APPLICATION OF MOLECULAR METHODS TO UNDERSTAND MICROBIAL PROCESSES, INCLUDING CARBON AND NITROGEN CYCLING AND CONTAMINANT BIODEGRADATION By Zheng Li A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Environmental Engineering – Doctor of Philosophy 2024 ABSTRACT Microorganisms play important roles in complex and dynamic environments such as agricultural soils and contaminated site sediments. Molecular methods have greatly advanced the understanding of microbial processes, such as nitrogen cycling, carbon cycling and contaminant biodegradation, by providing insights into the structure, function and dynamics of microbial communities. The first project evaluated the impact of four agricultural management practices (no tillage, conventional tillage, reduced input, biologically based) on the abundance and diversity of microbial communities regulating nitrogen cycling using shotgun sequencing. The relative abundance values, diversity and richness indices, taxonomic classification and genes associated with nitrogen metabolism were examined. The microbial communities involved in nitrogen metabolism are sensitive to varying soil conditions, which in turn, likely has important implications for N2O emissions. The second project examined the impact of plant diversity, soil pore size, and incubation time on soil microbial communities in responses to new carbon inputs (glucose). Soil cores from three plant systems (no plants, monoculture switchgrass, and high diversity prairie) were incubated with labeled and unlabeled glucose. The phylotypes responsible for the carbon uptake from glucose were identified using stable isotope probing (SIP). The microbial communities were influenced by plan diversity but not by pore size or incubation time. The differentiated carbon assimilators may be linked to different carbon assimilation strategies (r- vs. K-strategists) depending on pore size. The third and fourth projects focused on the biodegradation of the common groundwater contaminant, 1,4-dioxane. A major challenge in addressing 1,4-dioxane contamination concerns chemical characteristics that result in migration and persistence. Given the limitations associated with traditional remediation methods, interest has turned to bioremediation to address 1,4- dioxane contamination. The third project examined the impact of yeast extract and basal salts medium (BSM) on 1,4-dioxane biodegradation rates and the microorganisms involved in carbon uptake from 1,4-dioxane. For this, laboratory sample microcoms and abiotic controls were inoculated with three soils and amended with media (water or BSM and yeast) and 2 mg/L 1,4- dioxane. SIP was then utilized to identify the active phylotypes involved in the 1,4-dioxane biodegradation. The amendment of BSM and yeast enhanced the 1,4-dioxane degradation in all three soil types. Gemmatimonas, unclassified Solirubacteraceae and Solirubrobacter were associated with carbon uptake from 1,4-dioxane and may represent novel degraders. Solirubrobacter and Pseudonocardia were associated with propane monooxygenases genes which potentially function in 1,4-dioxane biodegradation. The fourth project further explored the impact of yeast extract on 1,4-dioxane degradation at low concentrations (< 500 µg/L) using sediment from three impacted sites and four agricultural soils. 1,4-Dioxane biodegradation trends differed between inocula sources and treatments. For two of the impacted sites, no 1,4-dioxane biodegradation was observed for any treatment, indicating a lack of 1,4-dioxane degraders. In contrast, 1,4-dioxane degradation occurred in all treatments in microcosms inoculated with the agricultural soil or the other impacted site sediments. Bioaugmentation with agricultural soils initiated 1,4-dioxane biodegradation in the sediments with no intrinsic degradation capacities. Overall, yeast extract enhances 1,4-dioxane biodegradation in specific sediments. Bioaugmenting site sediments with agricultural soils may represent a promising approach for the remediation of 1,4-dioxane contaminated sites. To my husband and my parents, for loving me and having my back as always. iv ACKNOWLEDGEMENTS Firstly, I would like to express my deepest gratitude to my Ph.D. advisor, Dr. Cupples, for her unwavering support, understanding, encouragement and guidance. Throughout these four years, I encountered many challenges, including the disruptions caused by the COVID-19 pandemic and difficulties in my research. I would not have moved forward and overcome these obstacles without her support. Thanks to the rest of my Ph.D. committee members, Dr. Syed Hashsham, Dr. Irene Xagoraraki, and Dr. Dawn Dechand, for their valuable research advice. I would like to thank the past and current members in our lab. Thanks to Dr. Hongyu Dang for his support and guidance during the early months of Ph.D. journey. Thanks to Zohre Eshghdoostkhatami and Mohsen Faghihinezhad for their insightful discussions and inspiration. I would like to thank Lori Larner and Bailey Weber for placing research supplies orders. Thanks to Yanlyang Pan for technical support. Also, thanks to the support and other administrative staff in the Department of Civil and Environmental Engineering. I would like to thank support of the Research Technology and Support Facility (RTSF) at Michigan State University. Thanks to Dr. Casey Johnny, James O’Keefe, and Dr. Anthony Schilmiller at the Mass Spectrometry Laboratory at the RTSF (MSU) for 1,4-dioxane analytical method support. Thanks to the genomics facility for their support in DNA sequencing. I would like to thank all my friends. Your presence has made my life so much better. Special thanks to Ze Zhang, Jiaxin Zhang, Yiyi Chu, Yiqi Wang, Chi Zhan, Tianxudong Tang, Zefang Qi, Her Jiang, Yichen Tao, Nafeisha Maimaiti, Siquan Sun and Bo Nan. I am deeply grateful for your companionship, support, thoughts, and the happiness you have brought to me. Finally, I would like to express my heartfelt thanks to my families throughout this journey. To my husband, thank you for coming into my life and filling it with so many colors v and warmth. Thank you for being a good listener, offering thoughtful advice and for telling me I don’t have to be perfect. Thank you for being kind, patient, gentle and respectful. Thank you for on my side no matter whatever I want to do, wherever I want to go, and whoever I want to be. Thank you for choosing me as your wife, for building a home together, and for showing me that I deserve all the love and support in the world. Your heart, encouragement, support, and love mean everything to me. To my parents, thank you for your unconditional love and support. I would never be more grateful for what you have done for me. I also want to extend my thanks to my parents-in-law for incredible support in the past few months. A special thanks to Piggy, the ragdoll cat that I raised for half a year. Without you, I would not have made it through the toughest times in 2020. vi TABLE OF CONTENTS LIST OF ABBREVIATIONS ........................................................................................................ ix CHAPTER 1: INTRODUCTION ............................................................................................. 1 1.1 Nitrogen cycling .................................................................................................................... 1 1.2 Carbon turnover in the soils .................................................................................................. 1 1.3 1,4-Dioxane degradation ....................................................................................................... 2 1.4 Dissertation outline and objectives ....................................................................................... 3 REFERENCES ............................................................................................................................ 5 DIVERSITY OF NITROGEN CYCLING GENES AT A MIDWEST LONG CHAPTER 2: TERM ECOLOGICAL RESEARCH SITE.................................................................................... 9 2.1 Abstract ................................................................................................................................. 9 2.2 Introduction ......................................................................................................................... 10 2.3 Methods ............................................................................................................................... 13 2.4 Results ................................................................................................................................. 18 2.5 Discussion ........................................................................................................................... 38 REFERENCES .......................................................................................................................... 45 APPENDIX ............................................................................................................................... 52 CHAPTER 3: SOIL MICROORGANISMS INVOLVED IN GLUCOSE ASSIMILATION IN SMALL AND LARGE PORE MICRO-HABITATS .................................................................. 70 3.1 Abstract ............................................................................................................................... 70 3.2 Introduction ......................................................................................................................... 71 3.3 Methods ............................................................................................................................... 75 3.4 Results ................................................................................................................................. 82 3.5 Discussion ........................................................................................................................... 99 3.6 Conclusions ....................................................................................................................... 105 REFERENCES ........................................................................................................................ 