Rickettsiales_u_g Succinivibrio pmrB Enterobacteria phage YYZ-2008 Lachnospira Enterobacteria phage BP-4795 pbp2 evgA Actinobacteria_u_g Gemella Slackia Candidatus Phytoplasma Shigella cfxA 2366 Shigella phage SfIV Alkaliphilus Enterobacteria phage cdtI Olsenella Methanosarcina Shigella phage SfIV rteC pmrA marA blc 3334641 transposon 46242809 Escherichia phage TL-2011b Dorea Escherichia virus pro483 Alphaproteobacteria_u_g Erysipelotrichaceae_u_g Salmonella virus SJ46 Desulfomicrobium tolC Enterobacteria phage mEp460 mdtG Pseudonocardia β-lactam resistance gene Stx2-converting phage 86 Roseburia Lactobacillus phage phiAT3 Catabacter Enterobacteria phage HK140 cpxA Thalassospira Treponema Enterobacteria phage Sf101 ARG, MGEs, Species Week 9 Control cfxA 2366 Intramammary Ceftiofur Lactobacillus phage phiAT3 Dorea Cytophaga Saccharomonospora Halorubrum Acidipropionibacterium Thermoanaerobacterales_u_g transposon 46242809 Bacillus bla 1030 Branch Helcococcus Deinococcus-Thermus_u_g Ochrobactrum Alphaproteobacteria_u_g Gordonia emrY Kandleria Kocuria Halocynthiibacter transposon 46242811 Rickettsiales_u_g Thermoanaerobacterales_u_g Desulfotomaculum Veillonellaceae_u_g Porphyromonas traF emrE Selenomonadaceae_u_g sugE Microbacterium Anaerococcus Saccharopolyspora Olsenella exc1 Hungateiclostridiaceae_u_g Pelosinus evgS blc Atopobium Thermoactinomyces tetL-Ef Micromonospora Cutibacterium Corynebacterium ant6 Ib acrE Turicibacter Peptostreptococcaceae_u_g int traF Roseburia Succinivibrio Lactobacillus phage phiAT3 Pseudobutyrivibrio Turicibacter tetBP Tyzzerella Thermobifida Succinatimonas Acidithiobacillus Intestinibacter Treponema bla 1029 Branch Halorubrum Intestinibacter Ureaplasma Clostridiales Family XIII. Incertae Sedis_u_g Clostridiales Family XIII. Incertae Sedis_u_g Cytophaga Kandleria Roseburia cmy-13 Saccharomonospora Coprococcus Peptostreptococcus Erysipelotrichaceae_u_g Veillonellaceae_u_g Succiniclasticum Thalassospira Parvimonas Cetobacterium Kocuria mdfA Megamonas Cardiobacterium Enterobacteria phage mEp237 Enterobacteria phage P4 mdtL Candidatus Paracaedibacteraceae_u_g Lachnobacterium Bacteroidales_u_g int Catabacter Escherichia phage TL-2011b marA Peptostreptococcaceae_u_g Megasphaera tnp Enterobacteria phage cdtI Treponema Butyricicoccus Escherichia cpxA evgS Paeniclostridium transposon 46242811 Salmonella phage RE-2010 Erysipelotrichaceae_u_g Escherichia phage D108 emrK mdtP mdtD Atopobium Pelosinus Pseudobutyrivibrio Lachnobacterium acrD baeS Lachnospira Desulfomicrobium Megasphaera crp blaAmpH Shigella virus Sf6 Enterobacteria phage SfI Corynebacterium pmrC Stx2-converting phage 1717 astA Escherichia virus Wphi Escherichia virus pro483 Enterobacteria phage IME10 tetBP cfxA 2366 pmrF Shewanella Methylobacterium Escherichia virus fiAA91ss phoP mdtB Enterobacteria phage SfV Nitrospiraceae_u_g Escherichia virus phiV10 Escherichia virus HK633 evgA Escherichia virus P2 traA Lachnoclostridium Peptococcaceae_u_g Coprococcus cpxR pmrF acrF bacA mdtL ant6 Ib Escherichia virus pro147 Tyzzerella Plasmid 38606113Enterobacteria phage VT2phi_272 Escherichia virus Seurat Catabacter mdtD bacA pfoA Shigella virus SfMu mdtM pmrB Enterobacteria phage mEp043 c-1 emrA Escherichia virus Cajan H NS traA Escherichia virus mEpX1 Olsenella Enterobacteria phage BP-4795 Punavirus_u_s Escherichia virus 24B Saccharopolyspora Imtechella gadX Myxococcaceae_u_g Paeniclostridium Salmonella virus SJ46 Enterobacteria phage mEp460 mdtO pbp4 Escherichia virus P2 acrE Shigella phage SfIVmdtE pmrC Caloramator emrA Salmonella virus SJ46 crp Plasmid 38606113 Peptostreptococcus Enterobacteria phage HK140 Paracaedibacteraceae_u_g Salmonella phage RE-2010 Salmonella phage g341c Salmonella phage vB_SemP_Emek Salmonella virus PsP3 Candidatus Enterobacteria phage Sf101 Enterobacteria phage SfV pbp2 Enterobacteria phage Sf101 mdtA Stx2-converting phage 1717 Escherichia phage D108 Candidatus Phytoplasma Succinivibrio Slackia Megamonas acrS lacY Enterobacterial phage mEp213 Escherichia virus mEpX2 Escherichia virus HK022 Enterobacteria phage cdtI Enterobacteria phage mEp460 pbp4 mdtO phoQ yddA Escherichia virus pro147 mdtG Enterobacteria phage IME10 Shigella virus Sf6 mdtK Enterobacteria phage P4 ybtP Escherichia virus 186 Lachnospira baeR pmrA Escherichia virus HK106 Enterobacteria phage SfI Salmonella virus HK620 Firmicutes_u_g mdtE baeS pbp2 Escherichia virus HK544 Hungateiclostridiaceae_u_g Pseudoxanthomonas emrB Enterobacteria phage mEp235 Salmonella phage SEN1 acrF Escherichia virus Wphi colA phoQ blaAmpC Shigella phage SfII Collinsella mdtK Myxococcaceae_u_g mdtH Enterobacteria phage YYZ-2008 ompA Escherichia phage slur01 tolC baeR Bacteroidales_u_g mdtN Enterobacteria phage YYZ-2008 mdtF Micromonospora evgA Sphingobacteriaceae_u_g cfxA6 Bacillus gadX Salmonella virus HK620 Escherichia virus HK544 arnA mdtF Escherichia phage TL-2011b cpxA Succinatimonas mdtC Lachnoclostridium Escherichia virus HK97 mdtH Collinsella Escherichia Escherichia virus phiV10 Enterobacteria phage BP-4795 Shigella Leclercia Escherichia virus If1 mdtB Escherichia virus Seurat Salmonella virus FSLSP004 rteC Thermobifida Escherichia virus Min27 lpfA ompA Pseudoxanthomonas acrD Shigella Escherichia virus FFH2 Microbacterium Gordonia Stx2-converting phage 86 tetQ Escherichia virus fiAA91ss Campylobacter Imtechella cpxR emrY tetL emrB transposon 46242809 Gallibacterium Shigella phage SfIV Thermoactinomyces tetQ MobA 32395281 Yersinia Escherichia virus pro483 Butyricicoccus Shigella virus SfMu mdtP Holophagaceae_u_g blaCMY 27 Escherichia virus HK022 2024 Branch Helcococcus blaACI 1 Porphyromonas Cetobacterium Methylobacterium Rickettsiales_u_g MobA 32395281 Alphaproteobacteria_u_g Microvirga cfxA 32395282 Shigella phage SfII β-lactam resistance gene cfxA 2367 Micrococcaceae_u_g Halocynthiibacter Firmicutes_u_g Holophagaceae_u_g cfxA 32395282 blc 3334641 cfxA 33413459 Punavirus_u_s Dorea cfxA 2367 cfxA 33413459 blaACI 1 pmrA Ureaplasma Caloramator Salmonella virus SPN3US ARG, MGEs, Species Saccharibacillus Peptococcaceae_u_g Micrococcaceae_u_g 146 CHAPTER 4 Untargeted metabolomics and microbial-functional profiling of the cattle hindgut associated with antibiotic dry-cow therapy and lactation phase. 147 ABSTRACT The gut microbiota in cattle is critical to produce protein, energy, and vitamins. As a result, changes in the microbiota induced by diet, physiology and antibiotic application can affect cattle performance. In this study, the effects of intramammary (IMM) ceftiofur on the functional microbiome and metabolome of feces were evaluated. A total of 40 dairy cows were enrolled; half of them were treated with IMM ceftiofur and a non-antibiotic teat sealant containing bismuth subnitrate (n = 20) and controls were treated only with the non-antibiotic teat sealant (n = 20). Fecal samples were taken a day prior to the IMM treatment, and in weeks 1, 5, and 9 post- treatment, which corresponded to three phases: late lactation, dry-off and fresh cows. Metagenomic sequences were used to predict microbial metabolic pathways with HUMAnN 3.0. Additionally, untargeted metabolomics was carried out to identify polar and nonpolar positive metabolites in feces. Long-term changes in the functional microbiome were observed in cows treated with IMM ceftiofur that included lower production of energy and higher activity of Bifidobacterium. The metabolome, however, was identical between treatment groups, though each lactation phase had a different microbial profile and metabolite composition. Cows in late lactation exhibited the highest number of metabolic pathways and the most distinct metabolome associated with a diet rich in metabolizable energy and protein. Multi-omics analyses showed higher levels of histamine-producing bacteria in the lactation phase. Furthermore, positive correlations were identified between natural antimicrobial compounds, pesticides, bacteriophages from enterobacteria, and the gene encoding the extended-spectrum β-lactamase CFXA2. These data demonstrate that IMM ceftiofur does not alter the metabolome of the gut, though slight differences were observed in microbiome functionality. Indeed, different lactation phases, which are associated with unique diets and physiology, exhibited important differences in the 148 metabolome and functionality of resident microbial communities. Importantly, the presence of bacterial stressors such as natural antimicrobial compounds and pesticides, can induce a higher activity of enterobacteria and the mobilization of resistance genes. INTRODUCTION Ruminant-gut microbiota produces proteins, vitamins, and over 70% of the energy necessary for their host through an obligatory symbiotic relationship. Rumen microorganisms ferment the plant biomass to generate energy in form of volatile fatty acids (VFAs) [1] and to convert nitrogen-containing compounds into protein [2]. Thus, multiple studies have sought to analyze the cattle microbiome composition to determine its relationship with animal production and methane emissions [3]. By contrast, the effects of antibiotics on the cattle fecal microbiome have been poorly studied [4, 5] and hence, little is known about the impact of antibiotics on the functional gut microbiome and host performance [6]. One of the most common classes of antibiotics used in dairy farms are β-lactams, which are applied to treat foot rot, metritis, mastitis, and respiratory infections [7]. These antibiotics have been shown to be excreted by the biliary system and gut where their active metabolites can alter the microbiota composition [8]. Ceftiofur, a third-generation cephalosporin used for intramammary (IMM) dry-cow therapy at the end of lactation to treat subclinical mastitis [9], can alter the gut microbiota of dairy cows due to activity against Gram-negative and Gram-positive bacteria. Indeed, our previous study showed that IMM ceftiofur treatment altered the abundance of some taxa in the short and long-term within the hindgut despite having no impact on microbiome diversity (Chapter 3). Specifically, we observed a higher abundance of Actinobacteria and Bacteroidetes and lower abundance of Proteobacteria and Firmicutes in ceftiofur-treated versus untreated cattle over a 9-week period. Understanding the function of 149 these taxa in the gut microbiota can ultimately help identify the effects of antibiotic therapy on cattle performance. Metagenomics enables the prediction of microbial metabolic capacity based on the detection of genes encoding enzymes and mapping them into metabolic pathways [10]. The characterization of metabolites from host, dietary, and microbiome sources can also provide a better understanding of the functional interactions between the microbiome and environment. Untargeted metabolomics, for instance, is a tool based on liquid chromatography–mass spectrometry (LC–MS) that can simultaneously detect multiple compounds based on their retention time and tandem mass spectrometry fragmentation (MS/MS). Metabolomics of the rumen content from dairy cows has improved the understanding of diet-related metabolism, particularly influenced by the introduction of grain into the diet [11] and helped identify differences between fecal and rumen metabolites [12]. Furthermore, integrated approaches comparing metagenomics, metatranscriptomics, and metabolomics data have been used to analyze the functional microbiome in rumen and its association with feed efficiency [13]. In this study we sought to explore the effects of IMM ceftiofur on the function of the microbiome and metabolome in the hindgut of dairy cows. To identify short- and long-term changes due to antibiotic therapy, samples were taken a day prior to dry-off and in weeks one and nine after the treatment, which were selected based on data generated in our metagenomic analyses (Chapter 3). These three time points represent different phases during lactation including late lactation (day -1), dry-off (week 1), and fresh (week 9). Comparative analyses were carried out between the three stages, which also differ based on diet and physiology of the animals. Indeed, cows in late lactation need a maintenance diet containing high levels of metabolizable protein and energy. During the dry period, however, cows do not produce milk, 150 which allows the mammary gland and udder tissue to rest and renew before the next lactation. The dry period lasts approximately 60 days prior to calf birth or calving. As opposed to the lactation phase, cows require lower quantities of dry-matter intake and energy in their diet than during the dry-off period. Comparatively, the fresh phase lasts approximately 30 days post- calving and represents the start of the lactation period. Higher levels of energy, calcium, and metabolizable protein are needed in fresh cows as compared to dry cows to compensate for the energy imbalance induced by milk production and low dry-matter intake [14]. Here we applied multi-omics approaches to identify interactions between the microbiome and metabolites present in fecal samples from ceftiofur-treated and untreated dairy cows. Our goal was to understand the potential effects of antibiotics and diet-derived metabolites on the host and its microbiota. The use of metagenomics and untargeted metabolomics enabled the identification of functional microbiome profiles that varied between lactation phases as well as unique connections between metabolites and microbial taxa. Collectively, these findings highlight the potential impact of antibiotics on cattle health and the dispersion of resistant enterobacteria. MATERIALS AND METHODS Animals and treatments Forty Holstein cows were enrolled to analyze the impact of the IMM cephalosporine application on the fecal metabolome and functional microbiome. Half of the cows (n =20) received 500 mg of IMM ceftiofur hydrochloride (CHCL) (SpectramastDC®; Zoetis Animal Health) in each udder quarter at the start of the dry-off period; the remaining 20 cows (controls) did not receive the CHCL IMM treatment. Both the ceftiofur-treated and control cows received a non-antibiotic internal IMM teat sealant containing bismuth subnitrate (Orbeseal®; Zoetis 151 Animal Health) to prevent mastitis. None of the cows received antibiotic treatment during the last 90 days of lactation and the somatic cell count (SCC) was <150,000 cells/mL at the most recent Dairy Herd Improvement Association (DHIA) test (Figure 4.1). Cows treated with IMM antibiotics and controls were matched based on parity and monthly milk production. Diet analysis A detailed diet ration report containing nutrient amounts and an analysis of nutrients was facilitated by the Dairy Cattle Teaching and Research Center at Michigan State University (MSU), where the study took place. The software Spartan Dairy 3 was used for diet formulation following the standards of the National Research Council's 2001 Nutrient Requirements of Dairy Cattle (hereafter NRC 01). Hierarchical clustering was applied to analyze patterns of change in the nutrient composition in each lactation phase. Sample collection Analyses were performed on fecal samples collected before treatment at the end of lactation when the cows received a maintenance diet (day -1, n = 40), a week after dry-off (week 1, n = 40) when they were given an early dry diet, and during calving (week 9, n = 39) when they were fed with the fresh formulation (Figure 4.1). Fecal grabs (n =119) were collected via the rectum using clean obstetric sleeves and were stored in whirl-pak bags for transport to MSU in a cooler as described previously (Chapter 3). Each sample was homogenized by hand and aliquots were stored for DNA and metabolite extractions consisting of 0.25 g of feces in 750 ul of 190 Proof ethanol and 0.25 g of pure feces, respectively. All fecal aliquots were subjected to flash freezing by liquid nitrogen for 1 minute and were stored at -80°C until further processing. 