MICROBIAL COMMUNITY RESPONSE TO ANTHROPOGENIC POLLUTION: ANTIBIOTIC RESISTANCE GENES AND DIOXIN BIODEGRADATION By Timothy A. Johnson A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Crop and Soil Sciences – Environmental Toxicology – Doctor of Philosophy 2013 ABSTRACT MICROBIAL COMMUNITY RESPONSE TO ANTHROPOGENIC POLLUTION: ANTIBIOTIC RESISTANCE GENES AND DIOXIN BIODEGRADATION By Timothy A. Johnson The release of anthropogenic chemicals into the environment is vast and frequently hazardous. For instance, millions of kg of antibiotics are used each year in agriculture in the US and are released into the environment, and correlates with and likely contributes to antibiotic resistance in human pathogens, rendering some infections as untreatable. A second class of chemicals is persistent organic pollutants, such as dioxins, which are immune disruptors and are priority pollutants requiring remediation from the environment. Bacteria respond to chemical perturbation, in order to survive, in many ways: activation of antibiotic resistance genes (ARGs), horizontal gene transfer, transcription of degradative pathway genes, and other related systems such as toxic shock response. Often we use molecular methods to monitor the bacterial community responses and we have reviewed, analyzed, developed and validated hundreds of PCR primer sets specific to ARGs and aromatic carbon metabolism. We have found that in-feed antibiotics increase the abundance and diversity of ARGs both in individual swine, as well as farm-wide in manure, compost and soil amended with compost. Resistance gene abundance correlates with transposase abundance, indicating that the resistance genes may be genetically mobile and represents a potential risk to medical antibiotic treatment in humans. Using gene-targeted metagenomics we see that the diversity of dioxygenases which degrade dioxins exist in a greater extent than we currently have characterized. We went on to isolate a novel dibenzofuran-degrading consortium consisting of Agromyces sp., Bacillales sp. and Comamonadaceae sp, which completely degrade dibenzofuran. This consortium can also cometabolize chlorinated dioxins including 2,3- dichlorodibenzo-p-dioxin. Microorganisms respond to anthropogenic pollution in order to survive; however, the response may have positive or negative human implications, both threatening human health (in the case of antibiotic resistance genes) or aid in the removal of toxic chemicals (in the case of dioxins). ACKNOWLEDGEMENTS The work presented in this dissertation would not have been possible without the support, encouragement and contribution from a great number of people. First of all I would like to thank my advisors, Dr. James Tiedje and Dr. Syed Hashsham, for their guidance, encouragement, instruction, and for being an example of life-long learners and innovators. I would also like to thank the other members of my advisory committee: Dr. Brian Teppen and Dr. Gerben Zylstra for their input, help and instruction. Next I would like to thank all the collaborators whose names are listed within this work for their contribution to successful science. I am sincerely grateful to my peers at Michigan State University (in no particular order): Drs. Tamara Cole, Shoko Iwai, Bob Stedtfeld, Ryan Penton, Woo Jun Sul, Erick Cardenas, Patrick Chain, Marius Vital, Adina Howe, Jim Cole and Benli Chai, as well as Qiong Wang, Jordan Fish, Tiffany Stedtfeld, Aaron Garoutte, Jiarong Guo, and James Kremer, for their friendship, ideas, conversations, and encouragement. All were so kind to me; they taught me needed methods, answered endless questions, and provided a helping hand. I especially want to thank Amanda Geaslin, Evan Ballard, and Weijia Wang who contributed much of the work to produce the data that is shown here. Finally I would like to thank my family and friends. We have so dearly enjoyed our time in Michigan and owe much of that to our friends and neighbors, the Seedall, Phillips, Overson, Firth, Oliver, Hutchinson and Landon families. I would like to thank my parents Martin and Leslie Johnson, and Angela’s parents Mark and Diane Soffe for their support and love as well as making many long trips to Michigan to visit us. I thank my sons Zach, Grant and Bennett for being so precious to their mother, loving soil, and trying to learn about bacteria. Finally, I thank iv my wife, Angela, for the patience, sacrifice, and love she has shown to me. She has truly made this possible. Financial support was provided by the Superfund Research Program grant P42 ES004911-20 from the U. S. National Institute of Environmental Health Sciences. v TABLE OF CONTENTS LIST OF TABLES x LIST OF FIGURES xii CHAPTER I TWO CASES OF MICROBIAL COMMUNITY RESPONSE TO ANTHROPOGENIC POLLUTION: AGRICULTURAL ANTIBIOTICS AND DIOXINS 1 Abstract 2 Introduction 3 Part I: Antibiotic resistant genes as pollutants: Implications and risk assessment 8 Introduction 9 Implications of considering ARGs as a novel category of hazard and risk 11 Horizontal gene transfer 11 Silent selection for ARGs 12 Co-selection prolongs residence time 12 Environmental reservoir of antibiotic resistance genes 13 Diversity of resistance genes and antibiotics 14 Risk assessment of resistance genes from animal farms 14 Hazard Identification: Farm ARGs 15 Exposure Characterization: Exposure pathways 16 Conceptual model 21 Part II: Bioremediation and detoxification of polychlorinated dioxin contaminated environments 24 Introduction 25 Bioremediation of PCDD. 29 Function of putative dechlorinases 29 Isolation of novel dioxin detoxification genes (DDGs) 33 Effective implementation procedure 34 Surface soils 35 Water sediments 37 Flyash 37 Conclusion 38 REFERENCES 40 CHAPTER II COMPARISON OF THE SPECIFICITIES AND EFFICACIES OF PRIMERS FOR AROMATIC DIOXYGENASE GENE ANALYSIS OF ENVIRONMENTAL SAMPLES 51 Abstract 52 Introduction 52 Biphenyl and PAH dioxygenase genes 53 vi Primer selection: importance of primer coverage, specificity, and PCR product length Role of primer design: lessons from previous studies Coverage of previous primers Control of primer specificity PCR product length of dioxygenase primers Choosing appropriate primer sets Conclusions Acknowledgements REFERENCES CHAPTER III IN-FEED ANTIBIOTIC EFFECTS ON THE SWINE INTESTINAL MICROBIOME Abstract Introduction Results Shifts in community membership with ASP250 Shifts in functional gene abundance with ASP250 Pervasive antibiotic resistance in the absence of antibiotic exposure qPCR and metagenomic analyses reveal shifts in resistance gene richness and abundance in medicated pigs Discussion Conclusions Material and methods Swine DNA extractions 16S rRNA sequencing DNA sequencing Phylotype analysis Metagenomic analysis Quantitative PCR Statistical Analysis of qPCR Results: Abundance and Diversity Culturing Escherichia coli Acknowledgements REFERENCES CHAPTER VI DIVERSE AND ABUNDANT ANTIBIOTIC RESISTANCE GENES IN CHINESE SWINE FARMS Abstract Introduction Results Antibiotics and metal concentrations Diversity of antibiotic resistance genes Abundance of antibiotic resistance genes. vii 54 56 56 57 60 61 61 61 62 66 67 68 70 70 72 73 74 77 82 82 82 83 83 84 84 84 86 86 87 87 89 95 96 97 99 99 101 101 Transposase enrichment Discussion Feed additive use Enlarged diversity and abundance of the environmental resistance reservoir Potential for horizontal gene transfer of ARGs Role of manure management in controlling ARGs Materials and Methods Sampling Antibiotic and metal quantitation DNA extraction Primer design Quantitative PCR Acknowledgments REFERENCES 105 106 106 107 110 111 113 113 114 115 115 115 116 117 CHAPTER V GENE-TARGETED METAGENOMICS OF PUTATIVE DIBENZO-P-DIOXIN DEGRADING ANGULAR DIOXYGENASES Abstract Introduction Materials and methods Results and discussion Acknowledgements REFERENCES 123 124 124 125 130 134 135 CHAPTER VI ISOLATION AND CHARACTERIZATION OF A NOVEL DIBENZOFURAN DEGRADING CONSORTIUM FROM A PRISTINE PRAIRIE SOIL 139 Abstract 140 Introduction 141 Materials and methods 142 Chemicals and media 142 Enrichment and isolation of DF, DD and 2-MCDD degrading bacteria 143 Degradation experiments 143 Chemical extraction and analysis 144 DNA extraction and 16S rRNA sequencing 145 Whole genome sequencing, assembly and analysis 146 Morphological characterization 147 Results 147 Enrichment for DF and DD degraders 147 Isolation of the DF degrading consortium: IA1I1#3 148 Attempts to purify #3 to a pure culture or reconstitute from pure culture inputs 152 Specificity of the degradative activity 152 Whole genome sequencing of IA1I1#3 153 Degradation pathway of IA1I1#3 156 viii Discussion REFERENCES 156 162 CHAPTER VII CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS Conclusions Future research directions REFERENCES 166 167 169 174 ix LIST OF TABLES Table 1.1 23 Description of parameters involved in the conceptual human gut resistance gene-centered risk assessment model. Table 1.2 30 Summary of studies that have demonstrated the successful dechlorination of PCDDs. Many studies show dechlorination of dioxin-like compounds including PCDFs, but this table only concerns PCDDs, which are the most recalcitrant of all dioxin-like compounds. The table shows the compound, the organism found to degrade it, the degradation rate (based on the indicated reference), the genes involved, metabolites, and references. It is important to note that the specific gene(s) relative to PCDD dechlorination have not been isolated, and no study has shown the complete dechlorination of PCDD under anaerobic conditions. Table 1.3 31 A summary of important studies that have demonstrated the successful oxidation of PCDDs. The table shows the compound, the organism(s) found to degrade it, the degradation rate (an average of many studies), the genes involved, the formed products, references. The three genes listed here are angular dioxygenases. MCDF oxidation is listed to show its oxidation relative to MCDD. Table 3.1 78 Antibiotic resistance genes differentially represented (P < 0.05) in the medicated vs. nonmedicated pig fecal samples as detected by metagenomics [number of sequences in the medicated (n = 1) vs. nonmedicated (n = 3) metagenomes per resistance gene] and qPCR (gene copy number/16S rRNA gene copy number) during the treatment period. Table 5.1 126 Reference sequences used in primer design, PCR validation of primer specificity and designation of reference sequences in clusters with obtained environmental sequences. Positive control column indicates if the strain DNA was used in PCR validation of primer specificity. PCR validation column indicated which primer set produced an amplicon with that strain. References listed detail the activity of the strain toward dioxins. * Rhodococcus sp. RHA1 produced only a faint band with the dbfA1 primer set. Table 5.2 128 Primer sequences and PCR conditions of the three primer sets. These PCR conditions were optimized for the soil samples described. The target positions described are for reference amino acid sequences: * position based on Sphingomonas wittichii RW1, dxnA1, and † position based on Sphingomonas sp. KA1, carAa. ‡ Ta = annealing temperature. x Table 5.3 128 Obtained sequences statistics. A mock community (MC) was composed of the strains used in validation of primer specificity (Table 1) and yielded the correct sequences and are not described further. Table 6.1. Enrichments of interest that showed some level of substrate depletion. 146 Table 6.2. Genome assembly results. 146 xi LIST OF FIGURES Fig. 1.1 17 Exposure pathways of antibiotic resistance genes among farm, natural environment and human biospheres. 1. Manure application, 2. surface runoff/leaching, 3. aerial dispersion, 4. water contact and recreation, 5. breathing bioaerosols, 6. soil contact and consumption, 7. farm animal, waste, and AB contact, 8. secondary transmission, and 9. consuming farm products. (A) represents selective pressure on resident resistant bacteria. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. Fig. 1.2 20 Framework from which to design an antibiotic resistance gene centered risk assessment model for the human gut. Nodes (circles) indicate concentrations of entities and arrows indicate rates. Rates 1-4 are the selection pressure due to presence of antibiotics. Rates 5 and 6 are the rates of the commensals and pathogens, respectively, gaining antibiotic resistance genes via horizontal gene transfer (HGT). Dashed lines indicate the source of the resistance genes to be horizontally transferred. Fig. 1.3 26 Structures of compounds. A) 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin (HCDD); B) 2,3,4,7,8pentachlorodibenzofuran (PeCDF); C) Dibenzo-p-dioxin (DD); D) carbazole (CAR). Different congeners (species) of these compounds are named by listing the locations of the chlorine subsitutions. Adapted from: Chang YS, 2008, J Mol Microbiol Biotechnol, 15:152-171. Fig. 1.4 27 Proposed PCDD dechlorination and oxidation pathways. Pathway A shows peri- and lateraldechlorination of 1,2,3,7,8-PeCDD. Pathway B shows the oxidation of 1,2,3,7,8-PeCDD by the indicated enzymes. These pathways are shown for illustrative purposes, and are not confirmed. Adapted from (Field et al. 2008, Wittich 1998, and Nam et al. 2006). Fig. 2.1 55 Primer coverage pattern against 204 reference dioxygenase genes. Each box indicates a perfect match between the gene and the primer. Primers can be classified into the six classes noted on the left, based on their coverages: class A targets all five reference subclades; class B targets PAH-GN; class C targets PAH-GN and T/B; class D targets T/B; class E targets dioxin dioxygenases in OT-I; class F targets PAH-GP. Fig. S1 in the supplemental material expands this figure to show each primer name, its computed degeneracies, and the number of perfect matches to the corresponding gene’s nucleic acid and protein identification number. Fig. 3.1 71 Shifts in fecal bacterial community membership with antibiotic treatment. (A) NMDS analysis of Bray-Curtis similarity coefficients calculated from 16S rRNA gene sequence data from individual animals at days 0 and 14 shows the similarity among replicate pig fecal samples. (B) Phylum-level composition of fecal microbial communities. Data were pooled for a given xii treatment and time point and are shown as percentage of abundance. (C) Genus-level composition of Proteobacteria, shown as the total number of sequences (normalized to 50,000 total reads). (D) Predicted genera of COG3188 homologs found in the swine metagenomes based on BLASTx analysis. COG3188 was overrepresented in the medicated metagenome vs. the nonmedicated metagenomes. Fig. 3.2 75 Changes in diversity and abundance of antibiotic resistance genes (ARG) in swine feces with antibiotic treatment. (A) Metagenomes were analyzed by BLASTx against the ARDB, and the number of reads were normalized to 100,000 total reads per metagenome. (B) Differences in the abundance of resistance genes were assessed by calculating the ratio of resistance gene copy number (ARG) to 16S rRNA gene copy number per sample as detected by qPCR. Columns denoted by the same letter are not statistically significant (P > 0.05) within each resistance type. Error bars represent the SEM. (C) Bray-Curtis similarity coefficients were calculated from qPCR-derived resistance gene abundance data and plotted in a multidimensional scaling graph. The distance between points indicates the degree of difference in the diversity of resistance genes between samples. The medicated sample outlier (square) is from one medicated pig on day 21. Measures for day 0 samples are not shown. Fig. 4.1 100 Antibiotic resistance gene detection statistics. Sample names are abbreviated with two letters representing location and sample type: first C, B, J, and P (control, Beijing, Jiaxing, and Putian, respectively) and second M, C, and S [manure, compost, and soil (with compost amendment), respectively]. Because many resistance genes were targeted with multiple primers, if multiple primer sets detected the same gene, this was only counted as detection of a single unique resistance gene. (A) Average number of unique resistance genes detected in each sample. Error bars represent SEM of four field replicates. The resistance genes detected in all samples were classified based on (B) the mechanism of resistance, and (C) the antibiotic to which they confer resistance. FCA, fluoroquinolone, quinolone, florfenicol, chloramphenicol, and amphenicol resistance genes; MLSB, Macrolide-Lincosamide-Streptogramin B resistance. Fig. 4.2 102 Resistance gene profile from the farm sites. Each column is labeled with the sample name (same abbreviation scheme as in Fig. 1, with numbers representing field replicates), and each row is the results from a single primer set. Values plotted are the ΔΔCT with the control soil being the reference sample for all samples. The legend denotes corresponding fold change values, which is a log scale. All primer sets (223) that showed amplification in at least one sample are shown. Columns were clustered based on Bray-Curtis diversity measures. Black boxes delineate resistance profiles: (A) enriched in all sam- ples, including control manure (CM); (B) enriched in all farm samples, but not the CM; (C) widely enriched in most of the farm samples but not the CM; (D) genes that were enriched in the Putian compost but not the Putian manure; and (E) strongly enriched in CM and farm manures. Fig. 4.3 Abundance of resistance genes and transposases. In the box plots, the symbols indicate the following: box, 25th to 75th percentile; horizontal line, median; whiskers, 10th and 90th xiii 104 percentile; and square, maximum value. The y axis is a log scale of fold increase: farm manure compared with the control manure, and farm compost or soil compared with the control soil. (A) Only statistically enriched resistance genes are represented. The number above each site indicates the number of primer sets that yielded statistically significant results. (B) Summary of all nine primer sets used to target different transposase alleles (in B, top whisker represents the maximum value). (C) Correlation of total resistance and transposase abundances, oxytetracycline concentration, and copper concentration. Total antibiotic resistance and total transposases values are the sum of ΔΔCT values of all assays of that type in each sample. The sample identifiers below B apply to both A and B. Fig. 4.4 108 Canonical correspondence analysis (CCA) compares the abundance of detected resistance genes (symbols) and the concentration of heavy metals and antibiotics (arrows). The results showed that pig manure samples were positively correlated to the concentrations of copper, zinc, arsenic, and total tetracyclines. Environmental variables were chosen based on signifi- cance calculated from individual CCA results and variance inflation factors (VIFs) calculated during CCA. The percentage of variation explained by each axis is shown, and the relationship is significant (P = 0.005). CCA analyses were performed in R 2.13.0 with vegan package 1.17-9. Fig. 5.1 131 Results of clustering obtained sequences with the reference sequences. (A) Results using the dxnA1/dfdA1 primer set. (B) Results using the carAa primer set. Clusters are only shown that contained at least four sequences. There were an additional 12 clusters that contained two or three sequences. Fig. 5.2 132 Nearest neighbor-joining tree of the representative sequences of each cluster shown in Fig. 5.1. Branch names designate: cluster name (from Fig. 1), name and accession number of reference sequence in that cluster (if applicable), number of obtained sequences from pyrosequencing, and the predominate sample from which the sequences originated. Sequences were aligned using MUSCLE, trimmed to a common region for all sequences, and the tree was made using MEGA 5.1. Fig. 5.3 133 Percent conservation of translated obtained nucleotide sequences to protein sequences. (A) Results using the dxnA1/dfdA1 primer set. (B) Results using the carAa primer set. Key conserved amino acid positions are indicated. The DX2HX3-4H iron-binding site is indicated as well as the uncharacterized, yet highly conserved NW(K/R) motif. The asterisk (*) indicates positions for which obtained sequences were conserved at a higher rate than reference sequences. Fig. 6.1. 149 DF degradation curves of a) IA1I1#3 and b) IA1#3-21. Optical density measurements were taken sequentially from the same cultures and DF concentrations were taken from individual sacrificed cultures. Points shown are the means of triplicate cultures and error bars indicate ± SEM. Vials xiv with no cells or autoclaved cells were sacrificed at the end timepoint and showed the same concentration of the substrate as at 0 h (data not shown). Fig 6.2. 150 Electron micrographs of IA1I1#3 (A-B) and IA1I1#3-21 (C-D). In panel A three cell morphotypes are visible, short rod, hairy rod and a club shaped cell. Panel B is a detailed image of the short rods. In panel C, three cell morphotypes are visible from #3-21, short rod, hairy rod and a coccus. Panel D is a detailed image of the short rods. White bars are all 1µm. Fig. 6.3. 154 Range of substrate degradation by IA1I1#3-21. The key shown in panel A applies to all panels. Bars indicate the mean of triplicate vials and error bars indicate ± SEM. 2-MCDD = 2monochlorodibenzo-p-dioxin, 2,3-DCDD = 2,3-dichlorodibenzo-p-dioxin. Fig. 6.4. 155 Mass spectra from three peaks specific to the IA1I1#3 culture. Two peaks at 19.618 and 19.915 min had the same mass spectra as shown in panel A and was identified as hydroxydibenzofuran. The metabolite shown in panel B eluted at 24.783 min and was identified as dihydroxydibenzofuran. The metabolite in panel C was identified as 2-hydroxy-6-(2’hydroxyphenyl)-6-oxo-2,4-hexadienoate (HOHPDA) and eluted at 25.768 min. Mass spectra could not be found which indicated the presence of 2,2’,3-trihydroxybiphenyl, salicylate, or 2oxopent-4-enoate. Fig. 6.5. 157 Phylogenetic relationships of A. the 16S rRNA sequence of members of the IA1I1#3 consortium compared to known DF degraders and B. ring hydroxylating dioxygenases from Acidovorax sp. W2 compared to known Rieske dioxygenases. In panel B clades with activity toward a specific substrate are indicated: BPH biphenyl, DF dibenzofuran, PAH polyaromatic hydrocarbons, BEN benzoate, CAR carbazole. xv CHAPTER I TWO CASES OF MICROBIAL COMMUNITY RESPONSE TO ANTHROPOGENIC POLLUTION: AGRICULTURAL ANTIBIOTICS AND DIOXINS   1   ABSTRACT The response of a microbial community to a perturbation is based in ecology and competition. A number of factors are involved in establishing a new stable state, and populations that can best respond to the perturbation will become more dominant than in the previous state. This microbial community response may have detrimental (in the case of antibiotic resistance) or beneficial (in the case of bioremediation) human consequences. The number of pathogens that are antibiotic resistant and the number of antibiotics to which they are resistant is increasing at a troubling rate. The cost of antibiotic resistance in human medicine is billions of dollars. We need a thorough understanding of the mechanisms of the development of multidrug resistance in order to contain or stop it. We propose that antibiotic resistance genes (ARGs), independent of their bacterial host, be treated as pollutants that cause harm to humans. The characterization of ARGs as pollutants introduces novel risk factors that are not presented by any other risk source, and may urge a lower acceptable level of exposure compared to other sources of risk such as cancer-causing chemicals. We provide the framework for the risk assessment of antibiotic resistance genes from animal agriculture. Antibiotic use in agriculture increases the abundance and diversity of ARGs in native soil bacteria and mammalian-associated bacteria in the manure and soil. Farmers are infected at very high rates of antibiotic resistance genes, which may be considered an occupational hazard. ARGs contamination can be spread in the environment, e.g. though water, dust, soil, and other vectors, which shows potential risk to human. ARGs are a significant source of risk and the knowledge gaps required to complete a full risk assessment from agriculture   2   should be completed. Animal farmers and meat produce from farms that use antibiotics are the major sources of risk. The environmental reservoir of ARGs provides novel genotypes of resistance to the general human population environment. Several critical control points are identified to reduce the spread of ARG pollution. Environmental PCDD contamination jeopardizes human health and requires economically feasible remediation that detoxifies the environment. Recent studies have shown the bacterial degradation of toxic PCDDs, indicating that PCDD contamination could be addressed via bioremediation. In order to implement successful bioremediation of PCDDs, three areas of research need to be developed. First, the functions of putative dechlorinases need to be defined, beginning with reductive-dehalogenase- homologous genes. Second, the search for novel PCDD RDases and angular dioxygenases needs to continue with emphasis on molecular isolation techniques. Third, an effective implementation procedure should be developed for soil, water, and flyash environments. INTRODUCTION Microbial communities, and their membership, are largely shaped by general physical factors including, environmental factors (oxygen availability, temperature, moisture content, pH, carbon and nutrient content), and spatial distance (historical disturbances, access to other environments, etc) (Ramette et al. 2007). The manner in which microbial communities respond to changes in the physical environment is best understood in an ecological context. In environments with constant conditions, for example the animal gastrointestinal (GI) tract, microbial communities show a fairly high level of background stability (Durbán et al. 2012), while microbial communities in a less   3   consistent environment, like soil, the background community stability is low (Lauber et al. 2013). In the long term in any environment, pulse and/or press perturbations (short- or long-term changes in environmental conditions) are likely to occur. The community will respond to the perturbation, resulting in a new and stable state community composition (Schloss et al. 2012) or return to a previous state (Fries et al. 1997, Yagi et al. 2009). A number of organism (e.g. dormancy and stress response), population (e.g. growth rate) and community (e.g. diversity and turnover) level factors influence the resistance and resilience of populations within the community and provide the biological mechanisms by which a stable state will again be achieved (Shade et al. 2012). Additionally, in the GI tract, the bacterial community has been reported to mature, or to age with the host in a form of microbial community succession (Chung et al. 2012, Kim et al. 2012), a process of progressing from one stable state to another. Anthropogenic pollution can be a chemical perturbation to natural microbial communities. This pollution may cause growth promoting or toxic effects to the microbial community. If the chemical is organic, it could be used as a carbon source, and the substrate and its metabolites may promote the growth of community members with the required enzymes for the metabolism of the substrate or those with an association with the pollutant metabolizers. Other chemicals, such as antibiotics, display toxic effects on microbial communities, killing a portion of the population, and allowing the resistant portion to multiply and become more dominant. For the purpose of this dissertation, we consider antibiotics and dioxins specifically as pollutant perturbations. Antibiotic exposure could be either a pulse (e.g. field application with manure) or press (e.g. in-feed antibiotics) perturbation, and dioxin contamination, depending on its recalcitrance and   4   length of persistence, may be either. Depending on the nature of the chemical perturbation, microbial communities will have a unique combination of factors most important to resilience of the bacterial community specific to that chemical. We will briefly describe these factors in our two situations. Antibiotic perturbations can broadly alter the bacterial community, and those communities might not return to the previous stable state (Dethlefsen et al. 2008, Dethlefsen et al. 2011, Jakobsson et al. 2010, Jernberg et al. 2007, Young et al. 2004), indicating the strength of the perturbation and the role of ecology in regaining community stability. Many ecological factors play into the resilience (Shade et al. 2012) of gut microbiomes due to antibiotic perturbations. The first is bacterial dormancy, including persister cells, which are susceptible to antibiotics but survive due to dormancy (Lewis 2007). Individual organism stress tolerance to antibiotics can come from antibiotic resistance genes, which is key to survive this perturbation (Benveniste et al. 1973). Population stress tolerance can be achieved if a portion (even a low number of cells) of the native population survives the antibiotic course and is able to recolonize the gut (Costello et al. 2012). Horizontal gene transfer can aid in the dispersal of antibiotic resistance genes (Salyers et al. 2004) and horizontal gene transfer has been observed after other types of perturbations as well (Lenski et al. 1993). The growth rate of individual resistant and susceptible populations is important in the establishment of a new stable state. A study in soil found that the respiration rate following antibiotic treatment resulted in an immediate drop in respiration (due to microbial death), followed by an increase in respiration rate higher than the no-antibiotic control (Butler et al. 2011). This elegant experiment shows that the native community was first disturbed and susceptible   5   populations were killed, followed by the recolonization by the fastest growers, which resulted in an elevated respiration rate. This change in proportions of bacterial populations results in a community turnover, which is important in community recovery (Shade et al. 2012) and has been observed in the gut in response to antibiotics (Antonopoulos et al. 2009, Dethlefsen et al. 2011). Thus, it is an interesting combination of ecological forces that shape the reestablishment of a stable community. The addition of an anthropogenic organic carbon pollutant follows similar but different principles in resilience and resistance to change. A toxic stress response to organic pollutants (Chai, in preparation) and chlorinated organics (Fries et al. 1997) has been observed but is likely less pronounced than with antibiotics. Dormancy would likely play a role in pulse perturbations, in that the carbon utilizing population would bloom during the perturbation and then may subside when the carbon source is depleted. Horizontal gene transfer has been observed in the transfer of initial oxygenases (Wilson et al. 2003) and may allow for the enrichment of more degrader phylotypes. In terms of carbon utilization, microbial community networks (Zhou et al. 2010), the carbon substrate and its metabolites are likely dispersed broadly in the community. Perturbations with biphenyl showed a response, mostly dominated by Proteobacteria, Actinobacteria, Acidobacteria, in the microbial community able to metabolize the parent molecule or its metabolite compounds (Sul, in preparation). The winner among potential pollution degraders will depend heavily on their comparative growth rates (Lauro et al. 2009). A pulse enrichment of organic carbon may more often result in the bloom of degrader populations with a gradual return to the previous state when the organic carbon is no longer present (Fries et al. 1997, Yagi et al. 2009).   6   We will now consider the ecological principles in the response of farm and gut microbial communities to perturbations with antibiotics and the associated risk, as well as the response of dioxin metabolizing populations and how to best implement dioxin bioremediation research and application strategies.   