108 APPENDIX ............................................................................................................................. 118 CHAPTER 4: IMPACT OF YEAST EXTRACT AND BASAL SALTS MEDIUM ON 1,4- DIOXANE BIODEGRADATION RATES AND THE MICROORGANISMS INVOLVED IN CARBON UPTAKE FROM 1,4-DIOXANE ............................................................................. 168 4.1 Abstract ............................................................................................................................. 168 4.2 Introduction ....................................................................................................................... 169 4.3 Methods ............................................................................................................................. 172 4.4 Results ............................................................................................................................... 182 4.5 Discussion ......................................................................................................................... 197 4.6 Conclusions ....................................................................................................................... 203 REFERENCES ........................................................................................................................ 205 APPENDIX ............................................................................................................................. 217 CHAPTER 5: BIODEGRADATION OF 1,4-DIOXANE AT LOW CONCENTRATIONS IN SITE SEDIMENTS USING YEAST AMENDMENT AND BIOAUGMENTATION ............. 228 5.1 Abstract ............................................................................................................................. 228 5.2 Introduction ....................................................................................................................... 229 5.3 Methods ............................................................................................................................. 233 vii 5.4 Results ............................................................................................................................... 239 5.5 Discussion ......................................................................................................................... 254 5.6 Conclusions ....................................................................................................................... 258 REFERENCES ........................................................................................................................ 260 APPENDIX ............................................................................................................................. 268 CHAPTER 6: CONCLUSIONS AND PERSPECTIVES .................................................... 284 viii LIST OF ABBREVIATIONS BSM —— Basal Salts Medium CsCl —— Cesium Chloride DIAMOND —— Double Index Alignment of Next-generation Sequencing Data HPCC —— High Performance Computing Cluster KEGG —— Kyoto Encyclopedia of Genes and Genomes KO —— KEGG Orthologs MG-RAST —— Meta Genome Rapid Annotation using Subsystem Technology N2O —— Nitrous Oxide NO2 - —— Nitrite NO3 - —— Nitrate OTU —— Operational Taxonomic Units PCA —— Principal Component Analysis PCoA —— Principal Coordinate Analysis PERMANOVA —— Permutational Multivariate Analysis of Variance RI —— Refractive Index RTSF —— Research Technology Support Facility SDIMO —— Soluble Di-iron Monooxygenase SIP —— Stable Isotope Probing SPME —— Solid Phase Micro Extraction STAMP —— Statistical Analysis of Taxonomic and Functional Profiles TCA cycle —— Citrate Cycle USEPA —— US Environmental Protection Agency ix CHAPTER 1: INTRODUCTION 1.1 Nitrogen cycling The emissions of greenhouse gas due to human activities have changed the global climate significantly over the years. Nitrous oxide (N2O) is a greenhouse gas that contributes to ozone layer depletion and has ~300 times greater global warming potential than CO2 (Richardson et al., 2009). Many researchers have studied the impacts of various agriculture management practices on N2O emissions. Nitrogen-based fertilizers are extensively utilized across the world to improve agricultural production and meet the consuming demand of the enlarged human population. The increasing use of nitrogen fertilizer in agricultural practice has accordingly increased the N2O production (Davidson, 2009). No till has gained much attention due to the potential to reduce soil erosion (Halvorson, Mosier et al. 2006) and improve the soil quality and crop productivity (Hungria, Franchini et al. 2009). Although the production of N2O involves complex biological pathways, denitrification is a dominant process (Zumft 1997). N2O emission rates are greatly influenced by the abundance and diversity of the genes related to the production and consumption of N2O. 1.2 Carbon turnover in the soils Bioenergy crops reduce the dependence of fossil fuels, mitigate the emission of atmospheric CO2 and enhance soil carbon sequestration. Switchgrass (Panicum virgatum L.) is a particularly promising perennial bioenergy feedstock in the USA (Sanderson, Adler et al. 2006). Switchgrass can increase carbon sequestration in the long-term (after 10 years) (Ma, Wood et al. 2000) and promote carbon accumulation compared to annual systems due to the more root- derived carbon inputs (Adkins, Jastrow et al. 2016). However, there are also reports suggesting that switchgrass is slow to accumulate the soil organic carbon (Chimento, Almagro et al. 2016, 1 Sprunger and Robertson 2018). Carbon stored in soils is driven by the balance of root litter production, root exudates and the microbial decomposition of these compounds (Jastrow, Amonette et al. 2007). Fine roots are important for carbon accumulation in soils and diverse biofuel cropping systems are likely to hold more fine roots than monoculture systems (Sprunger, Oates et al. 2017). Plant diversity influences carbon inputs in soils and further the resident microbial populations and activities (Carney and Matson 2006, Zhang, Wang et al. 2010, Lamb, Kennedy et al. 2011, Ravenek, Bessler et al. 2014, Lange, Eisenhauer et al. 2015). Soil pore structure also plays an important role in shaping the microbial community in soils (Chenu, Hassink et al. 2001, Carson Jennifer, Gonzalez-Quiñones et al. 2010, Sleutel, Bouckaert et al. 2012, Kravchenko, Negassa et al. 2014, Negassa, Guber et al. 2015, Kravchenko and Guber 2017, Kravchenko, Guber et al. 2021, Xia, Zheng et al. 2022). 1.3 1,4-Dioxane degradation 1,4-Dioxane is classified as a probable human carcinogen (Derosa, Wilbur et al. 1996) and is widespread in aquifers around the world. 1,4-Dioxane was used as a solvent and stabilizer for the chlorinated solvents, particularly 1,1,1-trichloroethane (USEPA 2013). The US Environmental Protection Agency (USEPA) set health advisory levels of 1,4-dioxane between 0.35 and 35 µg/l, corresponding to a lifetime cancer risk of 1 in a million and 1 in 10,000. Tradition remediation methods are challenging due to low Henry’s law constant (4.80×106 atm- m3/mol at 25 °C), low Kow (Log Kow: 0.27) and low Koc (Log Koc: 1.23) (USEPA 2013). Given the limitations associated with traditional remediation methods, interest has turned to bioremediation to address 1,4-dioxane contamination. Both metabolic (Parales, Adamus et al. 1994, Kelley, Aitchison et al. 2001, Mahendra and Alvarez-Cohen 2005, Kim, Jeon et al. 2009, Huang, Shen et al. 2014) and co-metabolism 2 processes (Vainberg, McClay et al. 2006, House and Hyman 2010) have been reported for 1,4- dioxane biodegradation.. In metabolic processes, degraders utilize 1,4-dioxane as a carbon and energy source for growth. Parales et al. (1994) first isolated Actinomycete CB1190 from a 1,4- dioxane-contaminated sludge sample. This Actinomycete CB1190 strain used 1,4-dioxane as a growth substrate and was further characterized as Pseudonocardia dioxanivorans sp. nov CB1190 (Mahendra and Alvarez-Cohen 2005). Isolates that could metabolize 1,4-dioxane also include Mycobacterium sp. PH-06 (Kim, Jeon et al. 2009), Acinetobacter baumannii DD1 (Huang, Shen et al. 2014), Afipia broomeae D1(Isaka, Udagawa et al. 2016) and Rhodococcus ruber 219 (Simmer, Richards et al. 2021). Numerous strains also grow on different substrates and degrade 1,4-dioxane co-metabolically (Vainberg, McClay et al. 2006, House and Hyman 2010, Sun, Ko et al. 2011). For example, Pseudonocardia sp. strain ENV478 can degrade 1,4- dioxane when growing on tetrahydrofuran, sucrose, lactate, yeast extract, 2-propanol and propane (Vainberg, McClay et al. 2006). Flavobacterium co-metabolically degrades 1,4-dioxane following tetrahydrofuran degradation (Sun, Ko et al. 2011). 1.4 Dissertation outline and objectives The dissertation work is described below in the following chapters. Chapter 2: This project examined shotgun sequencing data from agricultural soils under four different management practices. The overall objective was to investigate the impact of four agricultural management on the abundance and diversity of microbial communities regulating nitrogen cycling (primarily denitrification). Chapter 3: The objectives of this work were to investigate the effects of 1) cropping system (no plants, switchgrass, high diversity prairie), 2) soil pore size and 3) incubation time (24 hr and 30 days) on the microbial communities involved in the utilization of a newly added 3 carbon (glucose). The impacts of those factors on the overall composition and diversity of the soil microbial communities were also investigated. Chapter 4: The objectives were to 1) examine the impact of yeast extract and BSM on 1,4-dioxane degradation rates in microcosms amended with different inocula (agricultural soil, wetland sediment and impacted site sediments), 2) identify the phylotypes involved in carbon uptake from 1,4-dioxane using stable isotope probing (SIP), and 3) determine the functional genes putatively associated with 1,4-dioxane biodegradation. Chapter 5: The objectives of this study are to 1) examine the impact of varying concentrations of yeast extract on biodegradation kinetics of 1,4-dioxane at low concentrations in mixed microbial communities; 2) investigate the phylotypes deriving a growth benefit from the 1,4-dioxane of low concentration of 1,4-dioxane and 3) examine the impact of bioaugmentation with agricultural soil microorganisms on 1,4-dioxane removal rates in site sediments. Chapter 6: The conclusions of this dissertation are outlined in this chapter along with directions for future work. 4 REFERENCES Adkins, J., Jastrow, J. D., Morris, G. P., Six, J., & de Graaff, M.