152 Figure 4.1. Scheme of animal treatment and sampling collection by lactation phase. Metagenomic sequencing and analysis Fecal DNA was extracted with the DNeasy PowerSoil Pro Kit (Qiagen, Germantown, MD, USA) as described in our prior study (Chapter 3). Briefly, the samples were centrifuged for 5 minutes at 16,000 rpm and 4°C to remove the supernatant containing ethanol used for preservation. To ensure that the remaining ethanol was removed, two washes with 1 ml of 1X PBS, which was removed as described in the prior step. The DNA-extraction kit protocol was followed with an additional wash step using the C3 solution to improve the DNA-quality ratio (260/230). An average of 1277.3 ng (+/- 310.5 ng) of dsDNA measured with a Qubit were sent to CosmosID (Rockville, MD, USA) for metagenomic Next-generation sequencing (mNGS) using a Nextera™ XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA) and an Illumina HiSeq X platform 2x150 bp. 153 The CosmosID bioinformatics software package was used for metagenomics analyses. After the sequences were screened and cleaned for bovine (Bos taurus) genome sequences, the reference GeneBook® database [15] was used to characterize the microbiome, resistome and virulome composition. These data are presented in Chapter 3. Abundance scores were determined based on genome size and the number of reads in order to calculate the relative abundance of taxa and genes. Microbial metabolic profiling The functional profiling of cattle microbiomes was determined using the HUMAnN 3.0 pipeline [10] that allows for the identification of metabolic pathways with their microbial species-level contributions. These analyses were carried out using the following databases: ChocoPhlAn 3 [10] for taxonomic identification, UniRef90 [16] for enzyme commission number screening, and MetaCyc v24.0 [17] for the assignation of pathways. First, paired-raw sequences were processed with Trimommatic v.0.39 [18] to remove low-quality reads and adapters used for Illumina sequencing. BWA v.0.7.15 [19] and Samtools v.1.4.1 [20] removed bovine DNA reads (Bos taurus, ARS-UCD1.2 [21]). Trimmed non-host paired FASTQ reads were merged with the UNIX command cat. Merged reads were used as input for HUMAnN 3.0 and the resulting pathway abundances, reported as reads-per-kilobase (RPK), were normalized as the relative abundance per sample. A joined matrix containing pathway relative abundances for all the samples was generated with the command humann_join_tables. While pathways of interest were depicted with the humann_barplot function stratifying the pathway contributions by bacterial taxa. 154 Fecal metabolite extraction A protocol developed by Dr. Daniel Jones at the Mass Spectrometry and Metabolomics Core at MSU was utilized for metabolite extraction of cattle feces (n =119). First, internal standard solutions were prepared for quality control and normalization including: 1) labeled short-chain fatty acids (SCFAs) (10 uM each of [13C]sodium formate, [13C2]sodium acetate, [13C3]sodium propionate, and [13C4]sodium butyrate) in 50:50 (v/v) methanol/water; 2) [13C16]palmitic acid (10 uM in 100% isopropanol); 3) phenylalanine-d7 (10 uM in 50:50 methanol/water); 4) succinic acid-d4 (10 uM in 50:50 methanol/water); and 5) labeled bile acids (10 uM each of glycocholic acid-d4 and glycoursodeoxycholic acid-d4 in 50:50 methanol/water). A total of 20 mg of feces were weighed under sterile conditions and 350 ul of ice-cold methanol containing 0.1% butylated hydroxytoluene (BHT) was added followed by homogenization and incubation on ice for 10 minutes. To sediment the feces, 10 ul of each standard was added, homogenized by agitation for 30 seconds and centrifuged at 10,000 x g and 4˚C for 10 minutes. The supernatant was pipette-transferred to a sterile microcentrifuge tube on ice, while ice-cold HPLC-grade isopropanol (200 ul) was added to the pellet, homogenized for 30 seconds, and centrifuged at 10,000 x g and 4°C for 10 minutes. Finally, the isopropanol supernatant was combined with the initial extract and 100 ul aliquots of the mixed extracts were stored into glass vials inserted in 2-mL Amber glass autosampler vials sealed with 9 mm screw septum caps. Metabolite extracts were preserved at -80ºC until analyzed. Untargeted metabolomics Polar and nonpolar positive metabolites were analyzed through LC-MS in a Thermo Scientific Vanquish™ Ultra High-Performance Liquid Chromatography (UHPLC) coupled to a Q Exactive™ Hybrid Quadrupole-Orbitrap™ mass spectrometer (MS). Along with the samples 155 (n =119), blanks and pools were included at the beginning of each run (polar and nonpolar) and every 20 samples. Three blanks were analyzed between polar and nonpolar runs. The Thermo Scientific™ Xcalibur™ software was used for method setup and data acquisition. Polar metabolites were detected with reversed-phase chromatography using 10 ul of each sample injected with a column Waters Acquity Ethylene Bridged Hybrid (BEH)-C18 UPLC (2.1x100mm) at 60°C. A 0.4 ml/min flow rate was used for a gradient analysis that consisted of 98% mobile phase A (water plus 0.1% formic acid) and 2% mobile phase B (acetonitrile plus 0.1% formic acid) for 1 minute; then, phase B was ramped to a 100% until the minute 8 when it was hold for 2 minutes. Finally, the phase B was returned to 2% at 10.01 minutes and hold at that concentration for 2 more minutes. Nonpolar metabolites were analyzed through hydrophilic interaction liquid chromatography (HILIC). A Waters BEH-Amide UPLC column (2.1x100mm) held at 60°C was used to inject 10 uL of sample. The gradient analysis was carried out at a rate of 0.4 ml/min starting with 100% mobile phase B (10 mM ammonium formate/10 mM ammonium hydroxide in 95:5 acetonitrile/water (v/v)) and 0% mobile phase A (10 mM ammonium formate/10 mM ammonium hydroxide in water) for 1 min. Phase B was ramped to 40% until the minute 8 and hold at this concentration for 2 minutes. Finally, phase B was returned to a 100% at minute 10.01 and hold at this concentration for 2 minutes. In addition, the MS settings for both methods included: electrospray ionization in positive mode with a capillary voltage of 3.5 kV, transfer capillary temperature at 262.5°C, sheath gas at 50, auxiliary gas at 12.5, probe heater at 425°C, and S-lens RF level at 50. For data acquisition the MS/MS method was set of 35,000 resolution, automatic gain control (AGC) target of 1E6, maximum inject time 100 ms, and m/z range 100-1500. Only the top 5 ions were kept with a 156 resolution setting of 17,500, AGC target of 1E5, minimum AGC of 5E3, maximum inject time 50 ms, isolation window of 1.5, fixed first mass at m/z 50, dynamic exclusion setting of 3 seconds and stepped normalized collision energy settings of 20, 40 and 60. Mass-spectrometry (MS) data processing The chromatographic component separation of fecal metabolites was exported as raw files (.RAW) for each sample with the Thermo Scientific™ Xcalibur™ software. These files were transformed to mzXML format with the Global Natural Product Social Molecular Networking (GNPS) vendor conversion software. Next, MS data processing was performed using MZmine v.2.53 [22] where polar and nonpolar files were analyzed separately. First, mzXML files were imported to MZmine for mass detection at the levels MS1 and MS2 (centroided spectrum type) using a noise level of 4E04 for MS1 and 3.5E03 for MS2, which was set based on visual analyses of chromatograms from pools and blanks. Then, chromatograms were built with the ADAP (Automated Data Analysis Pipeline) [23] module using a scan retention time of 1.00 – 10.00 min for MS level 1, minimum group size in number of scans equal to 4, group intensity threshold of 4.0E4, minimum highest intensity of 5.0E4, and scan to scan accuracy of 0.002 m/z or 10.00 ppm. Chromatograms were smoothed with the Savitzky Golay algorithm with a filter width of 5 and deconvoluted with local minimum feature resolver. The deconvolution settings included MS/MS scan pairing with a retention time tolerance of 0.15 absolute min and MS1 to MS2 precursor tolerance of 0.002 Da. Additionally, the deconvolution algorithm was set up with a chromatographic threshold of 83.3999%, minimum search range RT/Mobility (absolute) of 0.05, minimum relative height of 0.0%, minimum absolute height of 5.0E4, min ratio of peak top/edge 1.80, and peak duration range (min/mobility) 0.00 – 1.51. Isotopes were grouped with a m/z 157 tolerance of 0.0015 m/z or 3.0 ppm, a retention time tolerance of 0.05 absolute mins, and a maximum charge of 2 while choosing the most intense representative isotopes. Next, an aligned feature list containing data from all samples was generated with module join aligner using a tolerance or 0.0015 m/z or 5.0 ppm, weight for m/z of 3, retention time (RT) tolerance of 0.1 absolute min, and weight for RT of 1. Gaps in the aligned list were filled with the module peak finder using an intensity tolerance of 20%, an m/z tolerance of 0.002 m/z or 10.0 ppm, and a retention time tolerance of 0.05 absolute min. Duplicate peaks generated during gap filling were removed at a m/z tolerance of 8.0E-4 m/z or 1.5 ppm and a RT tolerance of 0.035 absolute (min). To obtain a final feature list containing only features present in at least three samples, the module ‘feature list rows filter’ was used with at least 3 peaks in a row, keeping only peaks with MS2 scan, and resetting the peak number ID. Finally, the feature list was exported for analyses in GNPS for the Feature-Based Molecular Networking (FBMN) workflow using filter rows only with MS2. The exported files consisted of a feature quantification table (.CSV format) and a MS/MS spectral summary file (.MGF format) with a list of MS/MS spectra associated with the LC-MS ion features. Metabolite classification The FBMN workflow was carried out in GNPS [24, 25] by importing the MGF file and the feature quantification table generated in MZmine, as well as the metadata containing the sample attributes. Precursor ion mass and fragment ion mass tolerances were set at 0.02 Da. Additionally, default settings were used for the advanced options except for minimum matched fragment ions for networks and library search min matched peaks which were set at 4. All the spectra with IDs were downloaded; library ID and network component index were recorded for 158 each metabolite also referred to as ‘cluster’ for further analyses. Molecular networks were visualized in GNPS to explore metabolite components and identify clusters of interest. Metabolome data analyses The R package Phyloseq v.1.38 [26] was used to analyze metabolomics diversity and composition. First, a Phyloseq object was generated by merging metadata, feature table with cluster intensities, and cluster identifications which included three levels: network component, library ID, and cluster numbers. The package decontam v.1.14 [27] was used to remove contaminant clusters based on a combined method that uses the Fisher’s test to concatenate the probabilities of a cluster being present in a sample based on the amount of feces used for the metabolite extraction and the prevalence of a given cluster in negative controls vs. samples. Cluster intensities were normalized to their relative abundances per sample. Diversity analyses The alpha diversity was measured with Shannon and richness indexes. The paired, one- tailed Wilcoxon signed-rank test was used to compare the alpha diversity between treatments and time points, while the Friedman test examined the variability of the indexes per animal over time. Differences in the composition were evaluated using beta-diversity analyses based on Bray-Curtis dissimilarity distances, which were mapped with Principal Coordinate Analyses (PCoA) using the R packages Vegan v.2.5-7 [28] and ggplot2 v.3.3.5. The mean compositions, represented by the centroid of each group of samples (treatment and phase) in the PCoA, were compared with permutational multivariate analysis of variance (PERMANOVA) with 999 permutations, and the dispersion was compared with PERMDISP. 