7   Part I: Antibiotic resistant genes as pollutants: Implications and risk assessment   8   INTRODUCTION Antibiotic resistance, especially multidrug resistance, in pathogens is challenging the efficacy of infection treatment as we have known it since the discovery of penicillin (Arias et al. 2009). Antibiotic resistance is often manifested when a bacteria acquires an antibiotic resistance gene, which allows the cells to either degrade the drug, remove the drug from the cell, or alter the cellular target of the drug, so as to prevent its antibacterial action (Alekshun et al. 2007). Even more problematic is when pathogens acquire resistance genes to multiple antibiotics and doctors are required to prescribe one or more antibiotics with or without successful outcomes. Methicillin-resistant Staphylococcus aureus (MRSA) (Klein et al. 2007, Klevens et al. 2007), vancomycin-resistant enterococci (Moellering 1998, Soderblom et al. 2010), totally drug-resistant (TDR) Mycobacterium tuberculosis (Udwadia et al. 2012, Velayati et al. 2009), Acintobacter baumanii strain AYE (Fournier et al. 2006), and multidrug resistance (MDR) enterobacteriaceae (Kumarasamy et al. 2010) are known human pathogens that resist treatment by traditional antibiotic treatment regimes and increase U.S. health care costs billions of dollars each year and cause up to a two-fold increase in mortality (Cosgrove et al. 2003). In 2005, there were over quarter million MRSA-related hospitalizations, and its antibiotic resistance cost the healthcare system an extra $830 million – $9.7 billion (Klein et al. 2007). As pathogens assemble a larger and larger antibiotic resistance arsenal, new antibiotics are coming to market at rate lower than any time in history (Wright 2011). The problem of antibiotic misuse and resistance has been recognized for decades (RosenblattFarrell 2009). Governments are beginning to try to more tightly regulate antibiotic use in   9   humans (Hvistendahl 2012) and animals (Gilbert 2012, Hvistendahl 2012) but progress is slow because their use is often necessary and corresponding proof of harm, especially from agricultural use, is hotly debated (Phillips et al. 2004). Recently, it has been put forth that antibiotic resistance genes (ARGs) themselves are pollutants (Gillings et al. 2012, Pruden et al. 2006, Storteboom et al. 2007, Zhu et al. 2013). Risk assessment models are used to estimate the exposure and the known doseresponse to calculate the estimated risk due to hazardous pollutants and helps governments in knowing appropriate standards for release and cleanup. Traditional risk assessment can be divided into three major categories: non-cancer causing agents, carcinogenic agents, and biological agents. Toxic chemical dose-response curves are characterized by a no observed adverse effect level (NOAEL) and lowest observed adverse effect level (LOAEL), which describe a certain threshold dose under which there is no toxicity. Carcinogenic agents are treated more stringently because they follow the “one-hit model”, which describes that a single molecule could alter a single DNA molecule causing a mutation, transforming a normal cell to a cancer cell. Pathogens, in some cases, are treated more stringently still because their risk is characterized by the one-hit model, as well as additional biological factors, such as cellular replication and human-to-human (or secondary) transmission (Microbial Risk Assessment-Draft, 2011). We propose that harm-causing genes, like antibiotic resistance genes be added as a fourth category of risk, comparable to microbial risk. However, antibiotic resistance genes are unique because they can spread through microbial communities independent of their bacterial host via horizontal gene transfer, they can be promoted and enriched in a mammalian host during responsible and needed antibiotic therapy to treat infections, and   10   there is no known short-term method to eradicate resistance genes from a host. Considering these four categories of risk assessment (chemical, carcinogenic, biological or genetic), compiling layers of host sensitivity and outbreak potentials of the risk agents, progressively less risk should be allowed when considering the control of the risk agent. Implications of considering ARGs as a novel category of hazard and risk. Risk is the product of harm and exposure. A low exposure, high harm scenario results in sporadic and potentially unpredictable incidents of high risk. Antibiotic resistance genes, may present a similar low exposure, high harm scenario, with the key difference that the resistance gene can replicate and grow exponentially in the larger host population by secondary transmission. A number of factors differentiate gene risk assessment from microbial risk assessment which may facilitate the outbreak of a gene in large, high density populations: a) Horizontal gene transfer – Individual genes or clusters of genes can be transferred into a bacterial chromosome via transformation (uptake of naked DNA), transduction (phage-mediated) or conjugation (usually plasmid mediated) (Alekshun et al. 2007, Ochman et al. 2000). Other mobile genetic elements such as transposons (Mahillon et al. 1998) and integrons (Boucher et al. 2007, Mazel 2006) facilitate the transfer and/or assemblage of many resistance genes into a single genetic element (Stokes et al. 1989). Bacterial horizontal gene transfer is said to be promiscuous, freely sharing genes throughout bacterial communities (Caro-Quintero et al. 2011, Smillie et al. 2011). Horizontal gene transfer both increases the extent of ARG contamination and prolongs the residence time of ARGs in those communities.   11   b) Silent selection for ARGs – Antibiotic resistance genes in and of themselves are harmless. A person may carry an extensive resistance gene reservoir in harmless, non-pathogenic commensal bacteria and never manifest any disease symptom to the host (Levy 1978). Those same genes when integrated into a pathogenic bacterial host are then causing harm. In fact, normal responsible antibiotic therapies may eliminate the pathogen, but at the same time encourage bacterial “breeding” and promoting the growth and replication of the resistant populations or transfer of the gene to previously susceptible population, especially commensals (Dethlefsen et al. 2011, Salyers et al. 2004). For example, subtherapeutic agricultural use of antibiotics has been shown to increase the abundance of ARGs 50,000 fold in swine (Zhu et al. 2013) with no signs of harm to the host. The term “silent” selection indicates that the resistance genes are selected and enriched without any outward manifestation of that process. Pathogen infections are much the opposite: an individual with a severe bacterial infection displays obvious symptoms and can then be isolated, treated, and hazard is removed. Resistance genes may be residing in the normal commensal microflora subject to secondary transmission while the host interacts with large populations, increasing the chance for outbreak. c) Co-selection prolongs residence time – When antibiotics are present, organisms that hold an antibiotic resistance genes have a fitness advantage over those that are susceptible to the antibiotic. Thus, they increase in abundance in the community. Co-selection is when non-antibiotic chemicals enrich ARGs. Metals (Baker-Austin et al. 2006, Berg et al. 2010) and disinfectants (Gaze et al. 2011)   12   are commonly implicated in co-selection because the mechanisms of resistance to metals, disinfectants and antibiotics can be the same efflux pump (Knapp et al. 2011) or all the specific resistance genes to all the chemicals are co-localized to the same genetic element (Petrova et al. 2011). If an environment is contaminated with ARGs and any of these co-selective chemicals, there is an ecological pressure for the ARGs to persist increasing the residence time of the ARGs, and the probability for horizontal transfer to a pathogen. d) Environmental reservoir of antibiotic resistance genes – Antibiotics largely originated from environmental bacteria, especially Actinomycetes. These antibiotic-producing organisms are also resistant to the antibiotics (D'Costa et al. 2006) and are the likely source of antibiotic resistance genes as supported by some evidence (Benveniste et al. 1973, Price et al. 2012). Indeed, resistance genes evolved long ago (D'Costa et al. 2011) and have had millennia to disperse among environmental populations. The appearance of antibiotic resistance genes and MDR in human-associated pathogens is unlikely to be accomplished by point mutation of bacterial DNA, but through horizontal gene acquisition especially when considering that the phenotype emerged so rapidly (Rosenblatt-Farrell 2009). Additionally, resistance genes may have even been a contaminant in early antibiotic preparations (as discussed in (Chee-Sanford et al. 2009)). All combined, the environment can be thought to be the source of the arsenal of antibiotic resistance genes to the human-society microbiome. It is then in the collective human-society microbiome in which they are then selected and transferred to pathogens and multidrug resistance can develop. Increased environmental   13   contamination with antibiotic resistance genes increases their likelihood to have contact with humans, and human microbiome contamination with ARGs increases the likelihood that pathogens will develop resistance, or that the resistant pathogens will have an outbreak in the human population. Thus the environmental contamination and human use of antibiotics are both very important factors in the development of antibiotic resistance genes in humans. e) Diversity of resistance genes and antibiotics – The number of known antibiotic resistance genes is over 500 unique types. There are 34 unique resistance genes to tetracycline alone (Liu et al. 2009). Add to this there are about 50 antibiotic chemicals used in agriculture (FDA 2009) and more are used in human medicine. This diversity provides a multiplicity of ARGs for bacteria to incorporate into their genomes to acquire antibiotic resistance. It also complicates monitoring and detection of genetic determinants of resistance. All these important factors are unique to genetic elements of risk, like ARGs, and increase the exposure element of the risk equation. We now provide a basic framework for the risk assessment of antibiotic resistance genes from animal farms to the human gut. Risk assessment of resistance genes from animal farms. Up to this point, only a few quantitative risk assessments for antibiotic resistance genes have been attempted (as discussed in (Marshall et al. 2011)), but all have worked with limited datasets and have relied on severe assumptions. These models generally consider one endpoint (deaths due to antibiotic resistant foodborne infections) and do not address the molecular and physiological principles related to antibiotic resistance. These models have been challenged by pointing out that they have failed to address exposure pathways other than   14   contaminated food, horizontal gene transfer, interrelationships within a complex bacterial community like that of the gut microbiome, the rise of coselection and coselecting agents, and the cumulative resistome rather than only considering resistance in response to the corresponding antibiotic (McEwen 2012). For the purpose of our risk assessment, we consider the human gastrointestinal tract as the habitat, antibiotic resistance genes as the hazard, and a human pathogen as the response. In environmental settings, the response could be expanded to include any antibiotic resistant bacterium because it can act as a vector to transport the ARG downstream in the environment, or to transfer the ARG to a mammalian-associated bacterium. As a model example, we will examine the exposure pathways and doseresponse relationships in the passage of resistance genes from the farm to the farmer. Hazard Identification: Farm ARGs. Agricultural antibiotic use represents the largest user of antibiotics. In the U.S., up to 80% of antibiotics produced are used in agriculture (Mellon et al. 2001) for animal growth promotion, disease treatment, and disease prevention in the swine, chicken, fish, and beef industries. The prudence of agricultural use of antibiotics has been argued for decades. Farmers have long said that antibiotic use is indispensible to their industry and controls disease, while research continues to show the rise in resistance genes in arable soil (Heuer et al. 2011), livestock (Looft et al. 2012), farm workers (Levy et al. 1976), and the general population (Marshall et al. 2011) due to agricultural use. The hazard we identify for this risk assessment is antibiotic resistance genes of any type that are present on the farm. It is difficult to draw a definite link between farm antibiotic use and harm in human populations because the same antibiotics are often used in both humans and animals. However, recent work has begun to draw the   15   link between farm antibiotic use and human health. Methicillin susceptible S. aureus CC398 gained antibiotic resistance due to agricultural antibiotic use and was then conferred from animal to human (Price et al. 2012), a strong link between the farm and human disease. As DNA sequencing becomes more and more available and inexpensive for high throughput of sequencing entire bacterial genomes, as was done by Price et al. (2012), we will be able to more clearly link the source of the resistance gene and the practices that introduce problems. Additionally, human gut has been shown to be a hotspot for horizontal gene transfer (Salyers et al. 2004), thus if antibiotic resistance genes are introduced to the gut they could easily be transferred between commensals or to pathogens and from the farm to humans (Forsberg et al. 2012). The European Union, and Denmark in particular, has stopped the use of some growth promoting antibiotics and decreased resistance levels have resulted (Aarestrup et al. 2001). With the current antibiotic use policies in the U. S., resistant diseases steadily increase, for example MRSA (Klein et al. 2007). Thus any novel antibiotic resistant gene entering the human population is likely to remain in the individual (Jakobsson et al. 2010) and the population via secondary transmission and at some point be transferred to a pathogenic bacterial host.   16   Exposure Characterization: Exposure pathways. Opposed to the assumptions of previous risk assessments, there are many potential exposure routes to agricultural antibiotic resistance genes. The fate of ARGs and the total human exposure pathway includes the farm, environmental, and human biosphere (Fig. 1.1). Each biosphere can be a source of ARGs due to antibiotic use in animals and humans and natural antibioticproducing soil bacteria (D'Costa et al. 2006). Antibiotic resistance genes, and the bacteria that carry them, have various routes between the biomes. Within the farm environment, A   Fig. 1.1. Exposure pathways of antibiotic resistance genes among farm, natural environment and human biospheres. 1. Manure application, 2. surface runoff/leaching, 3. aerial dispersion, 4. water contact and recreation, 5. breathing bioaerosols, 6. soil contact and consumption, 7. farm animal, waste, and AB contact, 8. secondary transmission, and 9. consuming farm products. (A) represents selective pressure on resident resistant bacteria, not a bacterial source. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation.   17   animals are the major source of ARGs, which are transported to the soil or crops through manure application or to the farm worker through physical contact with the animal or its waste (Akwar et al. 2007). Direct exposure to in-feed antibiotics may select for antibiotic resistance genes in the farmer directly (Fig. 1.1) and represent a secondary source of ARGs from the farm. Farm resistance genes can be transported to the human biosphere through manure-contaminated agricultural products (especially fresh meat) and by secondary transmission from the farmer to other humans. ARGs from the farm soil and manure can also be transported to the environment through water erosion to surface waters and ground water, or by wind erosion into the atmosphere for later deposition to surface soil, water or human populations. In the environmental biosphere, ARGs could be transported among the environment components, e.g., water, soil, and air, and then to the human biosphere by human ingestion of soil, water or dust (Fig. 1.1). As an initial risk assessment, we will focus on just one step of the entire pathway, that being the exposure to and fate of ARGs from the farm to the farmer GI tract (farm-to-gut model), with future works extending these principles to secondary transmission to the general human population. In our estimation, the establishment of antibiotic resistant bacteria in the farm worker has been underemphasized in research and the discussion of transmission of antibiotic resistant bacteria. The adverse effects that could occur to the farm worker is the enrichment of antibiotic resistant bacteria in the microbiome of the farm worker, due to direct exposure to antibiotics by handling the chemicals themselves or feed amended with antibiotics, or exposure to antibiotic resistance genes from the animal, land, or farm products (meat or produce) (Akwar et al. 2007, Marshall et al. 2011). It has been written   18   that the primary route of ARG exposure to the general public is through the food chain and ARG contaminated meats (Phillips et al. 2004, Salyers et al. 2004). While this may be true, the farm worker is an important vector to carry ARGs from the farm to the general public. Levy et al. (1976) was fundamental in first finding that farm workers have a high load of antibiotic resistance in their gut microbiome. They found >80% of E. coli were antibiotic resistant in 31.3% of fecal samples from farm dwellers compared to 6.8% of fecal samples from farm neighbors (Levy et al. 1976). Another study showed a farm worker and his wife received a resistant plasmid from the animals (Hunter et al. 1994). At least nine other studies are summarized by Marshall and Levy (2011) showing that farm workers carry a high burden of antibiotic resistance genes in their gastrointestinal tract. A more recent study has shown that farm use of antibiotics, time spent in pig barns, contact with sick pigs, and intake of antimicrobials are all occupational risk factors associated with accumulating ARGs in the farm worker (Akwar et al. 2007). After workers establish resistance genes in their guts, the ARGs could be disseminated to the general public when they become sick and go to the hospital or by communicating disease to others (Hunter et al. 1994). These ARGs then enter the human community microbiome. This human-tohuman secondary transmission of antibiotic resistance genes has been implicated (Church 2004, Smillie et al. 2011) and observed in the infection of others with antibiotic resistant diseases like totally drug-resistant tuberculosis (Velayati et al. 2009), New Delhi metalloß-lactamase-1 (NDM-1) containing Enterobacteriaceae (Kumarasamy et al. 2010), vancomycin resistance enterococcus (Soderblom et al. 2010), MRSA (Davis et al. 2012), and others. MRSA is a highly communicable infection because the pathogen commonly colonizes the skin. Farmers in the Netherlands were >760 time more likely to contract the   19   disease than the general public, and in addition, the disease was shown to be passed from farmer to family to nurse (Voss et al. 2005). MRSA is perhaps the best-documented and most abundant antibiotic resistant disease. There has been a steady increase in the number of MRSA cases in recent years. In the period of 2000–2005 the number of MRSA cases doubled and the percent of total S. aureus infections that were methicillin resistant increased by more than 25% (Klein et al. 2007). It may be possible to identify the major paths of ARG exposure and first control those routes of exposure, also known as a critical control points. In the pathway from the farm back to the general human population, we see the farmer as an important vector for the transport of ARGs. For the occupational safety of farmers, their exposure to A) D) 4 Antibiotics Pathogen 1 Commensals HG C) ARGs 2 B) T 3 (commensal) 6 E) ARGs 5 HG (Pathogen) T Fig. 1.2. Framework from which to design an antibiotic resistance gene centered risk assessment model for the human gut. Nodes (circles) indicate concentrations of entities and arrows indicate rates. Rates 1-4 are the selection pressure due to presence of antibiotics. Rates 5 and 6 are the rates of the commensals and pathogens, respectively, gaining antibiotic resistance genes via horizontal gene transfer (HGT). Dashed lines indicate the source of the resistance genes to be horizontally transferred.   20   antibiotics and ARGs should be as limited as possible, in an effort to decrease their odds of contracting an infection. Conceptual model. We developed a conceptual model of the important biological interactions in the human gut that we would like to be able to describe in the future with a mathematical model (Fig. 1.2). The nodes represent the concentration of a biological factors and the arrows represent the rate of certain interactions between the nodes. Different exposure and response rates will apply to each state and data will be found in the literature to describe the exposure, persistence factors and interaction (Table 1.1) and inform a mathematical model that will predict the probability of acquiring a pathogen with an antibiotic resistance gene that originated from the farm. These risk estimates will be compared to actual studies that determined the risk of farmers acquiring certain observed levels of antibiotic resistance (e.g. (Levy et al. 1976)). Some assumptions will have to be made in the model, including: i) bacterial conjugation as the only form of horizontal resistance gene transfer, ii) an average conjugation rate, iii) nature of flow through the GI tract, and iv) the insignificance of the evolution of novel resistance determinants through genetic mutation, among others. Bacterial conjugation has been highlighted as the major mechanism of horizontal resistance gene transfer (Gillings et al. 2012). Transformation and transduction are other mechanisms of horizontal resistance gene transfer, but they have been shown to be minor contributors. Transformation, or the uptake of DNA outside of a bacterial cell – so called naked DNA, is a pathway of horizontal gene transfer that is most easily eliminated from our model. (Nordgård et al. 2007) found no evidence or resistance gene transformation in an in vivo model system. Additionally concerns over genetically modified crops were   21   settled when it was shown that transformation of resistance genes does not occur in the human gut or in the environment (Ramessar et al. 2007). Transduction, or the incorporation of DNA inserted by bacteriophages, can be a mechanism of horizontal resistance gene transfer (Davies et al. 2010), and it may be significant due to the vast abundance and antibiotic activation of viruses in a bacterial population (Allen et al. 2011), but for simplicity we do not consider it here. While a great deal of work has been done to describe conjugation rates between various organisms (Dionisio et al. 2002, Hunter et al. 2008), conjugation rates are too complex to be able to model at this point. For this purpose we chose strains for which we have data, that appear to not have extreme conjugation rates, and for which the acceptor is a opportunistic pathogen: Escherichia coli as the donor, and Enterococcus as the acceptor. Genetic mutations are another mechanism for the introduction of resistance genes in a bacterial community. While they can arise in bacterial genomes and lead to beneficial mutations, such as antibiotic resistance (Dai et al. 2012), we are not considering their contribution in this particular risk assessment. Genetic mutations are very rare, occurring in only 1 of 10 7 10 to 10 bacteria (Rosenblatt-Farrell 2009). Beneficial mutations are even more rare, so in this assessment, we consider the contribution to resistance from beneficial genetic mutations to be insignificant. This mathematical risk assessment model is the first step in producing a biologically relevant model that describes the ingestion rate, selection, replication, horizontal transfer, death rate and persistence of antibiotic resistance genes in the gut of   22   the farmer. With validation and additional modifications, the model can then be applied to describe outbreak models in larger human populations. Table 1.1. Description of parameters involved in the conceptual human gut resistance genecentered risk assessment model Node A Exposure ABs ingested from environmental sources, not therapeutic Persistence factors Residence time and body absorption Interactions none B Concentration of commensals Commensal ingestion rate Residence time, growth rate, and death rate AB selection C Concentration of ARGs in commensal bacteria ARG ingestion rate (from commensals) Residence time, growth rate, and death rate of the host AB selection and HGT D Concentration of pathogens Pathogen ingestion rate Residence time, growth rate, and death rate AB selection E   Node description Concentration of antibiotics (ABs) in gut Concentration of ARGs in pathogens ARG ingestion rate (from pathogens) Residence time, growth rate, and death rate of the host AB selection and HGT 23   Part II: Bioremediation and detoxification of polychlorinated dioxin contaminated environments The work in Chapter I: Part II has been published: Johnson, Timothy Alan (2008). Bioremediation and detoxification of polychlorinated dioxin contaminated environments. MMG 445 Basic Biotechnology eJournal [Online], 4 (1) and is reprinted here with the permission of the publisher.   24   INTRODUCTION Environmental contamination of polychlorinated dibenzo-p-dioxins (PCDDs), or dioxins, poses “one of the most challenging problems in environmental science and technology” (Yoshida et al. 2005) because of their toxicity, persistence, and biounavailability (Field et al. 2008). Dioxin contamination is important because the compound is carcinogenic (Mandal 2005). PCDDs are released into the environment from a variety of sources including: combustion, incineration, pulp and paper manufacturing, pesticides, and some natural sources (Kulkarni et al. 2008). Fig. 1.3 shows the structure of the compound, and many different chlorinated dibenzo-p-dioxin (CDD) congeners are defined by differing number of chlorine substituents and location of substitution (Chang 2008). Dioxin congeners will be abbreviated as listed above. The release of dioxins into the environment has resulted in contaminated soil that need treatment (Haglund 2007). Currently, PCDD contaminated sites are remediated only by physical and chemical processes that are very expensive (large remediation projects cost from $100 to $500 million) (Weber et al. 2008). Studies have demonstrated that these remediation practices are not sustainable, because of high cost and complex logistics related to containment or relocation of PCDDs as compared to the potential benefits of PCDD destruction (Weber et al. 2008). Bioremediation has been examined as a technique to degrade or detoxify dioxins at a lower cost. Microbial degradation of dioxins has been studied extensively, and takes place through anaerobic reductive dechlorination (ARD) or through aerobic dioxygenation   25   Fig. 1.3. Structures of compounds. A) 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin (HCDD); Figure 1. Structures of compounds. A) B) 2,3,4,7,8-pentachlorodibenzofuran (PeCDF); C) Dibenzo-p-dioxin (DD); D) 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin are named by carbazole (CAR). Different congeners (species) of these compounds (HCDD); listing the locations of the chlorine subsitutions. Adapted from: Chang YS, 2008, J Mol B) 2,3,4,7,8-pentachlorodibenzofuran (PeCDF); Microbiol Biotechnol, 15:152-171. C) Dibenzo-p-dioxin (DD); D) carbazole (CAR). D i ff e r e n t c o n g e n e r s ( s p e c i e s ) o f t h e s e compounds are named by listing the locations of the chlorine subsitutions. Adapted from: Chang YS, 2008, J Mol Microbiol Biotechnol, 15:152-171. Table 1 presents a summary of observed microbial dechlorination of CDDs. Dechlorination rates decrease with increasing chlorine substitution, and complete dechlorination of the compound by bacteria has not been observed. Recently, it was shown that toxic 1,2,3,4,7,8-HCDD was dechlorinated by “Dehalococcoides” ethenogenes strain 195 to less toxic   congeners [11••]. In 26   aerobic environments, microbes can degrade the compound through F o l P 1 T p F [ A" peri dechlorination angular dioxygenase lateral dechlorination A) DD); DF); AR). ese s of ang nol, ved Ds. with and d by y, it was es” xic nts, ugh age B" spontaneous dioxygenase chloro-catechol hydrolase six-carbon chain Krebs cycle fully metabolized Fig. Figure 2: Proposed PCDD dechlorination and 1.4. Proposed PCDD dechlorination and oxidation pathways. Pathway A shows peri-oxidation pathways. Pathway Pathway B shows the oxidation of and lateral-dechlorination of 1,2,3,7,8-PeCDD. A shows peri- and 1,2,3,7,8-PeCDD by the indicated enzymes. These pathways are shown for illustrative lateral-dechlorination of 1,2,3,7,8-PeCDD. purposes, and are not confirmed. Adapted from (Field et al. 2008, Wittich 1998, and P al. h w Nam et a t2006). a y B s h o w s t h e o x i d a t i o n o f   1,2,3,7,8-PeCDD by the indicated enzymes. These pathways are shown for illustrative purposes, and are not confirmed. Adapted from Field, 2008 [2•]; Wittich, 1998 [9]; and Nam, 2006 [13]. 27   aerobic angular dioxygenation detoxify (Chang 2008, Field et al. 2008, Hiraishi 2008, Wittich 1998). ARD takes place in anaerobic microbial environments when a chlorine atom is removed and replaced with a hydrogen atom (Mohn et al. 1992) (see Fig. 1.4A). The chlorinated compound acts as the terminal electron acceptor, and “Dehalococcoides” can save energy from the dechlorination process (Hiraishi 2008). Table 1.2 presents a summary of observed microbial dechlorination of CDDs. Dechlorination rates decrease with increasing chlorine substitution, and complete dechlorination of the compound by bacteria has not been observed. Recently, it was shown that toxic 1,2,3,4,7,8-HCDD was dechlorinated by “Dehalococcoides” ethenogenes strain 195 to less toxic congeners (Liu et al. 2008). In aerobic environments, microbes can degrade the compound through aerobic oxidation and subsequent cleavage of the aromatic rings. The oxidation pathway in Fig. 1.4B shows that after both ether bridges are broken, a chloro-catechol and a six-carbon chain are formed. Table 1.3 presents a summary of observed aerobic oxidation of PCDDs by bacteria. Increased chlorine substitution decreases the rate of oxidation (Habe et al. 2002). Recently, toxic 1,2,3,4,7,8-HCDD was shown to be oxidized to less toxic compounds (Nam et al. 2006). Sphingomonas (especially strain RW1), and Pseudomonas are the most efficient PCDD oxidizers (Field et al. 2008). It is critical that microbial degradation of PCDDs results in a less toxic product. 