-A. (2016). Effects of switchgrass cultivars and intraspecific differences in root structure on soil carbon inputs and accumulation. Geoderma, 262, 147-154. https://doi.org/https://doi.org/10.1016/j.geoderma.2015.08.019 Carney, K. M., & Matson, P. A. (2006). The influence of tropical plant diversity and composition on soil microbial communities. Microbial Ecology, 52(2), 226-238. Carson Jennifer, K., Gonzalez-Quiñones, V., Murphy Daniel, V., Hinz, C., Shaw Jeremy, A., & Gleeson Deirdre, B. (2010). Low Pore Connectivity Increases Bacterial Diversity in Soil. Applied and Environmental Microbiology, 76(12), 3936-3942. https://doi.org/10.1128/AEM.03085-09 Chenu, C., Hassink, J., & Bloem, J. (2001). 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Oikos, 123(12), 1528-1536. https://doi.org/10.1111/oik.01502 Sanderson, M. A., Adler, P. R., Boateng, A. A., Casler, M. D., & Sarath, G. (2006). Switchgrass as a biofuels feedstock in the USA. Canadian Journal of Plant Science, 86(Special Issue), 1315-1325. Simmer, R. A., Richards, P. M., Ewald, J. M., Schwarz, C., da Silva, M. L. B., Mathieu, J., Alvarez, P. J. J., & Schnoor, J. L. (2021). Rapid Metabolism of 1,4-Dioxane to below Health Advisory Levels by Thiamine-Amended Rhodococcus ruber Strain 219. Environmental Science & Technology Letters, 8(11), 975-980. https://doi.org/10.1021/acs.estlett.1c00714 Sleutel, S., Bouckaert, L., Buchan, D., Van Loo, D., Cornelis, W. M., & Sanga, H. G. (2012). Manipulation of the soil pore and microbial community structure in soil mesocosm incubation studies. Soil Biology and Biochemistry, 45, 40-48. https://doi.org/https://doi.org/10.1016/j.soilbio.2011.09.016 Sprunger, C. D., Oates, L. G., Jackson, R. D., & Robertson, G. P. (2017). Plant community composition influences fine root production and biomass allocation in perennial bioenergy cropping systems of the upper Midwest, USA. Biomass and bioenergy, 105, 248-258. https://doi.org/https://doi.org/10.1016/j.biombioe.2017.07.007 Sprunger, C. D., & Robertson, G. P. (2018). Early accumulation of active fraction soil carbon in newly established cellulosic biofuel systems. Geoderma, 318, 42-51. Sun, B., Ko, K., & Ramsay, J. A. (2011). Biodegradation of 1, 4-dioxane by a Flavobacterium. Biodegradation, 22, 651-659. USEPA. (2013). Integrated Risk Information System (IRIS) on 1,4-Dioxane Vainberg, S., McClay, K., Masuda, H., Root, D., Condee, C., Zylstra, G. J., & Steffan, R. J. (2006). Biodegradation of ether pollutants by Pseudonocardia sp. strain ENV478. Applied and Environmental Microbiology, 72(8), 5218-5224. Xia, Q., Zheng, N., Heitman, J. L., & Shi, W. (2022). Soil pore size distribution shaped not only compositions but also networks of the soil microbial community. Applied Soil Ecology, 170, 104273. https://doi.org/https://doi.org/10.1016/j.apsoil.2021.104273 Zhang, C.-B., Wang, J., Liu, W.-L., Zhu, S.-X., Ge, H.-L., Chang, S. X., Chang, J., & Ge, Y. (2010). Effects of plant diversity on microbial biomass and community metabolic profiles 7 in a full-scale constructed wetland. Ecological Engineering, 36(1), 62-68. https://doi.org/https://doi.org/10.1016/j.ecoleng.2009.09.010 Zumft, W. G. (1997). Cell biology and molecular basis of denitrification. Microbiology and Molecular Biology Reviews, 61(4), 533-+. https://doi.org/10.1128/.61.4.533-616.1997 8 CHAPTER 2: DIVERSITY OF NITROGEN CYCLING GENES AT A MIDWEST LONG TERM ECOLOGICAL RESEARCH SITE This chapter is a modified version of a published work in Applied Microbiology and Biotechnology: Li, Z. and A. M. Cupples (2021). "Diversity of nitrogen cycling genes at a Midwest long-term ecological research site with different management practices." Applied Microbiology and Biotechnology 105(10): 4309-4327. 2.1 Abstract Nitrogen fertilizer results in the release of nitrous oxide (N2O), a concern because N2O is an ozone-depleting substance and a greenhouse gas. Although the reduction of N2O to nitrogen gas can control emissions, the factors impacting the enzymes involved have not been fully explored. The current study investigated the abundance and diversity of genes involved in nitrogen cycling (primarily denitrification) under four agricultural management practices (no tillage [NT], conventional tillage [CT], reduced input, biologically based). The work involved examining soil shotgun sequencing data for nine genes (napA, narG, nirK, nirS, norB, nosZ, nirA, nirB, nifH). For each gene, relative abundance values, diversity and richness indices and taxonomic classification were determined. Additionally, the genes associated with nitrogen metabolism (defined by the KEGG hierarchy) were examined. The data generated were statistically compared between the four management practices. The relative abundance of four genes (nifH, nirK nirS and norB) were significantly lower in the NT treatment compared to one or more of the other soils. The abundance values of napA, narG, nifH, nirA and nirB were not significantly different between NT and CT. The relative abundance of nirS was significantly higher in the CT treatment compared to the others. Diversity and richness values were higher for 9 four of the nine genes (napA, narG, nirA, nirB). Based on nirS/nirK ratios, CT represents the highest N2O consumption potential in four soils. In conclusion, the microbial communities involved in nitrogen metabolism were sensitive to different agricultural practices, which in turn, likely has implications for N2O emissions. 2.2 Introduction An understanding of the terrestrial nitrogen cycle is important both for optimizing agricultural productivity as well as for minimizing environmental impacts, such as water pollution or global warming. Nitrous oxide (N2O) is a predominant ozone-depleting substance and an important and potent greenhouse gas with a global warming potential over 100 years of ~298 and 11.9 times that of CO2 and CH4, respectively (Ravishankara, Daniel et al. 2009, Domeignoz-Horta, Philippot et al. 2018). The majority (almost 70%) of the total global N2O atmospheric loading can be accounted for by terrestrial ecosystems, and at least 45% of this has been attributed to microbial cycling of nitrogen in agricultural systems (Rudy, Harris et al. 2008, Syakila and Kroeze 2011). The increasing use of nitrogen fertilizer in agricultural practice has accordingly increased N2O production (Davidson, 2009). The nitrogen cycle involves two key microbial processes for the emission of N2O from soils. During nitrification, bacteria produce N2O during the first step, when ammonia is oxidized to nitrite via hydroxylamine (Prosser and Nicol 2012). Denitrification is another key microbial process for the release of N2O, involving the respiratory reduction of nitrate (NO3 -) to nitrite (NO2 -) and their subsequent reduction to gaseous forms (NO, N2O, N2). Although the microbial reduction of N2O to nitrogen gas is vital for controlling emissions from terrestrial ecosystems, the determinants for a soil to act as a source or a sink remain uncertain (Butterbach-Bahl, Baggs et al. 2013). 10 A number of enzymes are associated with denitrification, including those encoded by nitrate reductases (napA/narG), nitrite reductases (nirk/nirS); nitric oxide reductase (norB) and nitrous oxide reductase (nosZ) (Philippot, Hallin et al. 2007). Many researchers have suggested that the abundance and diversity of such genes can impact N2O emission rates. For instance, researchers found corrections between the relative abundance of nosZ and the potential N2O production (Domeignoz-Horta, Putz et al. 2016). In another study, low N2O emission rates were explained by soils properties (up to 59%), whereas high rates were explained by the abundance and diversity of the microbial communities (up to 68%) (Domeignoz-Horta, Philippot et al. 2018). The same study found that the diversity of nosZ was important to explain the variation in N2O emissions (Domeignoz-Horta, Philippot et al. 2018). Others found that nirK gene copy numbers correlated with potential denitrification, but nirS gene copy numbers did not (Attard, Recous et al. 2011). Further, researchers have provided evidence of higher nirS/nirK ratios and higher N2O consumption (Jones, Spor et al. 2014). Agricultural practices are also known to influence denitrification trends. Although the impact of no tillage (NT) on N2O emissions has been widely investigated, the results have been varied. Some studies reported minimal differences of N2O emissions between NT and conventional tillage (CT) soil (Kaharabata, Drury et al. 2003, Lee, Six et al. 2006, Melero, Perez- de-Mora et al. 2011). For example, the potential denitrification rates and the ratios of N2O/N2 were similar in NT and CT after harvesting in a rainfed crop rotation system in Spain (Melero, Perez-de-Mora et al. 2011). Others found that NT stimulates denitrification (Calderon, Jackson et al. 2001, Baudoin, Philippot et al. 2009, Wang and Zou 2020). The denitrification enzyme activity and denitrification gene abundances (nirK and nosZ) were enhanced in NT in a soybean/rice crop system in Madagascar (Baudoin, Philippot et al. 2009). Similar results for the 11 increase of denitrification gene abundance in NT was also observed under sub-zero temperatures (Tatti, Goyer et al. 2015). The general trend that NT favored the denitrification rates, the abundance of denitrifying genes and N2O emission was demonstrated at a global scale (Wang and Zou 2020). Research has also addressed the differential consequences of tillage management on the microbial community structure and diversity. CT has a positive influence on the bacterial richness and diversity in clay soil in central Italy (Pastorelli, Vignozzi et al. 2013). However, some studies found opposite results. Minimal tillage enriched the microbial population and diversity