159 Biomarker detection The identification of significantly different features between treatment groups and lactation phases (time points) was evaluated with three methods: 1) Linear Discriminant Analysis (LDA) Effect Size (LEfSe) [29]; 2) Analysis of compositions of microbiomes with bias correction (ANCOM-BC) [30]; and 3) Microbiome Multivariable Associations with Linear Models (MaAsLin2) [31], as suggested in a prior study [32]. Features that were significantly different (adjusted p-value ≤ 0.05) in at least two of the three tests were considered true biomarkers. In addition, Random Forest with 5000 decision trees was used to estimate the out-of- bag error (OOB) rate that allows for correctly classifying the sample groups based on the metabolite composition. Random Forest was also used to predict important features (clusters and components) based on their discriminatory levels between sample groups, which were ranked based on their mean decrease accuracy (MDA). Multi-omics analyses Associations between the fecal microbiome and metabolome were carried out by correlating the relative abundances of known metabolites (with library ID) with microbial taxa at the phylum and species levels, antimicrobial resistance genes (ARGs), virulence genes, and microbial metabolic pathways across samples. Spearman correlations were calculated with the R package Hmisc [33], and coefficients (ρ) greater than 0.75 with p-values lower than 0.01 were filtered to construct networks with Gephi v.0.9.2 [34]. To characterize patterns of change in the abundance of microbial and metabolic features, hierarchical clustering was carried out with the R package stats v4.1.2 [35]. Only features identified as biomarkers (n = 684) in the previous step were included in this analysis. First, a distance matrix was constructed with the euclidean metric using the fold-change (FC) relative to 160 each feature average per sample. The FC was calculated by computing the mean abundance of each feature across samples, and by dividing the abundance of a feature from a given sample by their corresponding mean. The distance matrix was used for hierarchical clustering with the Ward method (ward.D) and the resulting tree was conveniently cut into 30 clusters. The optimal number of clusters was identified with the NbClust v3.0.1 package [36], which resulted in 5 clusters; however, a finer analysis of each branch was biologically more meaningful. Finally, boxplots of each hierarchical clustering group were depicted to observe the patterns of change between lactation phases. All metagenomics and metabolomics methods have been summarized schematically in Figure 4.2. Figure 4.2. Summary of the methodology applied to analyze the functional microbiome and metabolome composition from cattle feces. 161 RESULTS Overview of the study population In this study, a total of 40 cows were enrolled at the end of lactation with average days in milk (DIM) of 266.24 (antibiotic group, mean = 262.69; control, mean = 269.59). Mastitis was rule out in these cows through the somatic cell count (SCC) in milk, which had an average of 34,8718 +/- 23,602 cells/mL (antibiotic group, mean = 35,300 cells/mL; control, mean = 34,4211 cells/mL). Fecal samples from all animals were collected through the 9-week period, except for one cow from the IMM ceftiofur-treated group that had a C-section in week 9, hence no sample was taken from this animal in that week. Metagenomic sequencing was carried out in samples collected the day prior to dry-off and on weeks 1, 5, and 9 after the treatment (n = 159). Since our prior study (Chapter 3) showed that the microbiome diversity and composition in weeks 1 and 5 were similar, untargeted metabolomics was performed on samples from day -1, week 1 and week 9 (n = 119). Diet composition Different diets were provided to the enrolled cows throughout the study in accordance with their production demands. During lactation, which corresponds to a day prior to the IMM treatment, cows received the maintenance diet with 14% more metabolizable energy and 2.5 times more metabolizable protein (g) than the dry-off diet (weeks 1 – 5) (Table 4.1; Figure C.1). In week 9 animals received a diet for fresh cows where they were given 64% of as-fed intake (lb) when compared to lactating cows (Table 4.1) but with transitioning levels of energy and protein that were 15% and 64% higher than during dry-off, respectively (Figure C.1). Animals in all phases received corn silage, soybean meal with 47.5% crude protein, CFE MSU dairy base, and haylage (Table 4.1). It was only during the lactation and fresh periods that the 162 ration included corn (ground fine and fed dry) and MSU fresh high supplement to increase the energy density and to provide essential nutrients such as calcium, magnesium, potassium and niacin to prevent metabolic disorders that can occur during the transition into lactation. Comparatively, soybean hull pellets, cottonseed, and long bulk brown midrib (BMR) corn silage (CS) were exclusively given to cows in late lactation. Moreover, alfalfa hay was only provided to fresh cows; while grass silage, MSU straw, grass pasture, and QLF ignite dry cow 25 were given only during the dry period (Table 4.1). Table 4.1. Diet rations provided to dairy cows in four different lactation stages. Ration component Maintenance Early dry Close-up Fresh As-Fed lbs 107.51 71.82 43.82 69.08 DM Fed lbs 53.00 28.60 28.10 35.05 Corn grain ground fine (DM fed lbs) 9.50 0 0 5.00 Corn gluten feed dry (DM fed lbs) 4.00 0 0 1.00 Soybean Hulls Pellet (DM fed lbs) 4.50 0 0 0 Soybean meal 475 solvent (DM fed lbs) 2.50 2.10 6.00 3.00 Cottonseed Fuzzy (DM fed lbs) 3.00 0 0 0 MSU Corn silage (DM fed lbs) 9.00 9.00 8.38 12.30 MSU Haylage (DM fed lbs) 9.00 8.90 0 6.00 CFE MSU dairy base (DM fed lbs) 1.00 0.60 0.70 0.80 MSU Long bunk BMR CS (DM fed lbs) 8.00 0 0.00 0 MSU fresh high supplement (DM fed lbs) 2.50 0 0.00 2.35 MSU Purchased Alfalfa Hay (DM fed lbs) 0 0 0.00 4.60 CFE MSU PreFresh DE (DM fed lbs) 0 0 1.00 0 MSU Low K Grass Hay (DM fed lbs) 0 0 10.50 0 SoyChlor (DM fed lbs) 0 0 1.52 0 MSU grasslage (DM fed lbs) 0 6.00 0 0 MSU Straw (DM fed lbs) 0 2.00 0 0 Grass Pasture 16 CP 55 NDF 7 LNDF (DM fed lbs) 0 0.00* 0 0 QLF Ignite Dry Cow 25 (tub) (DM lb) 0 0.00* 0 0 DM = Dry matter; NDF = Neutral Detergent Fiber; LNDF = Lignin as a percent of the NDF; CP = Crude Protein; CS = Corn silage; BMR = brown midrib; *Provided only to dry cows. Untargeted metabolomics data overview Overall, mass-spectra analyzed through MZmine resulted in twice the amount of nonpolar (n = 11,199) than polar (n = 5,390) metabolite clusters (Figure 4.3A). Each cluster corresponds to consensus MS2 spectra from identical compounds detected across samples 163 (Figure 4.3B). Similarly, molecular networks aggregating metabolites based on their MS2 spectral similarity resulted in 2,604 nonpolar and 1,149 polar components (Figure 4.3A). Network components connect clusters (nodes) structurally related via edges that represent a modified cosine score calculated on ions that differ by the mass difference (Figure 4.3B). Only a small fraction of clusters had library identifications, corresponding to 2.48% of the total metabolites (polar, n =138; nonpolar, n =273), of which 68 were identified with both polar and nonpolar modes. FBMN data processed through GNPS is available online for polar and nonpolar metabolites (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d4a761f0a6be422c8b89db9408f57b0d and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=1d6f7e95d2f04f96a94fede8c195702d, respectively). A B Network component 12000 10000 8000 Number 6000 4000 2000 Library ID Cluster 0 Clusters Components Library ID Polar 5390 1149 138 Nonpolar 11199 2604 273 68 overlapped Figure 4.3. Number of metabolite clusters, network components, and metabolites with identification. A) Bar plot showing the number of clusters identified with MZmine, components grouped with FBMN, and clusters with library ID assigned with GNPS pipelines. B) Scheme of a network component containing clusters with and without identification. A plot depicting MS/MS spectra shows how clusters are aggregated. 164 Number of microbial metabolic pathways identified with HUMAnN 3.0 Across samples, 262 metabolic pathways were identified. These pathways were assigned to bacterial taxa, producing 797 pathways with different bacterial contributions. Only nine bacterial genera were assigned to the pathways, including Bifidobacterium (pathways, n = 75), Clostridium (n = 4), Escherichia (n = 25), Methanobrevibacter (n = 18), Olsenella (n = 2), Ruminococcaceae unclassified (n = 26), Sarcina (n = 12), Turicibacter (n = 12), other (n = 262), and unclassified (n =200). On average, 93% of the reads were classified as unmapped and 6% as unintegrated. Alpha diversity of metabolome and microbial pathways The within-sample diversity was measured with the number of observed features and the Shannon index. Significantly higher diversity of metabolites and microbial-metabolic pathways were detected during lactation than during the dry-off and fresh stages (p < 0.05) (Figure 4.4). Fresh cows exhibited a similar number of metabolites and microbial pathways as when they were dry (Observed, p > 0.05) but with lower evenness (Shannon, p < 0.006). Although the metabolite alpha diversity was similar between fresh and lactating cows, the Shannon index showed lower diversity in the fresh phase denoting a transition like the one detected in their diet composition (Figure 4.4). Curiously, the alpha diversity of metabolic pathways was more similar between dry and fresh phases than between fresh and lactating cows despite the similarity in diet between these last two phases. No significant differences in alpha diversity were observed between treatment groups, except for the number of metabolic pathways on week 5 of dry-off, when controls had significantly higher diversity than cows given IMM ceftiofur (p < 0.001) (Figure 4.4). Because samples from week 5 were not analyzed with untargeted metabolomics, we could not compare 165 between treatment groups at this time point. Nonetheless, no long-term effects in the number of metabolites were observed on week 9 (fresh cows). Figure 4.4. Alpha diversity of metabolites and microbial pathways. Beta diversity of metabolome and microbial pathways Bray-Curtis dissimilarity distances showed significant differences between lactation phases for polar and non-polar metabolome composition comprised of all metabolite clusters (PERMANOVA, p < 0.001) (Figure 4.5A). The microbial pathways had overlapping composition between dry and fresh phases (PERMANOVA, p > 0.3), however, samples from lactating cows showed a significantly higher dispersion in the PCoA (PERMDISP, F = 53.32, p = 1.34e-11) and different average composition (PERMANOVA, F = 63.69, p = 0.001) (Figure 4.5B). Thus, despite the metabolome composition differences associated with the lactation phase, 166 the microbial metabolic pathways were similar in dry and fresh cows. Furthermore, cows treated with IMM ceftiofur had an identical mean metabolite composition as the controls (PERMANOVA, p > 0.38), though differences in the composition of the microbial pathways were observed in weeks 5 (PERMANOVA, F = 4.25, p = 0.007) and 9 (PERMANOVA, F = 2.67, p = 0.045). These findings demonstrate that antibiotic dry-cow therapy does not significantly impact the metabolome at the time points examined but does result in some long- term changes in the mean composition of microbial pathways. Figure 4.5. Beta diversity of metabolites and microbial pathways. Biomarker identification After comparing the abundance of a total of 16,589 metabolites, 3,753 components, and 797 microbial-metabolic pathways, only 1 biomarker was significantly different between controls and cows treated with intramammary ceftiofur in week 1 after the treatment. This corresponded to the metabolite cluster 6574 with a parent mass of 422.206 and a precursor charge of 1 (Figure C.2). This cluster is not identifiable and was not part of a network component, thereby limiting the understanding of its occurrence in ceftiofur-treated animals. Random-Forest (RF) was not able to correctly classify the metabolomic composition by treatment group at any time point, giving an out-of-bag (OOB) estimate of error rate higher than 55%. Likewise, the OOB error rate for the microbial pathways was 55% for day -1 and week 1, 167 47.5% for week 5, and 41.3% for week 9. Given the differences in composition in weeks 5 and 9 observed in the PCoA, a less restrictive analysis of metabolic pathways was performed. To do so, we included pathways that were significantly different with a non-corrected p-value less than 0.05 in two or more tests, and could explain differences between groups through RF. Use of these criteria identified 16 pathways to be significantly lower in week 5, including those involved in the biosynthesis of L-isoleucine, glycogen, isoprene, preQ0, chorismate, and coenzyme A, as well as in the degradation of D-galactose and L-arginine. The most significant biomarkers in week 5, however, were unintegrated pathways of Ruminococcaceae bacterium P7 and methylerythritol phosphate pathway I, which were lower in ceftiofur-treated cows. In week 9, 38 pathways showed differences between treatments, of which 17 were assigned to Bifidobacterium pseudolongum that was higher in cows treated with antibiotics. The most important differential pathways identified in week 9 were a lower abundance of coenzyme A biosynthesis and glycogen degradation II in the ceftiofur-treated vs. untreated cows. Among lactation phases, the OOB error was 1.68%, as the metabolomes of two fresh cows were misclassified as lactating cows. Hierarchical clustering of the 50 most important metabolite components identified with RF showed a transitional composition in fresh cows between the dry and lactation phases (Figure 4.6). Only four of these 50 components had known clusters representing long-chain fatty acids, which were higher in dry-off, and amino acids that were increased during lactation (Figure C.3). 168 Figure 4.6. Heat map of the 50 most important nonpolar metabolite components for classifying samples by time point. Hierarchical clustering with the method Ward was used to aggregate samples and components. Differential abundance tests identified 9,850 biomarkers between lactation phases corresponding to 46.59% of the total features (metabolites and microbial pathways) (Figure 4.7). Dry and fresh cows showed a lower number of different features (53.88% of the biomarkers) than dry and lactating cows (64.64%) or lactating and fresh cows (66.9%). In particular, dry and fresh cows differed in only a few microbial pathways, whereas approximately a third of the microbial metabolic pathways had a different abundance during lactation than in the other phases. 