2,3,7,8-TCDD is the most toxic congener. Other congeners with all four lateral chlorines are also highly toxic. The compound decreases in toxicity when any lateral chlorine is removed (Mandal 2005), or when the aromatic structure is broken (Chang 2008). For this reason, lateral dechlorination is of great importance because it results in a detoxified   28   product (Liu et al. 2008). Anaerobic dechlorination and aerobic angular dioxygenation detoxify PCDDs, and the genes involved in these two reactions will be called dioxin detoxification genes (DDGs) for convenience in this paper. Bioremediation of PCDD. PCDDs are subject to microbial degradation and detoxification as demonstrated in Tables 1.2 and 1.3, but much advancement is needed in order to develop a successful PCDD bioremediation strategy. First, putative, or alleged, dechlorinases need to be described. Second, the isolation of novel DDGs should continue with increased emphasis on using molecular techniques. These two steps will describe PCDD degrading enzymes more fully. Then, an effective implementation procedure should be designed for the contaminated environment (soil, water sediment, or flyash) with a plan to promote the expression of PCDD degrading enzymes. When these steps are taken, bioremediation of PCDDs could make the transition from the laboratory bench to the field. Function of putative dechlorinases. “Dehalococcoides” sp. has been identified as the most successful PCDD dechlorinator (Hiraishi 2008), but we cannot implicate a specific gene or genes related to PCDD dechlorination (Hölscher et al. 2004). Progress is being made to describe these reductive dechlorinases (RDases). The entire genomes of two dioxin dechlorinators “Dehalococcoides” ethenogenes (Seshadri et al. 2005) and “Dehalococcoides” sp. strain CBDB1 (Bedard et al. 2007) were both published in 2005. Two tetrachloroethene RDases were characterized and named pceA and tceA. About 50 unique sequences that shared the characteristic features of these two genes were found among “Dehalococcoides” sp. strains CBDB1, FL2, BAV1 and 195 and were termed reductive-dehalogenase-homologous (RDH) genes. The function of these genes has not   29   Table 1.2. Summary of studies that have demonstrated the successful dechlorination of PCDDs. Many studies show dechlorination of dioxin-like compounds including PCDFs, but this table only concerns PCDDs, which are the most recalcitrant of all dioxin-like compounds. The table shows the compound, the organism found to degrade it, the degradation rate (based on the indicated reference), the genes involved, metabolites, and references. It is important to note that the specific gene(s) relative to PCDD dechlorination have not been isolated, and no study has shown the complete dechlorination of PCDD under anaerobic conditions. PCDD Compound Microbial Culture 23-DCDD Dehalococcoides sp. CBDB1 Degrad. Rate (% remov / time) 53% / 28 days Known Genes Involved PDGs d Productsd 2-CDD References of Interest Bunge et al. (2003) 123-TrCDD Dehalococcoides sp. CBDB1, c Anaerobic consortiumc 60% / 57 days PDGs 23-/ 13-DCDD, 2-MCDD Bunge et al. (2003) Ballerstedt et al. (2004) 124-TrCDD Dehalococcoides sp. CBDB1, c Anaerobic consortiumc 55% / 57 days PDGs 13-DCDD, 2MCDD Bunge et al. (2003) Ballerstedt et al. (2004) Bunge et al. (2008) PDGs 124-TrCDD 13-/ 23-DCDD 2MCDD Bunge et al. (2003) Fennell et al. (2004) Anaerobic, methogenic, and sulfatereducing enrichmentsc, 1234-TCDD Dehalococcoides sp. CBDB1, D. ethanogenes 195c 24% / 84 days 12378PeCDD Dehalococcoides sp. CBDB1 75% / 84 days PDGs 123478HCDD Mixed culture containing D. ethanogenes 195 10% / 200 days PDGs a 2378-TCDD, DCDDs, 237TrCDD 1378-/ 1248TCDD Bunge et al. (2003) Liu (2008) b Adapted from Field and Alvarez (2008). Putative dehalogenase genes. We cannot implicate a specific gene or genes to the dechlorination of c PCDD/Fs. Only genes similar to tceA or pceA have been implicated. The culture was first enriched on another clorinated electron acceptor. d General product list and not specific to one culture. Products are listed in decreasing order of rate of formation.   30   Table 1.3. A summary of important studies that have demonstrated the successful oxidation of PCDDs. The table shows the compound, the organism(s) found to degrade it, the degradation rate (an average of many studies), the genes involved, the formed products, references. The three genes listed here are angular dioxygenases. MCDF oxidation is listed to show its oxidation relative to MCDD. PCDD Degrad. Rate Genes References of Compound Microbial Culture (% remov/time) Involved Products Interest Rhodococcus, Pseudomonas, Bacillus, Klebsiella, Sphingomonas, 1-/ 2-MCDD Burkholderia, Terrabacter, Klebsiella, others Sphingomonas, Terrabacter, 2-/ 3-/ 4Klebsiella, Pseudomonas, MCDF Burkholderia, others Sphingomonas, Pseudomonas, 23-/ 27-/ 28Rhodococcus, Terrabacter, P450 DCDD enzyme 123-/ 237TrCDD Spingomonas wittichii RW1, Pseudomonas sp. EE41, P450 enzyme Sphingomonas wittichii RW1, Pseudomonas veronii PH-03 Pseudomonas testosteroni G1036, 2378-TCDD Bacillus magaterium 123478Spingomonas wittichii RW1 HCDD 1234-TCDD   75% / 2 days b CARDOb, c DFDOc, d DDDOd 88% / 1 day DFDO 42% / 4 days CARDO, DFDO, DDDO chlorocatechols, chloroketone, CDDs 31% / 30 days CARDO, DFDO, DDDO polychlorocatechol, polychloroethers, hydroxy PCDD 15% / 5 days DDDO DFDO 60% / 244 days DFDO 10% / 5 days DDDO polychlorocatechol, polychloroguaiacol hydroxy-TeCDD, unidentified tetrachlorocatechol, others 31   chlorocatechol, chloro acids, hydroxyl-CDD Habe et al. (2001a) Wittich et al. (1992) chlorosuccinyl acids, CBPs Habe et al. (2001a) Sulistyaningdhah et al. (2004) Hong et al. (2002) Nam et al. (2006) Sulistyaningdhah et al. (2004) Habe et al. (2001) Hong et al. (2002) Nam et al. (2006) Table 1.3 (cont’d) a Adapted from Field and Alvarez, 2008. All listed oxidations utilized another compound (DF, CAR, others) as the growth substrate. b c d carbazole 1,9a dioxygenase (Habe et al., 2001a) dibenzofuran 4,4a dioxygenase (Habe et al., 2001a) dibenzo-p-dioxygenase (Wittich et al., 1992)   32   been described (Hölscher et al. 2004). Determining if these genes encode for PCDD RDases would allow us to probe for these genes in PCDD contaminated environments by real-time PCR or microarray, to monitor their expression, and the extent of microbial PCDD dechlorination. This technique has been used to study polychlorinated biphenyl (PCB) biodegradation. “Dehalococcoides” was found to dechlorinate Aroclor 1260, a mixture of PCBs. Quantitative real-time PCR indicated that the “Dehalococcoides” population increased by nearly two orders of magnitude in the presence of Aroclor 1260 (Bedard et al. 2007). Isolation of novel dioxin detoxification genes (DDGs). Notwithstanding recent observation of highly chlorinated dioxin detoxification (Liu et al. 2008, Nam et al. 2006, Nam et al. 2008), the known diversity of DDGs is narrow. Dehalogenating bacteria of all chloro-organic compounds are phylogenetically diverse (Hiraishi 2008), but efficient PCDD dechlorinators are limited to the “Dehalococcoides” group. Efficient PCDD oxidizers are mainly only Sphingomonas and Pseudomonas (see Tables 1.2 and 1.3). Sipilä et al. has claimed, “... we are unfortunately only in the beginning of grasping the overwhelming diversity of bacteria involved in biodegradation in soil” (Sipilä et al. 2008). PCDD contaminated environments likely harbor many other DDGs that remain unknown. Multiple factors may contribute to why our knowledge of DDGs is limited. First, PCDD degradation is generally very slow in nature and yields little biomass production, which may contribute to the seemingly elusive nature of DDGs (Hölscher et al. 2004). Second, most studies have attempted to screen environmental samples to a pure culture that can detoxify PCDDs (Bunge et al. 2003). It might be that dioxin detoxifying   33   organisms rely on other organisms to provide substrates, and/or provide enzymes for reactions further down the pathway (biphenyl dioxygenase, hydrolase, chloro-catechol degrading genes), and thus screening to a pure culture may result in a loss of the PCDD degrading cultures. For these reasons it may be necessary to localize DDGs by way of molecular methods, before culturing and enrichment procedures. This type of procedure was followed in the description of RDH genes. Probing PCDD contaminated environments with broad primers from conserved regions of known angular dioxygenases may lead to the discovery of novel angular dioxygenases. Iwai et al. used a broad primer set targeting biphenyl/toluene dioxygenases together with pyrosequencing to obtain over 900 unique dioxygenase sequences in historically contaminated environmental samples, demonstrating large biodiversity of dioxygenases in the environment (Iwai et al. 2010). Effective implementation procedure. PCDDs are found in surface soils, water sediments, and incinerator flyash, and each environment presents unique challenges in the implementation of PCDD bioremediation An effective implementation procedure will promote growth of microbes of interest and the expression of DDGs under the environmental conditions of the contaminated site. Biostimulation and bioaugmentation have been shown to promote the growth of microbes of interest and the expression of DDGs. Biostimulation is the practice of amending the environment with a necessary growth substrate or co-substrates, and bioaugmentation is the inoculation of the environment with a microorganism, or microbial consortium known to have the desired degrading ability. 1,2,3,4-tetrachlorobenzene has been shown to increase the dechlorination rate of 1,2,3,4-TCDD by native microbial communities compared to samples with only a growth substrate (Ahn et al. 2005). Ahn et al. tested the affect of   34   bioaugmentation (with “Dehalococcoides” ethenogenes strain 195) and biostimulation (with 1,2,3,4-tetrachlorobenzene, and 2’,3’,4’- trichloroacetophenone) on the dechlorination of PCDDs, and at the same time monitored community and gene dynamics. They found that heavily contaminated sites harbored more indigenous dechlorinators than less contaminated sites, by denaturing gradient gel electrophoresis (DGGE). Dechlorination at the heavily contaminated sites was not greatly enhanced by biostimulation and bioaugmentation, while at less contaminated sites dechlorination was enhanced by these methods (Ahn et al. 2008). Surface soils. PCDDs are generally only found in the upper portions of the soil profile because the compound is extremely insoluble in water (water solubility equals 0.019 ppb) (Field et al. 2008), which prevents the compound from leeching or moving with ground water. Surface soils are generally aerobic and converting a large environment of this type to an anaerobic state to promote ARD would be difficult and expensive. Therefore, ARD in surface soils is likely to not occur at a significant rate, but dioxygenation would be the most common mode of dioxin detoxification by bacteria. Biodegradation of PCDDs with dioxygenation as the only designed mode of detoxification would result in the persistence of a significant fraction of toxic dioxin congeners, because dioxygenation of HpCDD and OCDD has not been observed (Field et al. 2008). In this situation, it may be helpful to employ a chemical treatment simultaneously. Zero valence iron (ZVI) has been shown to rapidly dechlorinate highly chlorinated PCDDs, even OCDD. This process was carried out in an anaerobic environment in conjunction with PCDD dechlorinating bacteria (Chang 2008). It is not known if this process would function in an aerobic environment, but if it does, highly   35   chlorinated PCDDs could be dechlorinated by ZVI, and the products would be subject to dioxygenation, which may result in a highly detoxified environment. Another promising technology in surface soils would be the use of phytoremediation and rhizoremediation. Phytoremediation is the removal and/or degradation of pollutants by plants, and rhizoremediation is the degradation of pollutants by soil microbes that grow very close to plant roots. As of 2007, there was no documentation of dioxin uptake by plants from the soil (Jou et al. 2007). A study by Jou et al. reported dioxin uptake by tappa (Boussonetia papyrifera) in highly contaminated soils, and by Physalis angulatal in low contaminated soils (Jou et al. 2007). Dioxins are taken up at lower levels in zucchini cultivars (Inui et al. 2008) and some other annual and perennial plants (Fan et al. 2009). The mechanism of PCDD accumulation was not stated. It is possible PCDDs are more bioavailable in the rhizosphere of these plants, which allows for its uptake. In a study by Sipilä et al. (2008), it was determined that the microbial community structure, as determined by terminal restriction fragment length polymorphism (TRFLP), changed with the addition of PAHs and the cultivation of birch trees. The diversity of dioxygenases was greater in the rhizosphere compared to the bulk soil. The PAH degrading microbial community in the bulk soil was 48.5% dissimilar from the PAH degrading microbial community in the rhizosphere. This study outlines an excellent method of determining the diversity of genes that execute the desired function (Sipilä et al. 2008). Similar studies should be carried out in regards to PCDD dioxygenases of the rhizosphere. Sipilä et al. also showed an increase in microbial diversity in the rhizosphere (Sipilä et al. 2008). This may be advantageous to PCDD oxidation, because as indicated in Fig. 1.4B, many different enzymes are required in   36   PCDD oxidation. These enzymes could be supplied by not just one key organism, but a variety of soil bacteria. The rhizosphere may provide a favorable environment for a diverse dioxin detoxifying microbial community. Water sediments. PCDD contaminated soil particles can be eroded and deposited in rivers and lakes. These PCDD contaminated water sediments sink to the floor of the body of water and remain in an anaerobic environment. Such an environment would facilitate the ARD of PCDDs, but M/DCDD would remain (see Table 1.2 for the products of ARD reactions). There is no known mechanism to biodegrade M/DCDD further under anaerobic conditions. Thus, the detoxified M/DCDD product may be the best solution to PCDD contaminated sediments (Bunge et al. 2003). Flyash. Flyash is a major source of environmental dioxin contamination (Kulkarni et al. 2008). PCDDs form chemically when organic matter is burned in the presence of chlorine and flyash is a residue after incineration. Nam et al. performed a series of experiments to test the degradability of PCDD in flyash by Sphingomonas wittichii strain RW1. The total organic carbon content of the flyash was 0.0014 ± 0.004%, which is very low compared to soil, indicating that the environment is nutrient depleted and a harse environment for microbial growth. In 2005, they observed 75.5% removal of all PCDD and 83.8% removal of 2,3,7,8-TCDD from flyash by way of degradation and adsorption onto live and dead cell biomass (Nam et al. 2005). In 2008, a mix of 4 bacterial and 5 fungal strains were combined to form a dioxin-degrading biocatalyst in flyash. This biocatalyst degraded 68.7% of all PCDD and 66.8% of 2,3,7,8-TCDD substituted congeners. In this study it was shown that fungal strains provided extra cellular nonspecific oxidases to degrade highly chlorinated congeners. These fungal oxidases are   37   non-specific, meaning they are not specific to a single substrate, but can degrade an array of compounds, including very stable PCDDs and lignin (Nam et al. 2008). Flyash is the only environment that has shown fungi to be effective degraders of PCDDs in situ. This may be because the microbes were in a carbon-depleted environment and PCDDs were available for carbon and energy use. In soil, the total organic carbon content is higher, fungi are not effective PCDD degraders because they preferencially degrade higher energy yielding compounds (Field et al. 2008). Conclusion. Current physical and chemical remediation of PCDD contaminated sites is not sustainable and bioremediation could be a more favorable alternative. There are two research initiatives that need to be completed before bioremediation is a viable option for PCDD clean-up. First, a higher percentage of toxic PCDD congeners need to be shown to be degraded by bacterial enzymes. Second, these laboratory methods must be successfully implemented in the field. The bacterial degradation of a few highly chlorinated congeners, and many mono-, and di-CDDs has been demonstrated. Recent studies have shown both the bacterial dechlorination and oxidation of 1,2,3,4,7,8-HCDD, which indicates that the bacterial enzymes may exist to degrade other toxic PCDD congeners, but have not yet been discovered, isolated, or characterized. Reductivedehalogenase- homologous (RHD) genes are suspected to dechlorinate PCDDs. Their function has not been confirmed, but is a likely area of future research. Use of broad primers from conserved regions of known angular dioxygenases together with molecular methods (real-time PCR, microarrays, pyrosequencing, DGGE, and TRFLP) to detect the expression of DDGs may lead to easier isolation and characterization of novel DDGs, rather than isolation through culturing methods. Once we have isolated a sufficient   38   number of DDGs, in situ bioremediation strategies need to be developed. PCDDs exist in surface soils, water sediments and flyash. Aerobic surface soils would allow for aerobic dioxygenation, but would not allow for anaerobic reductive dechlorination (ARD). Rhizoremediation may encourage a greater dioxygenase diversity, and higher PCDD bioavailability to increase bioremediation rates. PCDDs in water sediments may be detoxified through ARD. PCDD contaminated flyash may best be detoxified by a bacterial and fungal biocatalyst, which has already been shown to be successful.   39   REFERENCES   40   REFERENCES Aarestrup FM, Seyfarth AM, Emborg HD, Pedersen K, Hendriksen RS, Bager F. 2001 Effect of abolishment of the use of antimicrobial agents for growth promotion on occurrence of antimicrobial resistance in fecal Enterococci from food animals in Denmark. Antimicrob Agents Chemother 45(7):2054-2059. Ahn Y-B, Häggblom MM, Fennell DE. 2005 Co-amendment with halogenated compounds enhances anaerobic microbial dechlorination of 1,2,3,4tetrachlorodibenzo-p-dioxin and 1,2,3,4-tetrachlorodibenzofuran in estuarine sediments. Environ Toxicol Chem 24(11):2775-2784. Ahn Y-B, Liu F, Fennell DE, Häggblom MM. 2008 Biostimulation and bioaugmentation to enhance dechlorination of polychlorinated dibenzo-p-dioxins in contaminated sediments. FEMS Microbiol Ecol 66(2):271-281. Akwar TH, Poppe C, Wilson J, Reid-Smith RJ, Dyck M, Waddington J et al. 2007 Risk factors for antimicrobial resistance among fecal Escherichia coli from residents on forty-three swine farms. Microb Drug Resist 13(1):69-76. Alekshun MN, Levy SB. 2007 Molecular mechanisms of antibacterial multidrug resistance. Cell 128(6):1037-1050. Allen HK, Looft T, Bayles DO, Humphrey S, Levine UY, Alt D et al. 2011 Antibiotics in feed induce prophages in swine fecal microbiomes. MBio 2(6). Antonopoulos DA, Huse SM, Morrison HG, Schmidt TM, Sogin ML, Young VB. 2009 Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect Immun 77(6):2367-2375. Arias CA, Murray BE. 2009 Antibiotic-resistant bugs in the 21st century—a clinical super-challenge. N Engl J Med 360(5):439-443. Baker-Austin C, Wright MS, Stepanauskas R, McArthur JV. 2006 Co-selection of antibiotic and metal resistance. Trends Microbiol 14(4):176-182. Ballerstedt H, Hantke J, Bunge M, Werner B, Gerritse J, Andreesen JR et al. 2004 Properties of a trichlorodibenzo-p-dioxin-dechlorinating mixed culture with a Dehalococcoides as putative dechlorinating species. FEMS Microbiol Ecol 47(2):223-234. Bedard DL, Ritalahti KM, Loffler FE. 2007 The Dehalococcoides population in sediment-free mixed cultures metabolically dechlorinates the commercial polychlorinated biphenyl mixture Aroclor 1260. Appl Environ Microbiol 73(8):2513-2521.   41   Benveniste R, Davies J. 1973 Aminoglycoside antibiotic-inactivating enzymes in Actinomycetes similar to those present in clinical isolates of antibiotic-resistant bacteria. Proc Natl Acad Sci U S A 70(8):2276-2280. Berg J, Thorsen MK, Holm PE, Jensen J, Nybroe O, Brandt KK. 2010 Cu exposure under field conditions coselects for antibiotic resistance as determined by a novel cultivation-independent bacterial community tolerance assay. Environ Sci Technol 44(22):8724-8728. Boucher Y, Labbate M, Koenig JE, Stokes HW. 2007 Integrons: Mobilizable platforms that promote genetic diversity in bacteria. Trends Microbiol 15(7):301-309. Bunge M, Adrian L, Kraus A, Opel M, Lorenz WG, Andreesen JR et al. 2003 Reductive dehalogenation of chlorinated dioxins by an anaerobic bacterium. Nature 421(6921):357-360. Bunge M, Wagner A, Fischer M, Andreesen JR, Lechner U. 2008 Enrichment of a dioxin-dehalogenating Dehalococcoides species in two-liquid phase cultures. Environ Microbiol 10(10):2670-2683. Butler E, Whelan MJ, Ritz K, Sakrabani R, van Egmond R. 2011 Effects of triclosan on soil microbial respiration. Environ Toxicol Chem 30(2):360-366. Caro-Quintero A, Deng J, Auchtung J, Brettar I, Höfle MG, Klappenbach J et al. 2011 Unprecedented levels of horizontal gene transfer among spatially co-occurring Shewanella bacteria from the Baltic Sea. ISME J 5(1):131-140.   Chai B, Tsoi T, Iwai S, Liu C, Fish J, Gu C, Johnson TA, Teppen B, Hashsham SA, Boyd S, Cole JR, Tiedje JM. Sphingomonas wittichii strain RW1 genome-wide gene expression shifts in response to dioxins and clay (in preparation). Chang Y-S. 2008 Recent developments in microbial biotransformation and biodegradation of dioxins. J Mol Microbiol Biotechnol 15(2-3):152-171. Chee-Sanford JC, Mackie RI, Koike S, Krapac IG, Lin Y-F, Yannarell AC et al. 2009 Fate and transport of antibiotic residues and antibiotic resistance genes following land application of manure waste. J Environ Qual 38(3):1086-1108. Chung H, Pamp SJ, Hill JA, Surana NK, Edelman SM, Troy EB et al. 2012 Gut immune maturation depends on colonization with a host-specific microbiota. Cell 149(7):1578-1593. Church DL. 2004 Major factors affecting the emergence and re-emergence of infectious diseases. Clin Lab Med 24(3):559-586. Cosgrove SE, Carmeli Y. 2003 The impact of antimicrobial resistance on health and economic outcomes. Clin Infect Dis 36(11):1433-1437.   42   Costello EK, Stagaman K, Dethlefsen L, Bohannan BJM, Relman DA. 2012 The application of ecological theory toward an understanding of the human microbiome. Science 336(6086):1255-1262. D'Costa VM, McGrann KM, Hughes DW, Wright GD. 2006 Sampling the antibiotic resistome. Science 311(5759):374-377. D'Costa VM, King CE, Kalan L, Morar M, Sung WW, Schwarz C et al. 2011 Antibiotic resistance is ancient. Nature 477(7365):457-461. Dai M, Lu J, Wang Y, Liu Z, Yuan Z. 2012 In vitro development and transfer of resistance to chlortetracycline in Bacillus subtilis. J Microbiol 50(5):807-812. Davies J, Davies D. 2010 Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74(3):417-433. Davis MF, Iverson SA, Baron P, Vasse A, Silbergeld EK, Lautenbach E et al. 2012 Household transmission of meticillin-resistant Staphylococcus aureus and other Staphylococci. Lancet Infect Dis 12(9):703-716. Dethlefsen L, Huse S, Sogin ML, Relman DA. 2008 The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16s rRNA sequencing. PLoS Biol 6(11):e280. Dethlefsen L, Relman DA. 2011 Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci USA 108 Suppl 1:4554-4561. Dionisio F, Matic I, Radman M, Rodrigues OR, Taddei F. 2002 Plasmids spread very fast in heterogeneous bacterial communities. Genetics 162(4):1525-1532. Durbán A, Abellán JJ, Jiménez-Hernández N, Latorre A, Moya A. 2012 Daily follow-up of bacterial communities in the human gut reveals stable composition and hostspecific patterns of interaction. FEMS Microbiol Ecol 81(2):427-437. Fan G, Cui Z, Liu J. 2009 Interspecies variability of dioxin-like PCBs accumulation in five plants from the modern Yellow River delta. J Hazard Mater 163(2-3):967972. FDA. 2009 Summary report on antimicrobials sold or distributed for use in foodproducing animals. 1-4. Field JA, Sierra-Alvarez R. 2008 Microbial degradation of chlorinated dioxins. Chemosphere 71(6):1005-1018.     43   Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MOA, Dantas G 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337(6098): 1107-1111. Fournier P-E, Vallenet D, Barbe V, Audic S, Ogata H, Poirel L et al. 2006 Comparative genomics of multidrug resistance in Acinetobacter baumannii. PLoS Genet 2(1):e7. Fries MR, Hopkins GD, McCarty PL, Forney LJ, Tiedje JM. 1997 Microbial succession during a field evaluation of phenol and toluene as the primary substrates for trichloroethene cometabolism. Appl Environ Microbiol 63(4):1515-1522. Gaze WH, Zhang L, Abdouslam NA, Hawkey PM, Calvo-Bado L, Royle J et al. 2011 Impacts of anthropogenic activity on the ecology of class 1 integrons and integron-associated genes in the environment. ISME J 5(8):1253-1261. Gilbert N. 2012 Rules tighten on use of antibiotics on farms. Nature 481(7380):125. Gillings MR, Stokes HW. 2012 Are humans increasing bacterial evolvability? Trends Ecol Evol 27(6):346-352. Habe H, Chung JS, Lee JH, Kasuga K, Yoshida T, Nojiri H et al. 2001 Degradation of chlorinated dibenzofurans and dibenzo-p-dioxins by two types of bacteria having angular dioxygenases with different features. Appl Environ Microbiol 67(8):3610-3617. Habe H, Ide K, Yotsumoto M, Tsuji H, Hirano H, Widada J et al. 2001a Preliminary examinations for applying a carbazole-degrader, Pseudomonas sp. strain CA10, to dioxin-contaminated soil remediation. Appl Microbiol Biotechnol 56(5-6):788795. Habe H, Ide K, Yotsumoto M, Tsuji H, Yoshida T, Nojiri H et al. 2002 Degradation characteristics of a dibenzofuran-degrader Terrabacter sp. strain DBF63 toward chlorinated dioxins in soil. Chemosphere 48(2):201-207. Haglund P. 2007 Methods for treating soils contaminated with polychlorinated dibenzop-dioxins, dibenzofurans, and other polychlorinated aromatic compounds. Ambio 36(6):467-474. Heuer H, Solehati Q, Zimmerling U, Kleineidam K, Schloter M, Müller T et al. 2011 Accumulation of sulfonamide resistance genes in arable soils due to repeated application of manure containing sulfadiazine. Appl Environ Microbiol. 77(7): 2527-30 Hiraishi A. 2008 Biodiversity of dehalorespiring bacteria with special emphasis on polychlorinated biphenyl/dioxin dechlorinators. Microbes Environ 23(1):1-12.   44   Hölscher T, Krajmalnik-Brown R, Ritalahti KM, Von Wintzingerode F, Görisch H, Löffler FE et al. 2004 Multiple nonidentical reductive-dehalogenase-homologous genes are common in Dehalococcoides. Appl Environ Microbiol 70(9):52905297. Hong H-B, Chang Y-S, Nam I-H, Fortnagel P, Schmidt S. 2002 Biotransformation of 2,7-dichloro- and 1,2,3,4-tetrachlorodibenzo-p-dioxin by Sphingomonas wittichii RW1. Appl Environ Microbiol 68(5):2584-2588. Hunter JE, Bennett M, Hart CA, Shelley JC, Walton JR. 1994 Apramycin-resistant Escherichia coli isolated from pigs and a stockman. Epidemiol Infect 112(3):473480. Hunter PR, Wilkinson DC, Catling LA, Barker GC. 2008 Meta-analysis of experimental data concerning antimicrobial resistance gene transfer rates during conjugation. Appl Environ Microbiol 74(19):6085-6090. Hvistendahl M. 2012 China takes aim at rampant antibiotic resistance. Science 336:1-1. Inui H, Wakai T, Gion K, Kim Y-S, Eun H. 2008 Differential uptake for dioxin-like compounds by zucchini subspecies. Chemosphere 73(10):1602-1607. Iwai S, Chai B, Sul WJ, Cole JR, Hashsham SA, Tiedje JM. 2010 Gene-targetedmetagenomics reveals extensive diversity of aromatic dioxygenase genes in the environment. ISME J 4(2):279-285. Jakobsson HE, Jernberg C, Andersson AF, Sjölund-Karlsson M, Jansson JK, Engstrand L. 2010 Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS ONE 5(3):e9836. Jernberg C, Löfmark S, Edlund C, Jansson JK. 2007 Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J 1(1):56-66. Jou J-J, Chung J-C, Weng Y-M, Liaw S-L, Wang MK. 2007 Identification of dioxin and dioxin-like polychlorbiphenyls in plant tissues and contaminated soils. J Hazard Mater 149(1):174-179. Kim HB, Borewicz K, White BA, Singer RS, Sreevatsan S, Tu ZJ et al. 2012 Microbial shifts in the swine distal gut in response to the treatment with antimicrobial growth promoter, tylosin. Proc Natl Acad Sci USA 109(38):15485-15490. Klein E, Smith DL, Laxminarayan R. 2007 Hospitalizations and deaths caused by methicillin-resistant Staphylococcus aureus, United States, 1999-2005. Emerging Infect Dis 13(12):1840-1846.   45   Klevens RM, Morrison MA, Nadle J, Petit S, Gershman K, Ray S et al. 2007 Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA 298(15):1763-1771. Knapp CW, Mccluskey SM, Singh BK, Campbell CD, Hudson G, Graham DW. 2011 Antibiotic resistance gene abundances correlate with metal and geochemical conditions in archived Scottish soils. PLoS ONE 6(11):e27300. Kulkarni PS, Crespo JG, Afonso CAM. 2008 Dioxins sources and current remediation technologies—a review. Environ Int 34(1):139-153. Kumarasamy KK, Toleman MA, Walsh TR, Bagaria J, Butt F, Balakrishnan R et al. 2010 Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: A molecular, biological, and epidemiological study. Lancet Infect Dis 10(9):597-602. Lauber CL, Ramirez KS, Aanderud Z, Lennon J, Fierer N. 2013 Temporal variability in soil microbial communities across land-use types. ISME J. Apr 4. doi: 10.1038/ismej.2013.50. [Epub ahead of print] Lauro FM, McDougald D, Thomas T, Williams TJ, Egan S, Rice S et al. 2009 The genomic basis of trophic strategy in marine bacteria. Proc Natl Acad Sci USA 106(37):15527-15533. Lenski RE, Bennett AF. 1993 Evolutionary response of Escherichia coli to thermal stress. Am Nat 142 Suppl 1:S47-64. Levy SB, FitzGerald GB, Macone AB. 1976 Changes in intestinal flora of farm personnel after introduction of a tetracycline-supplemented feed on a farm. N Engl J Med 295(11):583-588. Levy SB. 1978 Emergence of antibiotic-resistant bacteria in the intestinal flora of farm inhabitants. J Infect Dis 137(5):689-690. Lewis K. 2007 Persister cells, dormancy and infectious disease. Nat Rev Microbiol 5(1):48-56. Liu B, Pop M. 2009 Ardb--antibiotic resistance genes database. Nucleic Acids Res 37(Database):D443-D447. Liu F, Fennell DE. 2008 Dechlorination and detoxification of 1,2,3,4,7,8hexachlorodibenzofuran by a mixed culture containing Dehalococcoides ethenogenes strain 195. Environ Sci Technol 42(2):602-607.   46   Looft T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD et al. 2012 In-feed antibiotic effects on the swine intestinal microbiome. Proc Natl Acad Sci USA 109 (5) pp. 1691-1696. Mahillon J, Chandler M. 1998 Insertion sequences. Microbiol Mol Biol Rev 62(3):725774. Mandal PK. 2005 Dioxin: A review of its environmental effects and its aryl hydrocarbon receptor biology. J Comp Physiol B, 175(4):221-230. Marshall BM, Levy SB. 2011 Food animals and antimicrobials: Impacts on human health. Clin Microbiol Rev 24(4):718-733. Mazel D. 2006 Integrons: Agents of bacterial evolution. Nat Rev Microbiol 4(8):608-620. McEwen SA. 2012 Quantitative human health risk assessments of antimicrobial use in animals and selection of resistance: A review of publicly available reports. Rev Off Int Epizoot 31(1):261-276. Moellering RC. 1998 Vancomycin-resistant enterococci. Clin Infect Dis 26(5):11961199. Mohn WW, Tiedje JM. 1992 Microbial reductive dehalogenation. Microbiol Rev 56(3):482-507. Nam I-H, Hong H-B, Kim Y-M, Kim B-H, Murugesan K, Chang Y-S. 2005 Biological removal of polychlorinated dibenzo-p-dioxins from incinerator fly ash by Sphingomonas wittichii RW1. Water Res 39(19):4651-4660. Nam I-H, Kim Y-M, Schmidt S, Chang Y-S. 2006 Biotransformation of 1,2,3-tri- and 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin by Sphingomonas wittichii strain RW1. Appl Environ Microbiol 72(1):112-116. Nam I-H, Kim Y-M, Murugesan K, Jeon J-R, Chang Y-Y, Chang Y-S. 2008 Bioremediation of PCDD/Fs-contaminated municipal solid waste incinerator fly ash by a potent microbial biocatalyst. J Hazard Mater 157(1):114-121. Nordgård L, Nguyen T, Midtvedt T, Benno Y, Traavik T, Nielsen KM. 2007 Lack of detectable DNA uptake by bacterial gut isolates grown in vitro and by Acinetobacter baylyi colonizing rodents in vivo. Environ Biosafety Res 6(12):149-160. Ochman H, Lawrence JG, Groisman EA. 2000 Lateral gene transfer and the nature of bacterial innovation. Nature 405(6784):299-304.   47   Petrova M, Gorlenko Z, Mindlin S. 2011 Tn5045, a novel integron-containing antibiotic and chromate resistance transposon isolated from a permafrost bacterium. Res Microbiol 162(3):337-345. Phillips I, Casewell M, Cox T, De Groot B, Friis C, Jones R et al. 2004 Does the use of antibiotics in food animals pose a risk to human health? A critical review of published data. J Antimicrob Chemother 53(1):28-52. Price LB, Stegger M, Hasman H, Aziz M, Larsen J, Andersen PS et al. 2012 Staphylococcus aureus CC398: Host adaptation and emergence of methicillin resistance in livestock. MBio 3(1). Pruden A, Pei R, Storteboom H, Carlson KH. 2006 Antibiotic resistance genes as emerging contaminants: Studies in northern Colorado. Environ Sci Technol 40(23):7445-7450. Ramessar K, Peremarti A, Gómez-Galera S, Naqvi S, Moralejo M, Muñoz P et al. 2007 Biosafety and risk assessment framework for selectable marker genes in transgenic crop plants: A case of the science not supporting the politics. Transgenic Res 16(3):261-280. Ramette A, Tiedje JM. 2007 Multiscale responses of microbial life to spatial distance and environmental heterogeneity in a patchy ecosystem. Proc Natl Acad Sci USA 104(8):2761-2766. Rosenblatt-Farrell N. 2009 The landscape of antibiotic resistance. Environ Health Perspect 117(6):A244-250. Salyers AA, Gupta A, Wang Y. 2004 Human intestinal bacteria as reservoirs for antibiotic resistance genes. Trends Microbiol 12(9):412-416. Schloss PD, Schubert AM, Zackular JP, Iverson KD, Young VB, Petrosino JF. 2012 Stabilization of the murine gut microbiome following weaning. Gut Microbes 3(4):383-393. Seshadri R, Adrian L, Fouts DE, Eisen JA, Phillippy AM, Methe BA et al. 2005 Genome sequence of the PCE-dechlorinating bacterium Dehalococcoides ethenogenes. Science 307(5706):105-108. Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H et al. 2012 Fundamentals of microbial community resistance and resilience. Front Microbio 3:417. Sipilä TP, Keskinen A-K, Akerman M-L, Fortelius C, Haahtela K, Yrjälä K. 2008 High aromatic ring-cleavage diversity in birch rhizosphere: PAH treatment-specific changes of I.E.3 group extradiol dioxygenases and 16s rRNA bacterial communities in soil. ISME J 2(9):968-981.   48   Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ. 2011 Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480(7376):241-244. Söderblom T, Aspevall O, Erntell M, Hedin G, Heimer D, Hokeberg I et al. 2010 Alarming spread of vancomycin resistant Enterococci in Sweden since 2007. Euro Surveill 15(29). Stokes HW, Hall RM. 1989 A novel family of potentially mobile DNA elements encoding site-specific gene-integration functions: Integrons. Mol Microbiol 3(12):1669-1683. Storteboom HN, Kim S-C, Doesken KC, Carlson KH, Davis JG, Pruden A. 2007 Response of antibiotics and resistance genes to high-intensity and low-intensity manure management. J Environ Qual 36(6):1695-1703.   Sul, W. J., R. C. Penton, T. V. Tsoi and J. M. Tiedje. Identification of biphenyl-utilizing populations by stable isotope probing and pyrosequencing. (in preparation) Sulistyaningdyah WT, Ogawa J, Li Q-S, Shinkyo R, Sakaki T, Inouye K et al. 2004 Metabolism of polychlorinated dibenzo-p-dioxins by cytochrome P450 BM-3 and its mutant. Biotechnol Lett 26(24):1857-1860. Udwadia ZF, Amale RA, Ajbani KK, Rodrigues C. 2012 Totally drug-resistant tuberculosis in India. Clin Infect Dis 54(4):579-581. Velayati AA, Masjedi MR, Farnia P, Tabarsi P, Ghanavi J, Ziazarifi AH et al. 2009 Emergence of new forms of totally drug-resistant tuberculosis bacilli: super extensively drug-resistant tuberculosis or totally drug-resistant strains in Iran. Chest 136(2):420-425. Voss A, Loeffen F, Bakker J, Klaassen C, Wulf M. 2005 Methicillin-resistant Staphylococcus aureus in pig farming. Emerging Infect Dis 11(12):1965-1966. Weber R, Gaus C, Tysklind M, Johnston P, Forter M, Hollert H et al. 2008 Dioxin- and pop-contaminated sites—contemporary and future relevance and challenges: Overview on background, aims and scope of the series. Environ Sci Pollut Res Int 15(5):363-393. Wilson M, Herrick J, Jeon C, Hinman D, Madsen E. 2003 Horizontal transfer of phnAc dioxygenase genes within one of two phenotypically and genotypically distinctive naphthalene-degrading guilds from adjacent soil environments. Appl Environ Microbiol 69(4):2172-2181.   49   Wittich RM, Wilkes H, Sinnwell V, Francke W, Fortnagel P. 1992 Metabolism of dibenzo-p-dioxin by Sphingomonas sp. strain RW1. Appl Environ Microbiol 58(3):1005-1010. Wittich RM. 1998 Degradation of dioxin-like compounds by microorganisms. Appl Microbiol Biotechnol 49(5):489-499. Wright GD. 2011 Molecular mechanisms of antibiotic resistance. Chem Commun 47(14):4055-4061. Yagi J, Madsen E. 2009 Diversity, abundance, and consistency of microbial oxygenase expression and biodegradation in a shallow contaminated aquifer. Appl Environ Microbiol 75(20):6478-6487. Yoshida N, Takahashi N, Hiraishi A. 2005 Phylogenetic characterization of a polychlorinated-dioxin- dechlorinating microbial community by use of microcosm studies. Appl Environ Microbiol 71(8):4325-4334. Young VB, Schmidt TM. 2004 Antibiotic-associated diarrhea accompanied by largescale alterations in the composition of the fecal microbiota. J Clin Microbiol 42(3):1203-1206. Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. 2010 Functional molecular ecological networks. MBio 1(4). Zhu Y-G, Johnson TA, Su J-Q, Qiao M, Guo G-X, Stedtfeld RD et al. 2013 Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci USA 110(9):3435-3440.   50   CHAPTER II COMPARISON OF THE SPECIFICITY AND EFFICACY OF PRIMERS FOR AROMATIC DIOXYGENASE GENE ANALYSIS OF ENVIRONMENTAL SAMPLES The work presented in this chapter has been published: Iwai S, Johnson TA, Chai B, Hashsham SA, Tiedje JM. 2011 Comparison of the specificity and efficacy of primers for aromatic dioxygenase gene analysis of environmental samples. Appl Environ Microbiol 77(11): 3551-3557 and is reprinted here with the permission of the publisher. The published version of this chapter, as well as all other published chapters, is the official record rather than this reconstituted version. Important supplemental online material are available in the published version of this chapter at the journal’s website. Author contributions: S.I. and B. C. determined the in silico specificities of the published primer sets. T.A.J. reviewed previously published primer sets and their efficacy. S.I. and T.A.J. made recommendations to future primer use. S.I., T.A.J., S.A.H and J.M.T. wrote the paper.   51   ABSTRACT Aromatic dioxygenase genes have long been of interest for bioremediation and aromatic carbon cycling studies. To date, 115 primers and probes have been designed and used to analyze dioxygenase gene diversities in environmental samples. Here we analyze those primers’ specificity, coverage and PCR product length compared to known aromatic dioxygenase genes by using in silico analysis as well as summarize their differing advantages or effectiveness from over 50 reported experimental studies. We also provide some guidance for primer use in future studies. INTRODUCTION Aromatic compounds such as polycyclic aromatic hydrocarbons (PAHs), biphenyls and dioxins are widespread contaminants due to human activities as well as from natural events (Field et al. 2008, Green et al. 2004). Aromatic dioxygenases, which initiate the aerobic degradation of these compounds, have been of considerable interest for bioremediation. Furthermore these aromatic hydrocarbons have basic structures similar to some plant and soil aromatic compounds, and hence these and similar dioxygenase genes are likely involved in the natural carbon cycle. Improved understanding of the microbial contribution to terrestrial carbon turnover, especially of its more resistant aromatic components, is of increasing importance for predicting and potentially influencing the carbon cycle, and hence affect climate change (Bardgett et al. 2008). The α-subunits of these multi-component dioxygenases have a well-known, common structure central to its electron transfer and catalysis called the Rieske center, and mononuclear iron, and are called Rieske non-heme iron oxygenases (Gibson et al. 2000). These dioxygenases have been divided into four groups based on the substrates metabolized, which also correspond   52   to their phylogeny: toluene/biphenyl (T/B), naphthalene (or PAH), benzoate and phthalate dioxygenases (Gibson et al. 2000). The first two groups have been the most studied and 115 primers and probes have been reported for their study (see Table S1 in the supplemental material). As more recent studies have revealed much greater diversity of these genes in nature (Ding et al. 2010, Iwai et al. 2010, Kimura et al. 2009, Marcos et al. 2009, Yagi et al. 2009), an evaluation of the current primers is timely for informing future studies. Primer coverage and specificity, which are important factors in choosing a primer set appropriate for the experimental purpose, have never been compared and discussed. In this review, we compared the 115 primer sets in terms of specificity, coverage and length of the products by using in silico analysis, and by summarizing their performance from published studies. Biphenyl and PAH dioxygenase genes. As reference sequences, we retrieved 464 Rieske nonheme iron dioxygenase genes from the Pfam protein family database (Finn et al. 2008), which are more than 350aa in length and have both the Rieske family domain (Pfam PF00355) (Rieske center) and the Ring_hydroxyl_A family domain (Pfam PF00848) (iron binding site). Retrieved protein sequences were aligned using MUSCLE (Edgar 2004), and dissimilarity matrices calculated and used in DOTUR (Schloss et al. 2005) for Complete Linkage Clustering. A distance cutoff of 0.2 produced 120 clusters. The middle distance sequence in each cluster was selected as a representative sequence for the cluster. The representative sequences were used for constructing a phylogenetic tree by the Neighbor-joining method using Phylip 3.67 software (Felsenstein 1989) (data not shown). Of the retrieved genes, the aromatic dioxygenase genes including well-characterized toluene/biphenyl and PAH dioxygenase genes, are located in red branches. The rest of the gene clusters located in bottom half of the tree are benzoate dioxygenases or not functionally characterized Rieske-type dioxygenases. Since aromatic   53   dioxygenase genes usually indicate genes in the red-branched clade and thus most of the primers were designed for those genes, we used the 44 clusters that represent 204 dioxygenase genes in this large clade as reference sequences in this study. We grouped those reference genes into five sub-clades: PAH dioxygenases from Gram-negative bacteria (PAH-GN, blue circles), toluene/biphenyl dioxygenases (T/B, green circles), other dioxygenases I and II (OT-I and -II, yellow circles) and PAH dioxygenases from Gram-positive bacteria (PAH-GP, orange circles). Primer selection: Importance of primer coverage, specificity and PCR product length. In addition to basic primer design strategies written elsewhere (Robertson et al. 1998), three criteria should be considered for dioxygenase primer selection: coverage, specificity, and PCR product length. Primer coverage, which is the size of allele of the target gene that should be amplified by the primer set during PCR as estimated from known sequences, is a key parameter because it suggests the possible diversity of the sequences that will be recovered from natural samples. The coverage range depends on the conservation of the primer region, degeneracy of the primer, and PCR conditions that may allow some primer-template mismatches. Secondly, specificity, or to what extent the primers align with the correct genes, while not aligning with other undesired genes, is also critical. Specificity is counter to coverage. Higher degeneracy is often used to increase coverage to more alleles; but this may result in lower specificity. Primer coveragespecificity relationships are especially critical when using primers for deep sequencing methodologies such as pyrosequencing – less specificity increases the chances of obtaining many non-target sequences. Thus, an appropriate balance of coverage and specificity for each primer should be examined. If a highly degenerate primer is selected for one end of the amplicon, a low degenerate one could be chosen for the other end to increase the specificity of the primer set. Another important consideration is that different samples have different dioxygenase gene   54   OL. 77, 2011 MINIREVIEW 3553 !   FIG. 2. Primer coverage pattern against 204 reference dioxygenase genes. Each boxEach box indicates perfect match between the gene and primer. Fig. 2.1. Primer coverage pattern against 204 reference dioxygenase genes. indicates a perfect match between the gene and the rimers can be classified into the six classes noted on the left,classes noted on the left, based on their coverages:five reference subclades; class B targets the primer. Primers can be classified into the six based on their coverages: class A targets all class A, targets all five reference AH-GN; class Csub-clades; class B, targets PAH-GN;D targets T/B; PAH-GN and T/B; class dioxygenases in OT-I; class dioxin dioxygenases inFigure S1 targets PAH-GN and T/B; class class C, targets class E targets dioxin D, targets T/B; class E, targets F targets PAH-GP. OT-I; class F, targets PAH-GP. the supplemental material expands this figure to show each primer name, its computed degeneracies, and the number of perfect matches to the rresponding gene’s nucleic acid and protein identification number.   55   contents. Therefore, it is always important to test multiple annealing temperatures on the particular samples to determine the specificity of the primer set. Third, PCR product length, or the distance between forward and reverse primers, is an important consideration since the currently available instruments provide sequence for only partial gene lengths. For traditional Sanger sequencing, up to around 700 bp can be sequenced. For pyrosequencing using the current Genome Sequencer FLX titanium system (454 Life Sciences, Branford, CT), sequences up to 400-500 bp can be obtained from PCR product sizes of 200 to 600 bp. Using Genome Analyzer (Illumina, San Diego, CA), 150 bp is currently the longest obtained sequence. Longer sequences of course lead to a better understanding of the gene, which is the ultimate goal, but the higher capacity of the 454 and Illumina technologies provide a much greater sampling of nature’s gene diversity, also an important goal. The Role of Primer Design: Lessons from Previous Studies. Considerable research has taken place in trying to understand the aromatic degradation potential of bacteria in terms of diversity of dioxygenases in the environment, and for the type of dioxygenase contained within bacterial isolate(s) with known aromatic degradation capacity. As noted, 115 primers have been reported for environmental studies (see Table S1 in the supplemental material). The question for future studies is ‘which primers do I use, and are further improvements possible?’ We considered the role of target gene specificity, clade coverage, amplicon length, and reference sequences used in primer design as the parameters for judging quality of primers and then relate this to the experimental outcome from use of these primer sets. Coverage of previous primers. To summarize the coverage of previously reported primers, each primer was searched against 204 reference dioxygenase genes using BLAST+ (Camacho et al. 2009) and perfect match genes were mapped. From overall patterns that indicate the coverage   56   of amplified genes (shown by density of boxes in sub-clades in Fig. 2.1), primers were grouped into six classes. The first class (class A) covers all five reference sub-clades. [DP2 or Rieske_f (Ni Chadhain et al. 2006) and Nah-for (Zhou et al. 2006) primers showed especially high coverage of more than 100 genes (Fig. S1). Those high coverage primers have high degeneracy and were designed to target the highly conserved Rieske motifs.] The other classes are more specific to each target sub-clade(s): class B targets PAH-GN, class C targets PAH-GN and T/B, class D targets T/B, class E targets especially dioxin dioxygenases in OT-I, and class F targets PAH-GP. An expanded, readable map of target genes for each primer is shown as Fig. S1 in the supplemental material. While the published studies used various techniques to accomplish different goals, in terms of coverage of primers, Lozada et al. (Lozada et al. 2008) highlighted a comparison of a broad coverage primer set with a narrow coverage primer set. The Ac114F/Ac596R primer set has been used frequently (Gomes et al. 2007, Jeon et al. 2003, Stach et al. 2002, Tuomi et al. 2004, Wilson et al. 1999) with fairly consistent success. In this study (Lozada et al. 2008), clone libraries were constructed using the Ac114F/Ac596R primer set yielding seven distinct sequence clusters, five of them novel (only 58% to 68% identical to known amino acid sequences). Using the narrow coverage Cyc372F/Cyc854R primer set, which targets phnA1, all sequences showed high amino acid identity (98.6 to 100%) to Cycloclasticus isolates. The authors were able to detect broad diversity with the Ac144F/Ac596R primer set and then find specific genes, possibly specific to Cycloclasticus, with the second primer set. Primers specific to other phylotypes were attempted for this second step, but they did not result in amplification. Primer set P8073/P9047 is also an instructive example. This primer set was designed with one reference sequence, phnAc (Lloyd-Jones et al. 1999), and our BLASTn results also show that   57   it is the only sequence with a perfect match (see Fig. S1). In multiple studies, this primer set was more specific to apparent PAH dioxygenases contained in uncultured bacteria. In two studies, strains isolated by PAH enrichment did not produce amplicons using these primers, yet the primer did produce amplicons in soils and a biofilm contaminated with phenanthrene (LloydJones et al. 1999, Stach et al. 2002). In two other studies this primer set was able to produce amplicons from PAH-degrading isolates while primer sets targeting the nahAc gene were less successful (Widada et al. 2002, Wilson et al. 2003). In the later study, they postulate that the phnAc gene was horizontally transferred, because identical copies of the gene were detected in phylogenetically diverse isolates (Wilson et al. 2003). Apparently the primer set targets a broad set of genes that may be dissimilar to those currently known. These examples indicate that previously known genes are only part of nature’s diverse gene pool and that in silico analysis based on previous sequence data is a helpful first step in estimating primer coverage but is not (and should not) be expected to be completely accurate. Control of primer specificity. Perfect match sequences are not only the products of PCR amplification. Some primers showed a lesser number of perfect match genes or even no matches although the primers were designed to target a wide range of genes. For example, the pah-rhdα primers by Ding et al. (2010) have no perfect match sequence in reference genes. Those primers were designed to use a lower annealing temperature and allow several mismatches during PCR in order to amplify a broader set of target genes. This effect could not be reflected in Fig. 2.1 because we were not able to estimate the effect of PCR conditions on specificity. Thus, the summary of previous studies in Table S3 can help in understanding actual primer coverage including the effect of PCR conditions.   58   Targeting conserved motifs within the target gene enhances primer specificity to the target gene. Ní Chadhain et al. (2006), as have others, designed a primer set targeting the Rieske center of the dioxygenase, a highly conserved portion of the gene. They include degenerate bases to increase coverage, but relied on the conserved positions to maintain specificity. Clone libraries were constructed in two studies (Ni Chadhain et al. 2006, Yagi et al. 2009), and in both studies the amplicons formed many clusters. Some clusters were distantly related to reference sequences, indicating that the primer amplified a gene similar to those from both cultured and uncultured bacteria. Another method of ensuring specificity is to use a large number of reference sequences with which to find consensus sequences within the gene to use in primer design. Recently we designed the BPHD-f3/BPHD-r1 primer set with 31 reference sequences to ensure the positions selected as primers were conserved between many strains. This allowed known conserved motifs to be contained within the amplicon to use as a quality filter during pyrosequencing data analysis. As a result we found that the bphA1 genes in the environment were mainly novel, with only a few similar to known sequences (Iwai et al. 2010). This result suggests that the current knowledge severely under-represents bphA1sequences in nature, which may be the case for many organic carbon-degrading genes, and indicates new strategies are needed for assessing nature’s gene repertoire. PCR product length of dioxygenase primers. Since PCR product length is limited for certain sequencing technologies, we summarize the positions of each primer for easy calculation of estimated product length with different combinations of primers. Using MUSCLE alignment of 204 dioxygenase genes, primer annealing positions were calculated based on bphA from Burkholderia xenovorans LB400 (GenBank: M86348). The length of the PCR product in each   59   primer pair varies from less than 100-bp to the entire α-subunit. Although two primer sets in class A target genes from all sub-clades, the amplified product lengths of both are 78bp, which limits classification and diversity assessment of these sequenced PCR products. The primers could be tried in different combinations after consideration of similar annealing temperatures and the coverage of sub-clades depending on the purpose of the study or the location of conserved motifs within the gene. Choosing appropriate primer sets. We recommend the following general steps in choosing primers for gene-targeted (amplicon) metagenomics: (i) Select the target sub-clade, e.g all, PAHGN, T/B or PAH-GP. (ii) Collect a set of candidate primers from each of the groups based on success of past studies (Table S3 in the supplemental material), and their coverage against the desired genes (Fig. S1 in the supplemental material). (iii) The amplicon length should be as long as possible to gain maximum information, but short enough so that it can be sequenced through the reverse primer for quality control (the current 454 Titanium version of pyrosequencing is able to sequence up to 500bp). (iv) Include conserved regions within the amplicon so that those sequences can be used as a quality filter in processing. We considered all primers as candidates for forward or reverse primers; however, it is possible that a primer will not function as well for the reverse complement, especially due to low GC content in the 3’ end (≤40% in last five bases). All primers should be tested empirically since predictions are not reality. Amplification conditions may also need further optimization and for the type of sample matrix. From our evaluation of past work, we offer a few primer pair suggestions. For the most comprehensive targeting of dioxygenase clades (all, our category A), we suggest DP1/Rieske_f and ARHD2R. For other sub-clades such as PAH-GN, T/B and PAH-GP, we suggest Ac596R (use as a reverse complement) and NAPH-2R, adoB1 (use as a reverse complement) and BPHD-   60   r1, NidA-forward and pdo1-r, respectively. We also recommend DP1/Rieske_f and Nah-for (use as a reverse complement) that covers 92 of the reference genes from all classes as general probes to determine if a novel isolate or gene contains a Rieske dioxygenase motif, but are not generally recommended for amplicon sequencing due to their short length of 77 bp. Conclusions. Many recent studies reveal a much higher diversity of aromatic dioxygenases in environmental samples than previously thought and hence suggest a need for a better approach. One key is the selection of primer sets. The primer coverage, specificity and length that we summarized as well as our summary of the experimental studies will help guide the experimenter in choosing the most appropriate primer sets for new studies. We also expect that the current expansion of environmental metagenome sequencing projects will provide an additional sequence resource for evaluating and improving primers as that data will not be limited by what is in GenBank, which is biased toward easily cultured strains. For example, about 100 Gbp of soil metagenomic data contains about 1,000 of biphenyl dioxygenase-like genes (personal communication from J. R. Cole). As we write there are already soil metagenome projects producing terabases of sequences. The next phase in primer design may entail how to detect and assess the dioxygenase-like genes in these resources for improving our knowledge of nature’s aromatic degradation capacity. Acknowledgements. This work was supported by the Superfund Basic Research Program grant P42 ES004911-20 from the U.S. National Institute of Environmental Health Sciences.   61   REFERENCES   62   REFERENCES Bardgett RD, Freeman C, Ostle NJ. 2008 Microbial contributions to climate change through carbon cycle feedbacks. ISME J 2(8):805-814. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K et al. 2009 BLAST+: architecture and applications. BMC Bioinformatics 10:421. Ding G-C, Heuer H, Zuhlke S, Spiteller M, Pronk GJ, Heister K et al. 2010 Soil type-dependent responses to phenanthrene as revealed by determining the diversity and abundance of polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase genes by using a novel PCR detection system. Appl Environ Microbiol 76(14):4765-4771. Edgar RC. 2004 MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5):1792-1797. Felsenstein J. 1989 PHYLIP—phylogeny inference package (version 3.2). Cladistics 5(2):164166. Field JA, Sierra-Alvarez R. 2008 Microbial degradation of chlorinated dioxins. Chemosphere 71(6):1005-1018. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR et al. 2008 The Pfam protein families database. Nucleic Acids Res 36(Database issue):D281-288. Gibson DT, Parales RE. 2000 Aromatic hydrocarbon dioxygenases in environmental biotechnology. Curr Opin Biotechnol 11(3):236-243. Gomes N, Borges L, Paranhos R, Pinto F, Krogerrecklenfort E, Mendonca-Hagler L et al. 2007 Diversity of ndo genes in mangrove sediments exposed to different sources of polycyclic aromatic hydrocarbon pollution. Appl Environ Microbiol 73(22):7392. Green NJ, Hassanin A, Johnston AE, Jones KC. 2004 Observations on historical, contemporary, and natural PCDD/Fs. Environ Sci Technol 38(3):715-723. Iwai S, Chai B, Sul WJ, Cole JR, Hashsham SA, Tiedje JM. 2010 Gene-targeted-metagenomics reveals extensive diversity of aromatic dioxygenase genes in the environment. ISME J 4(2):279-285. Jeon C, Park W, Padmanabhan P, DeRito C, Snape J, Madsen E. 2003 Discovery of a bacterium, with distinctive dioxygenase, that is responsible for in situ biodegradation in contaminated sediment. Proc Natl Acad Sci U S A 100(23):13591. Kimura N, Kamagata Y. 2009 Impact of dibenzofuran/dibenzo-p-dioxin amendment on bacterial community from forest soil and ring-hydroxylating dioxygenase gene populations. Appl Microbiol Biotechnol 84(2):365-373.   63   Lloyd-Jones G, Laurie A, Hunter D, Fraser R. 1999 Analysis of catabolic genes for naphthalene and phenanthrene degradation in contaminated New Zealand soils. FEMS Microbiology Ecology 29(1):69-79. Lozada M, Riva Mercadal JP, Guerrero LD, Di Marzio WD, Ferrero MA, Dionisi HM. 2008 Novel aromatic ring-hydroxylating dioxygenase genes from coastal marine sediments of Patagonia. BMC Microbiol 8(1):50. Marcos MS, Lozada M, Dionisi HM. 2009 Aromatic hydrocarbon degradation genes from chronically polluted subantarctic marine sediments. Lett Appl Microbiol 49(5):602-608. Ni Chadhain S, Norman R, Pesce K, Kukor J, Zylstra G. 2006 Microbial dioxygenase gene population shifts during polycyclic aromatic hydrocarbon biodegradation. Appl Environ Microbiol 72(6):4078. Robertson JM, Walsh-Weller J. 1998 An introduction to PCR primer design and optimization of amplification reactions. Methods Mol Biol 98:121-154. Schloss PD, Handelsman J. 2005 Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microbiol 71(3):1501-1506. Stach JEM, Burns RG. 2002 Enrichment versus biofilm culture: a functional and phylogenetic comparison of polycyclic aromatic hydrocarbon-degrading microbial communities. Environmental Microbiology 4(3):169-182. 14 Tuomi P, Salminen J, Jørgensen K. 2004 The abundance of nahAc genes correlates with the Cnaphthalene mineralization potential in petroleum hydrocarbon-contaminated oxic soil layers. FEMS Microbiology Ecology 51(1):99-107. Widada J, Nojiri H, Kasuga K, Yoshida T, Habe H, Omori T. 2002 Molecular detection and diversity of polycyclic aromatic hydrocarbon-degrading bacteria isolated from geographically diverse sites. Appl Microbiol Biotechnol 58(2):202-209. Wilson M, Bakermans C, Madsen E. 1999 In situ, real-time catabolic gene expression: Extraction and characterization of naphthalene dioxygenase mRNA transcripts from groundwater. Appl Environ Microbiol 65(1):80. Wilson M, Herrick J, Jeon C, Hinman D, Madsen E. 2003 Horizontal transfer of phnAc dioxygenase genes within one of two phenotypically and genotypically distinctive naphthalene-degrading guilds from adjacent soil environments. Appl Environ Microbiol 69(4):2172. Yagi J, Madsen E. 2009 Diversity, abundance, and consistency of microbial oxygenase expression and biodegradation in a shallow contaminated aquifer. Appl Environ Microbiol 75(20):6478.   64   Zhou H, Guo C, Wong Y, Tam N. 2006 Genetic diversity of dioxygenase genes in polycyclic aromatic hydrocarbon-degrading bacteria isolated from mangrove sediments. FEMS Microbiol Lett 262(2):148-157.   65   CHAPTER III IN-FEED ANTIBIOTIC EFFECTS ON THE SWINE INTESTINAL MICROBIOME The work presented in this chapter has been published: Looft T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD et al. 2012 In-feed antibiotic effects on the swine intestinal microbiome. Proc Natl Acad Sci USA 109(5): 1691-1696 and is reprinted here with the permission of the publisher. Supplemental material is provided with the published version of this chapter at the journal website. Author contributions: T.L., W.J.S., S.A.H., J.M.T., and T.B.S. designed research; T.L., T.A.J., H.K.A., W.J.S., T.M.S., and T.B.S. performed research; D.O.B., D.P.A., R.D.S., T.M.S., B.C., J.R.C., and S.A.H. contributed new reagents/analytic tools; T.L., T.A.J., and H.K.A. analyzed data; and T.L., T.A.J., H.K.A., J.M.T., and T.B.S. wrote the paper. 66 ABSTRACT Antibiotics have been administered to agricultural animals for disease treatment, disease prevention, and growth promotion for over 50 years. The impact of such antibiotic use on the treatment of human diseases is hotly debated. We raised pigs in a highly controlled environment with a portion of the littermates receiving a diet containing performance-enhancing antibiotics (chlortetracycline, sulfamethazine, and penicillin [known as ASP250]) and the other portion receiving the same diet but without the antibiotics. We employed phylogenetic, metagenomic, and qPCR-based approaches to address the impact of antibiotics on the swine gut microbiota. Bacterial phylotypes shifted after 14 days of antibiotic treatment, with the medicated pigs showing an increase in Proteobacteria (1% to 11%) compared to non-medicated pigs at the same timepoint. This shift was driven by an increase in Escherichia coli populations. Analysis of the metagenomes showed that microbial functional genes relating to energy production and conversion were increased in the antibiotic-fed pigs. The results also indicate that antibiotic resistance genes increased in abundance and diversity in the medicated swine microbiome despite a high background of resistance genes in non-medicated swine. Some enriched genes, such as aminoglycoside O-phosphotransferases, confer resistance to antibiotics that were not administered in this study, demonstrating the potential for indirect selection of resistance to classes of antibiotics not fed. The collateral effects of feeding subtherapeutic doses of antibiotics to agricultural animals are apparent and must be considered in cost-benefit analyses. 