relative to CT in a recent global meta-analysis (Li, Zhang et al. 2020). The bacteria diversity (represented by all the alpha-diversity indices) was higher in the NT soils compared to CT soils in a winter wheat cropping system in northern China (Dong, Liu et al. 2017). They also found that Actinobacteria, Alphaproteobacteria, Gammaproteobacteria and Betaproteobacteria were more abundant at class level in NT whereas CT had more sequences belong to Acidobacteria. In an experiment conducted in the agricultural fields in Indiana (USA), more DNA sequences related to the nitrogen metabolism were observed in the NT soils compared to CT soils, indicating the higher potential of nitrogen cycling (Smith, Blair et al. 2016). Although researchers have previously studied the impacts of various agricultural management practices on denitrification and N2O emission, the information on the taxonomic distributions and functional sequences related to nitrogen metabolism under different managements in the field-crop ecosystems is still limited, especially in the U.S Midwest. The objective of this research was to investigate the impact of four agricultural management on the abundance and diversity of microbial communities regulating nitrogen cycling (primarily denitrification). The work focused on the agricultural sites at the Long Term Ecological Research 12 (LTER) Site at Kellogg Biological Station (KBS), in southwest Michigan, southwest of the campus of Michigan State University (MSU). This LTER has field-crop ecosystems typical of the U.S. Midwest. The work is unique because it examines the key functional genes for nitrogen cycling over four long-term systems and detects a wider range of sequences through high throughput shotgun sequencing. 2.3 Methods 2.3.1 Sample Collection, DNA Extraction and Shotgun Sequencing The DNA examined in the current work was generated from a previous study by our group (Thelusmond, Strathmann et al. 2019), involving an examination of the genes associated with xenobiotic biodegradation. Our previous work did not investigate the genes involved in nitrogen cycling. Briefly, four soils were collected from 5 sampling stations in 6 replicate plots for Treatments 1, 2, 3 and 4 within the Michigan State University Main Cropping System Experiment at Kellogg Biological Station Long-Term Ecological Research (KBS LTER) (42o24'N, 85o23'W). The agricultural management practices for each Treatment are illustrated in Table 1 and for additional information see https://lter.kbs.msu.edu/research/site-description-and- maps/. The physical and chemical characteristics of the soils were previously determined (A & L Great Lakes Laboratories, Inc., Fort Wayne, IN) with all being classified as loam soils. DNA extraction was completed using the DNA extraction kit (DNeasy PowerLyzer PowerSoil Kit, Mo Bio, USA) according to the manual protocol. Shotgun sequencing was performed with the Illumina HiSeq 4000 (2 × 150 bp) platform at the Research Technology Support Facility (RTSF) at Michigan State University (MSU), as previously described (Thelusmond, Strathmann et al. 2019). 13 2.3.2 Processing, DIAMOND Alignment, Diversity Analysis and Enrichments in Each Soil Low quality sequences and Illumina adapters were removed from the HiSeq fastq.gz files using Trimmomatic with the Paired End Mode settings (Bolger, Lohse et al. 2014) (Version 0.36). Protein sequences for each of the nine genes were collected from the FunGene website (http://fungene.cme.msu.edu/) using a filter minimum HMM coverage of 70% (Cole, Wang et al. 2011, Fish, Chai et al. 2013). Following this, the FunGene Pipeline Dereplicator tool was used to dereplicate these sequences (Cole, Wang et al. 2011, Fish, Chai et al. 2013). Table A2.1 provides a summary of the sequences obtained at each step. The dereplicated sequences were then aligned against the trimmomatic files using DIAMOND (double index alignment of next-generation sequencing data) (Version 2.0.1) (Buchfink, Xie et al. 2015). Only reads that exhibited an identity of ≥ 60 % and an alignment length ≥ 49 amino acids to the reference sequences were retained. For each, relative abundance values were calculated using the number of aligned reads divided by the total number of sequences for each sample. The relative abundance values were then normalized by (divided by) the number of dereplicated reference sequences for each gene. Diversity indices (Chao 1, Chao2, Inverse Simpson and Shannon values) were determined (using the number of aligned reads for each gene) using EstimateS (Version 8.2.0) (Colwell 2006). The accession numbers of sequences statistically enriched in each soil (as described below) were determined. The R package Taxonomizr (Sherrill-Mix 2009) was used with R (Version 3.5.1) (R_Core_Team 2018) in RStudio (Version 0.9.24) (RStudio_Team 2020) to determine the taxonomic classification of each sequence. The data were illustrated with bar charts in Excel (Version 2010). 2.3.3 Phylogenetic Trees The 50 most abundant sequences for each gene, averaged across all samples, were 14 determined in Excel. The list of accession number for each were uploaded to COBALT: constraint-based alignment tool for multiple protein sequences (https://www.ncbi.nlm.nih.gov/tools/cobalt/cobalt.cgi) (Papadopoulos and Agarwala 2007). The downloaded alignments (fasta plus gaps) from COBALT were then submitted for MAFFT (multiple alignment using fast Fourier transform) alignment using an online server (https://mafft.cbrc.jp/alignment/server/) (Katoh, Rozewicki et al. 2019) (Version 7). Trees, also obtained from the same website, by the Neighbor-Joining method were exported in Newick format. The downloaded tree files were uploaded to the Interactive Tree of Life (https://itol.embl.de) (Letunic and Bork 2019) (Version 5.5.1). Sequences were colored depending on their classification and relative abundance values were added using the Datasets function called simple bar chart. 2.3.4 MG-RAST Analysis Shotgun sequences were also analyzed by MG-RAST (Meta Genome Rapid Annotation using Subsystem Technology, Version 4.0.2) (Meyer, Paarmann et al. 2008). The processing pipeline includes removing artificial replicate sequences by dereplication and removing low quality sequencing by using SolexaQA (Cox, Peterson et al. 2010). The taxonomic analysis included RefSeq (Pruitt, Tatusova et al. 2005) database and the KEGG (Kanehisa 2002) database. The sequences are available publicly on the MG-RAST and the summary of the MG- RAST data is presented in Table A2.2. 2.3.5 Statistical Analysis RStudio was used to perform a number of statistical tests, as follows (Version 0.9.24) (Team 2020). One-way ANOVA or Kruskal-Wallis tests were performed using the “aov” or “kruskal.test” functions as implemented in R package “car” (Fox, Weisberg et al. 2020) to 15 determine if there were statistically significant differences between 1) relative abundance of functional genes obtained by DIAMOND and 2) richness and diversity values (Chao 1, Chao2, Inverse Simpson and Shannon values). First, Levene’s test was carried out to assess the homogeneity of variance of the data using the “leveneTest” function in the R package “car” (Fox, Weisberg et al. 2020). The Shapiro-Wilk test was conducted to evaluate the normality of the data using the “shapiro_test” function in the R package ‘rstatix’ (Kassambara 2020). When the p values from both of the Levene’s and the Shapiro-Wilk tests were more than 0.05, the differences between the means were determined by one-way ANOVA. When the p values from the one-way ANOVA were less than 0.05, multiple pairwise comparison between the means were performed using Tukey’s Honest Significant Difference test using the “TukeyHSD” function in the “stats” R package. When the p values from the Shapiro-Wilk test were less than 0.05, the non-parametric alternative to a one-way ANOVA, the test Kruskal-Wallis (function “kruskal.test” in the “stats” package), was used. When p values were less than 0.05 for the Kruskal-Wallis text, Dunn’s test, using the “dunnTest” function in the R package “FSA”, (Ogle , Wheeler et al. 2020)) was utilized to determine differences between means. Spearman’s Rank Correlation test was carried out to explore the strength of correlation between the relative abundance of different genes using the “cor.test” function (with method = “spearman”) in the R package “stats”. Principle component analysis (PCA) was performed within Microsoft Excel using XLSTAT (Addinsoft 2020) (Version 2020.3.1) to visualize the effect of the addition of pharmaceuticals on the gene relative abundance in different managed soils. STAMP (Statistical Analysis of Taxonomic and Functional Profiles, Version 2.1.3) (Parks, Tyson et al. 2014) was used to statistically analyze the MG-RAST data. Specifically, extended error bars were generated 16 to illustrate significant differences (Welch’s two-sided t-test, two group analysis option, p < 0.05) in the gene relative abundance for the genes associated with nitrogen metabolism (as defined by the KEGG hierarchy). The data (generated in MG-RAST, six metagenomes for each soil) were analyzed using STAMP with the two group analysis option (each soil compared to the other three soils) and Welch’s two sided t-test (p<0.05). 2.3.6 Analysis of Assembled Sequences Shotgun sequences processed by Trimmomatic were assembled with Megahit (Li, Luo et al. 2016) (Version 1.2.4) with the pair end plus single end option (minimum and maximum kmer size were 27 and 127 with a kmer size step of 10). TaxIds for the FunGene nifH database (as described above, except no dereplication occurred) were obtained with the R package taxonomizr (Sherrill-Mix 2009), RStudio (Team 2020) (Version 0.9.24) and R (Team 2018) (Version 3.5.1). The analysis targeted nifH because no significant differences were found between soils in the analysis described above (before assembly). Following the deletion of duplicate values, the taxids obtained were used to analyze the assembled reads using the NCBI nucleotide database (nt) with the taxids option in BLASTN (Altschul, Gish et al. 1990) (Version 2.10.0-Linux_x86_64). BLASTN command lines also included the following options: identity ≥ 60 %, evalue ≤ 1× 10-5. The txt files generated from BLASTN were imported into Megan (Huson, Beier et al. 2016) (community edition Version 6.19.7). In Megan, the option “Compare” was used to combine all twenty-four data sets and then the combined dataset (at species level) was exported (using the STAMP export option) for analysis in STAMP. Additionally, the assembled contigs were aligned against the entire nt database using BLASTN without the taxids option (identity ≥ 60 %, evalue ≤ 1× 10-5). The output files were first imported and then in Megan, following this, the file was exported into STAMP to compare the communities between 17 soils. 