169 Figure 4.7. Venn diagram showing the number of biomarkers shared between lactation phases. Percentages represent the proportion of the total differential features. The top 8 most important pathways that enabled lactation phase classification through RF corresponded to three categories: 1) cell division, 2) amino acid biosynthesis, and 3) carbohydrate biosynthesis. These pathways were significantly higher during lactation, particularly those related to cell division that were mostly absent during the dry-off period. Cell- division pathways included inosine-5’-phosphate biosynthesis III, pyrimidine deoxyribonucleotides de novo biosynthesis IV, UDP-N-acetyl-D-glucosamine biosynthesis I, and O-antigen building blocks biosynthesis (E. coli) which were assigned to Bifidobacterium, Turicibacter, Olsenella, and Escherichia coli (Figure 4.8). The three main pathways related to amino acid biosynthesis involved the superpathway of L-lysine, L-lysine biosynthesis VI, L- valine biosynthesis, and L-threonine and L-methionine biosynthesis II (Figure 4.9). During lactation, these amino acid biosynthesis pathways were mainly assigned to Bifidobacterium spp., while no taxa was assigned to them during the dry and fresh phases. 170 Figure 4.8. Cell-division-related pathways significantly different between lactation phases. Figure 4.9. Amino acid biosynthesis pathways significantly different between lactation phases. 171 Finally, carbohydrate biosynthesis pathways included glycogen biosynthesis I (from ADP-D-Glucose), sucrose biosynthesis II, and chorismate biosynthesis I (Figure 4.10). Although Sarcina was the main taxa assigned to glycogen and sucrose biosynthesis, during the dry-off and fresh periods, Bifidobacterium was mainly associated with carbohydrate biosynthesis during lactation. Figure 4.10. Carbohydrate biosynthesis pathways significantly different between lactation phases. Multi-omics correlations Positive Spearman’s correlations among metabolites, microbial pathways, microbial species, and genes encoding antimicrobial resistance and virulence factors were analyzed. Interestingly, three nonpolar metabolites with antibiotic properties, monolinolenin, beauvericin [+NH4+], and neobavaisoflavone, were directly connected to bacteriophages from enterobacteria including Escherichia, Salmonella, and the extended-spectrum β-lactamase (ESBL) gene CFXA2 (Figure 4.11). Monolinolenin and beauvericin are produced by fungi and have activity against Gram-positive bacteria, while neobavaisoflavone is produced by plants and has activity against 172 Gram-negatives. Two fungal species, Byssochlamys nivea and Trichoderma atroviride were correlated with these compounds suggesting a potential role in the occurrence of beauvericin and monolinolenin. The lowest levels of these two compounds were observed during the dry-off period (Figure 4.12), whereas the level of monolinolenin was steady over time. Furthermore, these three antimicrobial metabolites were correlated with metabolic pathways for amino acid, nucleotide, and sucrose biosynthesis from Clostridium and E. coli (Figure C.4). Similarly, the pesticide piperonyl butoxide was related to three Salmonella and two E. coli bacteriophages (Figure 4.13A). This compound was significantly higher in fresh cows as compared to both the lactating and dry cows (Figure 4.13B). Fruchterman Reingold networks showing other correlations between microbiome and polar metabolites as well as nonpolar metabolites are shown in Appendices 5 and 6, respectively. Finally, a module network containing multiple ARGs, virulence factors, plasmids, bacteriophages, and microbial pathways suggest the co- occurrence of these features in a multi-resistant E. coli (Figure C.7). Escherichia virus K1-5 Byssochlamys nivea Lactobacillus phage LfeSau uncultured Ruminococcaceae bacterium Beauvericin [+NH4+] Taylorella Monolinolenin (9c,12c,15c) from NIST14 Paenibacillus antibioticophila neobavaisoflavone Salmonella phage BP12B Escherichia virus K1E Enterobacteria phage vB_EcoP_ACG-C91 Beta-lactam cfxA2 Paenibacillus macerans Trichoderma atroviride Lactobacillus phage iA2 Figure 4.11. Correlation network of a module containing three antimicrobial compounds. The size of the nodes corresponds to the degree of centrality. 173 Figure 4.12. Boxplots of the three antimicrobial compounds between time points. A B Escherichia virus K1G Salmonella virus SS3e Salmonella virus SE2 Pesticide4_Piperonyl butoxide_C19H30O5_Butacide Dyadobacter Salmonella virus LSPA1 Escherichia virus K1H Figure 4.13. Piperonyl butoxide correlations and abundance in cattle feces. A) Correlation network. B) Boxplot showing the abundance of the pesticide by timepoint. Changing metabolome and microbiome patterns among lactation phases To better explore the functional associations of differentially abundant features, hierarchical clustering was carried out only with known metabolic clusters that were significantly different. A hierarchical clustering tree was constructed based on a distance matrix with the fold- change of metabolites and metagenomic features identified as biomarkers between lactation 174 phases. One relevant group showed concomitant higher levels of Actinobacteria, Proteobacteria, and three forms of histamine including C16:0, C18:1, and C18:2 during lactation (Figure 4.14A), suggesting a role of these taxa in the production of some pro-inflammatory compounds. Interestingly, correlation networks also identified associations between the microbiome and histamine, including histamine C18:0 that correlated with Clostridium and histamine C18:1 that was associated with Prevotella, Atopobium, Asteriusvirus, and Lactobacillus virus LP65 (Figure C.4). During the dry-off period, traces of compounds found in pesticides and detergents such as nopol, aleuretic acid, and comphene, were identified at higher levels relative to the other lactation phases (Figure 4.14B). Similarly, the unsaturated fatty acids elaidic acid and petroselinic acid, and the diterpenes karuane and manool, were higher in dry cows, which was the only time when straw and grass silage were given (Figure 4.14B). Fresh cows showed higher levels of urate and nonpolar plant-derived compounds related to a diet rich in alfalfa hay, which was exclusively supplemented in this phase (Figure 4.14C). Monensin [M+NH4]+ also was detected at high levels in fresh cows although it was also detected during the lactating phase (Figure 4.14C, Figure C.8). This ionophore is used as a coccidiostat and growth promoter, though it was also shown to have activity against some Gram-positive bacteria [37]. No direct associations were observed between monensin and the microbiome composition. Other clusters also showed patterns with lower contrast across the lactation phases, with the most relevant HC groups displayed in Appendices 9 and 10. For instance, higher quantities of amino acids and dipeptides were detected with the polar mode on samples from the lactation phase, which was related to a higher abundance of bacterial amino acid synthesis pathways mentioned priorly 175 (Figure C.9). Not surprisingly, higher levels of androstane were also detected in fresh cows, which are expected to have peak levels of estrogens at this time (Figure C.10). Figure 4.14. Relevant patterns of FC of metagenome and metabolome features in three lactation phases. Features higher in each phase are shown A) Lactation; B) Dry-off; C) Fresh cows. 176 DISCUSSION In this study, we aimed to identify short and long-term changes in the gut metabolome of dairy cows due to intramammary treatment with a third-generation cephalosporin applied at dry- off. Although we observed no major changes in the metabolome associated with the IMM antibiotic treatment, the metabolome and microbial-metabolic pathways were significantly impacted by lactation phase. Furthermore, integrative analyses of metagenomics and untargeted metabolomics data revealed fingerprints unique to each lactation phase that help define the metabolite-microorganism interactions in the cattle gut. IMM ceftiofur treatment had no short- or persistent effects on the gut metabolome diversity or composition, which is consistent with data from our prior study of the same animals showing no significant differences in microbiome diversity (Chapter 3). Importantly, ceftiofur metabolites were not detected in feces through untargeted metabolomics, demonstrating that these compounds did not persist in high levels one week following treatment. This finding was expected since a prior study found the excretion of ceftiofur to occur as quickly as 24 hours after intramuscular application and that most of the residues were found in the urine (60-80%) [38]. No prior studies had examined the IMM application of ceftiofur, which likely results in the excretion of lower levels than parenteral administration, thereby contributing to fewer functional changes in the gut. One unidentified metabolite, however, was detected only in cows that received IMM ceftiofur a week post-treatment. This compound could belong to a constituent of Spectramast® DC, which not only contains ceftiofur hydrochloride but also microcrystalline wax, oleoyl oilyoxyglyceride, and cottonseed oil. Future characterization of this compound is therefore warranted. 177 Furthermore, IMM ceftiofur treatment had long-term effects on the functional microbiome composition. A lower abundance of coenzyme A biosynthesis and glycogen degradation was observed in weeks 5 and 9, suggesting lower bacterial production of energy. However, several pathways linked to Bifidobacterium were higher in ceftiofur-treated cows in week 9; this genus belongs to phylum Actinobacteria, which was significantly higher in antibiotic-treated cows in our prior study (Chapter 3). Similarly, at week 5 lower abundance of genes that did not contribute to pathways (unintegrated) of Ruminococcaceae were observed in ceftiofur-treated cows. This genus is part of the phylum Firmicutes and was also less abundant in our prior study (Chapter 3). Unfortunately, most pathways were unassigned or unmapped, thereby complicating the understanding of differences between groups. Each lactation phase, characterized by unique physiology and diet, determined the fecal metabolome composition. Even though the individual impact of these two factors cannot be explored based on our study design, diet seems to greatly impact the fecal metabolome and microbiome composition in cows as demonstrated previously [39–42]. For instance, increasing the grain-forage ratios are associated with a higher abundance of Proteobacteria and a lower abundance of Bacteroidetes in feces [41, 42]. Moreover, diets with over 30% grain given to cows in early lactation significantly changed the ruminal metabolome, increasing the abundance of short-chain fatty acids as well as toxins, inflammatory compounds, putrescine, methylamines, and ethanolamine [11]. In our study, cows in late lactation received the highest amount of grain in the diet, constituting about 39% of the dry matter intake vs. 26% in fresh cows and 7% in dry cows. Comparatively, those cows sampled during late lactation showed enhanced diversity of microbial pathways and metabolites, along with greater levels of histamine produced by Actinobacteria and Proteobacteria. Higher amounts of histamine-producing bacteria have been 178 associated with asthma in humans [43] and visceral hyperalgesia in mice colonized with human- fecal microbiota from patients with irritable bowel syndrome [44]. Bacterial-derived histamine can enhance cell-mediated (Th1) immune responses and suppress antibody-mediated immune responses (Th2), though their role in chronic inflammatory disorders is poorly studied [45]. A higher diversity of microbial pathways, as was observed during late lactation, has also been associated with enteric infection in a case-control clinical study performed by our group [46]. Despite the similarity in diet and the metabolome between the fresh and lactating cows, the microbial metabolic pathway diversity and composition were significantly different. In fact, the pathway profiles of fresh cows were similar to those observed in the dry phase suggesting a slow adaptation to a high grain diet. Although it only took a week on a forage-based diet at dry-off to identify changes in the functional gut microbiome, this was accompanied by lower levels of histamine-producing bacteria compared to the lactation phase. Since the core microbiome composition is unique to each farm due to factors that include housing, breed, and age [40, 47], changes in the diet are the most impactful on the cattle metabolome and microbial diversity. Consequently, manipulation of the functional microbiome through dietary changes is plausible; however, functional changes can take longer time to develop in a new environment as was observed herein. Interestingly, integrated analyses between untargeted metabolomics and metagenomics exposed natural antimicrobial compounds from plant and fungi sources to be associated with the expansion of lytic bacteriophages of enterobacteria and genes encoding ESBL. For instance, beauvericin is a mycotoxin commonly found in animal feed, particularly corn, wheat, and barley, contaminated with fungi representing Fusarium spp. or Beaveria bassiana [48]. Similarly, monolinolenin has been isolated from ryegrass and Rhizobium [49, 50], while neobavaisoflavone 179 is derived from plants [51]. These compounds are being studied as alternatives to treat multidrug resistant infections [48, 51, 52]. Thus, identifying levels of these compounds in different food sources as well as associations with the microbiome and resistome could enhance understanding of their role in the abundance of resistant bacteria and pathogens. Importantly, the positive correlation between these natural antimicrobials and bacteriophages of E. coli and Salmonella indicates their indirect effect on the reduction of some enterobacterial populations by inducing the lytic phase of these phages. However, bacterial stress could also promote the mobilization of other genetic elements [53, 54], which could explain their association with the β-lactam resistant gene CFXA2. Beavericin, monolinolenin and neobavaisoflavone were also related to a higher abundance of metabolic pathways from