67 INTRODUCTION Antibiotics are the most cost-effective way to maintain or improve the health and feed efficiency of animals raised with conventional agricultural techniques (Cromwell 2002, Dibner et al. 2005). In addition to improving feed efficiency, antibiotics are commonly given to livestock, poultry, and fish for disease treatment and prevention. The sum of agricultural antibiotic use reportedly accounts for as much as half of all antibiotics produced in the U. S. (Lipsitch et al. 2002). Despite the clear benefits of antibiotics to agriculture, liberal antibiotic use combined with rapid and widespread emergence of both animal and human pathogens resistant to multiple antibiotics has led some to question the prudence of current antibiotic use (Aarestrup et al. 1999, Levy 1978). Studies of environmental and intestinal microbial communities reveal enormous diversity of antibiotic resistance genes (Allen et al. 2010, Martinez et al. 2009, Sommer et al. 2009). The addition of antibiotics to feed introduces a selective pressure that may lead to lasting changes in livestock commensal micro-organisms. Also, reservoirs of antibiotic resistance genes have been shown to be stable in bacterial communities, even in the absence of antibiotics (Gotz et al. 1996, Salyers et al. 1997, Stanton et al. 2011, Stanton et al. 2011). A central concern of increased abundance of antibiotic resistance is the transfer of resistance to pathogens (Martinez 2008). As a result, the FDA recently released a draft guidance recommending restrictions on the use of antibiotics in animal agriculture (Health et al. 2010). The Infectious Diseases Society of America testified before a congressional subcommittee in support of such limitations (2010). Bacteria that inhabit the gastrointestinal tract of animals are important for the maintenance of host health. The intestinal microbiota assists the host in nutrient extraction, immune system and epithelium development, and are a natural defense against pathogens (Zoetendal et al. 2004). Contrary to these benefits, the gut microbiota may antagonize future 68 disease treatment by facilitating the dissemination of resistance alleles across distantly related organisms. For example, commensal bacteria of the human colon harbor antibiotic resistance genes and can transfer these genes to pathogens (Karami et al. 2007, Shoemaker et al. 2001). In fact, horizontal gene transfer is largely the cause of multidrug resistance in Gram-negative bacteria (Leverstein-van Hall et al. 2002). With the identification of antibiotic resistance genes in commensal bacteria in the human foodchain (Barbosa et al. 1999, Li et al. 2010, Stanton et al. 2003), the role of the gut microbiota as a reservoir of resistance genes for animal and foodborne pathogens needs to be explored. Valuable insights have been gained by culture- and PCR-based approaches to study narrow groups of bacteria or genes, such as erythromycin resistance in swine isolates (Wang et al. 2005); however, the comprehensive effects of daily feeding of subtherapeutic doses of antibiotics on livestock microbiotas have not been studied. We therefore sought to extensively evaluate the effects of in-feed antibiotics on the entire gut microbiota. Phylotyping, metagenomic, and parallel quantitative PCR (qPCR) approaches were used to track changes in microbial membership and encoded functions, enabling the detection of so-called “collateral” effects of antibiotics, i.e. effects outside of the intended growth promotion and disease prevention. These collateral effects included increases in Escherichia coli populations and in the abundance of certain antibiotic resistance genes. Piglets were birthed at the National Animal Disease Center, Ames IA, and housed together in highly-controlled, decontaminated rooms to avoid cross contamination among the medicated animals, non-medicated animals, and other resident barn animals. Neither the piglets nor the sow were exposed to antibiotics prior to the study. This design was to ensure that the inoculum for the piglets would come horizontally from their mother, minimizing variability so 69 that effects of antibiotic treatment could be detected. At 18 weeks of age, one group of littermates received ASP250 feed (medicated) and the other received the same but unamended feed (non-medicated) for three weeks. ASP250 is an antibiotic feed additive containing chlortetracycline, sulfamethazine, and penicillin that is commonly given to swine for the treatment of bacterial enteritis and for increased feed efficiency. Fecal samples were collected just before treatment (day 0), and after 3 days, 14 days, and 21 days of continued treatment. Day 0 samples were used to describe the swine intestinal microbiome prior to antibiotic treatment period. RESULTS Shifts in community membership with ASP250. We collected 133, 294 sequences of the V3 region of the 16S rRNA gene from a total of 12 fecal samples. Data from pigs of the same treatment and sampling date were grouped to appraise an antibiotic effect on community membership. As reported for a mammalian intestinal environment (Ley et al. 2008), and recently in a swine metagenome (Lamendella et al. 2011), the majority of classifiable sequences (7586%) belonged to the Bacteroidetes, Firmicutes, and Proteoba1cteria phyla (Table S1). Of the Bacteroidetes, the Prevotella genus was consistently abundant, as was shown to be a feature of the swine microbiome (Lamendella et al. 2011). The Bray-Curtis index was calculated for all sample combinations and an analysis of similarities (ANOSIM) was performed. A multidimensional scaling (MDS) plot of these data indicated divergence of the day 14 samples from the day 0 samples (p<0.01), and the medicated microbiome diverged from the non-medicated (p<0.05) (Fig. 1A), demonstrating changes in microbial community membership over time and with treatment. 70 antibiotic resistance gene database (ARDB). Three of the COGs with the lowest P value (3188, 3539, and 3121) contained genes genera (Table S1). The increase in Proteobacteria abundance with in-feed ASP250 was particularly striking: from 1% of the A" 0.48 A 0.48 10000 10 Normalized number of Normalized number of 16S 16S sequences (× 103) sequences Coordinate 2 0.32 0.32 Coordinate 2 C" C All animals day 0 All animals day 0 Nonmedicated day 14 Nonmedicated day 14 Medicated day 14 Medicated day 14 0.16 0.16 0 0 8000 8 6000 6 4000 4 2 2000 0 -0.16 -0.16 Nonmed Med Nonmed Med Nonmed Med Nonmed Med day 0 day 0 day 14 day 14 day 0 day 0 day 14 day 14 -0.48 -0.48 -0.32 -0.32 -0.16 0 -0.16 0 Coordinate 1 Coordinate 1 0.16 0.16 0.32 0.32 D" D 145 145 80 80 60 60 Unclassified Unclassified Other Other Proteobacteria Proteobacteria Firmicutes Firmicutes Bacteroidetes Bacteroidetes 40 40 20 20 0 0 150 150 Number of reads Number of reads B" 100 B 100 Percent ofof 16Ssequences Percent 16S sequences Desulfovibrio Desulfovibrio Campylobacter Campylobacter Escherichia/Shigella Escherichia/Shigella Succinivibrio Succinivibrio 20 20 Shigella Shigella 15 15 Desulfovibrio Desulfovibrio Oxalobacter Oxalobacter Prevotella Prevotella 10 10 Parabacteroides Parabacteroides Chitinophaga Chitinophaga 5 5 0 0 Nonmed Med Nonmed Med Nonmed Med Nonmed Med day 0 day 0 day 14 day 14 day 14 day 0 day 0 day 14 Escherichia Escherichia Bacteroides Bacteroides Other Other Nonmed Med Nonmed Med Med Nonmed Med Nonmed day 0 day 0 day 14 day 14 day 0 day 0 day 14 day 14   Fig. 1. Shifts in fecal bacterial community membership with antibiotic treatment. (A) NMDS analysis of Bray-Curtis similarity coefficients calculated from 16S Fig. 3.1. Shifts in fecal bacterial community membership with antibiotic treatment. (A) A multi-dimensional scaling (MDS) analysis of rRNA gene sequence data from individual animals at days 0 and 14 shows the similarity among replicate pig fecal samples. (B) Phylum-level composition of Bray-Curtis similarity coefficients calculated from 16S rRNA gene sequence data from individual animals at days 0 and 14 shows the   fecal microbial communities. Data were pooled for a given treatment and time point and are shown as percentage of abundance. (C) Genus-level composition of Proteobacteria, shown as the total number of sequences (normalized to 50,000 total reads). (D) Predicted genera of COG3188 homologs found in the swine metagenomes based on BLASTx analysis. COG3188 was overrepresented in the medicated metagenome vs. the nonmedicated metagenomes. 2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1120238109 71 Looft et al. Fig. 3.1 (cont’d) similarity among replicate pig fecal samples. (B) Phylum-level composition of fecal microbial communities. Data were pooled for a given treatment and timepoint and are shown as percent abundance. (C) Genus-level composition of Proteobacteria, shown as the total number of sequences (normalized to 50,000 total reads). (D) Predicted genera of COG3188 homologues found in the swine metagenomes based on BLASTx analysis. COG3188 was overrepresented in the medicated metagenome versus the non-medicated metagenomes. Specific changes in the microbial community associated with ASP250 treatment included a decrease in the abundance of Bacteroidetes, along with members of Anaerobacter, Barnesiella, Papillibacter, Sporacetigenium and Sarcina genera. Members of the Deinococcus-Thermus and Proteobacteria phyla increased with ASP250 treatment as well as Succinivibrio and Ruminococcus genera (Table S1). The increase in Proteobacteria abundance with in-feed ASP250 was particularly striking: from 1% of the population in non-medicated animals to 11% of the population with antibiotic treatment (Fig. 1B). Specifically, E. coli populations were the major difference between medicated and non-medicated animals, comprising 62% of the Proteobacteria in medicated animals (Fig. 1C). The increase in E. coli was confirmed in the metagenomic data (Fig. 1D) and by qPCR targeting the uidA gene of E. coli (p<0.05). A separate study using 12 pigs similarly treated but with analysis by culture-based techniques further established that swine fed ASP250 have an increased E. coli population at 14 days post treatment, showing a 20 to 100 fold greater E. coli abundance in medicated than non-medicated swine (Fig. S1). Shifts in functional gene abundance with ASP250. DNA samples from the feces of nonmedicated and medicated pigs at day 0 and 14 were isolated, and samples of like treatment and sampling date were pooled for pyrosequencing. Metagenome sequences (1, 202, 058 total) were analyzed in MG-RAST for SEED subsystems (Glass et al. 2010), and in-house for clusters of 72 orthologous groups (COGs). All metagenomes showed functional stability over time by both COG and subsystem analyses (Fig. S2). The most abundant SEED subsystem of known function was carbohydrate metabolism, mirroring what was previously reported for the swine metagenome (Lamendella et al. 2011). A statistical analysis of COGs revealed shifts in microbial community functions with ASP250: the medicated metagenome contained 169 COGs that were significantly more abundant than in the non-medicated metagenomes (Table S2). Three COGs (0477, permeases of the major facilitator superfamily; 1289, predicted membrane protein; 3570, streptomycin 6-kinase) contain swine metagenomic genes that are annotated as resistance genes in the antibiotic resistance gene database (ARDB). Three of the COGs with the lowest p-value (3188, 3539, and 3121) contained genes related to P pilus assembly, and additionally among the statistically significant COGs are transposases (0675, 1662, and 4644). To identify themes among differentially represented COGs between the medicated and non-medicated metagenomes, COGs of Table S2 were clustered by their respective COG category. Only one COG functional category, energy production and conversion (C), was found more frequently (p<0.05) in the medicated metagenome than in the non-medicated metagenomes (Table S3). Pervasive antibiotic resistance in the absence of antibiotic exposure. The discovery that resistance-related COGs fluctuated with antibiotic treatment led to further scrutiny of the metagenomes by BLAST against the ARDB (Liu et al. 2009). All metagenomes, regardless of antibiotic treatment, harbored sequences similar to diverse antibiotic resistance genes representing most mechanisms of antibiotic resistance: efflux pumps, antibiotic-modifying enzymes, and modified or protected targets of the antibiotic (Fig. 2A). This analysis detected 149 different resistance genes in the day 0 metagenomes. 73 The finding of diverse fecal antibiotic resistance genes in the non-medicated metagenomes was supported by parallel qPCR analysis. A rich array of 58 resistance genes was detected at least once in the swine fecal samples by qPCR. Samples from non-medicated animals showed a total of 51 different resistance genes, but few were shared between animals: only six (erm(A), erm(B), cfiA, mefA, tet(32), and aadA) were detected in 66% of the samples and none were found in more than 80% of the samples. No enrichment of these genes was observed in the medicated animals even though tet(32), a ribosomal protection protein, is known to confer resistance to an administered antibiotic (tetracycline). Samples from medicated animals yielded more homogenous resistance gene diversity: 39 genes were detected in at least one medicated sample, 20 were detected in 66% of samples, and 11 (cfiA, mefA, erm(A), erm(B), tet(32), tet(O), aadA, aph(3’)-ib, bcr, acrA, and bacA) were detected in at least 8/9 of the samples. qPCR and metagenomic analyses reveal shifts in resistance gene richness and abundance in medicated pigs. Statistical analysis of the ARDB results showed 23 genes to be differentially represented in the medicated and non-medicated metagenomes (Table 1). The 20 genes that were more abundant in the medicated metagenome were associated with efflux, sulfonamide resistance, and aminoglycoside resistance, the latter of which represents resistance to a class of antibiotics not present in ASP250 (Table 1). The qPCR results mirrored the metagenomic analysis, revealing six resistance-gene types with statistically significantly greater abundance in the medicated animals than in the nonmedicated animals (p<0.05): tetracycline efflux pumps, class A beta-lactamases, sulfonamide resistance genes, aminoglycoside phosphotransferases, and two types of multi-drug efflux (Fig. 2B, Table 1). No statistical difference in abundance was found for these six resistance gene types 74 350 300 300 Nonmedicated day 14 Medicated day 14 300 250 200 250 200 200 150 0 0.2 0 0 0 -0.2 100 50 0 Medicated 0.2 0.2 -0.2 -0.2 150 100 100 50 0 Nonmedicated Nonmedicated Medicated Nonmedicated Medicated C Coordinate 2 400 350 Number of reads Number of reads Number of reads A C C" Nonmed day 0 × Med day 0 0 Nonmedicated day Medicated day 0 Nonmed day 0 14 day Nonmedicated day 14 Nonmedicated × Med day 014 Medicated day 14 Medicated day 400 Coordinate 2 Coordinate 2 A"A 400 ases, and two types of multidrug efflux (Fig. 2B and Table 1). No sistance difference between the was found for these six restatistical gene types in abundance medicated and nonmedicated microbiomes types 0 (Fig. the medicated and nonmedicated sistance gene on daybetween 2B), suggesting that in-feed ASP250 microbiomes on day 0 (Fig. 2B), suggesting that in-feed ASP250 -0.4 -0.4 Enzymes that that EnzymesdeacƟvate efflux pumps AcƟviƟes that protect other or unknown Efflux cell other or the anƟbioƟc from the anƟbioƟc deactivate the efflux Mechanism protection other or unknown pumps AcƟviƟes that protect unknown Enzymes that deacƟvate pumps of resistance the anƟbioƟc from the anƟbioƟc antibiotic -0.4 10 10 -3 ab 10-4 ab# ab 10-4 10 -4 10-5 10-5 10 -5 10-6 a a a# ab ab ab# b b b# 10-6 10 b b b b# -6 10-7 10 10-7 Class A Beta Lactamase Class A Class A Beta Lactamase beta Tetracycline Efflux Pump TetracyTetracycline Efflux efflux cline Pump lactamase -7 a# a a a# ab ab ab# ab ab# a a pumps b b# a a# b b b# ab ab# b b# b ab b b# 0.2 0.2 0.2 day 0 days 3-21 ab ab# ab ab b b a a ab ab# a# ab b b# 0 NonMed Day 0 Med Day 0 NonMed Days 3-21 Med Days 3-21 Nonmed day 0 Med Day 0× Med NonMed Day 0 a NonMed Days 3-21 Med Days 3-21 a Nonmed days 3-21 × Med -3 (ARG(ARG / 16S rRNA) copy numbers / 16S rRNA) copy numbers (ARG/16S rRNA) copy number B" -0.2 -0.2 1 0 Coordinate -0.2 0 Coordinate 1 Coordinate 1 -0.4 Mechanism of resistance B Mechanism of resistance 10 B -3 ab -0.4 -0.4 a a# b ab ab# b b b b# b# b b b# Sulfonamide Aminoglycoside ResistanceMajor Facilitator Resistance O-phospho- NodulaƟon- Cell- Superfamily Major Sulfon- Aminoglycoside ResistanceSulfonamide Aminoglyc Resistance Major Facilitator transferase Division Transporter Resistance O-phosphoTransporter amide Resistanceo- NodulaƟon- Cell- Superfamily oside Type nodulation facilitator AnƟbioƟc transferase Division Transporter cell superfamily resistance phosphoTransporter AnƟbioƟc in swine Type Fig. 2. Changes in diversity and abundance of antibiotic resistance genes (ARG) Resistancefeces with antibiotic treatment. (A) Metagenomes were analyzed by transporter transferase division BLASTx against the ARDB, and the number of reads were normalized to 100,000 total reads per transporter (B) Differences in the abundance of resistance metagenome. Fig. 2. Changes in diversity and abundance of antibiotic resistance genes resistance feces with antibiotic treatment. (A) Metagenomes were analyzed by Antibiotic (ARG) in swine type genes were assessed by calculating the ratio of resistance gene copy number (ARG) to 16S rRNA gene copy number per sample as detected by qPCR. Columns   BLASTx against the ARDB, and the number of reads were normalized to 100,000 total reads per metagenome. (B) Differences in the abundance of resistance denoted by the same letter are not statistically significant (P > 0.05) within each resistance type. Error bars represent the SEM. (C) Bray-Curtis similarity genes were assessed by calculating the ratio of resistance gene copy number (ARG) to 16S rRNA gene copy number per sample as detected by qPCR. Columns coefficients 3.2. Changes from qPCR-derived resistance gene abundance data and plotted in a multidimensional scaling graph. The distance between Fig. were calculatedare not statistically significant of 0.05) within each resistance type. Error feces with antibiotic treatment. denoted by the same letter in diversity and abundance(P > antibiotic resistance genes in swinebars represent the SEM. (C) Bray-Curtis similarity points indicates the degree of difference in the diversity of resistance genes between samples. The medicated sample outlier (square) is from one medicated coefficients were calculated from qPCR-derived resistance gene abundance data and plotted in a multidimensional scaling graph. The distance between pig on day 21. Measures for day 0 samples are not shown. points indicates the degree of difference in the diversity of resistance genes between samples. The medicated sample outlier (square) is from one medicated pig on day 21. Measures for day 0 samples are not shown. 75 Looft et al. Looft et al. PNAS Early Edition | 3 of 6 PNAS Early Edition | 3 of 6 MICROBIOLOGY MICROBIOLOG animals showed a total of 50 different resistance genes, but few and aadA] between animals: 66% of [ermA, ermB, mefA, tet(32), were shared were detected in only five the samples and none were found in were detected of the of the No enrichment of these and aadA]more than 80% in 66%samples.samples and none were found in more than 80% of the samples. No enrichment of these Fig. 3.2 (cont’d) (A) Metagenomes were analyzed by BLASTx against the ARDB, and the number of reads were normalized to 100,000 total reads per metagenome. (B) Differences in the abundance of resistance genes were assessed by calculating the ratio of resistance gene copy number (ARG) to 16S rRNA gene copy number per sample as detected by qPCR. Columns denoted by the same letter are not statistically significant (p>0.05) within each resistance type. Error bars represent the standard error of the mean. (C) Bray-Curtis similarity coefficients were calculated from qPCR-derived resistance gene abundance data and plotted in a multidimensional scaling graph. The distance between points indicates the degree of difference in the diversity of resistance genes between samples. The medicated sample outlier (square) is from one medicated pig on day 21. Measures for day 0 samples are not shown.   between the medicated and non-medicated microbiomes on day 0 (Fig. 2B) suggesting that infeed ASP250 caused the effect. Resistance-gene abundance increased most dramatically in the 3and 14-day samples (Fig. S3), indicating that antibiotic treatment induced a rapid shift in the abundance of resistance genes. ASP250 treatment increased the diversity of resistance gene types as detected by qPCR (Shannon indices 1.4 [medicated] and 0.8 [non-medicated]; p = 0.04). A t-test comparing the mean number of resistance genes in the metagenomes at day 14 to the corresponding nonmedicated metagenome confirms this result (p<0.05). Additionally, the structure of the resistance gene communities (beta diversity) was altered by antibiotic treatment, as determined by a twoway ANOSIM (p<0.01) of Bray-Curtis measures; however, the comparison R-value was 0.25, indicating that the degree of separation is limited. Nevertheless, resistance gene diversity converges with ASP250 treatment, presumably due to the selective pressure of the antibiotics (Fig. 2C). Taken together, these results show that feeding antibiotics increases the diversity of resistance genes within an individual sample and homogenizes that diversity between treated samples. 76 DISCUSSION We assessed the effect of ASP250 on the swine antibiotic resistome using phylotype, metagenomic, and qPCR approaches. The results show that the swine microbiome harbors diverse resistance genes even in the absence of selective pressure. Six genes in particular were detected at high frequency in both the medicated and non-medicated microbiomes. These genes could represent a core antibiotic resistome for this cohort of swine. Indeed, it was suggested that tet(32) is abundant in farm animals (Melville et al. 2001), and our data support that conclusion for swine. Unfortunately, the core swine resistome also includes resistance of clinical importance: cfiA confers resistance to carbapenems, a class of broad-spectrum beta-lactam antibiotics of last resort (Maltezou 2009). The constant selective pressure of 50 years of in-feed antibiotics appears to have established a high background level of resistance in the swine microbiome. Antibiotic treatment caused a detectable increase in the abundance of resistance genes even above the high background of resistance, and many of these were likely enriched due direct interaction with the antibiotics in ASP250. For example, sulfamethazine presumably selected for the sulfonamide resistance genes sul2 or sul1 present in 8 of the 9 medicated samples. Additionally, class A beta-lactamases were overrepresented in the medicated animals and confer resistance by cleaving such beta-lactam antibiotics as penicillin. Many of the other enriched resistance genes function by exporting chemicals. Such efflux includes but is not limited to antibiotics and may allow bacteria that lack specific resistance genes to survive antibiotic pressure. Multidrug efflux is frequently associated with the medically alarming issue of multipledrug resistance and can be found on mobile genetic elements (Martinez 2009). In addition to the effects on specific gene families, in-feed antibiotics homogenized the richness of resistance 77 ! ! Table resistance genes differentially represented represented (P in the medicated vs. nonmedicated pig fecal samples fecal samples Table resistance genes genes genes differentially represented (P in the nonmedicated pig fecal samples as able 1. Antibiotic 1. Antibiotic resistancedifferentially(P < 0.05) in the< 0.05) < 0.05)medicated vs. nonmedicated pig fecal samples as Antibiotic1. Antibiotic resistance differentially represented (P < 0.05) medicated vs. in the medicated vs. nonmedicated pig as 0.05) the Table Table 3.1. by resistance genes differentially(P the medicated vs.in 1) vs. the = 3)nonmedicated vs. =pig metagenomes gene]samples Table [number ofresistance [number able metagenomics1. Antibioticgenes differentiallyofthe medicated<(p =nonmedicated vs.medicated vs. = 3) metagenomes per pig per resistance 1. by metagenomicsmetagenomicsgenes the medicated< 0.05) (n0.05) (n = (P medicatedversus non-medicatednonmedicated resistance as Antibiotic resistance differentially represented in (n (Prepresentednonmedicated vs. medicated (n pigfecalpig gene] as Antibiotic1. by metagenomics [number ofgenes represented=in the < 1)< 0.05) 80%, respectively, were found resistant to at least one antibiotic (Enne et al. 2008, Levy et al. 1976). We estimate about 43% of bacteria possess at least the aphA3 gene, hence it is feasible that upwards of 90% of the entire community would carry one of the other 148 resistance genes detected. Considering all antibiotic resistance genes combined in the manure or compost samples, we estimate a total of 50,000-fold enrichment (Table S4). While enrichment of individual resistance genes is similar to previous studies, we were able to capture a more complete picture of the total level of the antibiotic resistance reservoir. Potential for horizontal gene transfer of ARGs. This study highlights that ARGs in swine farms are not only diverse, but are also remarkably abundant, which together offers a higher statistical probability of dispersal, further selection and/or horizontal transfer in the environment. The emergence and spread of ARGs are closely associated with mobile genetic elements such as plasmids, integrases and transposases (Binh et al. 2008, Heuer et al. 2012, Zhang et al. 2011). The high degree of transposase enrichment and correlation with ARG abundance suggests that horizontal gene transfer may have aided the enrichment of ARGs. The transposases detected most frequently belong to the IS6 family of insertion sequences, which are typically found 110 flanking an array of genes, often resistance genes (Mahillon et al. 1998). The most abundant member of the IS6 family in these samples, IS26 has been isolated, along with integrons in multi-drug resistant plasmids in enterobacteria (Miriagou et al. 2005). Integrons most commonly contain resistance cassettes encoding aadA genes (Singh et al. 2005), as well as qacE∆1 and sul2, which were among the most enriched genes in this study. The Putian farm ARGs that are more enriched in the compost than the manure (Fig. 3, “D” boxes, Table S6) are predominately aadA and other aminoglycoside resistance genes and their enrichment may be due to their presence in integrons which also hold a resistance gene cassette relevant to the drugs used on the farm (Binh et al. 2009, Heuer et al. 2009). Additionally, the combination of antibiotics and metals may provide a stronger selection for realized horizontal gene transfer within the microbial community than either alone (Baker-Austin et al. 2006, Gillings et al. 2012, Heuer et al. 2009, Petrova et al. 2011). It appears that a number of factors in swine farms could contribute to elevated rates of horizontal gene transfer, including elevated concentrations of antibiotics, metals, ARGs, and mobile genetic elements, making subsequent dispersal, (co-) enrichment, or horizontal transfer, including to human-associated bacteria, more probable. The role of manure management in controlling ARGs. The long-term goal of manure management is to remove environmental contaminants, while disposing of this high volume waste product and capturing its value to improve soil fertility. The goal in the case of ARGs is to identify practices that decrease their concentration to a greater degree than by simple dilution (Chen et al. 2007). Manure composting decreased the abundance of ARGs at Beijing, but abundance remained nearly the same in the Jiaxing manure, and in Putian, composting actually increased the abundance of ARGs. Composting concentrated sulfonamides (Fig. S1), sulfonamide resistance genes and some metals (Fig. S2), consistent with the observation that 111 sulfonamide resistance genes are more recalcitrant than tetracycline genes (Dolliver et al. 2008, McKinney et al. 2010, Pruden et al. 2012). The common practice of spreading compost on soil was not sufficient to reduce ARG abundance to background levels, and the Putian soil showed up to 3,000-fold enrichment. However, the practice decreased ARG concentrations substantially below compost levels. The relatively high enrichment of ARGs in Putian soil may be due to higher manure/soil ratio and/or shorter time before sampling after amendment compared to other farms. These observations highlight the need to determine adequate composting time to reduce resistance levels before release to the more uncontrolled environment (Storteboom et al. 2007), as well as to define soil and landscape features that would minimize dispersal to the human food chain. Resistance gene diversity and abundance patterns specific to each management type indicate the influence of the antibiotics as a selective pressure. These profiles show that generally samples of the same management type clustered together (Fig. 4). The relationships between the structure of detected ARGs and antibiotic and heavy metal concentrations were assessed with canonical correspondence analysis (CCA). Manure samples grouped separately by the first axis and were strongly affected by arsenic, copper and tetracycline concentrations, which are likely among the dominant factors driving the changes in ARG structures on these farms (Fig. 4). Although only three farms are included in this study, regardless of their location (a separation of over 2000 km), composting technique, or antibiotic dosage, the ARGs resistance profiles are similar, indicating that similar ARG reservoirs are likely common across China and in other countries where management practices are similar. The diversity and abundance of ARGs reported in this study is alarming, and clearly indicates that unmonitored use of antibiotics and metals on swine farms has expanded the 112 diversity and abundance of the antibiotic resistance reservoir in the farm environment. The coenrichment of ARGs and transposases further exacerbates the risks of ARG transfer from livestock animals to human-associated bacteria, and then spread among human populations (Marshall et al. 2011, Smillie et al. 2011). Policies and management tools to facilitate prudent use of antibiotics and heavy metals, including their combined use, in animal industries and animal waste management are needed. Decreased resistance levels have been observed in Europe after the disuse of agricultural antibiotics (Aarestrup et al. 2001). Pig manure, with its abundant and diverse ARGs and sheer volume, is a major source of resistance genes and as such a public health hazard. Microbes from manure, compost, or soil containing the ARGs are subject to dispersal via runoff into rivers (Pruden et al. 2012), leaching to subsurface waters, air dispersal via dust, human travel, and distribution of agricultural products, including compost for gardening, which could expand a local contamination to regional and even global scales (Church 2004, Smillie et al. 2011). MATERIALS AND METHODS Sampling. A total of 36 samples were collected in 2010 from three Chinese provinces including (from north to south): Beijing (Beijing farm), Zhejiang (Jiaxing farm), and Fujian (Putian farm). The manure and compost samples were obtained from representative swine farms with an animal intensity of 10,000 market hogs or more per year. Soil samples were collected from a nearby agronomic field to which manure-based compost had been applied. Four replicates were taken from each sample type and farm, and all the samples were kept on dry ice during transportation and stored at -80°C before DNA extraction and chemical analysis. 