2.4 Results 2.4.1 Abundance and Diversity of Functional Genes The relative abundance of genes associated with nitrogen fixation, denitrification or dissimilatory nitrate reduction in all four management systems are presented (Figure 2.1A), with the lowest abundance for nifH and highest for nirK. Two sets of genes were observed to have the approximately same level of abundance in the soils: the nitrate reductase genes napA and narG; and the nitrite reductase genes nirA and nirB. The distribution of the relative abundance of nirK, norB and nosZ were not as tightly grouped compared to the other genes, suggesting a greater spread in abundance of these genes across the metagenomes. Principle component analysis of the functional genes (Figure 2.1B) indicated the nitrite reductase gene nirK was positively correlated with the nitrite reductase gene nirS, nitric oxide reductase gene norB and nitrous oxide reductase gene nosZ. Further, the nitrate reductase gene napA was positively correlated with the nitrite reductase gene nirA. In contrast, the nitrite reductase gene nirB did not appear to correlate with any other gene. The addition of pharmaceuticals impacted the functional genes in two treatments (conventional tillage and reduced input soils). The average relative abundance of the twelve genes across the four management systems is displayed in Figure 2.2. Four genes (nifH, nirK nirS and norB) were significantly lower in the NT treatment compared to one or more of the other treatments. The average relative abundance of nirS was significantly higher in the CT treatment compared to the other treatments. It was also interesting to note that the average relative abundance of nosZ was approximately 50% lower in the NT soil compared to the other soils, although the difference was not statistically confirmed. The results of the statistical analysis tests (Levene’s test, Shapiro-Wilk, One-way ANOVA, 18 Tukey’s Honest Significant Difference, Kruskal-Wallis, Dunn’s test) on these data sets are summarized (Tables A2.3-A2.5). Table 2.1 Summary of the KBS agricultural management approaches for the four soils examined. Conventional This system is practiced by most farmers in this region. Tilled corn–soybean– No-till Reduced Input Biologically Based winter wheat (c–s–w) rotation; standard chemical inputs, chisel-plowed, no cover crops, no manure or compost No-till c–s–w rotation; standard chemical inputs, permanent no-till, no cover crops, no manure or compost Biologically based c–s–w rotation managed to reduce synthetic chemical inputs; chisel-plowed, winter cover crop of red clover or annual rye, no manure or compost Biologically based c–s–w rotation managed without synthetic chemical inputs; chisel-plowed, mechanical weed control, winter cover crop of red clover or annual rye, no manure or compost; USDA-certified organic 19 Figure 2.1 Box and whisker plot of relative abundance of genes (A) and Principal Component Analysis of the genes across the four management practices (with or without pharmaceuticals added) (B). 20 Figure 2.2 Average relative abundance values (%, as determined by DIAMOND) for each soil (n=6) with standard deviations illustrated with the bars. Values that are statistically significantly different (ANOVA or Kruskal-Wallis test, p<0.05) are shown with different letters. Letters are missing for nosZ because the statistical assumptions were not met for either test (unequal variance). Note, all y-axis have different scales. 21 Figure 2.3 Scatterplots comparing relative abundance values of all genes across all samples. Correlations that were statistically significant (Spearman’s rank test, p<0.05) are boxed in red. 22 Figure 2.3 illustrates correlations between gene relative abundance percentages across all samples with all statistically significant positive correlations (Spearman’s rank test) being boxed in red. The abundance napA significantly correlated with six genes (narG, nirA, nirB, nirK, norB, nosZ), as did the abundance of narG (napA, nifH, nirA, nirB, nirK, norB). In contrast, nifH correlated with two genes (narG and nirK). The abundance of both nirA and nirB correlated with napA, narG and to each other. Additionally, nirK, nirS, norB, nosZ all correlated positively to each other. The p-values and Spearman’s correlation coefficients for the Spearman’s rank tests are shown in Tables A2.6& A2.7. The values of richness estimators (Chao 1 and Chao 2) and diversity indexes (Shannon and Inverse Simpson) determined by EstimateS are summarized (Figure 2.4). The results of the statistical analysis (Levene’s test, Shapiro-Wilk, One-way ANOVA, Tukey’s Honest Significant Difference, Kruskal-Wallis, Dunn’s test) on this data set are also summarized (Tables A2.8- 2.17). Overall, higher Chao 1 and Chao2 values (~8000-9000) were found for four genes (napA, narG, nirA, nirB), whereas lower values (~500-1500) were estimated for the five other genes (nifH, nirK, nirS, norB, nosZ). For Chao 2 no significant differences were found between the four treatments for all genes. The only significant difference for Chao 1 between treatments was for nirS, nirK and nifH. Chao1 values were higher in both the CT treatment and the biological based treatment compared to the NT treatment for nirS. For nirK, Choa 1 was lower for the CT treatment compared to the reduced input treatment. For nifH, the Choa 1 value in the reduced input treatment was lower than the biological based treatment. The average values for Shannon and Inverse Simpson were higher (~1000-2000 and ~7.2-8.1) for four genes (napA, narG, nirA, nirB) compared to the rest (~100-400 and ~5.4-6.4). For the Inverse Simpson values, at least one significant difference between treatments was noted 23 for six genes (napA, nirA, nirB, nirK, nirS, norB), with the most notable number of differences between treatments being for nirK, nirS and norB. For nirK and norB, Inverse Simpson values were significantly higher in the NT treatment compared to the other treatments. For nirA and nirB, Inverse Simpson values were significantly higher in the reduced input treatment compared to the CT and NT treatments. For the Shannon Index values, at least one significant difference was found between at least two treatments for all genes except nifH and nirB. For napA, Shannon Index (and Inverse Simpson) values were significantly higher in the reduced input treatment compared to the CT and NT treatments. For nosZ, Shannon values were significantly lower in CT treatment compared to the other treatments. It was also interesting to note that the Shannon values of nirA were higher in the reduced input treatment than in the conventional and no tillage treatments. The abundance of the genes associated with the nitrogen metabolism (as defined in the KEGG hierarchy) were investigated to determine if there were significant differences between management systems. For this, each dataset was compared individually with the other three datasets (Figure 2.5). Only one gene (norF; nitric-oxide reductase NorF protein) was more abundant in the CT soil compared to the other three (Figure 2.5A). In contrast, six genes were more abundant in the NT soil compared to the other three soils (nirA; ferredoxin-nitrite reductase, cynT, can; carbonic anhydrase, nitronate monooxygenase, nitrate reductase (NADH), nrfD; protein NrfD and hao; hydroxylamine oxidase) (Figure 2.5B). Three genes (nirB; nitrite reductase (NAD(P)H) large subunit, nosZ; nitrous-oxide reductase, and nirD; nitrite reductase (NAD(P)H) small subunit (Figure 2.5C)) and one gene (nitronate monooxygenase) were dominant in the reduced input soil and biological based soil, respectively (Figure 2.5D). 24 2.4.2 Phylotypes Associated with Nitrogen Metabolism The phylotypes (at the class level) associated with the nitrogen metabolism genes have been summarized (Figures 2.6 and 2.7). The CT soil was dominated by Betaproteobacteria for napA, narG, nirA, nirB, nirK, nirS and norB and by Cytophagia for nosZ. Further, in many cases (napA, narG, nirA, nirB, nirK, norB) Betaproteobacteria were more abundant in the CT soil compared to the other three soils. For several genes (napA, narG, nirK, nosZ), Alphaproteobacteria were more abundant in the NT soil compared to the other soils. While for nirA and nirB, Alphaproteobacteria were more abundant in the biological based soil compared to the other three soils. For the genes napA, narG, nirK and norB, Actinobacteria were more abundant in the NT soil compared to the other three soils. For nirA and nirB, Actinobacteria was approximately at the same level in biological based soil compared to the NT soil while somewhat lower in the conventional tillage and reduced input soils. Additional trends included the dominance of the Gammaproteobacteria for two of the four soils for norB as well as the dominance of unclassified sequences and Flavobacteriia across various soils for nosZ. As no significant differences were noted for nifH for the above analysis, differences were investigated for this gene within the assembled contigs. Significant differences at the genera level associated with nifH gene between the CT and the other three soils are shown (Figure A2.1). The genus Frankia was significantly more abundant in the NT, reduced input and biological based soils compared to the CT soils. Several genera were enriched for this gene in the CT soil compared to the NT soil (e.g. Rubrivivax, Leptothrix, Cupriavidus). Also, two (Paraburkholderia and Burkholderia) were more enriched in the CT compared to the reduced input soils (Figure A2.1B). Four genera were more highly enriched in the comparison between the CT and the biological based soil for this gene (Figure A2.1C). 