Clostridium and E. coli suggesting their role in the activation and growth of these taxa. Untargeted metabolomics also showed traces of pesticides in feces, like piperonyl butoxide. The chronic exposure to pesticides can induce dysbiosis, toxicity, metabolic diseases and reproductive disturbances [55], which are conditions that are critical to prevent in dairy farm production. Furthermore, the association between piperonyl butoxide with lytic phages from Salmonella and E. coli indicates the role of pesticides as bacterial stressors. Despite a higher abundance of compounds associated with lytic bacteriophages of enterobacterial populations in fresh cows, our prior analyses (Chapter 3) showed an increased number of total and β-lactam resistant Gram-negatives at the same time point/phase. Hence, these phages are either strain specific or in the lysogenic stage, where their expansion is linked to the multiplication of their bacterial hosts. Fresh cows are immunocompromised and under a higher level of stress as compared to dry cows, which can also contribute to differences in the microbiota. The higher amounts of monensin added in the diet of fresh cows, for instance, could be related to bacterial 180 stress and the higher abundance of resistant microorganisms during peripartum observed in our prior study (Chapter 3). Even though no direct associations were detected between monensin and the microbiome, a prior metagenomic investigation of steers found that the administration of monensin in the diet contributed to reduced levels of some Gram-positive genera [6]. Not surprisingly, the vast majority of metabolites and microbial-metabolic pathways was unknown in this study, which has also been observed in prior metabolomic studies [56, 57]. Despite this limitation, biologically important compounds and metabolic pathways enabled the interpretation of some associations that were observed between the microbiome and metabolome. Future studies should include GC/MS to promote the identification of short-chain fatty acids (SCFAs) since they have been previously linked to health outcomes in humans [58] and production in cattle [59]. Moreover, associations between metagenome, metabolome and milk production could guide improvements to diet formulations, health, and probiotic development. Serum metabolome analyses could also help identify associations between microbiome functionality and host factors such as hormonal levels (i.e., estrogens, cortisol, progesterone) or metabolic disorders in cattle. Finally, future studies should also explore the use of fecal proteomics to define and target biomarkers of immunity and inflammation for classifying host responses, as well as metaproteomics to characterize the functional microbiome and resistome [60]. CONCLUSIONS IMM ceftiofur treatment of dairy cattle at dry-off did not alter the fecal metabolome, though a lower production of energy and higher activity of Bifidobacterium was observed in the long term among the ceftiofur-treated cows. The presence of natural antimicrobial compounds and pesticides in feces was also correlated with Enterobacteria stress and ESBL genes. 181 Moreover, each lactation phase was characterized by a distinct metabolome composition that was related to the feed ration and physiology. During lactation, a higher level of microbial activity was observed as compared to dry and fresh cows; however, proinflammatory bacteria were more abundant during lactation that can negatively affect the performance of the animals. In all, these data highlight how integrative analyses of metagenomics and untargeted metabolomics data can be used to define the metabolite-microorganism interactions in the cattle gut. Comprehending the role of the gut environment in the microbial profile is key to identify factors related with health and production in cattle farms. 182 REFERENCES 1. Bergman, E. Energy Contributions of Volatile Fatty Acids from the Gastrointestinal Tract in Various Species. Physiological reviews 1990, 70, 567–590. 2. Bach, A.; Calsamiglia, S.; Stern, M.D. Nitrogen Metabolism in the Rumen*. Journal of Dairy Science 2005, 88, E9–E21, doi:10.3168/jds.S0022-0302(05)73133-7. 3. O’Hara, E.; Neves, A.L.A.; Song, Y.; Guan, L.L. The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger? Annual Review of Animal Biosciences 2020, 8, 199–220, doi:10.1146/annurev-animal-021419-083952. 4. Weinroth, M.D.; Martin, J.N.; Doster, E.; Geornaras, I.; Parker, J.K.; Carlson, C.R.; Metcalf, J.L.; Morley, P.S.; Belk, K.E. Investigation of Tylosin in Feed of Feedlot Cattle and Effects on Liver Abscess Prevalence, and Fecal and Soil Microbiomes and Resistomes1. Journal of Animal Science 2019, 97, 4567–4578, doi:10.1093/jas/skz306. 5. Holman, D.B.; Yang, W.; Alexander, T.W. Antibiotic Treatment in Feedlot Cattle: A Longitudinal Study of the Effect of Oxytetracycline and Tulathromycin on the Fecal and Nasopharyngeal Microbiota. Microbiome 2019, 7, 86, doi:10.1186/s40168-019-0696-4. 6. Thomas, M.; Webb, M.; Ghimire, S.; Blair, A.; Olson, K.; Fenske, G.J.; Fonder, A.T.; Christopher-Hennings, J.; Brake, D.; Scaria, J. Metagenomic Characterization of the Effect of Feed Additives on the Gut Microbiome and Antibiotic Resistome of Feedlot Cattle. Sci Rep 2017, 7, 12257, doi:10.1038/s41598-017-12481-6. 7. Beta-Lactam Antimicrobial Use in Animals - Pharmacology Available online: https://www.merckvetmanual.com/pharmacology/antibacterial-agents/beta-lactam- antimicrobial-use-in-animals?autoredirectid=19871 (accessed on 1 October 2022). 8. Foster, D.M.; Jacob, M.E.; Farmer, K.A.; Callahan, B.J.; Theriot, C.M.; Kathariou, S.; Cernicchiaro, N.; Prange, T.; Papich, M.G. Ceftiofur Formulation Differentially Affects the Intestinal Drug Concentration, Resistance of Fecal Escherichia coli, and the Microbiome of Steers. PLoS One 2019, 14, e0223378, doi:10.1371/journal.pone.0223378. 9. Hallberg, J.W.; Wachowski, M.; Moseley, W.M.; Dame, K.J.; Meyer, J.; Wood, S.L. Efficacy of Intramammary Infusion of Ceftiofur Hydrochloride at Drying off for Treatment and Prevention of Bovine Mastitis during the Nonlactating Period. Vet Ther 2006, 7, 35–42. 10. Beghini, F.; McIver, L.J.; Blanco-Míguez, A.; Dubois, L.; Asnicar, F.; Maharjan, S.; Mailyan, A.; Manghi, P.; Scholz, M.; Thomas, A.M.; et al. Integrating Taxonomic, Functional, and Strain-Level Profiling of Diverse Microbial Communities with BioBakery 3. eLife 2021, 10, e65088, doi:10.7554/eLife.65088. 11. Saleem, F.; Ametaj, B.N.; Bouatra, S.; Mandal, R.; Zebeli, Q.; Dunn, S.M.; Wishart, D.S. A Metabolomics Approach to Uncover the Effects of Grain Diets on Rumen Health in Dairy Cows. Journal of Dairy Science 2012, 95, 6606–6623, doi:10.3168/jds.2012-5403. 183 12. Malheiros, J.M.; Correia, B.S.B.; Ceribeli, C.; Cardoso, D.R.; Colnago, L.A.; Junior, S.B.; Reecy, J.M.; Mourão, G.B.; Coutinho, L.L.; Palhares, J.C.P.; et al. Comparative Untargeted Metabolome Analysis of Ruminal Fluid and Feces of Nelore Steers (Bos Indicus). Sci Rep 2021, 11, 12752, doi:10.1038/s41598-021-92179-y. 13. Xue, M.-Y.; Xie, Y.-Y.; Zhong, Y.; Ma, X.-J.; Sun, H.-Z.; Liu, J.-X. Integrated Meta- Omics Reveals New Ruminal Microbial Features Associated with Feed Efficiency in Dairy Cattle. Microbiome 2022, 10, 32, doi:10.1186/s40168-022-01228-9. 14. NRC, I. Nutrient Requirements of Dairy Cattle. National Research Council 2001, 519. 15. Connelly, S.; Bristol, J. a.; Hubert, S.; Subramanian, P.; Hasan, N. a.; Colwell, R. r.; Kaleko, M. SYN-004 (Ribaxamase), an Oral Beta-Lactamase, Mitigates Antibiotic- Mediated Dysbiosis in a Porcine Gut Microbiome Model. Journal of Applied Microbiology 2017, 123, 66–79, doi:10.1111/jam.13432. 16. Steinegger, M.; Söding, J. Clustering Huge Protein Sequence Sets in Linear Time. Nat Commun 2018, 9, 2542, doi:10.1038/s41467-018-04964-5. 17. Caspi, R.; Billington, R.; Keseler, I.M.; Kothari, A.; Krummenacker, M.; Midford, P.E.; Ong, W.K.; Paley, S.; Subhraveti, P.; Karp, P.D. The MetaCyc Database of Metabolic Pathways and Enzymes - a 2019 Update. Nucleic Acids Res 2020, 48, D445–D453, doi:10.1093/nar/gkz862. 18. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120, doi:10.1093/bioinformatics/btu170. 19. Li, H. Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA- MEM. arXiv:1303.3997 [q-bio] 2013. 20. Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M.; et al. Twelve Years of SAMtools and BCFtools. Gigascience 2021, 10, giab008, doi:10.1093/gigascience/giab008. 21. Rosen, B.D.; Bickhart, D.M.; Schnabel, R.D.; Koren, S.; Elsik, C.G.; Tseng, E.; Rowan, T.N.; Low, W.Y.; Zimin, A.; Couldrey, C.; et al. De Novo Assembly of the Cattle Reference Genome with Single-Molecule Sequencing. GigaScience 2020, 9, doi:10.1093/gigascience/giaa021. 22. Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. MZmine 2: Modular Framework for Processing, Visualizing, and Analyzing Mass Spectrometry-Based Molecular Profile Data. BMC Bioinformatics 2010, 11, 395, doi:10.1186/1471-2105-11-395. 23. Myers, O.D.; Sumner, S.J.; Li, S.; Barnes, S.; Du, X. One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks. Anal Chem 2017, 89, 8696–8703, doi:10.1021/acs.analchem.7b00947. 184 24. Wang, M.; Carver, J.J.; Phelan, V.V.; Sanchez, L.M.; Garg, N.; Peng, Y.; Nguyen, D.D.; Watrous, J.; Kapono, C.A.; Luzzatto-Knaan, T.; et al. Sharing and Community Curation of Mass Spectrometry Data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016, 34, 828–837, doi:10.1038/nbt.3597. 25. Nothias, L.-F.; Petras, D.; Schmid, R.; Dührkop, K.; Rainer, J.; Sarvepalli, A.; Protsyuk, I.; Ernst, M.; Tsugawa, H.; Fleischauer, M.; et al. Feature-Based Molecular Networking in the GNPS Analysis Environment. Nat Methods 2020, 17, 905–908, doi:10.1038/s41592- 020-0933-6. 26. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data Available online: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061217 (accessed on 13 May 2021). 27. Davis, N.M.; Proctor, D.M.; Holmes, S.P.; Relman, D.A.; Callahan, B.J. Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data. Microbiome 2018, 6, 226, doi:10.1186/s40168-018-0605-2. 28. Dixon, P. VEGAN, a Package of R Functions for Community Ecology. Journal of Vegetation Science 2003, 14, 927–930, doi:https://doi.org/10.1111/j.1654- 1103.2003.tb02228.x. 29. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery and Explanation. Genome Biol 2011, 12, R60, doi:10.1186/gb-2011-12-6-r60. 30. Lin, H.; Peddada, S.D. Analysis of Compositions of Microbiomes with Bias Correction. Nat Commun 2020, 11, 3514, doi:10.1038/s41467-020-17041-7. 31. Mallick, H.; Rahnavard, A.; McIver, L.J.; Ma, S.; Zhang, Y.; Nguyen, L.H.; Tickle, T.L.; Weingart, G.; Ren, B.; Schwager, E.H.; et al. Multivariable Association Discovery in Population-Scale Meta-Omics Studies. PLOS Computational Biology 2021, 17, e1009442, doi:10.1371/journal.pcbi.1009442. 32. Nearing, J.T.; Douglas, G.M.; Hayes, M.G.; MacDonald, J.; Desai, D.K.; Allward, N.; Jones, C.M.A.; Wright, R.J.; Dhanani, A.S.; Comeau, A.M.; et al. Microbiome Differential Abundance Methods Produce Different Results across 38 Datasets. Nat Commun 2022, 13, 342, doi:10.1038/s41467-022-28034-z. 33. Harrell Jr, F.E.; Harrell Jr, M.F.E. Package ‘Hmisc.’ CRAN2018 2019, 2019, 235–236. 34. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third international AAAI conference on weblogs and social media; 2009. 35. Team, R.C. R: A Language and Environment for Statistical Computing, Vienna, Austria. 2013. 185 36. Charrad, M.; Ghazzali, N.; Boiteau, V.; Niknafs, A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software 2014, 61, 1–36, doi:10.18637/jss.v061.i06. 37. Huczyński, A.; Ratajczak-Sitarz, M.; Stefańska, J.; Katrusiak, A.; Brzezinski, B.; Bartl, F. Reinvestigation of the Structure of Monensin A Phenylurethane Sodium Salt Based on X- Ray Crystallographic and Spectroscopic Studies, and Its Activity against Hospital Strains of Methicillin-Resistant S. Epidermidis and S. Aureus. J Antibiot 2011, 64, 249–256, doi:10.1038/ja.2010.167. 38. Brown, S.; Jaglan, P.; Banting, A. Ceftiofur Sodium: Disposition, Protein-Binding, Metabolism, and Residue Depletion Profile in Various Species. Acta Veterinaria Scandinavica. Supplementum (Denmark) 1991. 39. Vasco, K.; Nohomovich, B.; Singh, P.; Venegas-Vargas, C.; Mosci, R.E.; Rust, S.; Bartlett, P.; Norby, B.; Grooms, D.; Zhang, L.; et al. Characterizing the Cattle Gut Microbiome in Farms with a High and Low Prevalence of Shiga Toxin Producing Escherichia coli. Microorganisms 2021, 9, 1737, doi:10.3390/microorganisms9081737. 40. Hagey, J.V.; Bhatnagar, S.; Heguy, J.M.; Karle, B.M.; Price, P.L.; Meyer, D.; Maga, E.A. Fecal Microbial Communities in a Large Representative Cohort of California Dairy Cows. Frontiers in Microbiology 2019, 10. 41. Zhang, J.; Shi, H.; Wang, Y.; Cao, Z.; Yang, H.; Li, S. Effect of Limit-Fed Diets With Different Forage to Concentrate Ratios on Fecal Bacterial and Archaeal Community Composition in Holstein Heifers. Frontiers in Microbiology 2018, 9, doi:10.3389/fmicb.2018.00976. 42. Liu, J.; Liu, F.; Cai, W.; Jia, C.; Bai, Y.; He, Y.; Zhu, W.; Li, R.W.; Song, J. Diet-Induced Changes in Bacterial Communities in the Jejunum and Their Associations with Bile Acids in Angus Beef Cattle. Animal Microbiome 2020, 2, 33, doi:10.1186/s42523-020-00051-7. 43. Barcik, W.; Pugin, B.; Westermann, P.; Perez, N.R.; Ferstl, R.; Wawrzyniak, M.; Smolinska, S.; Jutel, M.; Hessel, E.M.; Michalovich, D.; et al. Histamine-Secreting Microbes Are Increased in the Gut of Adult Asthma Patients. Journal of Allergy and Clinical Immunology 2016, 138, 1491-1494.e7, doi:10.1016/j.jaci.2016.05.049. 44. De Palma, G.; Shimbori, C.; Reed, D.E.; Yu, Y.; Rabbia, V.; Lu, J.; Jimenez-Vargas, N.; Sessenwein, J.; Lopez-Lopez, C.; Pigrau, M.; et al. Histamine Production by the Gut Microbiota Induces Visceral Hyperalgesia through Histamine 4 Receptor Signaling in Mice. Science Translational Medicine 2022, 14, eabj1895, doi:10.1126/scitranslmed.abj1895. 45. Barcik, W.; Wawrzyniak, M.; Akdis, C.A.; O’Mahony, L. Immune Regulation by Histamine and Histamine-Secreting Bacteria. Current Opinion in Immunology 2017, 48, 108–113, doi:10.1016/j.coi.2017.08.011. 186 46. Hansen, Zoe A. Characterization of the Human Gut Resistome, Microbiome, and Metabolome during Enteric Infection, Michigan State University. 