113 These are typical large-scale swine farms. Pigs are continuously housed on concrete. The manure was sampled within one day after excretion in all cases. In Beijing, compost was managed in outdoor windrows with aeration, for 2 weeks. In Jiaxing, pile composting was used with regular stir (1-2 times per day), for about 10 days. In Putian they used pile composting with limited aeration, for 2-4 weeks. In Jiaxing and Putian, compost products are packed and sold as commercial organic fertilizer for local farmers. For soil amendment, the composted manure spreading rate varies, but is approximately 10 tons / hectare, applied once per year. At the Beijing and Jiaxing farms, the soil had been receiving manure compost for more than 2 years, and the most recent application was 2 months before sampling. At the Putian farm, the soil had been receiving manure compost for more than 3 years, and the most recent application was one week before sampling. Control samples received no known antibiotic input. The control soil is from a pristine forest in Putian, China. This soil has had no anthropogenic antibiotic input, and has a similar ARG abundance and diversity profile as another temperate region, antibiotic-free grassland soil we studied. The control pig manure samples were mixtures of DNA extracted from feces from pigs birthed from a mother with no antibiotic exposure and grown in facilities with no antibiotic exposure but fed a normal grower diet (see Looft et al. 2012 for further details). Sample CM1 was taken from six 84 day-old pigs not feed antibiotics. Samples CM2-4 were each taken from a single animal at three time points between 86 and 104 days old. The control manure was used as a comparison against the farm manures, while the control soil was used as a comparison against both the farm compost and farm soil. Antibiotic and metal quantitation. Sulfonamides (SAs) and quinolones (FQs) concentrations were analyzed in this study, including 114 sulfadiazine (SD), sulfamerazine (SM1), sulfamethoxydiazine (SMT), sulfamethazine (SM2), sulfamethoxazole (SMZ), norfloxacin (NOF), ofloxacin (OF), enrofloxacin (ENR) and ciprofloxacin (CIP). Previously, five target tetracyclines, and ten degradation products were analyzed (Qiao et al. 2012). Metals were analysed in air-dried, milled samples after oxidative digestion in sealed tubes by inductively coupled plasma-mass spectrometry (Agilent 7500cx, USA). Quantities were determined relative to reference standards. Sample extraction and analysis procedures for both antibiotics and metals are described in supplemental materials. DNA extraction. High molecular weight community DNA was extracted by the freeze-grinding, SDS-based method (Zhou et al. 1996) and was purified using a low melting agarose gel followed by phenol extraction. DNA concentration and quality was determined with a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE). Primer design. A majority of the primer sets (247) were designed, used, and validated in a previous study (Looft et al. 2012). For this study, 89 new primer sets were designed for categories of resistance genes not previously targeted as thoroughly. The same design parameters were used as before (Looft et al. 2012). Reference sequences were harvested from the ARDB (http://ardb.cbcb.umd.edu/). Additional validation of the primer sets was performed and is described in the supplemental materials. Quantitative PCR. All quantitative PCR reactions were performed using the Applied Biosystems OpenArray® platform, as described previously (Looft et al. 2012), except that a threshold cycle (CT) of 27 was used as the detection limit. Generally the technical triplicates were tested during separate testing occasions (plate and day of testing) as a method of quality control. The ΔΔCT method of comparison (Livak et al. 2001) was used to compare relative abundance between samples: 115 ΔCT = CT, (ARG) – CT, (16S) [1] ΔΔCT = ΔCT, (Target) – ΔCT, (Ref), [2] where: CT is the threshold cycle, ARG is one of the 313 antibiotic resistance gene assays, 16S is the 16S rRNA gene assay, Target is the experimental sample, and Ref is the reference sample. The reference sample used as a comparison depended on the purpose of the analysis. When the purpose was to reveal changes among all farm types and the dynamics or ARGs because of manure management, the control soil was the reference sample for all farm samples, as was the case in Fig. 2. Average CT values were calculated by averaging the four field replicates. If there was no amplification in one of the four field replicates, it was considered a false negative and discarded. In calculation of the ∆CT of the reference sample, if there was no amplification, the detection limit CT (27.0) was used. Genes were considered statistically enriched if the range created by three standard deviations of the mean fold change was entirely > 1. Acknowledgments. We thank Benli Chai and Amanda Geaslin for technical assistance. Funding for Y.G.Z. was provided by the Ministry of Science & Technology of China (2009DFB90120 & 2011DFB91710) and at Michigan State University by their Pharmaceuticals in the Environment Initiative. 116 REFERENCES 117 REFERENCES Aarestrup FM, Seyfarth AM, Emborg HD, Pedersen K, Hendriksen RS, Bager F. 2001 Effect of abolishment of the use of antimicrobial agents for growth promotion on occurrence of antimicrobial resistance in fecal enterococci from food animals in Denmark. Antimicrob Agents Chemother 45(7):2054-2059. Alcock RE, Sweetman A, Jones KC. 1999 Assessment of organic contaminant fate in waste water treatment plants. I: Selected compounds and physicochemical properties. Chemosphere 38(10):2247-2262. Arias CA, Murray BE. 2009 Antibiotic-resistant bugs in the 21st century—a clinical superchallenge. N Engl J Med 360(5):439-443. Baker-Austin C, Wright MS, Stepanauskas R, McArthur JV. 2006 Co-selection of antibiotic and metal resistance. Trends Microbiol 14(4):176-182. Barlow M. 2009 What antimicrobial resistance has taught us about horizontal gene transfer. Methods Mol Biol 532:397-411. Berg J, Thorsen MK, Holm PE, Jensen J, Nybroe O, Brandt KK. 2010 Cu exposure under field conditions coselects for antibiotic resistance as determined by a novel cultivationindependent bacterial community tolerance assay. Environ Sci Technol 44(22):87248728. Binh CTT, Heuer H, Kaupenjohann M, Smalla K. 2008 Piggery manure used for soil fertilization is a reservoir for transferable antibiotic resistance plasmids. FEMS Microbiology Ecology 66(1):25-37. Binh CTT, Heuer H, Kaupenjohann M, Smalla K. 2009 Diverse aadA gene cassettes on class 1 integrons introduced into soil via spread manure. Res Microbiol 160(6):427-433. Bolan NS, Khan MA, Donaldson J, Adriano DC, Matthew C. 2003 Distribution and bioavailability of copper in farm effluent. Sci Total Environ 309(1-3):225-236. Chee-Sanford JC, Mackie RI, Koike S, Krapac IG, Lin Y-F, Yannarell AC et al. 2009 Fate and transport of antibiotic residues and antibiotic resistance genes following land application of manure waste. Journal of Environment Quality 38(3):1086. Chen J, Yu Z, Michel FC, Wittum T, Morrison M. 2007 Development and application of realtime PCR assays for quantification of erm genes conferring resistance to macrolideslincosamides-streptogramin B in livestock manure and manure management systems. Appl Environ Microbiol 73(14):4407-4416. Church DL. 2004 Major factors affecting the emergence and re-emergence of infectious diseases. Clin Lab Med 24(3):559-586, v. 118 Critically important antimicrobials for human medicine (2009) Report of the WHO advisory group on integrated surveillance of antimicrobial resistance (AGISAR, Copenhagen), 3rd Ed:1-26. D'Costa VM, McGrann KM, Hughes DW, Wright GD. 2006 Sampling the antibiotic resistome. Science 311(5759):374-377. Dolliver H, Gupta S, Noll S. 2008 Antibiotic degradation during manure composting. J Environ Qual 37(3):1245-1253. Enne VI, Cassar C, Sprigings K, Woodward MJ, Bennett PM. 2008 A high prevalence of antimicrobial resistant Escherichia coli isolated from pigs and a low prevalence of antimicrobial resistant E. coli from cattle and sheep in Great Britain at slaughter. FEMS Microbiol Lett 278(2):193-199. Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MOA, Dantas G. 2012 The shared antibiotic resistome of soil bacteria and human pathogens. Science 337(6098):1107-1111. Fournier P-E, Vallenet D, Barbe V, Audic S, Ogata H, Poirel L et al. 2006 Comparative genomics of multidrug resistance in Acinetobacter baumannii. PLoS Genet 2(1):e7. Ghosh S, LaPara TM. 2007 The effects of subtherapeutic antibiotic use in farm animals on the proliferation and persistence of antibiotic resistance among soil bacteria. ISME J 1(3):191-203. Gilbert N. 2012 Rules tighten on use of antibiotics on farms. Nature 481(7380):125. Gillings MR, Stokes HW. 2012 Are humans increasing bacterial evolvability? Trends in Ecology & Evolution 27(6):346-352. Heuer H, Kopmann C, Binh CTT, Top EM, Smalla K. 2009 Spreading antibiotic resistance through spread manure: Characteristics of a novel plasmid type with low %G+C content. Environ Microbiol 11(4):937-949. Heuer H, Schmitt H, Smalla K. 2011 Antibiotic resistance gene spread due to manure application on agricultural fields. Curr Opin Microbiol 14(3):236-243. Heuer H, Binh CTT, Jechalke S, Kopmann C, Zimmerling U, Krögerrecklenfort E et al. 2012 IncP-1ε plasmids are important vectors of antibiotic resistance genes in agricultural systems: Diversification driven by class 1 integron gene cassettes. Front Microbio 3:2. Heuer H, Smalla K. 2012 Plasmids foster diversification and adaptation of bacterial populations in soil. FEMS Microbiol Rev 36(6):1083-1104. Hvistendahl M. 2012 China takes aim at rampant antibiotic resistance. Science 336:1-1. 119 Jackson CR, Fedorka-Cray PJ, Barrett JB, Ladely SR. 2004 Effects of tylosin use on erythromycin resistance in enterococci isolated from swine. Appl Environ Microbiol 70(7):4205-4210. Knapp CW, Dolfing J, Ehlert PAI, Graham DW. 2010 Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environ Sci Technol 44(2):580587. Levy SB, FitzGerald GB, Macone AB. 1976 Changes in intestinal flora of farm personnel after introduction of a tetracycline-supplemented feed on a farm. N Engl J Med 295(11):583588. Levy SB. 1978 Emergence of antibiotic-resistant bacteria in the intestinal flora of farm inhabitants. J Infect Dis 137(5):689-690. Livak KJ, Schmittgen TD. 2001 Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)) method. Methods 25(4):402-408. Looft T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD et al. 2012 In-feed antibiotic effects on the swine intestinal microbiome. Proceedings of the National Academy of Sciences:1-6. Mahillon J, Chandler M. 1998 Insertion sequences. Microbiol Mol Biol Rev 62(3):725-774. Marshall BM, Levy SB. 2011 Food animals and antimicrobials: Impacts on human health. Clin Microbiol Rev 24(4):718-733. McKinney CW, Loftin KA, Meyer MT, Davis JG, Pruden A. 2010 tet and sul antibiotic resistance genes in livestock lagoons of various operation type, configuration, and antibiotic occurrence. Environ Sci Technol 44(16):6102-6109. Miriagou V, Carattoli A, Tzelepi E, Villa L, Tzouvelekis LS. 2005 IS26-associated In4-type integrons forming multiresistance loci in enterobacterial plasmids. Antimicrob Agents Chemother 49(8):3541-3543. Pan X, Qiang Z, Ben W, Chen M. 2011 Residual veterinary antibiotics in swine manure from concentrated animal feeding operations in shandong province, china. Chemosphere 84(5):695-700. Peak N, Knapp CW, Yang RK, Hanfelt MM, Smith MS, Aga DS et al. 2007 Abundance of six tetracycline resistance genes in wastewater lagoons at cattle feedlots with different antibiotic use strategies. Environ Microbiol 9(1):143-151. Petrova M, Gorlenko Z, Mindlin S. 2011 Tn5045, a novel integron-containing antibiotic and chromate resistance transposon isolated from a permafrost bacterium. Res Microbiol 162(3):337-345. 120 Price LB, Stegger M, Hasman H, Aziz M, Larsen J, Andersen PS et al. 2012 Staphylococcus aureus CC398: Host adaptation and emergence of methicillin resistance in livestock. MBio 3(1). Pruden A, Arabi M, Storteboom H. 2012 Correlation between upstream human activities and riverine antibiotic resistance genes. Environ Sci Technol. Qiao M, Chen W, Su J, Zhang B, Zhang C. 2012 Fate of tetracyclines in swine manure of three selected swine farms in china. Journal of Environmental sciences (China) 24:1047-1052. Shi J, Yu X, Zhang M, Lu S, Wu W, Wu J et al. 2011 Potential risks of copper, zinc, and cadmium pollution due to pig manure application in a soil-rice system under intensive farming: A case study of Nanhu, China. J Environ Qual 40(6):1695-1704. Singh R, Schroeder CM, Meng J, White DG, McDermott PF, Wagner DD et al. 2005 Identification of antimicrobial resistance and class 1 integrons in Shiga toxin-producing Escherichia coli recovered from humans and food animals. J Antimicrob Chemother 56(1):216-219. Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ. 2011 Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480(7376):241-244. Storteboom HN, Kim S-C, Doesken KC, Carlson KH, Davis JG, Pruden A. 2007 Response of antibiotics and resistance genes to high-intensity and low-intensity manure management. Journal of Environment Quality 36(6):1695. Udwadia ZF, Amale RA, Ajbani KK, Rodrigues C. 2012 Totally drug-resistant tuberculosis in india. Clin Infect Dis 54(4):579-581. Varga C, Rajić A, McFall ME, Reid-Smith RJ, Deckert AE, Checkley SL et al. 2009 Associations between reported on-farm antimicrobial use practices and observed antimicrobial resistance in generic fecal Escherichia coli isolated from Alberta finishing swine farms. Prev Vet Med 88(3):185-192. Wang FH, Ma WQ, Dou ZX, Ma L, Liu XL, Xu JX, Zhang FS, et al. 2006 The estimation of the production and amount of animal manure and its environmental effect in china. China Environ Sci 26:614-617. Wu N, Qiao M, Zhang B, Cheng W-D, Zhu Y-G. 2010 Abundance and diversity of tetracycline resistance genes in soils adjacent to representative swine feedlots in china. Environ Sci Technol 44(18):6933-6939. Zhang T, Zhang X-X, Ye L. 2011 Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS ONE 6(10):e26041. 121 Zhou J, Bruns MA, Tiedje JM. 1996 DNA recovery from soils of diverse composition. Appl Environ Microbiol 62(2):316-322. 122 CHAPTER V GENE-TARGETED METAGENOMICS OF PUTATIVE DIBENZO-P-DIOXIN DEGRADING ANGULAR DIOXYGENASES 123 ABSTRACT Novel primer sets were designed to target angular dioxygenases with potential to degrade dioxins, the most toxic and persistent pollutants. Dibenzo-p-dioxin, dibenzofuran and carbazole dioxygenase targeted metagenomics elucidated that current understanding of these gene families underestimates actual diversity in nature, both in human-contaminated and pristine environments. INTRODUCTION Aromatic hydrocarbons comprise a chemically diverse class of organic compounds that include monoaromatics (e.g. benzoate), biphenyls, polycyclic aromatics (e.g. naphthalene), and more toxic oxygen-linked aromatics (e.g. dibenzofuran, and dibenzo-p-dioxin). Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are released into the environment from a variety of sources including: combustion, incineration, pulp and paper manufacturing, pesticides, and some natural sources (Kulkarni et al. 2008). Anthropogenic release of dioxins has resulted in contaminated soil that need treatment (U.S. Congress 1991). However, even pristine rural soils with low human impact have detectable levels of dioxins at low concentrations (1 - 1000 µg kg-1) (Schaum et al. 2007). Gene-targeted amplicon sequencing offers an important alternative to culturing individual isolate strains to identify novel biodiversity, as was done with biphenyl dioxygenase (Iwai et al. 2010). We have progressed to characterize dioxygenases with activity toward problematic dioxin chemicals. Microbial degradation of dioxins has been studied, but there are few tangible results, especially in terms of isolated degraders. Other than the well characterized (chloro)dioxin degrading strain Sphingomonas wittichii RW1 (Wittich et al. 1992), only one other strain has been reported to use dibenzo-p-dioxin as a sole carbon and energy source: Pseudomonas veronii 124 PH-03 (Hong et al. 2004). Angular dioxygenation adds molecular oxygen to the two carbons adjacent to the ether oxygen and results in catechol and a six-carbon aliphatic acid (Field et al. 2008). Three angular dioxygenases are known to catalyze the first step of this pathway: dioxin dioxygenase (dxnA1), dibenzofuran dioxygenase (dbfA1 or dfdA1), and carbazole dioxygenase (carAa) (Field et al. 2008). A significant number of PCR primers exist to probe samples for aromatic ring-hydroxylating dioxygenase genes (ARDHs), including a quantitative PCR primer specific to only Sphingomonas sp. RW1 dxnA1 (Hartmann et al. 2012), but no previously published primer set suitable for amplicon sequencing targets dioxygenases active toward dioxins specifically, without targeting other dioxygenases that do not have activity against dioxins (Iwai et al. 2011). MATERIALS AND METHODS A manual search of the GenBank database was performed to harvest reference sequences related to angular or dioxin or dibenzofuran or carbazole and dioxygenase. An initial analysis based on amino acid similarity of these reference sequences showed that these genes formed three groups within the superfamily of Rieske dioxygenases. These three groups are represented by the following genes (Table 1): group 1: dioxin 1,10a dioxygenase (dxnA1) and dibenzofuran 4,4a dioxygenase (dfdA1), group 2: dibenzofuran 4,4a dioxygenase (dbfA1), and group 3: carbazole 1,9a dioxygenase (carAa). The DDBJ/EMBL/GenBank non-redundant protein database was searched using Hidden Markov Models built from all reference sequences from each cluster with HMMER (Eddy 2009). The results were obtained from the December 2010 release of FunGene (http://fungene.cme.msu.edu). An HMM bits saved score cutoff of 700 was used, and no 125 Reference Reference Reference Reference Reference Primer group Primer group Primer Positive group control Positive Primer Primer control group Positive group PCR control Positive validation PCR Positive control validation control PCR validation PCR PCR validation validation Table 5.1: Reference sequences used in primer design, PCR validation of primer specificity and designation of reference sequences in clusters with obtained environmental sequences. Positive control column indicates if the strain DNA was used in PCR validation of primer specificity. PCR validation column indicated which primer set produced an amplicon with that strain. References listed detail the activity of the strain toward dioxins. * Rhodococcus sp. RHA1 produced only a faint band with the dbfA1 primer set. Cluster Cluster Cluster ClusterCluster no. Organism (Protein no. no. no. no. Organism (Protein ID) ID) Organism (Protein(Protein ID) Organism Organism (Protein ID) ID) str. RW1 str. RW1 (Wittich(Wittich et et 1992) et1992) al. al. al. d1wittichiiwittichii str. RW1 (CAA51365) Y dxnA1dxnA1 et al. (Wittich1992) S. S. RW1 (CAA51365) (CAA51365) 1 1 Y Y 1Y dxnA1 dxnA1 (Wittich et 1992) al. 1992) 1 1 dxnA1 Y d1 d1 d1 wittichii str.S. wittichii (CAA51365) S. S. d1 wittichii str. RW1 (CAA51365) (Wittich (Iida et2002)a 2002)a d2 Terrabacter YK3 (BAC06602) et 2002)a al. d2 Terrabacter sp. YK3 d2 d2 d2 Terrabacter YK3 (BAC06602) (BAC06602) 1 1 1 1 1 Terrabacter Terrabacter sp. YK3 (BAC06602) sp. sp. sp. YK3 (BAC06602) (Iida et (Iida(Iida et et al. 2002)a al. al.(Iida al. 2002)a (Miyauchi(Miyauchi al. et al. al. d2 Nocardioides DF412 (BAG06223) Y dxnA1dxnA1 (Miyauchi 2008) 1 1 dxnA1 Y dxnA1 (Miyauchi et 2008) 2008) d2 Nocardioides(BAG06223) d2 d2 d2 Nocardioides DF412 DF412DF412 (BAG06223)1 1 Y Y 1Y dxnA1 (Miyauchi et al. 2008) et et al. 2008) Nocardioides Nocardioides sp. (BAG06223) sp. sp. sp. sp. DF412 (BAG06223) (Aly et2008) 2008) d2 Rhodococcus sp. HA01 (ACC85677) et 2008) al. d2 Rhodococcus sp. HA01 (ACC85677) Rhodococcus (ACC85677) d2 d2 d2 Rhodococcus HA01 (ACC85677) (ACC85677) 1 1 1 1 1 Rhodococcus sp. sp. HA01 sp. HA01 (Aly et (Aly (Aly et et al. 2008) al. al. (Aly al. 2008) (Iida et2002)a 2002)a d2 Terrabacter YK1 (BAG80728) et 2002)a al. d2 Terrabacter sp. YK1 d2 d2 d2 Terrabacter YK1 (BAG80728) (BAG80728) 1 1 1 1 1 Terrabacter Terrabacter sp. YK1 (BAG80728) sp. sp. sp. YK1 (BAG80728) (Iida et (Iida(Iida et et al. 2002)a al. al.(Iida al. 2002)a (Iida et al.(Iida al. 2002)b d2 Rhodococcus sp. YK2 (BAG80733) Y dxnA1dxnA1 al. 2002)b 2002)b 1 1 dxnA1 Y et 2002)b al. d2 Rhodococcus (BAG80733) Rhodococcus sp. YK2 d2 d2 d2 Rhodococcus YK2 (BAG80733) (BAG80733) 1 1 Y Y 1Y dxnA1 dxnA1 et (Iida(Iida et et al. 2002)b Rhodococcus sp. sp. YK2sp. YK2 (BAG80733) (Iida d3 Bacillus DSM 2912 (YP_003590130) d3 d3 d3 d3 BacillusBacillus DSM 2912 (YP_003590130) tusciae tusciae DSM (YP_003590130) Bacillus DSM DSM 2912 (YP_003590130) Bacillus tusciae tusciaetusciae 29122912 (YP_003590130) (Kasuga(Kasuga et et 2001) et2001) al. al. al. d3 Terrabacter DBF63 (BAB55886) Y dbfA1 dbfA1 2 2 Y dbfA1 (Kasuga et al. (Kasuga2001) d3 Terrabacter sp. DBF63 d3 d3 d3 Terrabacter DBF63 DBF63 (BAB55886) Terrabacter Terrabacter(BAB55886) (BAB55886) 2 2 Y Y 2dbfA1 Y dbfA1 (Kasuga et 2001) al. 2001) sp. sp. sp. sp. DBF63 (BAB55886) (Aly et2008) 2008) d3 Rhodococcus sp. HA01 (ACC85681) et 2008) al. d3 Rhodococcus sp. HA01 (ACC85681) Rhodococcus (ACC85681) d3 d3 d3 Rhodococcus HA01 (ACC85681) (ACC85681) 2 2 2 2 2 Rhodococcus sp. sp. HA01 sp. HA01 (Aly et (Aly (Aly et et al. 2008) al. al. (Aly al. 2008) (Schuler(Schuler et et al. et2008) al. al. al. d3 Sphingomonas sp. (ABV68886) (Schuler et 2008) 2008) d3 Sphingomonas sp.(ABV68886) Sphingomonas LB126 (ABV68886) 2 d3 d3 d3 Sphingomonas LB126 LB126LB126 (ABV68886) 2 2 2 2 Sphingomonas sp. sp. LB126sp. (ABV68886) (Schuler et al. (Schuler2008) 2008) d3 Paenibacillus YK5sp. YK5 (BAE53401) Y dbfA1 dbfA1 (Iida(Iida 2006) 2 2 Y dbfA1 et 2006) al. d3 Paenibacillus (BAE53401) d3 d3 d3 Paenibacillus YK5 (BAE53401) (BAE53401) 2 2 Y Y 2dbfA1 Y dbfA1(Iida et2006) et et al. 2006) Paenibacillus Paenibacillus sp.(BAE53401) sp. sp. sp. YK5 YK5 (Iida et al. al.(Iida al. 2006) (Iida d3 Rhodococcus sp. YK2 (BAC00802) Y dbfA1 dbfA1 et al.(Iida al. 2002)b 2 2 Y dbfA1 (Iida al. 2002)b 2002)b et 2002)b al. d3 Rhodococcus (BAC00802) Rhodococcus sp. YK2 d3 d3 d3 Rhodococcus YK2 (BAC00802) (BAC00802) 2 2 Y Y 2dbfA1 Y dbfA1 et (Iida(Iida et et al. 2002)b Rhodococcus sp. sp. YK2sp. YK2 (BAC00802) (Noumura(Noumura al. et al. al. d3 Rhodococcus DFA3 (BAD51811) Y dbfA1 dbfA1 (Noumura 2004) Y dbfA1 dbfA1 (Noumura et 2004) 2004) d3 Rhodococcus sp. DFA3 (BAD51811) d3 d3 d3 Rhodococcus DFA3 (BAD51811) (BAD51811) Rhodococcus Rhodococcus sp. DFA3 sp. sp. sp. DFA3 (BAD51811) Y Y dbfA1 Y (Noumura et al. 2004) et et al. 2004) (Shepherd(Shepherd1998) et al. al. d4 Sphingomonas sp. CB3 (AAC38616) (Shepherd al. (Shepherd et 1998) 1998) d4 Sphingomonas (AAC38616) Sphingomonas sp. CB3 d4 d4 d4 Sphingomonas CB3 (AAC38616) (AAC38616) 1 1 1 1 1 Sphingomonas sp. sp. CB3 sp. CB3 (AAC38616) (Shepherd et al. 1998) et et al. 1998) Sphingomonas sp. sp. KA1 (YP_718182) (Urata et2006) 2006) al. 2006) (Urata (Urata al. 2006) et al. NA Sphingomonas (YP_718182) Sphingomonas sp. KA1 NA NANA NA Sphingomonas KA1sp. KA1 (YP_718182) 2 2 2 2 2 Sphingomonas sp. KA1 (YP_718182) (YP_718182) (Urata et al. (Urata et et al. 2006) (Urata et2006) al. 2006) al. 2006) c1c1 Sphingomonas (YP_717942) Y carAa(Urata (Urata (Urata2006) Sphingomonas sp. KA1 (YP_717942) 3 3 carAa Y et al. Sphingomonas sp. KA1 carAa c1 c1 c1 Sphingomonas KA1 (YP_717942) (YP_717942)3 3 Y Y 3Y carAa carAa et al. (Urata et et al. 2006) Sphingomonas sp. sp. KA1sp. KA1 (YP_717942) c1c1 Sphingomonas sp. JS1 (ACH98389) Sphingomonas (ACH98389) (ACH98389) 3 3 3 3 3 Sphingomonas sp. JS1 c1 c1 c1 Sphingomonas JS1 JS1JS1 (ACH98389) Sphingomonas sp. sp. sp. (ACH98389) c1c1 Sphingomonas (YP_717981) Y carAa carAa Sphingomonas sp. KA1 (YP_717981) Sphingomonas sp. KA1 c1 c1 c1 Sphingomonas KA1 (YP_717981) (YP_717981) Sphingomonas sp. sp. KA1sp. KA1 (YP_717981) Y Y Y carAa carAa carAa Y c1c1 Sphingomonas sp. XLDN2-5 (ADC31794) Sphingomonas sp. XLDN2-5 (ADC31794) Sphingomonas (ADC31794) c1 c1 c1 Sphingomonas XLDN2-5 sp. XLDN2-5 (ADC31794) Sphingomonas sp. sp. XLDN2-5 (ADC31794) c4c4 Pseudomonas stutzeri OM1 (BAA31266) Y carAa (Ouchiyama et1998) 1998) carAa Pseudomonas stutzeri OM1 (BAA31266) 3 3 3 carAa Y (Ouchiyama et al. al. carAa et al. al. 1998) 1998) (Ouchiyama c4 c4 c4 Pseudomonas stutzeri OM1 (BAA31266) Pseudomonas Pseudomonas (BAA31266) (BAA31266) 3 Y Y 3Y carAa(Ouchiyama(Ouchiyama et et al. 1998) stutzeri OM1 stutzeri OM1 c4 resinovorans sp. sp. CA10 CA10 resinovorans sp. CA10 (NP_758566)3 Y carAa (Ouchiyama et1993) 1993) carAa P. P.P. resinovorans sp. (NP_758566) 3 3 carAa Y (Ouchiyama et al. al. carAa et al. al. 1993) 1993) (Ouchiyama c4 c4 c4 resinovorans sp. CA10 (NP_758566) (NP_758566) 3 Y Y 3Y carAa(Ouchiyama(Ouchiyama et et al. 1993) P. P.c4 resinovorans CA10 (NP_758566) (Nojiri et2005) al. 2005) al. 2005) c4c4 Janthinobacterium sp. J3J3 Y carAa(Nojiri (Nojiri(Nojiri2005) Janthinobacterium sp. J3 (BAC56742) 3 3 carAa Y et al. Janthinobacterium sp. (BAC56742) 3 carAa c4 c4 c4 Janthinobacterium J3 (BAC56742) (BAC56742) 3 Y Y 3Y carAa carAa et al.(Nojiri et et al. 2005) Janthinobacterium sp. sp. J3 (BAC56742) (Li (Li et 2004) 2004) et al.(Li 2004) c4c4 Pseudomonas sp. XLDN4-9 (AAY56339) Pseudomonas XLDN4-9 (AAY56339) al. al. (Li c4 c4 c4 Pseudomonas XLDN4-9 sp. XLDN4-9 (AAY56339)3 3 3 3 Pseudomonas Pseudomonas (AAY56339) sp. sp. sp. XLDN4-9 (AAY56339) 3 (Li et al. 2004) et et al. 2004) carbazole-degrading bacterium bacterium CAR-SF carbazole-degrading bacterium carbazole-degrading bacterium CAR-SF carbazole-degrading CAR-SF carbazole-degrading bacterium CAR-SF CAR-SF c4c4 c4 c4 c4 (BAG30826) (Fuse et2003) et et 2003) al. al. al. (BAG30826) (BAG30826) (Fuse (BAG30826) (BAG30826) 3 3 3 33 (Fuse et(Fuse et 2003) al. 2003) al. (Fuse 2003) c4c4 Pseudomonas (BAC56726) Pseudomonas sp. K23 (BAC56726) c4 c4 c4 Pseudomonas K23K23 sp. K23 (BAC56726) Pseudomonas Pseudomonas sp. K23 (BAC56726) sp. sp. (BAC56726) (Inoue et2005) 2005) al. 2005) c5c5 Nocardioides sp. IC177 (BAD95466) Nocardioides IC177 (BAD95466) (Inoue(Inoue al. 2005) et al. c5 c5 c5 Nocardioides IC177 (BAD95466) (BAD95466) 3 3 3 3 3 Nocardioides Nocardioides sp. IC177 sp. sp. sp. IC177 (BAD95466) (Inoue et al. (Inoue et et al. 2005) Burkholderia xenovorans LB400 bphA1 bphA1 NA Burkholderia xenovorans LB400 Burkholderia xenovorans none NA NANA NA Burkholderia xenovorans LB400LB400 bphA1none none none nonenone Burkholderia xenovorans LB400 bphA1 bphA1 none none none none Rhodococcus sp. sp. RHA1 none NA Rhodococcus sp.bphA1 bphA1 Rhodococcus RHA1 none dbfA1* NA NANA NA Rhodococcus RHA1 sp. RHA1 bphA1 Rhodococcus sp. RHA1 bphA1 bphA1 none nonenone dbfA1* dbfA1* dbfA1*dbfA1* 126 additional sequences were obtained through this search, which reinforces the low number of sequenced (or cultured) strains with activity toward dibenzo-p-dioxin. Degenerate primers were designed from amino acid consensus regions (Table 2). No non-specific microbial genes are targeted by the primer sets (when allowing no mismatches), as determined by an NCBI nucleotide BLAST. The specificity of the primer sets was determined experimentally. DNA was obtained from positive control cultured organisms. An average of four positive controls were used for each primer set and the correct length amplicon was observed with 100% frequency. Positive controls from one cluster were then used as template with primers targeting other clusters as negative controls for those clusters. Two organisms with biphenyl/toluene dioxygenases (Burkholderia xenovorans str. LB400, and Rhodococcus sp. RHA1) were also used as negative controls. With the exception of a single weak amplification out of 42 total validation reactions, the primers were specific to only the gene cluster for which they were designed and did not produce amplicons from the closely neighboring gene clusters (Table 1). These results indicate that these primers are extremely specific to the targeted reference sequences. The above primer sets were used in gene-targeted metagenomics as has been done previously targeting other genes (Iwai et al. 2011a, Iwai et al. 2010, Vital et al. 2013). Two environmental samples were chosen as template DNA: a well characterized polychlorinated biphenyl (PCB)-contaminated rhizosphere (Leigh et al. 2007), and a pristine Kansas prairie soil (KS), from the Konza Prairie (39°05'N, 96°35'W). We suspect that both these soils are contaminated with polyaromatic hydrocarbons, and likely low levels of dioxins, due to industrial activity (in the case of the PCB-rhizosphere sample) or with sequencing adapters, 8 base oligo multiplex sequencing barcodes, together with the gene specific sequence. PCR conditions with 127 Table 5.2. Primer sequences and PCR conditions of the three primer sets. These PCR conditions were optimized for the soil samples described. The target positions described are for reference amino acid sequences: * position based on Sphingomonas  wittichii RW1, dxnA1, and † position based on Sphingomonas sp. KA1, carAa. ‡ annealing temperature. Primer Set Target position dxnA1/dfdA1 145-150* 312-307* dbfA1 205-210* 373-368* carAa 69-74† 268-263† 51 Mg conc. (mM) 4 51 0.8 3.5 63 0.8 2.5 Ta (ºC) Sequence (5' - 3') TACAAVGGGCTGRTTTTCGG GARAAVTTVGGGAACAC GGCGACGACTAYCACGTGCT TCGAAGTTCTCGCCRTCRTC TGCCTNCAYCGHGGBGT TTSAGHACRCCBGGSAGCCA 2+ Primer conc. (µM) 1.2 ‡ Table 5.3. Obtained sequences statistics. A mock community (MC) was composed of the strains used in validation of primer specificity (Table 1) and yielded the correct sequences and are not described further. Primer set Sample name Rhizosphere dxnA1/ dfdA1 KS Environmental sample total MC carAa Rhizosphere KS Environmental sample total MC Match barcode 2319 2844 Passed initial processing 2095 2521 Contains frame-shift 456 451 Passed FrameBot 641 690 5163 4616 907 1331 1247 1204 626 1204 450 720 673 543 594 128 194 193 339 460 465 1393 1137 322 532 612 501 340 500 128 Avg. length 389 375 476 sequencing adapters, 8 base oligo multiplex sequencing barcodes, together with the gene specific sequence. PCR conditions were optimized (Table 2). PCR products were prepared as described previously (Iwai et al. 2010) and mixed with other samples for pyrosequencing (Roche 454 GSFLX Titanium Sequencer). No amplicons were obtained using the dbfA1 primer set. Raw reads were filtered through barcode matching and quality filtered using the Ribosomal Database Project II (RDP-II) Pyro Initial Process tool (Cole et al. 2009) using the following parameters: forward primer maximum mismatches: 2, and minimum length: 300. Because many reads were not read through the reverse primer, the reverse primer filter and quality score filter were not applied at the initial filter stage. Reads passing the initial filters were aligned, frameshift corrected, and translated into protein sequences using the RDP FrameBot tool. The FrameBot reference set was obtained from the FunGene repository using a manual selection for genes related to polyaromatic dioxygenases in general. A broad diversity of reference genes was selected to assure that the correct reading frame to the obtained sequences would be found even if the obtained sequence was outside the intended target range. Protein reads with a length greater than 100 amino acids and ≥ 30% identity to the nearest reference sequence were for further analysis (Table 3). Quality filtered protein sequences and corresponding reference amino acid sequences were aligned by HMMER and trimmed to same length. The combined sequences were clustered using a 0.5 identity cut-off, using the RDP mcClust tool on the FunGene site. Clusters not containing a reference sequence were considered novel clusters (Iwai et al. 2010). The 0.5 identity cutoff was chosen because it is about at this distance that reference sequences were clustered to determine primer design groups. One representative sequence from each cluster was selected using the representative sequence tool on the RDP FunGene pipeline. These sequences were used to construct a nearest neighbor-joining tree using MEGA 5.1 software. 129 RESULTS AND DISCUSSION The obtained sequences (1863 total) revealed the in situ diversity of putative dioxygenases, similar to those that have been shown to degrade dioxins (Fig. 1). The majority of dxnA1/dfdA1 sequences formed novel clusters. Many clusters were shared in both the KS soil and the rhizosphere soil. However, the dominant dxnA1/dfdA1 clusters differ between sites. Two clusters (clusters d1, d5; Fig 1A) comprise 68% of sequences in the KS sample while these same clusters only represent 4% of the rhizosphere sequences. Notably cluster d1 contains the reference sequence from (chloro)dioxin oxidizing Sphingomonas wittichii str. RW1 and represents nearly 10% of the KS soil angular dioxygenase gene community. This indicates that genes similar to this important dioxygenase may be present in this prairie soil. The rhizosphere sample was dominated (49% of sequences) by two clusters (d6 and d7), while these same clusters only Known Clusters 250 Novel Clusters 300 Kansas Prairie PCB-rhizosphere Reference 250 200 150 100 50 50 0 0 c1 c2 c3 c4 c5 c6 c7 c8 c9 100 Known Clusters 350 200 150 B Novel Clusters d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 d15 d16 d17 d18 d19 d20 d21 d22 d23 d24 d25 No. Sequences in Cluster A Clusters (0.5 identity cutoff) Fig. 5.1. Results of clustering obtained sequences with the reference sequences. (A) Results using the dxnA1/dfdA1 primer set. (B) Results using the carAa primer set. Clusters are only shown that contained at least four sequences. There were an additional 12 clusters that contained two or three sequences. 