25 2.4.3 Comparison of Microbial Communities When the entire microbial community from the assembled contigs was compared between treatments significant differences were found and are illustrated at the genus level (Figure A2.2). No enriched genera were found in the NT soil compared to the CT soil. Four genera (Nocardioides, Mycobacterium, Nakamurella and Microvirga) were enriched in both the reduced input and biological based compared to the CT soil. The other more abundant genera identified in the reduced input compared to the CT soil were Pseudonocardia and Archangium. The other enriched genera identified in the biological based compared to the CT soil included Candidatus Nitrosotalea, Nitrospira, Bradyrhizobium, Actinoplanes, Nonomuraea, Skermanella, Sulfuritortus, Pigmentiphaga and Variibacter. 26 Figure 2.4 Average index diversity values and richness estimators for each soil (as determined by EstimateS, n=6) with standard deviations. Values that are statistically significantly different (ANOVA or Kruskal-Wallis test, p<0.05) are shown with different letters. In some cases, letters are missing because the statistical assumptions were not met for either test. Note, the scale on the y-axis differs between graphs. 27 Figure 2.5 Extended error bars illustrating the differences between each treatment compared to the other three treatments for the genes associated with nitrogen metabolism (as defined by the KEGG hierarchy). The data (generated in MG-RAST, six metagenomes for each soil) were analyzed using STAMP with the two-group analysis option (each soil compared to the other three soils) and Welch’s two sided t-test (p<0.05). 28 Figure 2.6 Phylotypes enriched in each soil associated with the genes napA, narG, nirA and nirB at the level of class. All y-axis have the same scale. 29 Figure 2.7 Phylotypes enriched in each soil associated with the genes nirK, nirS, norB and nosZ at the level of class. Note, the y-axis scales on each are different. There was minimal enrichment for any soil for nifH, therefore no graph was generated. 30 (1) napA Figure 2.8 Neighbour-Joining phylogenetic trees of fifty most abundant sequences in each soil associated with the genes napA, narG, nifH, nirA, nirB, nirK, nirS, norB and nosZ. Note, the bar charts illustrate the relative abundance (%) of the sequences in each soil. The three most abundant sequences are highlighted in yellow. 31 Figure 2.8 (cont’d) (2) narG 32 Figure 2.8 (cont’d) (3) nifH 33 Figure 2.8 (cont’d) (4) nirA 34 Figure 2.8 (cont’d) (5) nirB 35 Figure 2.8 (cont’d) (6) nirK 36 Figure 2.8 (cont’d) (7) nirS 37 2.4.4 Phylogeny of Most Abundant Sequences The phylogenetic relationships of the representative sequences (fifty most abundant sequences, before contigs were assembled) for the genes related to nitrogen metabolism in the four soils are shown (Figure 2.8). The three most abundant sequences for napA and narG were the same sequences and classified as Betaproteobacteria and Alphaproteobacteria. Similarly, the three most abundant sequences for nirA and nirB were the same and belonged to Deltaproteobacteria, Opitutae and the unclassified. The three most abundant sequences for norB belonged to Alphaproteobacteria and were phylogenetically close to each other. For nirK and nirS, both the majority of the fifty most abundant sequences and the three most abundant sequences belonged to Betaproteobacteria. Moreover, the predominant representative sequences belonged to Betaproteobacteria for nirB, nirK and nirS and belonged to Alphaproteobacteria, and Flavobacteriia for nifH and nosZ, respectively. 2.5 Discussion The influence of different agricultural management practices on nitrogen metabolism is important for understanding N2O emissions from agricultural soils. Here, the taxonomic and functional profiles of the soil microbial communities associated with nitrogen metabolism, primarily denitrification, were characterized in Mid-West agricultural soils under four different management practices. From the nine nitrogen metabolism genes examined in the soil metagenomes, the most abundant was nirK. Denitrifying microorganisms contain either a Cu- nitrite reductase or a cytochrome cd1 nitrite reductase, encoded by nirK and nirS respectively (Zumft 1997). In the current study, nirK was approximately 17.9 times more abundant than nirS when all of the soil metagenomes were considered together. Further, consistent with other researchers, nirK and nirS gene abundance were significantly correlated (Enwall, Throback et al. 38 2010). Others have also reported higher levels of nirK compared to nirS in soil metagenomes, for example, nirK was up to 3.8 times more abundant than nirS in 35 from 37 soils (Jones, Spor et al. 2014). Additionally, nirK was more abundant compared to nirS during agricultural waste composting (Zhang, Zeng et al. 2015). In another study, nirK copy numbers were approximately two orders of magnitude higher than nirS, regardless of tillage treatment (Kim, Riggins et al. 2021). These two genes are considered to be mutually exclusive, representing two ecologically distinct denitrifying communities (Enwall, Throback et al. 2010, Jones and Hallin 2010). To date, no microorganism has been reported to contain both types of reductases. It has been suggested that higher nirS/nirK ratios may indicate higher N2O consumption trends (Jones, Spor et al. 2014). Based on this, in the current study, CT represents the highest N2O consumption potential in four soils examined. Specifically, the average nirS/nirK ratios were 0.074, 0.045, 0.045 and 0.056 for CT, NT, reduced input and biological based, respectively. Concerning other genes impacting N2O depletion and formation, here, nosZ was less abundant compared to norB and (nirK+nirS). Others have also reported that nir gene abundance can exceed that of nosZ by up to one order of magnitude (Hallin, Jones et al. 2009, Garcia-Lledo, Vilar-Sanz et al. 2011, Philippot, Andert et al. 2011). This may be explained by the absence of nosZ in nearly one-third of genomes which contained nir and nor genes (Jones, Stres et al. 2008) and because nosZ has been found on plasmids (Zumft 1997). When considering the different management practices, the abundance of napA, narG, nifH, nirA and nirB was not significantly different between NT and CT. The same trend was reported for nifH and narG by others (Liu, Carvalhais et al. 2016). In contrast, we found that nirK, nirS and norB were statistically significantly lower in the NT compared to the CT treatment. Others have reported an increase in the abundance of denitrifying genes in response to 39 NT (Baudoin, Philippot et al. 2009, Wang and Zou 2020) or minimal tillage (Kaurin, Mihelic et al. 2018). However, in an experiment across arable soils, the abundance of nirS- and nirK- denitrifiers were not significantly different between agricultural practices (Domeignoz-Horta, Philippot et al. 2018). Similarly, in another study, the abundance of nirK and nirS did not differ between NT and CT (Puerta, Six et al. 2019). The authors speculated that NT could have promoted denitrification in the form of higher activity but not the abundance of denitrifying genes. NT was reported to greatly increase the RNA/DNA ratios for nirS and nosZ denitrifiers (Tatti, Goyer et al. 2015). They hypothesized that anoxic conditions (e.g., water content) contributed more to the nirS and nosZ transcription under NT compared to CT (Tatti, Goyer et al. 2015). NT tends to reduce the oxygen level below the surface (Pastorelli, Vignozzi et al. 2013) and increase the water-filled pore space because of greater soil moisture and bulk density (Wang and Zou 2020). These two factors may contribute to the potential enhanced anaerobic denitrification in NT soil. No correlation was found between denitrification enzyme activity and the abundances of nirK- and nirS- denitrifiers (Yin, Fan et al. 2014). More information is needed to determine the real impact of lower nirK, nirS and norB gene abundances in NT in the current system. The microbial community richness (Chao1 and Chao2) and diversity (Shannon index and Inverse of Simpson) indices were generally higher for the genes associated with nitrate reduction (napA and narG) and dissimilatory nitrite reduction (nirA and nirB) compared to the other genes. For nirK, norB and nosZ for at least one and up to three richness and diversity indexes were significantly greater in NT soil compared to CT soil, indicating a potential higher species richness and diversity in the current NT soil for these genes. In other research, higher alpha diversity of soil bacterial community was found in NT treatment compared to tilled treatment 40 (Dong, Liu et al. 2017, Liu, Li et al. 2020). Similarly, the richness and diversity of bacteria (characterized by phospholipid fatty acids analysis) were greater in NT over CT soil (Zhang, Li et al. 2015). This may be due to crop residues under the soil surface in NT soils being utilized as food sources (Zhang, Li et al. 2015). Another possible reason is that NT soil contains larger soil aggregates which could provide more organic matter for the microorganisms, therefore enhancing the bacterial diversity (Peixoto, Coutinho et al. 2006). It was demonstrated that denitrification activity was greatly influenced by denitrifier diversity but not the abundance. Using a dilution approach to manipulate the soil microbial community, researchers found that a decrease in the potential denitrification activity could be a result of denitrifier diversity loss and not the lower denitrifier biomass (Philippot, Spor et al. 2013). These trends could suggest that the NT examined in the current study may have a higher denitrification potential due to higher norB and nosZ diversity, although more research is needed to confirm this hypothesis. It is interesting to note that Chao 1, Shannon and Inverse of Simpson were significantly higher in CT soil compared to NT soils for nirS. Inversely, for nirK, Shannon and Inverse of Simpson were significantly higher in NT soil over CT soil. A previous study reported that diversity indices targeting nirS were more sensitive to environmental factors compared to nirK (e.g., ammonium content, total organic