47. Furman, O.; Shenhav, L.; Sasson, G.; Kokou, F.; Honig, H.; Jacoby, S.; Hertz, T.; Cordero, O.X.; Halperin, E.; Mizrahi, I. Stochasticity Constrained by Deterministic Effects of Diet and Age Drive Rumen Microbiome Assembly Dynamics. Nat Commun 2020, 11, 1904, doi:10.1038/s41467-020-15652-8. 48. Wang, Q.; Xu, L. Beauvericin, a Bioactive Compound Produced by Fungi: A Short Review. Molecules 2012, 17, 2367–2377, doi:10.3390/molecules17032367. 49. Kusumah, D.; Wakui, M.; Murakami, M.; Xie, X.; Yukihito, K.; Maeda, I. Linoleic Acid, α-Linolenic Acid, and Monolinolenins as Antibacterial Substances in the Heat-Processed Soybean Fermented with Rhizopus Oligosporus. Bioscience, Biotechnology, and Biochemistry 2020, 84, 1285–1290, doi:10.1080/09168451.2020.1731299. 50. Kyohkon, M.; Komiya, T.; Ohwaki, S.; Katsuzaki, H.; Imai, K.; Nishide, R.; Ikeda, H.; Hibasami, H. Sodium 1-Monolinolenin Isolated from Italian Ryegrass (Lolium Multiflorum Lam) Induces Apoptosis in Human Lymphoid Leukemia Molt 4B Cells. International Journal of Molecular Medicine 2001, 7, 265–268. 51. Porras, G.; Chassagne, F.; Lyles, J.T.; Marquez, L.; Dettweiler, M.; Salam, A.M.; Samarakoon, T.; Shabih, S.; Farrokhi, D.R.; Quave, C.L. Ethnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery. Chem. Rev. 2021, 121, 3495–3560, doi:10.1021/acs.chemrev.0c00922. 52. Abreu, A.C.; Coqueiro, A.; Sultan, A.R.; Lemmens, N.; Kim, H.K.; Verpoorte, R.; van Wamel, W.J.B.; Simões, M.; Choi, Y.H. Looking to Nature for a New Concept in Antimicrobial Treatments: Isoflavonoids from Cytisus Striatus as Antibiotic Adjuvants against MRSA. Sci Rep 2017, 7, 3777, doi:10.1038/s41598-017-03716-7. 53. von Wintersdorff, C.J.H.; Penders, J.; van Niekerk, J.M.; Mills, N.D.; Majumder, S.; van Alphen, L.B.; Savelkoul, P.H.M.; Wolffs, P.F.G. Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer. Frontiers in Microbiology 2016, 7. 54. Fornelos, N.; Browning, D.F.; Butala, M. The Use and Abuse of LexA by Mobile Genetic Elements. Trends in Microbiology 2016, 24, 391–401, doi:10.1016/j.tim.2016.02.009. 55. Yuan, X.; Pan, Z.; Jin, C.; Ni, Y.; Fu, Z.; Jin, Y. Gut Microbiota: An Underestimated and Unintended Recipient for Pesticide-Induced Toxicity. Chemosphere 2019, 227, 425–434, doi:10.1016/j.chemosphere.2019.04.088. 56. da Silva, R.R.; Dorrestein, P.C.; Quinn, R.A. Illuminating the Dark Matter in Metabolomics. Proceedings of the National Academy of Sciences 2015, 112, 12549– 12550, doi:10.1073/pnas.1516878112. 187 57. Peisl, B.Y.L.; Schymanski, E.L.; Wilmes, P. Dark Matter in Host-Microbiome Metabolomics: Tackling the Unknowns–A Review. Analytica Chimica Acta 2018, 1037, 13–27, doi:10.1016/j.aca.2017.12.034. 58. Tan, J.; McKenzie, C.; Potamitis, M.; Thorburn, A.N.; Mackay, C.R.; Macia, L. Chapter Three - The Role of Short-Chain Fatty Acids in Health and Disease. In Advances in Immunology; Alt, F.W., Ed.; Academic Press, 2014; Vol. 121, pp. 91–119. 59. Bionaz, M.; Vargas-Bello-Pérez, E.; Busato, S. Advances in Fatty Acids Nutrition in Dairy Cows: From Gut to Cells and Effects on Performance. Journal of Animal Science and Biotechnology 2020, 11, 110, doi:10.1186/s40104-020-00512-8. 60. Van Den Bossche, T.; Arntzen, M.Ø.; Becher, D.; Benndorf, D.; Eijsink, V.G.H.; Henry, C.; Jagtap, P.D.; Jehmlich, N.; Juste, C.; Kunath, B.J.; et al. The Metaproteomics Initiative: A Coordinated Approach for Propelling the Functional Characterization of Microbiomes. Microbiome 2021, 9, 243, doi:10.1186/s40168-021-01176-w. 188 APPENDIX Figure C.1. Bar plot showing four clusters of diet components given to cows in four lactation phases. The abundance is shown as fold change relative to the mean for each feature. Hierarchical clustering with the Ward method was used to classify nutrients into four groups based on their abundance in each lactation stage. Hierarchical cluster 1 contains nutrient components with the most important differences between diets provided to lactating (maintenance and fresh diets) and dry cows (early dry and close-up diets). Cluster 2 groups nutrients and amount of feed intake significantly higher in the maintenance diet compared to the other ones. Similarly, cluster 3 has nutrients given in higher amounts in the close-up phase. Group 4 shows the rest of the nutrients given at different levels in each diet. 189 Cluster 6574 A B Parent mass 422.20666 Precursor charge 1 Figure C.2. Cluster 6574 significantly different between treatments in week 1. A) Box plot based on the relative abundance per sample. B) Mass spectra of the unknown cluster. 190 Component 105 Component 1144 (Z,E)-Tetradeca-9,12-dienol Phase Lactation Dry-off Fresh cows Component 81 Component 13 Figure C.3. Components containing clusters with library ID that were among the 50 most important features for classifying the metabolome by lactation phase with RF. Edge numbers represent the cosine score of spectral similarity between two fragmentation spectra. The size of the clusters represents the number of files. 191 7-hydroxy-3-(4-methoxyphenyl)-4H-chromen-4-one ILEUSYN-PWY: L-isoleucine biosynthesis I (from threonine)|g__Methanobrevibacter.s__Methanobrevibacter_thaueri P461-PWY: hexitol fermentation to lactate, formate, ethanol and acetate|unclassified UNINTEGRATED|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY-6277: superpathway of 5-aminoimidazole ribonucleotide biosynthesis|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY0-166: superpathway of pyrimidine deoxyribonucleotides de novo biosynthesis (E. coli)|unclassified PWY-7184: pyrimidine deoxyribonucleotides de novo biosynthesis I|unclassified PWY-5097: L-lysine biosynthesis VI|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY-2942: L-lysine biosynthesis III|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PANTO-PWY: phosphopantothenate biosynthesis I|g__Clostridium.s__Clostridium_sp_CAG_964 PWY-8112: factor 420 biosynthesis I (archaea)|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY-5198: factor 420 biosynthesis II (mycobacteria)|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY-6749: CMP-legionaminate biosynthesis I Monolinolenin (9c,12c,15c) from NIST14 PWY-6122: 5-aminoimidazole ribonucleotide biosynthesis II|g__Methanobrevibacter.s__Methanobrevibacter_thaueri 2'-DEOXYADENOSINE - 50.0 eV PWY-6151: S-adenosyl-L-methionine salvage I|g__Clostridium.s__Clostridium_sp_CAG_964 PWY-5103: L-isoleucine biosynthesis III|g__Methanobrevibacter.s__Methanobrevibacter_thaueri neobavaisoflavone PWY-6749: CMP-legionaminate biosynthesis I|g__Methanobrevibacter.s__Methanobrevibacter_thaueri Beauvericin [+NH4+] PWY-6163: chorismate biosynthesis from 3-dehydroquinate|g__Methanobrevibacter.s__Methanobrevibacter_thaueri VALSYN-PWY: L-valine biosynthesis|g__Methanobrevibacter.s__Methanobrevibacter_thaueri PWY-7238: sucrose biosynthesis II|g__Clostridium.s__Clostridium_sp_CAG_964 BRANCHED-CHAIN-AA-SYN-PWY: superpathway of branched chain amino acid biosynthesis|g__Methanobrevibacter.s__Methanobrevibacter_thaueri UNINTEGRATED|g__Clostridium.s__Clostridium_sp_CAG_964 PWY-6292: superpathway of L-cysteine biosynthesis (mammalian) 1-Hexadecylpyridinium from NIST14 PWY-7000: kanamycin biosynthesis|unclassified PWY-6545: pyrimidine deoxyribonucleotides de novo biosynthesis III|unclassified LU042103 Ethoxyquin|6-ethoxy-2,2,4-trimethyl-1H-quinoline PWY-6292: superpathway of L-cysteine biosynthesis (mammalian)|unclassified PWY-7000: kanamycin biosynthesis TAG (16:0/18:1/18:2) PWY-6731: starch degradation III|unclassified Figure C.4. Correlation network between nonpolar metabolites and microbial-metabolic pathways. 192 Ectothiorhodospiraceae Asteriusvirus 7-hydroxy-3-(4-methoxyphenyl)-4H-chromen-4-one Atopobium sp. UBA7261 Escherichia phage Envy histamine-C18:1 histamine-C18:0 Streptomyces sp. Gammaproteobacteria bacterium UBA2917 Prevotella sp. UBA7743 Lactobacillus virus LP65 Bacteroidales bacterium UBA4363 Decynediol, tetramethyl, di(2-hydroxyethyl) ether from NIST14 PR311024 Diferuloyl putrescine bacterium UBA4272 Catabacter sp. UBA2862 Kandleria sp. UBA3822 TAG (16:0/18:1/18:2) MLS001048954-01! Porphyromonadaceae bacterium UBA7062 Murine mastadenovirus B Lactobacillus acetotolerans Porphyromonadaceae bacterium UBA7104 (Z)-3-(4-hydroxyphenyl)-N-[4-[[(E)-3-(4-hydroxyphenyl)prop-2-enoyl]amino]butyl]prop-2-enamide 1-Hexadecylpyridinium from NIST14 Lactococcus virus bIL67 1,2-Dilinoleoyl-sn-glycero-3-phosphocholine from NIST14 Clostridium sp. L2-50 LU042103 Ethoxyquin|6-ethoxy-2,2,4-trimethyl-1H-quinoline Prevotella sp. UBA640 Gammaproteobacteria bacterium UBA7282 Porphyromonadaceae bacterium UBA7149 Shigella virus 7502Stx3-Hydroxy-4-methoxycinnamic acid from NIST14 Bacillales bacterium UBA5578 Prevotella sp. UBA2859 Alphaproteobacteria Clostridiales bacterium UBA7001 Thermothelomyces thermophila Tenericutes Lactobacillus salivarius Cyanobacteria bacterium UBA1221 Byssochlamys nivea Alphaproteobacteria bacterium UBA3650 Escherichia virus K1-5 Succinatimonas sp. UBA1264 Penicillium chrysogenum virus NCGC00179905-02!(E)-3-(4-methoxyphenyl)-1-[2,4,6-trimethoxy-3-(3-methylbut-2-enyl)phenyl]prop-2-en-1-oneTriphenylphosphine oxide from NIST14 uncultured Slackia sp. Pediococcus Lachnospira sp. UBA4290 uncultured Ruminococcaceae bacterium Arachidic acid Treponema saccharophilum Taylorella Sorbitane Monooleate - Polysorbate 20 in-source fragment Saccharomyces Myristamidopropyl betaine|carboxymethyl-dimethyl-[3-(tetradecanoylamino)propyl]azanium Lysobacter Lactobacillus phage LfeSau Beauvericin [+NH4+] Clostridium paraputrificum Sorbitane Monopalmitate - Polysorbate 40 in-source fragment Kurthia Thermanaerovibrio Escherichia virus K1H Salmonella phage BP12B neobavaisoflavone Clostridium colicanis Seuratvirus [Propionibacterium] humerusii Sorbitane Monostearate - Polysorbate 60 in-source fragment Paenibacillus antibioticophila Monolinolenin (9c,12c,15c) from NIST14 Clostridiales bacterium UBA3223 TAG (18:1/18:2/18:2) 5-Hydroxy-2',4',7,8-Tetramethoxyflavone Trichococcus Coprococcus comes Pseudomonas stutzeri Alphaproteobacteria bacterium UBA3760 Escherichia virus K1E Salmonella virus SS3e Enterobacteria phage vB_EcoP_ACG-C91 Dyadobacter Beta-lactam cfxA2 Pesticide4_Piperonyl butoxide_C19H30O5_Butacide traF Alphabaculovirus Trichoderma atroviride Paenibacillus macerans lumichrome Rhizopus azygosporus Aurantioobtusin Salmonella virus LSPA1 Methylacidiphilum Methanococcus maripaludis Redivirus Lactobacillus phage iA2 NCGC00386021-01!8,8-dimethyl-3-(2,4,5-trimethoxyphenyl)pyrano[2,3-f]chromen-4-one Azonexaceae Palmitoyl ethanolamide from NIST14 9(10)-EpOME from NIST14 Salmonella virus SE2 Microbacterium sp. MEJ108Y Pepper chlorotic spot virus Methanobrevibacter sp. UBA187 Acinetobacter sp. 983759 [Eubacterium] hallii NCGC00180139-02!3-(6-methoxy-1,3-benzodioxol-5-yl)-8,8-dimethylpyrano[2,3-f]chromen-4-one Agrobacterium RP003003 Myristoyl Ethanolamide|N-(2-hydroxyethyl)tetradecanamide Escherichia virus K1G Porphyromonas gulae 2'-DEOXYADENOSINE - 50.0 eV Dimethyldioctadecylammonium cation from NIST14 Succinivibrio sp. (2S,3S,4S,5R,6R)-6-[[(3S,4S,6aR,6bS,8aR,9R,12aS,14bR)-9-hydroxy-4-(hydroxymethyl)-4,6a,6b,8a,11,11,14b-heptamethyl-1,2,3,4a,5,6,7,8,9,10,12,12a,14,14a-tetradecahydropicen-3-yl]oxy]-5-[(2S,3R,4S,5R,6R)-4,5-dihydroxy-6-(hydroxymethyl)-3-[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxyoxan-2-yl]oxy-3,4-dihydroxyoxane-2-carboxylic acid 1,3-Dicyclohexylurea from NIST14 Elizabethkingia 9,10-Dihydroxy-12Z-octadecenoic acid from NIST14 Streptococcus dysgalactiae Scedosporium apiospermum Dehydrocostus lactone Debaryomyces hansenii uncultured Acinetobacter sp. [Clostridium] saccharogumia [(3aR,4S,6E,10Z,11aR)-10-(hydroxymethyl)-6-methyl-3-methylidene-2-oxo-3a,4,5,8,9,11a-hexahydrocyclodeca[b]furan-4-yl] (Z)-4-acetyloxy-2-(hydroxymethyl)but-2-enoate Figure C.5. Correlation network between nonpolar metabolites, microbial taxa, and genes. 193 Succinivibrio sp. PTERIN - 50.0 eV Verrucomicrobia bacterium UBA3841 Escherichia phage Envy Succinivibrio dextrinosolvens fyuA Microbacterium virus Min1 Prevotellaceae bacterium UBA3702 1-Octadecanoyl-2-octadecenoyl-sn-glycero-3-phosphocholine from NIST14 AU252106 4-Hydroxy-1-(2-hydroxyethyl)-2,2,6,6-tetramethylpiperidine|1-Piperidineethanol, 4-hydroxy-2,2,6,6-tetramethyl-|1-(2-hydroxyethyl)-2,2,6,6-tetramethylpiperidin-4-ol Mycobacterium phage Enkosi Succinatimonas sp. UBA4709 Androstane-3,17-diol Acetobacter Lactococcus virus bIL67 Corynebacterium camporealensis Vequintavirus Bacillus sp. J13 1,2-Diheptadecanoyl-sn-glycero-3-phosphocholine from NIST14 Candidatus Saccharibacteria bacterium UBA3723 MDR-Efflux-pump bmr N-Acetylcadaverine - 40.0 eV Weissella phage phiYS61 Clostridium paraputrificum Balneolaceae Bos taurus polyomavirus 1 RP025602 Stearoyl-L-Carnitine|stearoylcarnitine|3-octadecanoyloxy-4-(trimethylazaniumyl)butanoate Selenomonas sp. AE3005 Macrolide l 2009 Branch Lachnospira sp. UBA4290 Acinetobacter Saccharopolyspora 2,3-dimethoxybenzaldehyde N,N-Dimethyldodecylamine N-oxide Isonicotinamide from NIST14 N,N,N-TRIMETHYLLYSINE 3695-24-7 Thermobispora bispora uncultured Spirochaetes bacterium [Propionibacterium] humerusii Figure C.6. Correlation network between polar metabolites, microbial taxa, and genes. 