130 represent 1% of the KS sequences. These data show site-specific populations composed of novel dioxygenases. The specificity of the primer sets was again confirmed because reference sequences used to design the dbfA1 primer set did not cluster with any of the obtained sequences using the dxnA1/dfdA1 primer set (using our 0.5 similarity cutoff). Similarly, many of the obtained sequences, including the most abundant novel cluster, formed a clade on the same branch as dxnA1 and dfdA1 (Fig. 2). Other sequences were more distantly related to this clade and their function is less predictable. While a majority of carAa sequences clustered with reference sequences at 50% similarity as is shown (Fig. 1B, cluster c1), similar ecological trends, including site specific populations, were also observed with the carAa obtained sequences. The carAa obtained sequences cluster separately from the reference sequences at 70% similarity cutoff. A consensus amino acid sequence of obtained sequences was compared to an aligned consensus sequence of reference sequences, and each searched for conserved amino acids. A known iron-binding motif (DX2HX3-4H (Nojiri et al. 2005), where X is any given amino acid) was observed in >95% of sequences of both dxnA1/dfdA1 and carAa obtained sequences (Fig. 3). In addition, another conserved motif (>95%), NWK or NWR, was observed and is shown in Fig. 3. Although no associated function could be found in the literature regarding a role of the NW(K/R) motif, it appears essential to the protein due to its high conservation. It is possible that the NW(R/K) motif plays a role in positioning of the substrate binding amino acid. In the case of carAa, Gly-178 is implicated to hydrogen bond to carbazole and the NWR motif is situated on the same alpha helix as Gly-178 (Nojiri et al. 2005). The identity of the third amino acid of this motif is specific to each group 131 B. xenovorans LB400 bphA1 Sphingomonas sp. CB3 carAa S. sp. RW1 dxnA1 N. sp. DF412 dfdA1 Terrabacter sp. DBF63 dbfA1 P. putida nahAc P. putida benA P. resinovorans carAa Fig. 5.2. Nearest neighbor-joining tree of the representative sequences of each cluster shown in Fig. 1. Branch names designate: cluster name (from Fig. 5.1), name and accession number of reference sequence in that cluster (if applicable), number of obtained sequences from pyrosequencing, and the predominate sample from which the sequences originated. 132 !"#$"%&'()%$"%*+*' ,' :;<" 8"9"""9" '!!" &!" %!" $!" ,-./0123" #!" 425262172" !" '()" '*)" '+)" #')" #))" #()" #*)" #+)" ,-.%)',$./'!)*.0)%'123.4%"/'&)'!"#$%&'('%)*+*56'789:',-%./;' !"#$"%&'()%$"%*+*' =' :;4" 8"9""9" =" =" '!!" =" &!" %!" $!" ,-./0123" #!" 425262172" !" '#(" '$(" '%(" '&(" #!(" ##(" #$(" #%(" ,-.%)',$./'!)*.0)%'123.4%"/'&)'!"#$%&'('%)*+*56'<,9:'0)1.);' Fig. 5.3. Percent conservation of translated obtained nucleotide sequences to protein sequences. (A) Results using the dxnA1/dfdA1 primer set. (B) Results using the carAa primer set. Key conserved amino acid positions are indicated. The DX2HX3-4H iron-binding site is indicated as well as the uncharacterized, yet highly conserved NW(K/R) motif. The asterisk (*) indicates positions for which obtained sequences were conserved at a higher rate than reference sequences. 133 While some recent advancements have been made in dioxin degradation with previously isolated strains, especially S. wittichii str. RW1 (Nam et al. 2006), progress in isolating novel dioxin degrading strains has been slow for several decades (Field et al. 2008). In the neighboring biphenyl dioxygenase clade, despite having many more degrader strains isolated, gene-targeted metagenomics still revealed extensive novel diversity (Iwai et al. 2010). This study reveals a number of novel dioxygenase sequence clusters of intermediate sequence similarity between the dxnA1 and dfdA1 genotypes. This reveals that there is likely a continuum of genetic diversity between these two relatively distinct but functionally similar groups. According to obtained dioxygenase sequences, the majority of putative dioxin degraders in these communities, have no cultured representatives and their diversity, in terms of number of clusters, far exceeds that of known degraders, as was previously found for bphA1 diversity (Iwai et al. 2010). Acknowledgements. We thank Dr. Gerben Zylstra for his suggestions and guidance, Dr. Hideaki Nojiri and Dr. Keisuke Miyauchi for providing reference strains of previously isolated dibenzofuran degraders, and Hyung-Inn and Jihee Lee for technical assistance. This work was supported by Superfund Research Program grant P42 ES004911-20 from the U. S. National Institute of Environmental Health Sciences. 134 REFERENCES 135 REFERENCES Aly H, Huu N, Wray V, Junca H, Pieper D. 2008 Two angular dioxygenases contribute to the metabolic versatility of dibenzofuran-degrading Rhodococcus sp. strain HA01. Appl Environ Microbiol 74(12):3812. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ et al. 2009 The ribosomal database project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37(Database issue):D141-145. Eddy SR. 2009 A new generation of homology search tools based on probabilistic inference. Genome Inform 23(1):205-211. Field JA, Sierra-Alvarez R. 2008 Microbial degradation of chlorinated dioxins. Chemosphere 71(6):1005-1018. Fuse H, Takimura O, Murakami K, Inoue H, Yamaoka Y. 2003 Degradation of chlorinated biphenyl, dibenzofuran, and dibenzo-p-dioxin by marine bacteria that degrade biphenyl, carbazole, or dibenzofuran. Biosci Biotechnol Biochem 67(5):1121-1125. Hartmann EM, Badalamenti JP, Krajmalnik-Brown R, Halden RU. 2012 Quantitative PCR for tracking megaplasmid-borne biodegradation potential of a model sphingomonad. Appl Environ Microbiol. Hong H-B, Nam I-H, Murugesan K, Kim Y-M, Chang Y-S. 2004 Biodegradation of dibenzo-pdioxin, dibenzofuran, and chlorodibenzo-p-dioxins by Pseudomonas veronii PH-03. Biodegradation 15(5):303-313. Iida T, Mukouzaka Y, Nakamura K, Kudo T. 2002 Plasmid-borne genes code for an angular dioxygenase involved in dibenzofuran degradation by Terrabacter sp. strain YK3. Appl Environ Microbiol 68(8):3716-3723. Iida T, Mukouzaka Y, Nakamura K, Yamaguchi I, Kudo T. 2002 Isolation and characterization of dibenzofuran-degrading actinomycetes: analysis of multiple extradiol dioxygenase genes in dibenzofuran-degrading Rhodococcus species. Biosci Biotechnol Biochem 66(7):1462-1472. Iida T, Nakamura K, Izumi A, Mukouzaka Y, Kudo T. 2006 Isolation and characterization of a gene cluster for dibenzofuran degradation in a new dibenzofuran-utilizing bacterium, Paenibacillus sp. strain YK5. Arch Microbiol 184(5):305-315. Inoue K, Habe H, Yamane H, Omori T, Nojiri H. 2005 Diversity of carbazole-degrading bacteria having the car gene cluster: isolation of a novel gram-positive carbazole-degrading bacterium. FEMS Microbiol Lett 245(1):145-153. 136 Iwai S, Chai B, Sul WJ, Cole JR, Hashsham SA, Tiedje JM. 2010 Gene-targeted-metagenomics reveals extensive diversity of aromatic dioxygenase genes in the environment. ISME J 4(2):279-285. Iwai S, Chai B, Jesus EC, Penton CR, Lee TK, Cole JR Tiedje JM 2011 Gene-targetedmetagenomics (GT-metagenomics) to explore the extensive diversity of genes of interest in microbial communities. p 235-244. In de Bruijn, FJ (ed), Handbook of Molecular Microbial Ecology I: Metagenomics and Complementary Approaches. John Wiley & Sons, Inc., Hoboken, NJ, USA. Iwai S, Johnson TA, Chai B, Hashsham SA, Tiedje JM. 2011 Comparison of the specificities and efficacies of primers for aromatic dioxygenase gene analysis of environmental samples. Appl Environ Microbiol 77(11):3551. Kasuga K, Habe H, Chung JS, Yoshida T, Nojiri H, Yamane H et al. 2001 Isolation and characterization of the genes encoding a novel oxygenase component of angular dioxygenase from the gram-positive dibenzofuran-degrader Terrabacter sp. strain DBF63. Biochem Biophys Res Commun 283(1):195-204. Kulkarni PS, Crespo JG, Afonso CAM. 2008 Dioxins sources and current remediation technologies—a review. Environ Int 34(1):139-153. Leigh MB, Pellizari VH, Uhlík O, Sutka R, Rodrigues J, Ostrom NE et al. 2007 Biphenylutilizing bacteria and their functional genes in a pine root zone contaminated with polychlorinated biphenyls (PCBs). ISME J 1(2):134-148. Li L, Xu P, Blankespoor HD. 2004 Degradation of carbazole in the presence of non-aqueous phase liquids by Pseudomonas sp. Biotechnol Lett 26(7):581-584. Miyauchi K, Sukda P, Nishida T, Ito E, Matsumoto Y, Masai E et al. 2008 Isolation of dibenzofuran-degrading bacterium, Nocardioides sp. DF412, and characterization of its dibenzofuran degradation genes. J Biosci Bioeng 105(6):628-635. Nam I-H, Kim Y-M, Schmidt S, Chang Y-S. 2006 Biotransformation of 1,2,3-tri- and 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin by Sphingomonas wittichii strain RW1. Appl Environ Microbiol 72(1):112-116. Nojiri H, Ashikawa Y, Noguchi H, Nam J-W, Urata M, Fujimoto Z et al. 2005 Structure of the terminal oxygenase component of angular dioxygenase, carbazole 1,9a-dioxygenase. J Mol Biol 351(2):355-370. Noumura T, Habe H, Widada J, Chung J-S, Yoshida T, Nojiri H et al. 2004 Genetic characterization of the dibenzofuran-degrading Actinobacteria carrying the dbfA1A2 gene homologues isolated from activated sludge. FEMS Microbiol Lett 239(1):147-155. Ouchiyama N, Zhang Y, Omori T, Kodama T. 1993 Biodegradation of carbazole by Pseudomonas spp. CA06 and CA10. Biosci Biotechnol Biochem 57(3):455-460. 137 Ouchiyama N, Miyachi S, Omori T. 1998 Cloning and nucleotide sequence of carbazole catabolic genes from Pseudomonas stutzeri strain OM1, isolated from activated sludge. J Gen Appl Microbiol 44(1):57-63. Schuler L, Ni Chadhain SM, Jouanneau Y, Meyer C, Zylstra GJ, Hols P et al. 2008 Characterization of a novel angular dioxygenase from fluorene-degrading Sphingomonas sp. strain LB126. Appl Environ Microbiol 74(4):1050-1057. Shepherd JM, Lloyd-Jones G. 1998 Novel carbazole degradation genes of Sphingomonas CB3: sequence analysis, transcription, and molecular ecology. Biochem Biophys Res Commun 247(1):129-135. Urata M, Uchimura H, Noguchi H, Sakaguchi T, Takemura T, Eto K et al. 2006 Plasmid pCAR3 contains multiple gene sets involved in the conversion of carbazole to anthranilate. Appl Environ Microbiol 72(5):3198-3205. U. S. Congress, Office of Technology Assessment 1991 Dioxin Treatment Technologies + Background Paper. OTA-BP-O-93. NTIS order no. PB92-152511. U. S. Environmental Protection Agency 2007 Pilot survey of levels of polychlorinated dibenzop-dioxins, polychlorinated dibenzofurans, polychlorinated biphenyls, and mercury in rural soils of the United States. National Center for Environmental Assessment, Washington, DC; EPA/600/R-05/048F. Vital M, Penton CR, Wang Q, Young VB, Antonopoulos DA, Sogin ML et al. 2013 A genetargeted approach to investigate the intestinal butyrate-producing bacterial community. Microbiome 1(8). Wittich RM, Wilkes H, Sinnwell V, Francke W, Fortnagel P. 1992 Metabolism of dibenzo-pdioxin by Sphingomonas sp. strain RW1. Appl Environ Microbiol 58(3):1005-1010. 138 CHAPTER VI ISOLATION AND CHARACTERIZATION OF A NOVEL DIBENZOFURAN DEGRADING CONSORTIUM FROM A PRISTINE PRAIRIE SOIL 139 ABSTRACT Dioxin chemicals, including furans, are U.S. Environmental Protection Agency priority pollutants to be removed from the environment. Only a few dioxin-degrading microorganisms, enzymes and pathways have been reported. We isolated a novel dibenzofuran-degrading consortium from a native prairie soil. Growth of the consortium is accompanied simultaneous disappearance of the substrate, indicating mineralization of the latter (6 h doubling time). The minimum required membership of the consortium is currently being defined, but there is some evidence to indicate that it is composed of three phyla: Firmicutes (Paenibacilles sp.), Proteobacteria (unclassified Comamonadaceae sp.), and Actinobacteria (Agromyces sp.). The consortium has grown together stably during several serial transfers. When the members were cultured separately with dibenzofuran, no growth was observed. Electron microscopy confirmed the presence of three cell types surrounding dibenzofuran crystals. The consortium degrades dibenzofuran by the traditional degradation pathway described elsewhere. Whole genome sequencing of the unclassified Comanomadaceae strain revealed genes potentially involved in initial dioxygenation of dibenzofuran, and salicylate degradation, but lacks genes required for 2oxopent-4-enoate, which gene is provided by Agromyces sp. from the consortium. The consortium cometabolizes dibenzo-p-dioxin, 2-monochlorodibenzo-p-dioxin, as well as 2,3dichlorodibenzo-p-dioxin. A number of studies imply that degradation of environmental pollutants occurs via consortia action, and this is direct evidence of this mechanism for degradation of the difficult to biodegrade angular polyaromatic ether hydrocarbons. 140 INTRODUCTION Polyaromatic hydrocarbons are a class of organic compounds that include polychlorinated dibenzo-p-dioxins (DD) and dibenzofurans (DF). Environmental contamination of polyaromatic hydrocarbons poses a challenging problem (Yoshida et al. 2005) because of their toxicity and persistence (Field et al. 2008). Furthermore, certain members of this class of chemicals are included on the EPA priority list as potentially dangerous or carcinogenic to humans (Mandal 2005), and bioaccumulate in the food chain (Van den Berg et al. 1998). Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are released into the environment by: combustion of chlorine containing organic materials, pulp and paper manufacturing, pesticides, and natural sources (Chang 2008, Kulkarni et al. 2008). While soils contaminated with dioxins are mandated to be remediated (U. S. Congress, 1991), even rural soils have detectable levels of dioxins at low concentrations (1 – 1000 ppt) (Schaum et al. 2007). Currently, only physical and chemical processes are practiced to remediate contaminated soils and sediments (Field et al. 2008). Bioremediation is under consideration as a technique to sustainably remove or detoxify dioxins. The fate of dioxins in the environment is not well understood, and there are large discrepancies in the estimated atmospheric deposition, and the amount measured in soil (Baker et al. 2000). Additionally, an EPA survey of dioxin levels in air and soil samples from 29 sites 2 showed a slight correlation between total organic soil carbon and some PCDD/F congeners (r = 0.51), and that octachlorodibenzo-p-dioxin is the most abundant PCDD/F congener at all 29 sites. The relative proportion of octochlorodioxin compared to other PCDD congeners increases in soils compared to air samples, while the relative proportion of all other congeners decreases (Schaum et al. 2007). It appears some processes occur within the soil matrix that causes a shift in 141 the relative proportion of PCDD/F congeners, which likely includes biological metabolism. Thus, we sought to isolate novel dioxin degrading organisms from natural soils with a history of burning of organic residues. Microbial degradation of dioxins has been studied, and a few strains have been isolated capable of degrading DD or DF as the sole carbon and energy source (Miyauchi et al. 2008, Wittich et al. 1992). These strains were generally isolated from industrial areas with human contamination of polyaromatic hydrocarbons. However, the diversity of the initial enzymes of the degradation pathways from previous isolates is low, comprising three tight clusters, represented by: Sphingomonas wittichii str. RW1 (dxnA1), Nocardioides sp. DF412 (dfdA1), and Paenibacillus sp. YK5 (dbfA1) (Field et al. 2008, Iwai et al. 2011). To dibenzofuran, these angular dioxygenases add oxygen to the two carbons adjacent to the ether oxygen, which results in the cleavage of the ether bridge (Chang 2008, Field et al. 2008) and the formation of 2,2’,3trihydroxybiphenyl, which is later cleaved forming salicylic acid and 2-oxopent-4-enoate. Here, we report the isolation and characterization of a novel dibenzofuran-degrading consortium. It is able to cometabolize mono- and dichlorodibenzo-p-dioxins as well as unchlorinated dibenzo-pdioxin. MATERIALS AND METHODS Chemicals and media. All chemicals used in this study were of the highest purity available. Growth substrates were purchased from the following manufactures: dibenzofuran (SigmaAldrich; St. Louis, MO, USA), dibenzo-p-dioxin (Wako Chemicals; Richmond, VA, USA), 2MCDD and 2,3-DCDD (AccuStandard, Inc.; New Haven, CT, USA). Two mineral media used which have been described previously, K2 (Zaitsev et al. 1991) but substituting 20 ml l -1 Hunter’s Mix in place of yeast extract, and MM4Y (Iida et al. 2006). Dilute undefined media, 142 10% Luria-Bertani (AccuMedia; Neogen, Lansing, MI, USA) and 20% R2A (Himedia, Mumbai, India), were prepared according to manufacture’s instructions except for the dilution factor. Solid -1 media was prepared with 15 g L Agar Select (AccuMedia; Neogen, Lansing, MI, USA). Enrichment and isolation of DF, DD and 2-MCDD degrading bacteria. Soil samples were taken from protected pristine prairie sites in Morris Prairie, Jasper County, Iowa, USA (41.768, 92.963) and Konza Prairie Kansas, USA, as well as an Amazonian black earth soil, or terra preta. All soils were enriched for degraders by using either K2 or MM4Y media, either DF, DD or 2MCDD in duplicate serial transfers for every condition. The first step of enrichment was a 20% (by weight) soil slurry in fresh medium and carbon source, and subsequently 10% culture transfer into fresh medium and carbon source. All enrichment cultures were in bottles (Wheaton; Millville, NJ, USA) with Teflon-lined lids allowing for at least 10X headspace volume and were opened every 48 hours for 10 min in a laminar flow hood to refresh O2 content. The timing of the transfer depended on observation of depletion of the substrate in separate bottles, but generally was at least 30 d. The Iowa soil was incubated at 22 °C, while the Kansas and black earth soils were incubated at 30 °C, all with continuous shaking (200 rpm). Pure cultures were obtained by plating onto the corresponding solid medium with DF crystals on the lid of the dish as the carbon source. Individual colonies were picked, serially transferred from solid to liquid medium, and tested for their ability to deplete the carbon source in liquid culture. Degradation experiments. Degradation experiments of DF were carried out in 25 ml vials with an aluminum sealed lid containing 2.5 ml of K2 medium. Liquid cultures with DF as the sole carbon source were prepared by adding the growth substrate dissolved in acetone (50 or 100 mg -1 ml ) to empty flasks, allowing the acetone to evaporate in a laminar flow hood for 5 h, adding the mineral media, and sonicating the flask for 30 s using a FS20 mechanical ultrasonic cleaner 143 (Fisher Scientific, USA) in order to disperse substrate crystals. Tubes were incubated at 30 °C on a reciprocal shaker at 200 rpm. Bacterial growth on using DF as a sole carbon source was -1 confirmed by measuring optical density, cell concentration (CFU ml ), and substrate depletion. Three vials were dedicated to more frequent optical density measurements, while a series of tubes were dedicated to measure optical density, cell concentration, and the vial was sacrificed to determine substrate depletion. A resting cell assay was used to test for the range of substrates that could be degraded #3-21. Cells were grown, concentrated to an optical density of 1.5 with fresh media, and aliquoted to a series of 7.5 ml vials with Teflon lined lids. The substrates (DF, DD, 2-monochlorodibenzo-p-dioxin, and 2,3-dichlorodibenzo-p-dioxin), dissolved in acetone, were added to the cells directly, in a volume of 1 or 2 µl, without removal of acetone. In parallel, cultures with autoclaved cells were established in the same manner. Each condition was tested in triplicate and data are shown as the mean of the experimental triplicates with error bars indicating the standard error. Chemical extraction and analysis. Vials were sacrificed in order to extract residual substrate. The substrates were extracted using a method modified from Halden et al. (Halden et al. 1999) by first acidifying the culture, addition of an internal control polyaromatic (biphenyl or 2monochlorodibenzo-p-dioxin), then extraction with acetonitrile, and agitation (60 min, 200 rpm). Particles were allowed to settle (10 min), and the supernatant was passed though a PTFE (0.2 µm). Aliquotes were analyzed by high-pressure liquid chromatography using a Perkin Elmer 200 Series equipped with a Supelco Discovery C-18 column (4.6 by 150 mm; particle size, 5µm) and a UV/Vis detector set to 220 nm. Mobile phase was 85% acetonitrile, 15% deionized water. An injection of 50 µl was by autosampler. Obtained peak areas and retention times were compared to known concentrations of standard chemicals. 144 To isolate DF metabolites, #3 cultures were harvested at different growth phases, acidified with HCl to pH 1, extracted with 1 ml of ethyl acetate, similar to that done previously (Peng et al. 2012). The organic phase was dried in 1.8 ml polypropylene tubes. The residue was dissolved in 100 µL of acetonitrile. The sample was derivatized with 50 µL of MSTFA (Nmethyl-N-(trimethylsilyl) trifluoroacetamide), inverted, vortexed, sonicated (5 min), and immersed in a 60 °C water bath for 1 hour. A positive control sample (gentisic acid) and solvent blanks of acetonitrile and acetonitrile/MSTFA underwent this process as well. The reacted samples were then transferred into autosampler vials as above and submitted to analysis by GCMS. Samples were analyzed on an Agilent 6890N GC coupled to an Agilent 5973 mass spectrometer. A DB5-MS capillary column was installed in the GC (30 m length x 0.250 mm ID x 0.250 µm film thickness). The GC inlet temperature was 250 °C, the He carrier flow rate was 1.0 mL/min, the m/z range was 30-600, and the oven ramp was as follows: 70 °C (hold for 1 minute), then 7 °C/min to 300 °C. DNA extraction and 16S rRNA sequencing. Consortium and individual strain DNA was extracted using the MoBio Utraclean Soil DNA isolation kit following the manufacture’s instructions, with the addition of a incubation for 5 min at 65 °C prior to bead beating. The 16S rRNA gene was amplified in 25 µl reactions containing 2mM MgCl2, 100 µg/ml bovine serum albumin, 2 U Taq DNA high fidelity polymerase (Roche), 200 µM deoxynucleoside triphosphates, 400 µM forward and reverse primers (8F: 5’-AGA GTT TGA TCC TGG CTC AG-3’ (Ley et al. 2006) and 1522R: 5’-AAG GAG GTG ATC CAR CCG CA-3’ (Zhang et al. 2002)). Amplicons were extracted from a 1% agarose gel (QIAquick Gel Extraction Kit; Qiagen, Valencia, CA, USA) and purified (QIAquick PCR Purification Kit; Qiagen). Purified amplicons 145 Table 6.1. Enrichments of interest that showed some level of substrate depletion. Starting soil Iowa prairie Terra preta Terra preta Iowa prairie Terra preta Kansas prairie Terra preta Medium MM4Y K2 MM4Y MM4Y MM4Y K2 MM4Y Substrate DF DF DF DD DD DD 2CDD % removal 74% 50% 70% 24% 70% 22% 20% Time 30 d 30 d 30 d 90 d 50 d 45 d 60 d Table 6.2. Genome assembly results. Raw sequences % ≥ Q30 Yield Strain (Mbp) R1 R2 Sphingomonas sp. RW1 NA* NA Sphingomonas sp. RW1 357.47 0.945 0.901 Y3 346.12 0.959 0.926 W2 376.60 0.943 0.900 IA1 I1 #3 815.19 0.952 0.913 Y3 + W2 Assembled Sequences # Size Avg. Contigs (Mbp) ORFs coverage 5.93 5345 282 7.18 6748 49.8 66 4.93 4656 70.2 352 5.15 4818 73.1 365 9.82 9327 83.0 10.08 9474 were directly sequenced using a ABI 3730 Genetic Analyzer from both ends to obtain the complete 16S rRNA sequence. Whole genome sequencing, assembly and analysis. Cell pellets from the IA1I1#3 consorium grown with DF as the sole carbon source, as well as the Commamonas sp. W2 and Agromyces sp. Y3 strains (designated W2 and Y3 because they are from the white and yellow colony morphotypes, respectively) grown in 10% LB, were resuspended in 400 µl TES (10mM TrisHCl (pH 7.5), 100 mM NaCl and 1 mM EDTA). Lytic enzymes were added to aid in cell lysis (400 µg Lysozyme, 25 U/ml lysostaphin, 1000 U/ml achromopeptidase, and 100 µg/ml 146 proteinase K) followed by incubation at 55 °C for up to 4 h. DNA was isolated from the cell lysates by phenol chloroform extraction and ethanol precipitation. Genomic DNA of multiple strains was multiplexed and sequenced using the Illumina HiSeq TM 2000 (Illumina, Inc. USA). Sequences were assembled using Velvet (Zerbino et al. 2008) with various kmer lengths (19-51) using digitally normalized kmers (Brown et al. 2012). The assemblies that resulted in the most bases assembled, the longest contig and the fewest contigs were then merged to create a multi-k assembly. Assembled contigs were uploaded to RAST (http://rast.nmpdr.org/) for automatic annotation including Enzyme Commission number (EC number). EC numbers relevant to the known dibenzofuran degradation pathway were obtained from the KEGG database (http://www.genome.jp/kegg/) and searched all sequences with the same EC numbers were extracted from the assembled sequences. Known dioxin, dibenzofuran and carbazole dioxygenases were used as query in a BLAST analysis of the assembled sequences. Morphological characterization. Electron microscopy was used both to determine the morphology of cells and to characterize the membership of the bacterial consortium. Cultures were grown in K2 medium with DF as the sole carbon source. Samples were prepared for electron microscopy as has been described previously, (Takeuchi et al. 2001), but the mounted samples were coated with osmium and imaged at the Center for Advanced Microscopy, Michigan State University, using a JEOL 7500F scanning electron microscope (Japan Electron Optics Laboratories, Ltd; Tokyo, Japan). RESULTS Enrichment for DF and DD degraders. Of the three soils, three substrates and two media used to create active enrichment cultures which degrade the supplied dioxin compound, only one 147 enrichment using the Iowa prairie soil, K2 medium and dibenzofuran (enrichment was named IA1) was concluded to be active. In fact, this enrichment culture completely removed dibenzofuran in four consecutive transfers. Other enrichments that showed varying degrees of substrate removal (Table 1). Our primary interest, when the enrichments were initiated, was to isolate bacterial strains that could utilize 2-MCDD or DD as a sole carbon and energy source, but despite screening hundreds of individual strains picked from agar plates, we were unable to isolate any strain proven to utilize 2-MCDD or DD as a growth substrate. Many strains were able to grow on solid medium with DD or 2-MCDD vapors, but we suspect that many of these strains were utilizing agar as a carbon source. Isolation of the DF degrading consortium: IA1I1#3. When the IA1 enrichment culture was grown on solid K2 media, a white morphotype CFU dominated the plate, as well as a translucent spreading morphotype. At the time of transfer this spreading member was largely overlooked, and what was thought to be about 40 single white colonies were transferred from solid to liquid medium. Two cultures, IA1I1 and IA1I4, showed optical density of about 0.4. These cultures were preserved and we sought to purify the culture. Two separate attempts to transfer 10 single colonies from IA1I1 and IA1I4 into liquid culture failed to produce any dibenzofuran degrading strains. Another attempt was made to transfer 200 single colonies for growth on DD and this also resulted in no growth in all instances. In a third attempt, 20 single colonies were transferred into liquid culture with DF as the growth substrate, accidentally incubated at 30 °C, and one of the IA1I1 cultures became turbid and turned yellow, typical of ring meta-cleavage. This culture, IA1I1#3 (hereafter called #3), was proven to completely degrade dibenzofuran with a 9.8 h doubling time. Growth of the culture is accompanied by simultaneous disappearance of the substrate (Fig. 1A). Initially, it was believed that #3 was a pure culture, but when the culture was 148 0.5 300 0.4 250 200 0.3 150 0.2 100 0.1 50 0 0 0 10 20 30 40 50 60 0.6 300 0.5 250 0.4 200 0.3 150 0.2 100 0.1 50 0 Dibenzofuran conc. (ppm) Optical density (600nm) B" Dibenzofuran conc. (ppm) Optical density (600nm) A" 0 0 10 20 30 40 50 Time (h) Fig. 6.1. DF degradation curves of a) IA1I1#3 and b) IA1#3-21. Optical density measurements were taken sequentially from the same cultures and DF concentrations were taken from individual sacrificed cultures. Points shown are the means of triplicate cultures and error bars indicate ± SEM. Vials with no cells or autoclaved cells were sacrificed at the end timepoint and showed the same concentration of the substrate as at 0 h (data not shown). 149 A" B" C" D" Fig 6.2. Electron micrographs of IA1I1#3 (A-B) and IA1I1#3-21 (C-D). In panel A three cell morphotypes are visible, short rod, hairy rod and a club shaped cell. Panel B is a detailed image of the short rods. In panel C, three cell morphotypes are visible from #3-21, short rod, hairy rod and a coccus. Panel D is a detailed image of the short rods. White bars are all 1µm. 150 grown on 20% R2A medium, two morphotypes were observed, a yellow and white types. These two morphotypes multiply in unison in a single growth rate phase. In fact, pyrosequencing of 16S rRNA amplicons from this consortium revealed three members are from three phyla: Firmicutes (unclassified Bacillales sp., accounting for 90% of sequences), Proteobacteria (unclassified Comamonadaceae sp., 10% of sequences), and Actinobacteria (Agromyces sp., 0.5% of sequences). Additionally, full-length 16S rRNA sequencing was preformed with white and yellow colonies, prepared for whole genome sequencing (called W2 and Y3, respectively). W2 was identified as Comamonadaceae sp. strain W2 (designated W2 because it is from the white colony morphotype) and the same 16S rRNA sequence was found in the whole genome sequencing. The 16S rRNA full-length amplicon sequencing of Y3 was not successful, but the whole genome sequence contained the same 16S rRNA sequence from pyrosequencing, which identified it as Agromyces sp. Y3 (designated strain Y3 because it came from the yellow colony morphotype). According to RDP SeqMatch tool, Agromyces sp. Y3 was most closely related (98%) to Agromyces humatus, and Comamonadaceae sp. W2 sequence was most closely related (95%) to Acidovorax avenae subsp. Avenae. The three members grow together stably during at least 9 serial transfers and the population ratio of 30-20% white colonies remained consistant. Electron microscopy confirmed the presence of three cell morphotypes (rod, long hairy rod, and club shaped) surrounding dibenzofuran crystals (Fig. 2A). From electron microscopy, it seems that the rod and hairy rod cell morphotypes are in approximate equal abundance, and the club morphotype is present at about 15%. The individual yellow and white morphotypes could be grown in pure culture in 10% LB (but not in undiluted LB), but when the two cultures were used as a combined inoculum into a single DF culture, no growth resulted. 