carbon and total N) (Li, Li et al. 2017). It was also found that nirS- denitrifiers rely more on the full anaerobic conditions than nirK-denitrifiers (Yuan, Liu et al. 2012). The greater diversity of nirK in NT in the current study could indicate oxygen levels and other environmental conditions in NT soil may be more favorable for nirK-denitrifiers than nirS- denitrifiers. Several trends were noted concerning the taxonomy of the microorganisms associated with the functional genes studied. For example, the most abundant sequences classified within 41 the Proteobacteria (primarily Betaproteobacteria) for nirK and nirS. Further, NT illustrated equal or more abundant levels of Betaproteobacteria (phylum Proteobacteria) compared to CT soil for a number of the genes examined (nirK, nirS, norB and nosZ). In other systems, Betaproteobacteria often dominates microbial populations due to high growth rates under available carbon substrates (Jenkins, Rushton et al. 2010). For nifH, Frankia (phylum Actinobacteria) was enriched in NT reduced input and biologically based soils compared to the CT soil. Frankia is a typical nitrogen-fixed organism both in free-living and symbiotic conditions (Sellstedt and Richau 2013). Two phyla, Actinobacteria and Acidobacteria, were notably less enriched or absent in CT for several genes (napA, narG, nirA, nirB and nirK) compared to the other three soils. Relating these results to previous research, others have examined soil microbial communities under different management systems. For example, one-time tillage increased the abundance of Actinobacteria and Acidobacteria in an acidic Solonetz with a 19-year NT management in Australia (Liu, Carvalhais et al. 2016). In another study, the abundance of Acidobacteria was higher in CT over NT, with the pH of 7.4 and 7.5, respectively (Dong, Liu et al. 2017). Acidobacteria are acidophilic and could be favored by slightly to moderately acidic growth conditions (Sait, Davis et al. 2006). Moreover, Acidobacteria exhibit the functional ability of the degradation of plant-derived organic matter (Naumoff and Dedysh 2012) and thus play an important role in the decomposition of organic matter (Rampelotto, Ferreira et al. 2013). For nosZ, the most abundant sequences belonged to the Bacteroidetes (with the dominant class of Flavobacteria). Others have reported Bacteroidetes display copiotrophic characteristics and are favored by increased nutrient availability (McHugh and Schwartz 2015). We found Flavobacteria was absent in NT soil but dominated in CT soil for nosZ. Consistent with these 42 results, microbial community studies have reported more Bacteroidetes in CT compared to NT soil (Yin, Mueth et al. 2017). Bacteroidetes were also more dominant in one soil compared to the same soil under non-disturbed grass systems (Acosta-Martinez, Dowd et al. 2008). However, others have reported that Bacteroidetes were more abundant under NT compared to CT in winter wheat cropping system (Dong, Liu et al. 2017) and non-disturbed grass system in comparison with agricultural rotation system (Zhang, Wu et al. 2014). Besides, NT increased the abundance of Flavobacteria compared to the tilled treatment under semi-arid conditions (Liu, Li et al. 2020). Notably, the above studies did not examine the taxonomy of the microorganisms linked with nosZ and so it is difficult to conclude if our results are typical of NT compared to CT soils. In conclusion, the agricultural management practices investigated here impacted gene abundance as well as the taxonomy of microorganisms associated with the nitrogen metabolism. From the nine genes examined, nirK was the most abundant and nifH was the least abundant. The nirS/nirK ratios were highest for the CT system, which may indicate a greater potential for N2O consumption. Three genes (nirK, nirS and norB) were statistically significantly lower in the NT compared to the CT treatment. The microbial community richness and diversity indices were generally higher for the genes associated with nitrate reduction (napA and narG) and dissimilatory nitrite reduction (nirA and nirB) compared to the other genes. For nirK, norB and nosZ a number of the richness and diversity indexes were significantly greater in NT soil compared to CT soil, indicating a potentially a higher denitrification potential. A number of trends were noted for the taxonomy of the functional genes across agricultural systems. The genus Frankia was significantly more abundant in the NT, reduced input and biological based soils compared to the CT soils. The CT soil was dominated by Betaproteobacteria for seven genes and by Cytophagia for nosZ. Also, for six of these genes, Betaproteobacteria were more 43 abundant in the CT soil compared to the other three soils. Alphaproteobacteria were more abundant in the NT soil compared to the other soils for several genes. While for nirA and nirB, Alphaproteobacteria were more abundant in the biological based soil compared to the other three soils. For napA, narG, nirK and norB, Actinobacteria were more abundant in the NT soil compared to the other three soils. 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On Fungene 74937 50753 5304 13238 19514 7988 25330 54085 90760 Minimum HMM Coverage 70% 40226 49174 3787 7054 3474 3367 3020 51514 45767 napA narG nosZ norB nifH nirK nirS nirA nirB Dereplicated 11395 11395 1266 1778 1562 556 993 12955 12955 52 Table A2.2 MG-RAST sequence data summary. Soil type Conventional tillage Conventional tillage Conventional tillage Conventional tillage Conventional tillage Conventional tillage No tillage No tillage No tillage No tillage No tillage No tillage Reduced input Reduced input Reduced input Reduced input Reduced input Reduced input Biological based Biological based Biological based Biological based Biological based Biological based MG-RAST ID mgm4887245.3 mgm4889385.3 mgm4887247.3 mgm4887261.3 mgm4887259.3 mgm4887263.3 mgm4887248.3 mgm4887249.3 mgm4887251.3 mgm4887262.3 mgm4887265.3 mgm4887264.3 mgm4887252.3 mgm4887253.3 mgm4887254.3 mgm4887267.3 mgm4887266.3 mgm4887268.3 mgm4887255.3 mgm4887256.3 mgm4887258.3 mgm4887270.3 mgm4887289.3 mgm4887290.3 Post QC: bp Count 1,049,991,462 bp 865,087,512 bp 873,760,585 bp 1,042,733,021 bp 1,186,811,683 bp 1,049,806,246 bp 978,574,572 bp 1,021,883,457 bp 893,615,124 bp 1,052,482,005 bp 1,171,824,030 bp 1,151,447,486 bp 1,020,227,225 bp 1,156,421,815 bp 845,604,740 bp 904,740,521 bp 1,216,560,266 bp 923,078,351 bp 1,070,768,940 bp 1,048,398,089 bp 1,095,942,092 bp 1,410,382,064 bp 1,149,249,456 bp 1,303,754,397 bp 53 Post QC: Sequences Count Post QC: Mean Sequence Length bp 4,562,115 3,773,767 3,805,693 4,497,130 5,171,096 4,606,628 4,289,260 4,491,203 3,901,326 4,556,161 5,106,093 5,131,392 4,473,295 5,084,544 3,689,278 3,896,151 5,320,030 4,016,875 4,666,479 4,589,220 4,834,482 6,169,872 5,008,186 5,670,793 230 ± 37 bp 229 ± 38 bp 230 ± 38 bp 232 ± 37 bp 230 ± 37 bp 228 ± 38 bp 228 ± 38 bp 228 ± 38 bp 229 ± 38 bp 231 ± 37 bp 229 ± 37 bp 224 ± 39 bp 228 ± 38 bp 227 ± 38 bp 229 ± 38 bp 232 ± 37 bp 229 ± 38 bp 230 ± 37 bp 229 ± 38 bp 228 ± 38 bp 227 ± 38 bp 229 ± 38 bp 229 ± 37 bp 230 ± 37 bp Table A2.3 P-values for statistical tests with the relative abundance of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Shapiro-Wilk test Levene's test One- way ANOV A Null Hypothesis Soil groups napA narG nifH nirA nirB nirK nirS norB nosZ The sample distribution is normal σ1=σ2 μ1= μ2 Convention al tillage 1.57E-01 1.59E-01 6.03E-01 7.19E-02 8.65E-01 7.12E-01 9.31E-01 5.97E-01 N/A No tillage 7.77E- 01 7.66E- 01 7.31E- 01 2.20E- 01 2.11E- 02 3.27E- 01 1.16E- 01 3.68E- 01 N/A Reduce d input 2.50E- Biologica l based 04 7.69E-01 5.35E-01 6.31E- 01 1.71E-01 9.14E-02 2.20E- 01 2.44E-01 8.27E-01 3.40E- 04 5.73E-01 5.45E-01 1.34E- 02 5.69E-01 3.49E-01 6.55E- 01 5.24E-01 1.33E-01 3.44E- 01 4.63E-02 4.37E-01 8.43E- 01 5.68E-01 3.81E-01 N/A 5.81E-03 N/A N/A 2.74E- 01 1.26E- 02 N/A N/A 1.61E- 05 2.22E- 05 1.85E- 02 N/A Kruskal-Wallis test Median1=Medi an2 6.02E-02 N/A N/A 4.81E-01 4.09E-01 N/A N/A N/A N/A 54 Table A2.4 P-values for Tukey HSD test with the relative abundance of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage- Biological based Reduced input - Biological based Tukey's HSD test μ1= μ2 napA narG nifH nirA nirB N/A N/A 4.14E-01 N/A N/A N/A N/A 9.03E-01 N/A N/A N/A N/A 1.89E-01 N/A N/A N/A N/A 1.42E-01 N/A N/A N/A N/A 7.59E-03 N/A N/A N/A N/A 5.06E-01 N/A N/A nirK 1.14E- 05 3.84E- 01 1.79E- 01 4.10E- 04 1.26E- 03 9.60E- 01 nirS 2.07E- 05 2.33E- 04 9.27E- 03 6.94E- 01 5.82E- 02 3.88E- 01 norB nosZ 1.16E- 02 N/A 4.65E- 01 N/A 1.65E- 01 N/A 2.22E- 01 N/A 5.68E- 01 N/A 9.00E- 01 N/A 55 Table A2.5 P-values for Dunn’s test with the relative abundance of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage - Biological based Reduced input - Biological based Dunn's test μ1= μ2 napA narG nifH nirA nirB nirK nirS norB nosZ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A nirB nifH narG 1.66E-07 4.74E-01 Table A2.6 Summary of the p values from Spearman’s rank correlation tests with gene relative abundance data. Values in bold indicate a significant difference (p ≤ 0.05). Genes napA napA narG nifH nirA nirB nirK nirS norB nosZ nirA < 2.2e-16 1.43E-08 2.30E-06 8.67E-02 1.28E-08 4.26E-06 4.11E-02 2.74E-04 8.32E-03 3.30E-03 4.70E-01 3.12E-02 3.63E-01 9.83E-01 7.64E-02 7.37E-03 2.02E-01 8.42E-01 2.86E-01 4.60E-02 5.80E-01 6.15E-01 1.08E-01 5.87E-01 8.05E-01 6.89E-01 5.47E-01 9.87E-01 4.84E-03 1.98E-02 1.26E-02 2.12E-04 1.76E-03 7.65E-05 1.66E-07 4.74E-01 4.11E-02 < 2.2e-16 2.74E-04 9.83E-01 1.43E-08 8.32E-03 7.64E-02 4.60E-02 2.30E-06 3.30E-03 7.37E-03 8.67E-02 4.70E-01 2.02E-01 1.28E-08 3.12E-02 8.42E-01 4.26E-06 3.63E-01 2.86E-01 5.80E-01 8.05E-01 6.15E-01 6.89E-01 4.84E-03 1.08E-01 5.47E-01 1.98E-02 2.12E-04 5.87E-01 9.87E-01 1.26E-02 1.76E-03 7.65E-05 norB nosZ nirK nirS 56 nirB nifH Table A2.7 Summary of Spearman’s correlation coefficient (rho) for Spearman’s rank correlation test with gene relative abundance data. Rho values in bold indicate a statistically significant correlation (p ≤ 0.05), as shown above. Genes napA nirA napA narG nifH nirA nirB nirK nirS norB nosZ narG 8.48E-01 1.54E-01 9.81E-01 8.80E-01 8.03E-01 3.57E-01 8.81E-01 