194 Escherichia phage slur01 Enterobacteria phage mEp460 PWY-6608: guanosine nucleotides degradation III|unclassified PWY-6606: guanosine nucleotides degradation II SALVADEHYPOX-PWY: adenosine nucleotides degradation PWY0-1319:II|unclassified CDP-diacylglycerol biosynthesis II PWY-5675: nitrate reduction V (assimilatory)|unclassified Regulator cpxR Escherichia virus Cajan Membrane-fusion-protein emrA Salmonella phage vB_SemP_Emek PWY-5667: CDP-diacylglycerol biosynthesis I MDR-regulator gadX Membrane-fusion-protein acrE MDR-Efflux-pump mdtD Escherichia virus Seurat Escherichia virus pro147 yddA PWY-5675: nitrate reduction V (assimilatory) MDR-Efflux-pump mdtA SALVADEHYPOX-PWY: adenosine nucleotides degradation II PWY-6353: purine nucleotides degradation II (aerobic) Sensor-kinase baeS Repressor-for-mdr-efflux-pump crp MDR-transporter mdtK Sensor-protein evgS UNINTEGRATED|g__Escherichia.s__Escherichia_coli H NS Enterobacteria phage Sf101 Involved-in-polymyxin-resistance pmrB Salmonella virus HK620 THRESYN-PWY: superpathway of L-threonine biosynthesis|g__Escherichia.s__Escherichia_coli traA Bacitracin-resistance bacA Escherichia virus HK022 Salmonella phage PWY-3001: RE-2010 superpathway of L-isoleucine biosynthesis I|g__Escherichia.s__Escherichia_coli Beta-lactam-resistance blaAmpC PWY4FS-7: phosphatidylglycerol biosynthesis I (plastidic)|unclassified ILEUSYN-PWY: L-isoleucine biosynthesis I (from threonine)|g__Escherichia.s__Escherichia_coli Leclercia adecarboxylata PHOSLIPSYN-PWY: superpathway of phospholipid biosynthesis I (bacteria)|unclassified Escherichia virus HK106 PWY-7222: guanosine MDR-protein mdtMPWY-7211: superpathway of pyrimidine deoxyribonucleotides deoxyribonucleotides de novo biosynthesis de novo biosynthesis II|g__Escherichia.s__Escherichia_coli PWY-6527: stachyose PWY-702: L-methionine biosynthesis II|g__Escherichia.s__Escherichia_coli Repressor-of-efflux-complex phoP degradation|g__Escherichia.s__Escherichia_coli Escherichia virus HK633 Escherichia virus mEpX1 Enterobacteria phage mEp237 lpfA Enterobacteria phage mEp043 c-1 MDR-Efflux-pump mdtB PWY0-301: L-ascorbate degradation I (bacterial, anaerobic) PWY0-1261: anhydromuropeptides recycling I Shigella virus Sf6 PHOSLIPSYN-PWY: superpathway of phospholipid biosynthesis I (bacteria) Escherichia virus 24B Escherichia virus HK97 MDR-translocase mdfA Enterobacteria phage P4 PWY-7663: gondoate biosynthesis (anaerobic)|g__Escherichia.s__Escherichia_coli Escherichia coli ORNDEG-PWY: superpathway PWY-7220: adenosine of ornithine deoxyribonucleotides de novo biosynthesis BRANCHED-CHAIN-AA-SYN-PWY: degradation II|g__Escherichia.s__Escherichia_coli superpathway of branched chain amino acid biosynthesis|g__Escherichia.s__Escherichia_coli MDR-protein mdtH PWY0-1586: peptidoglycan maturation (meso-diaminopimelate containing)|g__Escherichia.s__Escherichia_coli NONOXIPENT-PWY: pentose phosphate pathway (non-oxidative branch) I|g__Escherichia.s__Escherichia_coli PWY-6895: superpathway of thiamine VALSYN-PWY: diphosphate L-valine biosynthesis II|unclassified biosynthesis|g__Escherichia.s__Escherichia_coli FOLSYN-PWY: superpathway of tetrahydrofolate biosynthesis and salvage Shigella phage SfIV Beta-lactam pbp4 Ecoli UDPNAGSYN-PWY: UDP-N-acetyl-D-glucosamine biosynthesis I|g__Escherichia.s__Escherichia_coli PWY-7783: plasmalogen degradation Escherichia virus Min27 PWY-5136: fatty acid β-oxidation II (plant peroxisome)|g__Escherichia.s__Escherichia_coli MDR-Efflux-pump mdtC PWY4FS-7: phosphatidylglycerol biosynthesis I (plastidic) PWY4FS-8: phosphatidylglycerol biosynthesis II (non-plastidic)|unclassified PWY-5189: tetrapyrrole biosynthesis II (from glycine) ASPASN-PWY: PWY-7357: thiamine phosphate superpathway formation of L-aspartate from pyrithiamine and L-asparagine and oxythiamine biosynthesis|g__Escherichia.s__Escherichia_coli (yeast)|g__Escherichia.s__Escherichia_coli Enterobacteria phage cdtI PWY-5913: partial TCA cycle (obligate autotrophs)|g__Escherichia.s__Escherichia_coli Sensor-protein phoQ Escherichia virus fiAA91ss Efflux-pump baeR PWY-6353: purine nucleotides degradation II (aerobic)|unclassified Enterobacteria phage VT2phi_272 Salmonella virus PsP3 PWY0-301: L-ascorbate degradation I (bacterial, anaerobic)|g__Escherichia.s__Escherichia_coli PWY4FS-8: phosphatidylglycerol biosynthesis II (non-plastidic) Enterobacteria phage IME10 GLUCARGALACTSUPER-PWY: superpathway of D-glucarate and D-galactarate degradation Enterobacteria phage mEp235 Salmonella phage SEN1 Enterobacteria phage SfV PWY-5103: L-isoleucine biosynthesis III|g__Escherichia.s__Escherichia_coli PWY-5136: fatty acid β-oxidationShigella PENTOSE-P-PWY: pentose phosphate pathway|g__Escherichia.s__Escherichia_coli II (plantdysenteriae peroxisome) Escherichia virus 186 Plasmid 38606113 MDR-Efflux-pump mdtG Shigella sp. Inner-membrane-transporter acrF GLUCARDEG-PWY: D-glucarate degradation I Escherichia virus P2 GALACTITOLCAT-PWY: galactitol degradation Enterobacterial phage mEp213 PWY-6897: thiamine diphosphate PWY-6606: guanosine nucleotides degradation II|unclassified salvage II|g__Escherichia.s__Escherichia_coli CITRULBIO-PWY: L-citrulline biosynthesis ARG+POLYAMINE-SYN: superpathway of arginine and polyamine biosynthesis PWY-6803: phosphatidylcholine acyl editing|g__Escherichia.s__Escherichia_coli Translocase emrB polymyxin-and-cationic-antimicrobial-peptides arnA Yersinia virus L413C PWY-7210: pyrimidine deoxyribonucleotides biosynthesis from CTP Transporter-for-efflux-complex mdtF PWY-6612: superpathway of tetrahydrofolate biosynthesis GALACTARDEG-PWY: GLUCARDEG-PWY: D-glucarateD-galactarate degradation degradation I I|g__Escherichia.s__Escherichia_coli Enterobacteria phage HK140 astA MDR-Efflux-pump mdtO Enterobacteria phage SfI Shigella phage SfII MDR-Efflux-pump mdtN PWY-7323: superpathway of GDP-mannose-derived O-antigen building blocks biosynthesis Involved-in-polymyxin-resistance pmrF Beta-lactam-resistance blaAmpH PWY-6545: pyrimidine deoxyribonucleotides dePWY-5656: novopbp2 Beta-lactam mannosylglycerate biosynthesis I biosynthesis Ecoli III Ruminococcaceae PWY-7184: pyrimidine deoxyribonucleotides bacterium UBA1207 de novo biosynthesis I PWY0-166: superpathway of pyrimidine deoxyribonucleotides de novo biosynthesis (E. coli) POLYAMSYN-PWY: superpathway of polyamine biosynthesis I Escherichia phage TL-2011b MDR-Efflux-pump acrD PWY-6305: superpathway of putrescine biosynthesis Enterobacteria phage YYZ-2008 Escherichia virus pro483 Figure C.7. Network module with the highest number of connections including microbial pathways, microbial taxa, and genes. 195 Figure C.8. Relative abundance of Monensin and Avermectin detected with the nonpolar mode. 196 Figure C.9. Group of polar metabolites significantly higher on lactation phase 197 Figure C.10. Cluster of microbial pathways and polar and nonpolar metabolites significantly higher in fresh cows. 198 CHAPTER 5 Conclusions and Future Directions 199 Bovines are the most important source of protein for humans, and their demand is expected to increase abreast with the human population [1]. The symbiotic relationship with their complex gastrointestinal (GI) microbiome makes ruminants the most efficient food-producing animals [2, 3]. Despite the benefits of bovine production, the intensification and expansion of cattle farms will escalate, and potentially their negative footprints such as the transmission of foodborne pathogens [4] and antibiotic-resistant bacteria [5–14]. The main public health concerns associated with bovines are their role as primary carriers of STEC [15, 16] and the use of critically important antibiotics for human health applied in these animals [17]. Thus, we sought to identify ecological associations between the fecal microbiome of bovines with both STEC shedding and intramammary (IMM) use of third-generation cephalosporins. While EHEC O157:H7 is the most studied STEC serotype in cattle [18, 19], prior studies failed to characterize the microbiome of animals carrying non-O157 STEC serotypes, even though they cause about two-thirds of STEC infections in humans [20]. Hence, in Chapter 2 we used 16s rRNA sequencing to characterize the hindgut microbiome of cattle carrying non-O157 STEC at two levels: 1) between farms with a high and low prevalence of STEC; and 2) between animals carrying STEC and controls. At the farm level, significant differences were observed in the microbiome composition between facilities with a low and high prevalence of STEC, also referred to as LSP and HSP, independently of breed (i.e., beef and dairy). Though, these differences in the microbiome were associated with the diet composition. HSP farms had forage- dominant diets characterized by a higher abundance of Firmicutes and Bacteroidetes and a lower abundance of Proteobacteria. Whereas LSP farms had diets with higher levels of grains and presented a lower microbiome diversity. When comparing the microbiome composition of shedders and non-shedders of STEC, differences were observed in the abundance of some taxa 200 but not in the microbiome richness. Yet, longitudinal approaches of STEC carriage over time revealed different patterns of microbiome alpha diversity during STEC shedding related to the pathogen prevalence in the farms. For instance, STEC shedding was associated with a higher richness in animals from LSP but not in HSP farms, suggesting that preexposure to STEC plays a role in the microbiome richness during its shedding which can potentially be associated with immunotolerance. Finally, factors priorly identified with a higher prevalence of STEC in dairy farms, such as days in milk and warm temperatures affected the abundance of taxa associated with STEC shedding. In summary, Chapter 2 identified that STEC shedding by cattle is multifactorial, where diet independently of the breed is the main force shaping the hindgut microbiome of cattle carrying the pathogen. Despite the identification of distinctive microbiome diversity and bacterial taxa associated with STEC shedding at the farm and individual levels, different dynamics were observed among animals with varying levels of exposure. Through our approach, however, we cannot distinguish if STEC carriage affects the microbiome composition or if a rich microbiome enables better colonization of STEC. Therefore, this investigation calls for the identification of other factors that may affect STEC colonization in the bovine’s gut. Examples of factors that can alter the gut environment with consequences in the microbiome composition and STEC establishment include quorum sensing signals [21], antimicrobial peptides [22], diet- associated metabolites [23, 24], immunoglobulin A [25, 26], mobile genetic elements (MGEs) [27] and intestinal pH [28]. Future approaches should include metabolomics, metaproteomics, and MGE (i.e., bacteriophage) analyses of the intestinal content and mucosa where STEC colonizes [29, 30]. Identifying molecular factors in-vivo that influence the establishment of 201 STEC could help design alternatives to decrease the levels of this foodborne pathogen in cattle, such as diet modifications and probiotics. Furthermore, the use of medically important antibiotics in cattle farms is a global concern, particularly for the potential emergence of resistant bacteria to last-resort drugs [17, 31]. Third-generation cephalosporins are critically important antibiotics of highest priority given the number of rising infections with ESBL Enterobacteriaceae, which are the main cause of deaths by multi-resistant bacteria worldwide [31, 32]. Ceftiofur is a third-generation cephalosporin widely used in dairy farms to treat mastitis, metritis, respiratory infections, and foot rot [33–35]. Studies demonstrated the increase of ESBL genes in the resistome of cattle after its parenteral application [36, 37], but there is no evidence of the effect of intramammary (IMM) ceftiofur in the abundance of resistant bacteria in cattle feces. Therefore, in Chapter 3 we studied the effects of a single application of 2 g of IMM ceftiofur in dairy cows at dry off in the fecal resistome and microbiome over a 9-week time frame. Plate counting of Gram-negative and Gram-positive bacteria showed no effect of the antibiotic IMM treatment in the number of β-lactam (i.e., ceftiofur and ampicillin) resistant bacteria found in feces. Low levels of Gram-negative bacteria resistant to ceftiofur were detected in dairy cows and were not associated with IMM treatment. Metagenomics sequencing did not identify associations between the IMM ceftiofur and the diversity of the fecal microbiome and resistome. However, a persistent increment of β-lactam ARGs was observed in cows treated with IMM ceftiofur, particularly of genes encoding ESBLs (i.e., bla-CFX-A, bla-CMY, and bla-ACI). Correlation networks revealed the association of ESBL genes with bacteriophages and plasmids that can play a role in the mobilization of these genes across different taxa. Contig analysis and in-vitro identification showed that β-lactam resistance was carried by three phyla: Bacteroidetes, 202 Firmicutes, and Proteobacteria. Enhanced β-lactam ARG dispersion in the microbial communities was influenced by IMM ceftiofur treatment, evidenced a week after the treatment by a higher number of correlations between ARGs, different bacterial species, and MGEs. In short, this study revealed that a single application of IMM ceftiofur in dairy cows at dry-off persistently increased the levels of ESBL genes in cattle feces. Moreover, third-generation cephalosporin application enhanced the mobilization of ARGs across different bacterial phyla in the intestinal microbiome. However, the detection of ESBL Enterobacteriaceae by culture-based methods showed low levels of shedding by dairy cattle with no significant differences between treatments. Future studies should focus on the characterization of taxa carrying ESBLs to identify with better resolution the MGEs implicated in their dispersion, as well as the co-selection of other ARGs. Furthermore, molecular identification of ESBL bacterial hosts can enable the comparison of isolates recovered from cattle with the ones from human infections to understand their genetic relationship and zoonotic potential. Enrichment of ESBL-resistant bacteria with subsequent long-read sequencing has been identified as an effective method for the characterization of plasmids and bacterial hosts that could constitute minorities in the microbiota of fresh feces [38, 39]. Whole-genome sequencing (WGS) of ceftiofur-resistant isolates can also help identify virulence genes and the evolution events associated with MGEs and ARG acquisition. Finally, identifying the evolution and dispersion of ESBL Enterobacteriaceae in manure is necessary to measure the risks of dispersion of these resistant threats into the environment; particularly, given that ceftiofur is mainly eliminated by urine [35, 40] and that Enterobacteriaceae can increment their abundance in open environments [41]. 