151 Attempts to purify #3 to a pure culture or reconstitute from pure culture inputs. Multiple attempts were made to combine white and yellow colony types from #3 into a DF culture with the purpose of understanding the minimum membership requirements for growth on DF. However, transfer of the #3 group from solid to liquid medium is painstakingly unpredictable, resulting in growth in the liquid medium only three times in over 100 attempts. However, liquidto-liquid transfers result in growth in every case. When combining 10 random colonies from 20% R2A plates were combined into DF cultures, 10 out of 22 attempts resulted in growth. When using 1 up to 5 colonies as the inoculum, a total of 190 colonies were distributed among 67 individual cultures, of which 3 resulted in growth. These three cultures were inoculated with 4, 2 and 1 colonies. The culture that grew from a single colony, called IA1I1#3-21 (hereafter referred to as #3-21), was then studied intensively. Initially it was thought that #3-21 was a pure culture of the white morphotype, but in subsequent transfers contaminating yellow colonies were observed on 20% R2A. Electron microscopy revealed 3 cell morphotypes: short rod, hairy rod and coccus. The rod shaped morphotype clearly dominated the culture with the hairy rod present at about 10%. There was a third minor member, a coccus shaped cell, which might make up 1% of the cells. These observations do not agree completely with colony morphology abundances, as white colonies make up about 99% of the colonies and yellow colonies are present at about 1%. The identity of one single white colony, which was grown in 10% LB, by 16S rRNA sequencing was Agromyces sp., while the identity of the culture grown with DF as the substrate was Paenibacillus sp. in two instances. Therefore, the #3-21 culture is not a pure strain and it may be the same consortium as #3, with the loss of the club shaped morphotype and contamination by the coccus. The #3-21 consortium grows faster than #3 (Fig. 1B) with a doubling time of 6.1 h. 152 Specificity of the degradative activity. Resting cell assays were used to determine if similar dioxin compounds could be degraded by #3-21. In comparison to the autoclaved cell controls, #3-21 showed considerable activity toward the compounds tested within the 24 h time period allowed (Fig. 3). DF (200ppm) was nearly completely degraded in 5 h (Fig. 3A). Dibenzo-pdioxin was the least well degraded of the four substrates tested and 48% of 50 ppm was removed in 24 h. Chlorinated dioxins were degraded to a higher degree than unchlorinated dioxin – 79% of 50 ppm of 2-MCDD and 82% of 20 ppm of 2,3-DCDD were removed in 24 h. Little to no loss of the substrates was observed in the control vials, except the DF 24 h samples, which may be due to evaporation. Whole genome sequencing of IA1I1#3. Sphingomonas wittichii str. RW1 was sequenced as a control genome. The combined assembly resulted in a size of 7.18 Mbp, while the actual genome is 5.93 Mbp. The cause behind the difference is unknown. Choice of kmer length had little influence on the total size of the assembly – the merged assembly of W2 was 0.2Mbp larger than the smallest assembly of all individual kmers (19-mer) assembled (Table 6.2). Genes of interest (dxnA1A2) were found by BLAST within the RAST web interface, but not by EC number search. The Comamonadaceae sp. strain W2 genome holds genes that could be responsible for the upper pathway and one half of the lower pathway. Three candidate genes for initial dioxygenation (with corresponding beta subunits identified) were distantly related to the two known clusters of dibenzofuran dioxygenases (Fig. 5) and were annotated as 3-phenylproprionate dioxygenase (PP), benzoate 1,2-dioxygenase (ben), and ring hydroxylating dioxygenase (RHD). No gene was annotated as 2,2',3-trihydroxybiphenyl dioxygenase (dbfB), but there are a number of candidate genes based on EC number (data not shown). 2-Hydroxy-6-oxo-6-phenyl-2,4-hexadienoic acid 153 0" DD"conc."(ppm)" B" live" 70" 60" 50" 40" 30" 20" 10" 0" 200" autoclaved" 60" 25" C" 21MCDD"conc."(ppm)" 250" 150" 100" 50" 2,31DCDD"conc."(ppm)" DF"conc."(ppm)" A" D" 50" 40" 30" 20" 10" 0" 0" 2" 5" Time"(h)" 20" 15" 10" 5" 0" 0" 24" 2" 5" 24" Time"(h)" Fig. 6.3. Range of substrate degradation by IA1I1#3-21. The key shown in panel A applies to all panels. Bars indicate the mean of triplicate vials and error bars indicate ± SEM. 2MCDD = 2-monochlorodibenzo-p-dioxin, 2,3-DCDD = 2,3-dichlorodibenzo-p-dioxin. 154 Abundance A" Abundance B" 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 220000 200000 180000 160000 140000 120000 100000 80000 60000 40000 20000 0 241 256 139 45 73 113 183 211 281312 362 415 Abundance 592 344 73 45 271301 182 228 99 157 C" 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0 486 371 427 481 514 558599 333 73 45 147 115 257 216 304 435 407 464 372 525 591 50 100 150 200 250 300 350 400 450 500 550 600 m/z Fig. 6.4. Mass spectra from three peaks specific to the IA1I1#3 culture. Two peaks at 19.618 and 19.915 min had the same mass spectra as shown in panel A and was identified as hydroxydibenzofuran. The metabolite shown in panel B eluted at 24.783 min and was identified as dihydroxydibenzofuran. The metabolite in panel C was identified as 2-hydroxy-6-(2’hydroxyphenyl)-6-oxo-2,4-hexadienoate (HOHPDA) and eluted at 25.768 min. Mass spectra could not be found which indicated the presence of 2,2’,3-trihydroxybiphenyl, salicylate, or 2oxopent-4-enoate. 155 hydrolase (dbfC), as well as salicylate hydroxylase, were identified in Comamonadaceae sp. strain W2. These genes were identified in all different contigs within the Comamonadaceae sp. strain W2 genome. The only gene in the pathway of any significance that was not seen in the Comamonadaceae sp. strain W2 genome was 2-keto-4-pentenoate hydratase, which was found in the Agromyces sp. strain Y3 genome. The #3 consortium metagenome sequences did not result in the sequencing of any Bacillus strain, but was composed completely of the W2 and Y3 genotypes (Table 6.2). Degradation pathway of IA1I1#3. During early growth of both #3 and #3-21, the medium becomes turbid and yellow-orange simultaneously. This is traditionally known as build up of products from meta-cleavage of the ring. The metabolites that accumulated during incubation of #3 were determined by mass spectrometry. The metabolite in Fig 4A represents two peaks of significant size found in the gas chromatogram and was identified as hydroxydibenzofuran. The two peaks showed the same mass spectra profile and are likely different hydroxydibenzofuran congeners. Dihydroxydibenzofuran and 2-hydroxy-6-(2’-hydroxyphenyl)-6-oxo-2,4- hexadienoate (HOHPDA) were also identified (Fig. 3B,C). No other compounds of significance were identified, including salicylate or potentially novel degradation pathway intermediates. DISCUSSION Despite extensive effort, and clear depletion of DF and DD (Table 1), it appears that bacterial dioxin degradation is generally a function of a community rather than an individual strain. Generally we transferred 50-100 colonies from each enrichment dilution plating back into liquid culture and with the exception of #3, we never saw reproducible growth and simultaneous substrate depletion. Even in the case of #3, we still see that degradation requires a consortium 156 Fig. 6.5 A" B" 157 Fig. 6.5 (cont’d) B" Fig. 6.5. Phylogenetic relationships of A. the 16S rRNA sequence of members of the IA1I1#3 consortium compared to known DF degraders and B. ring hydroxylating dioxygenases from Acidovorax sp. W2 compared to known Rieske dioxygenases. In panel B clades with activity toward a specific substrate are indicated: BPH biphenyl, DF dibenzofuran, PAH polyaromatic hydrocarbons, BEN benzoate, CAR carbazole. 158 and not a simple pure strain. This indicates that dioxin degradation in these microcosms may generally be due to microbial community or consortia action rather than being due to a single strain. Multiple strains may be required to provide enzymes and or compounds not present in a single strain, e.g., upper or lower degradation pathway genes, or extracellular compounds that increase the solubility of the substrate. Other research teams have indicated that attempts to isolate dioxin-degrading bacteria more often results in the isolation of bacterial consortia rather than individual strains is common (Wittich et al. 1999), and carbon sharing in a bacterial community, in the case of biphenyl, was shown to be extensive (Sul, in preparation). Bacterial consortia may provide required services in the degradation of recalcitrant organic pollutants by providing necessary enzymes (Arfmann et al. 1997) or rapidly remove toxic metabolites and increase consortium resilience with parallel carbon utilization (Pelz et al. 1999). Indeed, it was reported that the majority of 3-chlorobenzoate (3CBA) degraders were not efficient 3CBA degraders, suggesting that xenobiotic compounds are generally degraded by cross-feeding generalists, rather high efficiency specialists (Rhodes, in preparation). In our case, one possible explanation for the requirement of the consortium is that Paenibacillus sp. completes initial dioxygenation (based on similarity to known dibenzofuran degraders, abundance in #3-21, and lack of dibenzofuran dioxygenases in the other sequenced strains) Comamonadaceae sp. W2 provides salicylate mineralization (based on identification of salicylate hydroxylase in genome sequence), and Agromyces sp. Y3 provides 2-oxopent-4-enoate mineralization (based on identification of 2-keto-4-pentenoate hydratase in genome sequence). The members the #3-21 consortium are phylogenetically diverse, each being a member of separate phyla, Bacillus, Actinobacteria, and Proteobacteria. The Bacillus member was more specifically identified as Paenibacillus sp. and its 16S rRNA sequence is 95% similar to the 159 dibenzofuran-degrading Paenibacillus sp. YK5 (Iida et al. 2006). The other two strains are more distantly related to know dibenzofuran degrading strains (Fig. 6.5). Other Paenibacillus strains have been characterized as gram-positive rods that form colonies that are yellow, glossy, and circular with complete margins (Xie et al. 2012). Unfortunately, the Paenibacillus strain was not sequenced in the shotgun metagenome of #3. We are not certain the cause for certain why no sequences arose from this metagenome assignable to Paenibacillus. It is possible the cells did not lyse, or were so overly dominated by the other two strains. While Acidovorax strains have been shown to degrade polyaromatic hydrocarbons (Singleton et al. 2009) the closest 16S rRNA match (95%) to the Acidovorax sp. W2 sequence was the phytopathogen Acidovorax avenae subsp. Avenae (Willems et al. 1992). While Agromyces sp. Y3 was most closely related (98%) to Agromyces humatus, isolated from Roman catacombs (Jurado et al. 2005) another species Agromyces bauzanensis was found to degrade phenol, but not polyaromatic hydrocarbons (Zhang et al. 2012). From the evidence that we now have, it appears that the dioxygenase that will be found responsible for initial dioxygenation will be unique from those previously found. Ring hydroxylating dioxygenases, found in the Acidovorax sp. strain W2 genome, were distantly related to known dibenzofuran dioxygenases. One of these dioxygenases was most similar to 3phenylproprionate dioxygenase from Bordetella bronchiseptica, a human pathogen. Because the #3 16S rRNA pyrosequencing showed a dominance of the Paenibacillus strain, as well as obtaining the Paenibacillus sequence from 16S rRNA PCR amplification from a #3-21 DF culture, this strain may indeed carry out initial dibenzofuran dioxygenation of its relation to other dibenzofuran degraders. This will have to be determined in the future. 160 The Rieske-type family of dioxygenases (to which dibenzofuran dioxygenases belong) generally has a broad substrate range and can cometabolize a variety of related compounds (Gibson et al. 2000). For example, a recently isolated dibenzofuran degrader was able to cometabolize 14 related polyaromatics including dibenzo-p-dioxin (Peng et al. 2012). Our findings are no different, in that #3-21 cometabolizes dibenzo-p-dioxin, 2-monochlorodibenzo-pdioxin (2-MCDD) and 2,3-dichlorodibenzo-p-dioxin (2,3-DCDD). The most toxic polychlorinated dioxin congener is 2,3,7,8-tetrachlorodibenzo-p-dioxin. Thus 2,3-DCDD is an important chemical to cometabolize because it is dichlorinated in two of four of these toxic lateral positions. According to a review by Field et al. (2008), there are 7 known strains that have been reported to cometabolize 2,3-DCDD with varying degrees of efficiency. Sphingomonas wittichii str. RW1 was able to remove 28.4% (18ppm) of an initial concentration of 63 ppm 2,3DCDD in 0.7 d (Wilkes et al. 1996). Strain IA1I1#3-21 was able to remove 15 ppm in 24 h which makes it nearly as efficient as S. wittichii str. RW1 at cometabolism of 2,3-DCDD and is as or more efficient than all the other 6 strains (Field et al. 2008). Overall, we have isolated a novel phylogenetically diverse dibenzofuran-degrading bacterial consortium with activity toward polychlorinated dioxin. This is an uncommon metabolic range as polychlorinated dioxins are generally unmetabolized. Much remains to be uncovered in regards to the carbon network within this consortium and their biological interplay and may serve as an interesting model system for symbiosis, community carbon metabolism, simple metagenomics and assembly method development, as well as the bioremediation of dioxins. 161 REFERENCES 162 REFERENCES Arfmann H, Timmis KN, Wittich R. 1997 Mineralization of 4-chlorodibenzofuran by a consortium consisting of Sphingomonas sp. strain RW1 and Burkholderia sp. strain JWS. Appl Environ Microbiol 63(9):3458-3462. Baker J, Hites R. 2000 Is combustion the major source of polychlorinated dibenzo-p-dioxins and dibenzofurans to the environment? A mass balance investigation. Environ Sci Technol 34(14):2879-2886. Brown CT, Howe A, Zhang Q, Pyrkosz AB, Brom TH. 2012 A reference-free algorithm for computational normalization of shotgun sequencing data. arXiv:1-18. Chang Y-S. 2008 Recent developments in microbial biotransformation and biodegradation of dioxins. J Mol Microbiol Biotechnol 15(2-3):152-171. Field JA, Sierra-Alvarez R. 2008 Microbial degradation of chlorinated dioxins. Chemosphere 71(6):1005-1018. Gibson DT, Parales RE. 2000 Aromatic hydrocarbon dioxygenases in environmental biotechnology. Curr Opin Biotechnol 11(3):236-243. Halden RU, Halden BG, Dwyer DF. 1999 Removal of dibenzofuran, dibenzo-p-dioxin, and 2chlorodibenzo-p-dioxin from soils inoculated with Sphingomonas sp. strain RW1. Appl Environ Microbiol 65(5):2246-2249. Iida T, Nakamura K, Izumi A, Mukouzaka Y, Kudo T. 2006 Isolation and characterization of a gene cluster for dibenzofuran degradation in a new dibenzofuran-utilizing bacterium, Paenibacillus sp. strain YK5. Arch Microbiol 184(5):305-315. Iwai S, Johnson TA, Chai B, Hashsham SA, Tiedje JM. 2011 Comparison of the specificity and efficacy of primers for aromatic dioxygenase gene analysis of environmental samples. Appl Environ Microbiol 77(11):3551- 3557. Jurado V, Groth I, Gonzalez JM, Laiz L, Schuetze B, Saiz-Jimenez C. 2005 Agromyces italicus sp. nov., Agromyces humatus sp. nov. and Agromyces lapidis sp. nov., isolated from Roman catacombs. Int J Syst Evol Microbiol 55:871-875. Kulkarni PS, Crespo JG, Afonso CAM. 2008 Dioxins sources and current remediation technologies—a review. Environ Int 34(1):139-153. Lane DJ. (1991). 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M (eds). Nucleic Acid Techniques in Bacterial Systematics. John Wiley & Sons: Chichester, England. pp 115–147. 163 Ley RE, Harris JK, Wilcox J, Spear JR, Miller SR, Bebout BM et al. 2006 Unexpected diversity and complexity of the Guerrero Negro hypersaline microbial mat. Appl Environ Microbiol 72(5):3685-3695. Mandal PK. 2005 Dioxin: A review of its environmental effects and its aryl hydrocarbon receptor biology. J Comp Physiol B 175(4):221-230. Miyauchi K, Sukda P, Nishida T, Ito E, Matsumoto Y, Masai E et al. 2008 Isolation of dibenzofuran-degrading bacterium, Nocardioides sp. DF412, and characterization of its dibenzofuran degradation genes. J Biosci Bioeng 105(6):628-635. Pelz O, Tesar M, Wittich RM, Moore ER, Timmis KN, Abraham WR. 1999 Towards elucidation of microbial community metabolic pathways: Unravelling the network of carbon sharing in a pollutant-degrading bacterial consortium by immunocapture and isotopic ratio mass spectrometry. Environ Microbiol 1(2):167-174. Peng P, Yang H, Jia R, Li L. 2012 Biodegradation of dioxin by a newly isolated Rhodococcus sp. with the involvement of self-transmissible plasmids. Appl Microbiol Biotechnol, DOI: 10.1007/s00253-012-4363-y. Rhodes AN, Fulthorpe RR, Tiedje JM. Probing functional diversity of global pristine soil communities with 3-chlorobenzoic acid reveals communities of generalists dominate catabolic degradation capacities. (in preparation) Schaum J, Schrock M, Chuang J, Abbgy A, Coutant B, Riggs K et al. 2007 Pilot survey of levels of polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, polychlorinated biphenyls, and mercursoils of the United States. US EPA/600/R-05/048F April:1-67. Singleton DR, Ramirez LG, Aitken MD. 2009 Characterization of a polycyclic aromatic hydrocarbon degradation gene cluster in a phenanthrene-degrading Acidovorax strain. Appl Environ Microbiol 75(9):2613-2620. Sul WJ, Penton CR, Tsoi TV, Tiedje JM. Identification of biphenyl-utilizing populations by stable isotope probing and pyrosequencing. (in preparation) Takeuchi M, Hatano K. 2001 Agromyces luteolus sp. nov., Agromyces rhizospherae sp. nov. and Agromyces bracchium sp. nov., from the mangrove rhizosphere. Int J Syst Evol Microbiol 51(Pt 4):1529-1537. U. S. Congress, Office of Technology Assessment. 1991. Dioxin Treatment Technologies + Background Paper. OTA-BP-O-93. NTIS order no. PB92-152511. Van den Berg M, Birnbaum L, Bosveld AT, Brunström B, Cook P, Feeley M et al. 1998 Toxic equivalency factors (TEFs) for PCBS, PCDDS, PCDFS for humans and wildlife. Environ Health Perspect 106(12):775-792. 164 Wilkes H, Wittich R, Timmis KN, Fortnagel P, Francke W. 1996 Degradation of chlorinated dibenzofurans and dibenzo-p-dioxins by Sphingomonas sp. strain RW1. Appl Environ Microbiol 62(2):367-371. Willems A, Goor M, Thielemans S, Gillis M, Kersters K, De Ley J. 1992 Transfer of several phytopathogenic Pseudomonas species to Acidovorax as Acidovorax avenae subsp. Avenae subsp. nov., comb. nov., Acidovorax avenae subsp. Citrulli, Acidovorax avenae subsp. Cattleyae, and Acidovorax konjaci. Int J Syst Bacteriol 42(1):107-119. Wittich R-M, Strömpl C, Moore ERB, Blasco R, Timmis KN. 1999 Interaction of Sphingomonas and Pseudomonas strains in the degradation of chlorinated dibenzofurans. J Ind Microbiol Biotechnol 23(4-5):353-358. Wittich RM, Wilkes H, Sinnwell V, Francke W, Fortnagel P. 1992 Metabolism of dibenzo-pdioxin by Sphingomonas sp. strain RW1. Appl Environ Microbiol 58(3):1005-1010. Xie J-B, Zhang L-H, Zhou Y-G, Liu H-C, Chen S-F. 2012 Paenibacillus taohuashanense sp. nov., a nitrogen-fixing species isolated from rhizosphere soil of the root of caragana kansuensis pojark. Antonie Van Leeuwenhoek 102(4):735-741. Yoshida N, Takahashi N, Hiraishi A. 2005 Phylogenetic characterization of a polychlorinateddioxin-dechlorinating microbial community by use of microcosm studies. Appl Environ Microbiol 71(8):4325-4334. Zaitsev GM, Tsoi TV, Grishenkov VG, Plotnikova EG, Boronin AM. 1991 Genetic control of degradation of chlorinated benzoic acids in Arthrobacter Globiformis, Corynebacterium sepedonicum and Pseudomonas cepacia strains. FEMS Microbiol Lett 65(2):171-176. Zerbino DR, Birney E. 2008 Velvet: Algorithms for de novo short read assembly using de bruijn graphs. Genome Res 18(5):821-829. Zhang YC, Ronimus RS, Turner N, Zhang Y, Morgan HW. 2002 Enumeration of thermophilic Bacillus species in composts and identification with a random amplification polymorphic DNA (RAPD) protocol. Syst Appl Microbiol 25(4):618-626. Zhang D-C, Mörtelmaier C, Margesin R. 2012 Characterization of the bacterial archaeal diversity in hydrocarbon-contaminated soil. Sci Total Environ 421-422:184-196. 165 CHAPTER VII CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS 166 CONCLUSIONS Human society has a vast impact on the environment especially in terms of chemical disposal in the environment. Microbial communities respond to these chemical perturbations following general ecological principles, which may result in altered composition of the microbial community. In some instances the microbial response could have negative human impacts, such as enrichment of antibiotic resistance in response to antibiotic use and/or disposal. In other instances, changes to the bacterial community helps human society goals, such as the degradation of polyaromatic hydrocarbons and restoration of a natural chemical profile. The goal of this work was two-fold: (i) to describe the antibiotic resistance gene reservoir in response to agricultural antibiotic use and (ii) to identify and isolate novel dioxin dioxygenases/ degraders in response to dioxin enrichment. The key accomplishments and findings of this work include: 1. Developed and validated highly parallel quantitative PCR. This technology will help fill critical knowledge gaps for the determination of the antibiotic resistance gene reservoir in environmental samples. We are already using the qPCR approach to determine the abundance of resistance genes in many important scenarios: water treatment plants, fisheries, human disease diagnostics, public parks, and ancient mammoth gut samples. 2. Resistance genes are enriched in individual swine guts in response to subtherapeutic antibiotics. Co-selection for resistance to antibiotics not in the feed is also observed. This finding highlights the importance of characterizing the complete antibiotic resistance gene reservoir because there may be enrichment of ARGs beyond what might be expected. Taking this a step farther, in active swine farms, resistance genes are highly enriched in manure, compost and soil receiving the manures. Thus management strategies 167 need to be discussed with the goal of reducing antibiotic resistance gene release and accumulation in the environment. 3. Enrichment of resistance genes due to horizontal gene transfer appears to be common on swine farms. We observed a high correlation between resistance gene and transposase allele abundance. This correlation should be investigated further by determining the genetic organization of resistance genes and mobile genetic elements, including plasmids. 4. In general, antibiotics impose a strong selective pressure both on the microbiome of individual swine and in the farm manure, compost and soil ecosystems. Resistance genes are enriched due to vertical and horizontal transfer. Agricultural antibiotic use is creating a large resistance gene reservoir and may pose a risk to human health, hence appropriate disposal and remediation strategies are needed to remove resistance genes from this environment and contain their dispersal. Risk assessment models may play an important role in determining the critical control points in limiting the dispersal of ARGs. 5. Developed and validated novel dioxygenase primers. These primers are important new tools to detect dioxygenases with potential activity toward dioxins. 6. Dioxygenase sequences obtained from the environment are generally unique from known dioxin/ dibenzofuran dioxygenases. This is a crucial finding because it indicates that potentially many novel dioxin or dibenzofuran degrading strains could be isolated from the environment to increase our known arsenal of dioxin biodegraders. 7. Dioxin degraders are present in “pristine” soils. The newly isolated #3-21 originated from a pristine prairie soil and can utilize dibenzofuran as a sole carbon and energy source. It can cometabolize chlorinated dioxins with similar efficiency to RW1. Isolation of novel chlorinated dioxin degraders was one of our primary goals and #3-21 meets this goal. 168 8. Recalcitrant hydrocarbon degradation may commonly be the action of consortia. Traditional isolation of a pure culture that utilizes a substrate as its sole carbon and energy source may limit the number of degraders that could be isolated if mixed consortia were considered. With metagenomic sequencing the genomes of all the members of the #3-21 consortium were determined and the role of each can be determined and confirmed with additional strain characterization. FUTURE DIRECTIONS We have shown that antibiotic resistance genes are enriched in swine farms due to the use of subtherapeutic in-feed antibiotic use and we have provided important data on the abundance of different classes of resistance genes. However, much of this story remains to be told. Following the risk assessment framework, there are many pieces of the story that remain to be uncovered: 1. Extent of horizontal antibiotic resistance gene transfer (HGART) in the gut microbiome. We provide indirect evidence for horizontal gene transfer by associating the abundance of resistance genes and transposase alleles. Direct observation of HGART is difficult because it would require a known recipient and a known donor and subsequent observation of the recipient with a resistance gene from the donor. A good deal of past work on this topic has been completed either in vivo or in culture (Hunter et al. 2008) but these studies do not attempt to determine a community level transfer rate. 2. Also important for risk assessment is the concept of dose-response. This concept has largely been ignored in the field of agricultural antibiotic use and resistance, but is a foundation principle of risk assessment. These could be very simple experiments. For 169 example, antibiotic resistance genes could be the dose and the change in abundance is the response. This could be determined by feeding animals decreasing concentrations of commensal bacteria with known antibiotic resistance genes, and then monitoring the abundance of the resistance gene with qPCR in the gut through time. Dose-response relationships should be described for resistance genes, antibiotic use and/or supplemental metals use, or mixtures of the three with a comparison to the response: resulting resistance gene levels. This is a sort of black box approach to describing what happens to resistance genes in the gut, because it does not describe bacterial growth rate, HGART rates, or transduction rates individually, but it does give a total quantitative value for all these process occurring simultaneously in vivo. There could also be interesting coselection studies done in this way -- feed the animal a resistance gene and a noncorresponding antibiotic and see what happens. These experiments could be done in any mammalian host so that results could be applicable to human processes also. The key here is that the dose of the resistance gene is the independent variable and the dose response relationship can be determined. 3. Another very interesting part of this story is secondary transmission, or animal-animal or human-human bacterial transfer. This can be determined by introducing an antibiotic free animal to conventional antibiotic-fed animals a few hours a day (or multiple animals with different contact times for dose-response) but the diet of the test individual remains antibiotic free. The response is the concentration of resistance genes changes in their gut. This could be a model for a farmer’s contact with animals, population modeling, etc. 4. In regards to perturbation, are antibiotics a pulse or press perturbation and does this depend on the recalcitrance of the chemical? For example, tetracycline was degraded 170 significantly in the Chinese manure and composting stages, yet sulfonamides and quinolones were not. Would a one-time perturbation with sulfonamides provide a press perturbation manifested by long-term community changes and a new stable state, whereas a pulse perturbation with tetracycline would only cause temporary changes and a return to the previous stable state? Would repeated use of tetracyclines provide a press perturbation and the establishment of a new stable state? With the limited data that I have, I would hypothesize in the affirmative to these questions in regard to the resistance gene composition, but I do not have a good estimation in regards to the phylogeny of the community, which could be resolved with phylotyping. 5. Additionally, in general, I think gene-targeted metagenomics of resistance genes has not been studied in much detail. This oversight could stem from the fact that there are hundreds of resistance genes already identified, and because of that interest in diversity has waned, but the ecology of ARGs is still critically important. Additionally using technology like the Access Array from Fluidigm, one could amplify many resistance genes and sequence them all simultaneously. This can contribute to questions regarding horizontal and vertical gene transfer and the fate of genes in an environment. 6. The study of antibiotic resistance also would be a good avenue to develop other metagenomic analysis tools, such as nucleating assembly and synthetic biology of genes of unknown function. Antibiotic resistance genes can be quite abundant, depending on the source of the sample. Therefore it may make it an easier case to find resistance genes in a shotgun metagenome and from there begin assemblies. When full-length resistance genes are assembled, these open reading frames could be synthesized and inserted into an appropriate bacterial host to test the function of the gene. Often metagenomes are only 171 annotated in terms of similarity to genes of known function. If we could use resistance genes as a model gene category and determine the genetic divergence that maintains the same function, it would provide some range of confidence to homology-based pipeline metagenomic annotations. We have isolated a dibenzofuran-degrading consortium. We are working on isolating all the members of the consortium in pure culture and being able to reconstruct the consortium by mixing the members back together from their pure cultures. While the role of each member of the consortium is yet to be defined, there are two foreseeable outcomes: a) one of the members (possibly Paenibacillus) can degrade dibenzofuran in pure culture and the other members of the consortium are community cheaters (especially Micrococcus since it was not even detectable in the #3 lineage of the consortium) or b) all the members are required in a true symbiotic consortium relationship. Additionally, perhaps a combination of the two situations is possible, with Micrococcus as a cheater and Agromyces as a symbiont. In either case, interesting ecological relationships could be examined. Community cooperation and cheaters are an interesting topic in ecology and can lead to the determination of community stability or collapse simply by altering the percent composition of the cheater population (Dai et al. 2013, Gore et al. 2009). Microbial cooperation, evolution (Lawrence et al. 2012) and community metabolism networks (Pelz et al. 1999) could also be studied in this simple microbial consortium: 1. Determine the dynamics of the cheater population of 3-21 and if there is a stable cheater concentration density and if cheater concentration affects the degrader growth rate. 2. Determine the interplay of carbon compounds between symbionts. 3. Carry out an evolution experiment with the consortium, similar to what was done in Lawrence et al. The consortium could evolve in polyculture, and the individuals could 172 evolve in monoculture being provided the carbon source they require in monoculture that is relevant to dibenzofuran degradation. Then test fitness characteristics of all the lineages after the evolutionary period. 4. In regards to perturbation: does a truly recalcitrant chemical, one that the community cannot degrade, actually provide a perturbation to the community, or does it simply pass through with the chemical and the community unchanged? This could be attempted with perhaps a chlorinated dioxin. 5. Can more dioxin degrading consortia be isolated if the culture strain purification process is omitted and it were simply a serial liquid transfer? 6. Can the complete degradative potential of a community, such as the soil community that gave rise to 3-21, be uncovered through metagenomics and metatranscriptomics of these soils or soil microcosms? This is an exciting time to study microbial ecology. The ideas laid out here for future investigation would advance both basic science and ecology and aid in discoveries, with useful human application both to limit the spread of antibiotic resistance and to further dioxin bioremediation. In research, it is critical to balance and address all the following primary goals: basic science, scientific theory, method technology, application and looking for solutions to human problems. 173                       REFERENCES 174 REFERENCES Dai L, Korolev KS, Gore J. 2013 Slower recovery in space before collapse of connected populations. Nature 496(7445):355-358. Gore J, Youk H, van Oudenaarden A. 2009 Snowdrift game dynamics and facultative cheating in yeast. Nature 459(7244):253-256. Hunter PR, Wilkinson DC, Catling LA, Barker GC. 2008 Meta-analysis of experimental data concerning antimicrobial resistance gene transfer rates during conjugation. Appl Environ Microbiol 74(19):6085-6090. Lawrence D, Fiegna F, Behrends V, Bundy JG, Phillimore AB, Bell T et al. 2012 Species interactions alter evolutionary responses to a novel environment. PLoS Biol 10(5):e1001330. Pelz O, Tesar M, Wittich RM, Moore ER, Timmis KN, Abraham WR. 1999 Towards elucidation of microbial community metabolic pathways: Unravelling the network of carbon sharing in a pollutant-degrading bacterial consortium by immunocapture and isotopic ratio mass spectrometry. Environ Microbiol 1(2):167-174. 175