203 Cattle rely on their microbiota to produce cofactors, 80% of their protein [42], and three- quarters of their energy [3]. However, different factors including diet, pathogens, and antibiotics can induce dysbiosis in the cattle microbiota with negative consequences on the bovine’s health and production [43]. Thus, in Chapter 4 we explored the potential effects of IMM ceftiofur treatment in the functional microbiome and metabolome of cattle feces from Chapter 3. Our results identified slight changes in the functional microbiome induced by the third-generation cephalosporin. Differences in the functional microbial profiles were only observed in weeks 5 and 9 when cows treated with IMM ceftiofur had a lower abundance of energy-related pathways and higher metabolic activity of Bifidobacterium. Although differences were identified in the microbial pathway abundance, the metabolome analyzed through LC/MS did not differ between treatment groups. Unique metabolome composition was observed between time points that corresponded to three phases of lactation (i.e., lactating, dry, and fresh cows) characterized by unique diets and physiology. Multi-omics analyses combining metagenomics and metabolomics data allowed the identification of interactions between microorganisms and the gut environment. For instance, high levels of histamine were associated with a greater abundance of Actinobacteria and Proteobacteria in the lactation phase. Moreover, correlations between lytic bacteriophages, ESBL genes, natural antimicrobial compounds and pesticides indicate the role of diet-associated compounds in the selection of MGEs and ARGs. In sum, Chapter 4 revealed that IMM ceftiofur has minor impacts on the functional microbiome that were observed at the end of the dry-off period. Furthermore, the multi-omics approach exposed the role of natural antimicrobial compounds in the activation of lytic bacteriophages of Enterobacteriaceae which can also enhance the mobilization of ARGs. Given the identification of differences in the microbial profiles induced by the IMM ceftiofur treatment in fresh cows, information regarding 204 the milk performance efficiency of these bovines can help associate the impact of changes in microbial pathways in the cow’s productivity. In addition, future studies can include the identification of short-chain fatty acids (SCFAs) given their relevance in production in cattle [58], along with analysis of host-derived metabolites (i.e., hormones and biomarkers of inflammation) to determine interactions between cattle and their microbiome. Altogether, this study identified that changes in the gut microbiome associated with the shedding of non-O157 STEC are multifactorial, with diet and level of exposure as the most relevant factors shaping changes in the microbiome composition of STEC shedders. Despite identifying differences in the abundance of certain taxa associated with the carriage of STEC, more research is needed to understand the influence of metabolites derived from the host, diet, and microbiota in the colonization of this pathogen to help design alternative methods for STEC reduction in cattle farms. Furthermore, the use of IMM third-generation cephalosporins in dairy cows at dry-off selectively and persistently increased the abundance of ESBL genes with no repercussions in the microbiome or resistome diversity. ESBL genes were distributed across different phyla and their associations with MGEs may aid their mobilization in the GI microbial communities. Nevertheless, ESBL Enterobacteriaceae were minorities in the cattle feces and their abundance was not associated with the antibiotic treatment. Finally, metabolic profiling of the microbial communities showed minor effects of the IMM treatment with ceftiofur and no changes in the metabolic profile. Multi-omics approaches revealed the presence of pesticides and natural antibiotics associated with bacteriophages and ESBL genes, highlighting the importance of metabolites present in the diet in ARG mobility. These studies revealed complex interactions between microbial communities, pathogens, and antibiotics in cattle. Future research should 205 include the influence of these interactions with the host as well as environmental factors that may risk the persistence of pathogens and resistance in cattle farms. 206 REFERENCES 1. McLeod, A. World Livestock 2011-Livestock in Food Security.; Food and agriculture organization of the United Nations (FAO), 2011; ISBN 92-5-107013-X. 2. Mottet, A.; de Haan, C.; Falcucci, A.; Tempio, G.; Opio, C.; Gerber, P. Livestock: On Our Plates or Eating at Our Table? A New Analysis of the Feed/Food Debate. Global Food Security 2017, 14, 1–8, doi:10.1016/j.gfs.2017.01.001. 3. Mackie, R.I. Mutualistic Fermentative Digestion in the Gastrointestinal Tract: Diversity and Evolution. Integrative and Comparative Biology 2002, 42, 319–326. 4. Gilchrist, M.J.; Greko, C.; Wallinga, D.B.; Beran, G.W.; Riley, D.G.; Thorne, P.S. The Potential Role of Concentrated Animal Feeding Operations in Infectious Disease Epidemics and Antibiotic Resistance. Environmental Health Perspectives 2007, 115, 313– 316, doi:10.1289/ehp.8837. 5. Thomas, M.; Webb, M.; Ghimire, S.; Blair, A.; Olson, K.; Fenske, G.J.; Fonder, A.T.; Christopher-Hennings, J.; Brake, D.; Scaria, J. Metagenomic Characterization of the Effect of Feed Additives on the Gut Microbiome and Antibiotic Resistome of Feedlot Cattle. Sci Rep 2017, 7, 12257, doi:10.1038/s41598-017-12481-6. 6. Noyes, N.R.; Yang, X.; Linke, L.M.; Magnuson, R.J.; Dettenwanger, A.; Cook, S.; Geornaras, I.; Woerner, D.E.; Gow, S.P.; McAllister, T.A.; et al. Resistome Diversity in Cattle and the Environment Decreases during Beef Production. eLife 2016, 5, e13195, doi:10.7554/eLife.13195. 7. Noyes, N.R.; Yang, X.; Linke, L.M.; Magnuson, R.J.; Cook, S.R.; Zaheer, R.; Yang, H.; Woerner, D.R.; Geornaras, I.; McArt, J.A.; et al. Characterization of the Resistome in Manure, Soil and Wastewater from Dairy and Beef Production Systems. Sci Rep 2016, 6, 24645, doi:10.1038/srep24645. 8. Doster, E.; Rovira, P.; Noyes, N.R.; Burgess, B.A.; Yang, X.; Weinroth, M.D.; Lakin, S.M.; Dean, C.J.; Linke, L.; Magnuson, R.; et al. Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period. Front. Microbiol. 2018, 9, 1715, doi:10.3389/fmicb.2018.01715. 9. Bickhart, D.M.; Watson, M.; Koren, S.; Panke-Buisse, K.; Cersosimo, L.M.; Press, M.O.; Van Tassell, C.P.; Van Kessel, J.A.S.; Haley, B.J.; Kim, S.W.; et al. Assignment of Virus and Antimicrobial Resistance Genes to Microbial Hosts in a Complex Microbial Community by Combined Long-Read Assembly and Proximity Ligation. Genome Biol 2019, 20, 153, doi:10.1186/s13059-019-1760-x. 10. Rovira, P.; McAllister, T.; Lakin, S.M.; Cook, S.R.; Doster, E.; Noyes, N.R.; Weinroth, M.D.; Yang, X.; Parker, J.K.; Boucher, C.; et al. Characterization of the Microbial Resistome in Conventional and “Raised Without Antibiotics” Beef and Dairy Production Systems. Frontiers in Microbiology 2019, 10. 207 11. Haley, B.J.; Kim, S.-W.; Salaheen, S.; Hovingh, E.; Van Kessel, J.A.S. Differences in the Microbial Community and Resistome Structures of Feces from Preweaned Calves and Lactating Dairy Cows in Commercial Dairy Herds. Foodborne Pathogens and Disease 2020, 17, 494–503, doi:10.1089/fpd.2019.2768. 12. Lim, S.-K.; Kim, D.; Moon, D.-C.; Cho, Y.; Rho, M. Antibiotic Resistomes Discovered in the Gut Microbiomes of Korean Swine and Cattle. GigaScience 2020, 9, giaa043, doi:10.1093/gigascience/giaa043. 13. Pitta, D.W.; Indugu, N.; Toth, J.D.; Bender, J.S.; Baker, L.D.; Hennessy, M.L.; Vecchiarelli, B.; Aceto, H.; Dou, Z. The Distribution of Microbiomes and Resistomes across Farm Environments in Conventional and Organic Dairy Herds in Pennsylvania. Environmental Microbiome 2020, 15, 21, doi:10.1186/s40793-020-00368-5. 14. Xue, M.-Y.; Xie, Y.-Y.; Zhong, Y.-F.; Liu, J.-X.; Guan, L.L.; Sun, H.-Z. Ruminal Resistome of Dairy Cattle Is Individualized and the Resistotypes Are Associated with Milking Traits. anim microbiome 2021, 3, 18, doi:10.1186/s42523-021-00081-9. 15. Duffy, G.; McCabe, E. Veterinary Public Health Approach to Managing Pathogenic Verocytotoxigenic Escherichia coli in the Agri-Food Chain. Microbiol Spectr 2014, 2, 2.5.21, doi:10.1128/microbiolspec.EHEC-0023-2013. 16. Gyles, C.L. Shiga Toxin-Producing Escherichia coli: An Overview. Journal of Animal Science 2007, 85, E45–E62, doi:10.2527/jas.2006-508. 17. FDA Summary Report on Antimicrobials Sold or Distributed for Use in Food-Producing Animals; FDA: USA, 2021; pp. 1–49. 18. Berry, E.D.; Wells, J.E. Escherichia coli O157:H7: Recent Advances in Research on Occurrence, Transmission, and Control in Cattle and the Production Environment. Adv Food Nutr Res 2010, 60, 67–117, doi:10.1016/S1043-4526(10)60004-6. 19. Callaway, T.R.; Carr, M.A.; Edrington, T.S.; Anderson, R.C.; Nisbet, D.J. Diet, Escherichia coli O157:H7, and Cattle: A Review after 10 Years. Curr Issues Mol Biol 2009, 11, 67–79. 20. Scallan, E.; Hoekstra, R.M.; Angulo, F.J.; Tauxe, R.V.; Widdowson, M.-A.; Roy, S.L.; Jones, J.L.; Griffin, P.M. Foodborne Illness Acquired in the United States—Major Pathogens. Emerging infectious diseases 2011, 17, 7. 21. Sperandio, V. SdiA Sensing of Acyl-Homoserine Lactones by Enterohemorrhagic E. coli (EHEC) Serotype O157: H7 in the Bovine Rumen. Gut Microbes 2010, 1, 432–435, doi:10.4161/gmic.1.6.14177. 22. Marquardt, R.R.; Li, S. Antimicrobial Resistance in Livestock: Advances and Alternatives to Antibiotics. Animal Frontiers 2018, 8, 30–37, doi:10.1093/af/vfy001. 208 23. Diard, M.; Garry, L.; Selva, M.; Mosser, T.; Denamur, E.; Matic, I. Pathogenicity- Associated Islands in Extraintestinal Pathogenic Escherichia coli Are Fitness Elements Involved in Intestinal Colonization. J Bacteriol 2010, 192, 4885–4893, doi:10.1128/JB.00804-10. 24. Paddock, Z.D.; Renter, D.G.; Shi, X.; Krehbiel, C.R.; DeBey, B.; Nagaraja, T.G. Effects of Feeding Dried Distillers Grains with Supplemental Starch on Fecal Shedding of Escherichia coli O157:H7 in Experimentally Inoculated Steers1. Journal of Animal Science 2013, 91, 1362–1370, doi:10.2527/jas.2012-5618. 25. Malmuthuge, N.; Guan, L.L. Understanding Host-Microbial Interactions in Rumen: Searching the Best Opportunity for Microbiota Manipulation. J Animal Sci Biotechnol 2017, 8, 8, doi:10.1186/s40104-016-0135-3. 26. Hooper, L.V.; Littman, D.R.; Macpherson, A.J. Interactions between the Microbiota and the Immune System. science 2012, 336, 1268–1273. 27. Pinto, G.; Sampaio, M.; Dias, O.; Almeida, C.; Azeredo, J.; Oliveira, H. Insights into the Genome Architecture and Evolution of Shiga Toxin Encoding Bacteriophages of Escherichia coli. BMC Genomics 2021, 22, 366, doi:10.1186/s12864-021-07685-0. 28. Mao, S.; Zhang, R.; Wang, D.; Zhu, W. Impact of Subacute Ruminal Acidosis (SARA) Adaptation on Rumen Microbiota in Dairy Cattle Using Pyrosequencing. Anaerobe 2013, 24, 12–19. 29. Dziva, F.; van Diemen, P.M.; Stevens, M.P.; Smith, A.J.; Wallis, T.S. Identification of Escherichia coli O157 : H7 Genes Influencing Colonization of the Bovine Gastrointestinal Tract Using Signature-Tagged Mutagenesis. Microbiology 2004, 150, 3631–3645, doi:10.1099/mic.0.27448-0. 30. Naylor, S.W.; Roe, A.J.; Nart, P.; Spears, K.; Smith, David.G.E.; Low, J.C.; Gally, D.L. Escherichia coli O157 : H7 Forms Attaching and Effacing Lesions at the Terminal Rectum of Cattle and Colonization Requires the LEE4 Operon. Microbiology 2005, 151, 2773–2781, doi:10.1099/mic.0.28060-0. 31. World Health Organization Critically Important Antimicrobials for Human Medicine; 6th rev.; World Health Organization: Geneva, 2019; ISBN 978-92-4-151552-8. 32. Murray, C.J.; Ikuta, K.S.; Sharara, F.; Swetschinski, L.; Aguilar, G.R.; Gray, A.; Han, C.; Bisignano, C.; Rao, P.; Wool, E.; et al. Global Burden of Bacterial Antimicrobial Resistance in 2019: A Systematic Analysis. The Lancet 2022, 399, 629–655, doi:10.1016/S0140-6736(21)02724-0. 33. U.S. Food & Drug Administration (FDA) Code of Federal Regulations, Title 21, Volume 6, CITE: 21CFR522.313a. Implantation or Injectable Dosage Form New Animal Drugs. Ceftiofur Crystalline Free Acid Available online: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=522.313a (accessed on 23 October 2022). 209 34. U.S. Food & Drug Administration (FDA) Code of Federal Regulations, Title 21, Volume 6, CITE: 21CFR526.313. Intramammary Dosage Form New Animal Drugs. Ceftiofur Available online: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=526.313 (accessed on 23 October 2022). 35. Hornish, R.; Katarski, S. Cephalosporins in Veterinary Medicine - Ceftiofur Use in Food Animals. CTMC 2002, 2, 717–731, doi:10.2174/1568026023393679. 36. Alali, W. q.; Scott, H. m.; Norby, B.; Gebreyes, W.; Loneragan, G. h. Quantification of the BlaCMY-2 in Feces from Beef Feedlot Cattle Administered Three Different Doses of Ceftiofur in a Longitudinal Controlled Field Trial. Foodborne Pathogens and Disease 2009, 6, 917–924, doi:10.1089/fpd.2009.0271. 37. Weinroth, M.D.; Scott, H.M.; Norby, B.; Loneragan, G.H.; Noyes, N.R.; Rovira, P.; Doster, E.; Yang, X.; Woerner, D.R.; Morley, P.S.; et al. Effects of Ceftiofur and Chlortetracycline on the Resistomes of Feedlot Cattle. Applied and Environmental Microbiology 2018, 84, e00610-18, doi:10.1128/AEM.00610-18. 38. Marano, R.B.M.; Gupta, C.L.; Cozer, T.; Jurkevitch, E.; Cytryn, E. Hidden Resistome: Enrichment Reveals the Presence of Clinically Relevant Antibiotic Resistance Determinants in Treated Wastewater-Irrigated Soils. Environ. Sci. Technol. 2021, 55, 6814–6827, doi:10.1021/acs.est.1c00612. 39. Qian, X.; Gunturu, S.; Sun, W.; Cole, J.R.; Norby, B.; Gu, J.; Tiedje, J.M. Long-Read Sequencing Revealed Cooccurrence, Host Range, and Potential Mobility of Antibiotic Resistome in Cow Feces. PNAS 2021, 118, doi:10.1073/pnas.2024464118. 40. Foster, D.M.; Jacob, M.E.; Farmer, K.A.; Callahan, B.J.; Theriot, C.M.; Kathariou, S.; Cernicchiaro, N.; Prange, T.; Papich, M.G. Ceftiofur Formulation Differentially Affects the Intestinal Drug Concentration, Resistance of Fecal Escherichia coli, and the Microbiome of Steers. PLoS One 2019, 14, e0223378, doi:10.1371/journal.pone.0223378. 41. Manyi-Loh, C.E.; Mamphweli, S.N.; Meyer, E.L.; Makaka, G.; Simon, M.; Okoh, A.I. An Overview of the Control of Bacterial Pathogens in Cattle Manure. International journal of environmental research and public health 2016, 13, 843. 42. Bach, A.; Calsamiglia, S.; Stern, M.D. Nitrogen Metabolism in the Rumen*. Journal of Dairy Science 2005, 88, E9–E21, doi:10.3168/jds.S0022-0302(05)73133-7. 43. Khalil, A.; Batool, A.; Arif, S. Healthy Cattle Microbiome and Dysbiosis in Diseased Phenotypes. Ruminants 2022, 2, 134–156. 210