7.91E-01 4.20E-01 6.78E-01 5.26E-01 5.75E-01 1.55E-01 4.41E-01 1.94E-01 -4.57E-03 3.69E-01 5.33E-01 2.70E-01 4.29E-02 2.27E-01 4.11E-01 1.19E-01 -1.08E-01 3.37E-01 1.17E-01 5.31E-02 -8.62E-02 1.29E-01 3.48E-03 5.55E-01 4.72E-01 5.01E-01 6.87E-01 6.04E-01 7.19E-01 8.48E-01 1.54E-01 4.20E-01 9.81E-01 6.78E-01 -4.57E-03 8.80E-01 5.26E-01 3.69E-01 4.11E-01 8.03E-01 5.75E-01 5.33E-01 1.19E-01 5.31E-02 3.57E-01 1.55E-01 2.70E-01 -1.08E-01 -8.62E-02 5.55E-01 8.81E-01 4.41E-01 4.29E-02 3.37E-01 1.29E-01 4.72E-01 6.87E-01 7.91E-01 1.94E-01 2.27E-01 1.17E-01 3.48E-03 5.01E-01 6.04E-01 7.19E-01 norB nosZ nirK nirS 57 Table A2.8 P-values for statistical tests with the richness index chao 1 of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Shapiro-Wilk test Levene's test One- way ANOV A Null Hypothesis Soil groups napA narG nifH nirA nirB nirK nirS norB nosZ The sample distribution is normal σ1=σ2 μ1= μ2 Convention al tillage 4.89E-01 4.66E-01 9.76E-01 5.72E-01 4.85E-01 2.50E-01 8.12E-01 9.10E-01 7.47E-01 No tillage 6.42E- 01 5.43E- 01 3.87E- 03 4.34E- 01 4.86E- 01 3.67E- 02 1.16E- 01 2.92E- 03 4.99E- 01 Reduce d input 2.90E- Biologica l based 01 2.63E-01 9.37E-01 2.78E- 01 2.82E-01 9.42E-01 6.53E- 01 9.76E-01 7.80E-01 4.55E- 01 5.53E-01 9.60E-01 3.71E- 01 5.47E-01 9.99E-01 7.66E- 02 1.05E-03 9.88E-01 3.55E- 01 8.13E-01 6.72E-01 9.44E- 01 5.08E-01 2.25E-01 8.99E- 01 4.85E-01 5.12E-01 3.56E- 01 9.37E- 01 N/A 2.65E- 01 7.99E- 01 N/A 8.52E- 04 N/A 5.86E- 01 Kruskal-Wallis test Median1=Medi an2 N/A N/A 1.80E-02 N/A N/A 1.68E-02 N/A 2.35E-01 N/A 58 Table A2.9 P-values for Tukey’s HSD test with the richness index chao 1 of counts of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage- Biological based Reduced input - Biological based napA narG nifH Tukey's HSD test μ1= μ2 nirA nirB nirK nirS norB nosZ N/A N/A N/A N/A N/A N/A 7.37E-04 N/A N/A N/A N/A N/A N/A N/A N/A 8.59E-03 N/A N/A N/A N/A N/A N/A N/A N/A 2.11E-01 N/A N/A N/A N/A N/A N/A N/A N/A 7.05E-01 N/A N/A N/A N/A N/A N/A N/A N/A 6.50E-02 N/A N/A N/A N/A N/A N/A N/A N/A 4.07E-01 N/A N/A 59 Table A2.10 P-values for Dunn’s test with the richness index chao 1 of counts of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage - Biological based Reduced input - Biological based napA narG nifH nirA Dunn's test μ1= μ2 nirB nirK nirS norB nosZ N/A N/A 1.00E+00 N/A N/A 1.00E+00 N/A N/A N/A N/A N/A 1.33E-01 N/A N/A 2.91E-02 N/A N/A N/A N/A N/A 1.00E+00 N/A N/A 2.03E-01 N/A N/A N/A N/A N/A 5.39E-02 N/A N/A 1.65E-01 N/A N/A N/A N/A N/A 1.00E+00 N/A N/A 7.85E-01 N/A N/A N/A N/A N/A 3.30E-02 N/A N/A 1.00E+00 N/A N/A N/A 60 Table A2.11 P-values for statistical tests with the richness index chao 2 of counts of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Soil groups Shapiro-Wilk test Levene' s test One-way ANOVA The sample distribution is normal σ1=σ2 μ1= μ2 Kruskal-Wallis test Median1=Medi an2 Conventi onal tillage No tillage Reduce d input Biologic al based napA 3.27E-01 4.60E-01 narG 2.88E-01 4.18E-01 nifH 7.54E-01 2.38E-01 nirA nirB nirK 3.24E-01 2.55E-01 2.55E-01 2.75E-01 5.32E-02 7.63E-03 nirS 3.70E-01 2.47E-01 norB 3.29E-01 6.27E-02 nosZ 2.30E-01 6.70E-01 4.26E- 01 2.19E- 01 1.62E- 01 4.23E- 01 2.57E- 01 8.64E- 03 2.80E- 01 7.21E- 01 7.22E- 01 2.38E- 01 2.26E- 01 2.98E- 01 3.80E- 01 3.74E- 01 6.93E- 03 4.17E- 01 1.90E- 01 5.40E- 01 9.19E- 01 4.17E-01 9.91E- 01 9.51E-01 5.93E- 01 3.00E-01 9.32E- 01 4.55E-01 9.98E- 01 9.55E-01 N/A N/A N/A N/A N/A N/A 2.18E-01 N/A N/A N/A 9.95E- 01 9.87E- 01 2.57E-01 8.45E- 01 4.62E-01 6.34E- 01 6.51E-01 61 Table A2.12 P-values for statistical tests with the Simpson diversity of the counts of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Soil groups Shapiro-Wilk test Levene' s test One-way ANOVA The sample distribution is normal σ1=σ2 μ1= μ2 Kruskal-Wallis test Median1=Medi an2 Conventi onal tillage No tillage Reduce d input Biologic al based napA 4.73E-01 6.37E-03 narG N/A N/A nifH 1.27E-01 2.66E-01 nirA nirB nirK 9.00E-02 1.25E-01 1.49E-01 2.33E-01 6.00E-02 9.95E-02 nirS 2.26E-01 7.08E-02 norB 2.15E-01 7.04E-02 nosZ N/A N/A 5.07E- 03 6.37E- 03 N/A 2.08E- 01 1.70E- 01 9.44E- 02 1.81E- 01 1.79E- 01 1.06E- 01 N/A N/A 1.60E- 01 2.87E- 02 2.87E- 02 2.05E- 01 1.15E- 01 8.94E- 02 N/A 3.63E- 01 4.44E- 05 2.00E- 01 6.90E- 01 6.16E- 01 4.85E- 01 6.44E- N/A N/A 6.52E-01 N/A N/A <2e-16 01 1.57E-05 3.11E- 01 1.33E- 04 8.76E-15 N/A 6.96E-05 N/A N/A 8.84E-04 5.77E-04 N/A N/A N/A N/A 62 Table A2.13 P-values for Tukey’s HSD test with the Simpson diversity of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage- Biological based Reduced input - Biological based Tukey's HSD test μ1= μ2 napA narG nifH nirA nirB nirK nirS 2.73E- norB nosZ N/A N/A N/A N/A N/A 0.00E+00 03 0.00E+00 N/A N/A N/A N/A N/A N/A 0.00E+00 02 5.00E-07 N/A 5.67E- 9.98E- N/A N/A N/A N/A N/A 0.00E+00 01 2.62E-02 N/A N/A N/A N/A N/A N/A 0.00E+00 06 0.00E+00 N/A 6.40E- N/A N/A N/A N/A N/A 0.00E+00 03 0.00E+00 N/A N/A N/A N/A N/A N/A 3.45E-01 02 3.18E-04 N/A 3.86E- 4.18E- 63 Table A2.14 P-values for Dunn’s test with the Simpson diversity of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage - Biological based Reduced input - Biological based Dunn's test μ1= μ2 napA narG nifH nirA nirB nirK nirS norB nosZ 8.35E-01 N/A N/A 1.00E+00 1.00E+00 N/A N/A N/A N/A 1.86E-02 N/A N/A 1.00E+00 4.31E-01 N/A N/A N/A N/A 5.49E-05 N/A N/A 1.81E-01 1.12E-01 N/A N/A N/A N/A 8.35E-01 N/A N/A 2.87E-02 9.14E-03 N/A N/A N/A N/A 1.86E-02 N/A N/A 6.14E-04 1.18E-03 N/A N/A N/A N/A 8.35E-01 N/A N/A 5.15E-01 1.00E+00 N/A N/A N/A N/A 64 Table A2.15 P-values for statistical tests with the Shannon diversity of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Soil groups Shapiro-Wilk test Levene' s test One-way ANOVA The sample distribution is normal σ1=σ2 μ1= μ2 Kruskal-Wallis test Median1=Medi an2 Conventi onal tillage No tillage Reduce d input Biologic al based napA 3.28E-02 3.17E-02 narG 2.47E-02 1.55E-02 nifH 2.08E-01 2.63E-01 nirA nirB nirK 9.44E-02 6.95E-02 5.13E-02 5.13E-02 7.78E-02 5.51E-02 nirS 1.88E-01 1.35E-01 norB 1.61E-01 1.55E-02 nosZ 7.63E-01 5.08E-02 2.50E- 01 9.26E- 01 3.25E- 01 2.21E- 01 6.92E- 02 3.29E- 02 1.73E- 01 7.96E- 02 1.61E- 01 3.28E- 02 6.07E- 01 3.08E- 01 1.31E- 01 2.07E- 01 9.11E- 02 1.52E- 01 1.01E- 02 7.03E- 02 2.09E- 01 1.38E- 01 6.31E- N/A N/A 2.03E-04 1.90E-04 01 8.35E-01 9.65E- 01 3.55E-03 9.59E- 01 5.85E-02 N/A N/A N/A N/A 1.77E-04 N/A 1.44E-03 N/A 9.24E- 01 8.03E- 01 2.76E-03 9.52E- 01 8.11E- 01 N/A <2E-16 65 Table A2.16 P-values for Tukey’s HSD test with the Shannon diversity of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage- Biological based Reduced input - Biological based Tukey's HSD test μ1= μ2 napA narG nifH nirA nirB nirK nirS norB nosZ N/A N/A N/A N/A N/A 0.00E+00 2.73E-03 0.00E+00 N/A N/A N/A N/A N/A N/A 0.00E+00 5.67E-02 5.00E-07 N/A N/A N/A N/A N/A N/A 0.00E+00 9.98E-01 2.62E-02 N/A N/A N/A N/A N/A N/A 0.00E+00 6.40E-06 0.00E+00 N/A N/A N/A N/A N/A N/A 0.00E+00 4.18E-03 0.00E+00 N/A N/A N/A N/A N/A N/A 3.45E-01 3.86E-02 3.18E-04 N/A 66 Table A2.17 P-values for Dunn’s test with the Shannon diversity of genes associated with nitrogen metabolism copies. “N/A” indicates the test was not appropriate and p-values in bold indicate a significant difference (p ≤ 0.05). Test Null Hypothesis Genes Conventional tillage - No tillage Conventional tillage - Reduced input Conventional tillage - Biological based No tillage - Reduced input No tillage - Biological based Reduced input - Biological based napA narG nifH nirA Dunn's test μ1= μ2 nirB nirK nirS norB nosZ 1.00E+00 1.00E+00 N/A N/A N/A 4.88E-05 N/A 0.00E+00 N/A 1.37E-02 1.75E-01 N/A N/A N/A 1.63E-01 N/A 5.00E-07 N/A 1.77E-04 1.42E-01 N/A N/A N/A 1.46E-01 N/A 2.62E-02 N/A 9.11E-01 1.42E-01 N/A N/A N/A 1.46E-01 N/A 0.00E+00 N/A 6.29E-02 1.75E-01 N/A N/A N/A 1.63E-01 N/A 0.00E+00 N/A 6.35E-01 5.27E-05 N/A N/A N/A 1.00E+00 N/A 3.18E-04 N/A 67 Figure A2.1 Genera with nifH genes significantly different between conventional tillage soil (in blue) and the other soils from the assembled contigs. Those enriched in no tillage soil compared to conventional tillage soil are shown in yellow (A), those enriched in reduced input soil compared to conventional tillage soil are shown in green (B) and those enriched in biologically based soil compared to conventional tillage soil are shown in purple (C). The data (generated in Megan, six metagenomes for each soil) were analyzed using STAMP with the two group analysis option (each soil compared to conventional tillage soil) and Welch’s two sided t-test (p<0.05). 68 Figure A2.2 Genera significantly different between conventional tillage (in blue) and the other three soils from the assembled contigs. Those enriched in soil 1 compared to no tillage soil are shown in blue (no genera were enriched in no tillage soil compared to conventional tillage soil) (A), those enriched in reduced input soil compared to conventional tillage soil are shown in green (B) and those enriched in biologically based soil compared to conventional tillage soil are shown in purple (C). The data (generated in Megan, six metagenomes for each soil) were analyzed using STAMP with the two group analysis option (each soil compared to conventional tillage soil) and Welch’s two sided t-test (p<0.05). 69 CHAPTER 3: SOIL MICROORGANISMS INVOLVED IN GLUCOSE ASSIMILATION IN SMALL AND LARGE PORE MICRO-HABITATS This chapter is a modified version of a published work in Nature Communications: Li, Z., A. N. Kravchenko, A. Cupples, A. K. Guber, Y. Kuzyakov, G. Philip Robertson and E. Blagodatskaya (2024). "Composition and metabolism of microbial communities in soil pores." Nature Communications 15(1): 3578. 3.1 Abstract High plant diversity is known to increase carbon inputs to soils, impact soil microbial community composition and promote soil microbial activity. Large pores are likely to hold more roots residues, provide more efficient oxygen supply, and have more dissolved nutrients and carbon carried by water fluxes. Soil pore structure also impacts the activities of soil microbial communities. The objectives of this study were to investigate the effects of 1) plant systems, representing a 9-year gradient of plant diversity (no plants, monoculture switchgrass (Panicum virgatum L.), and high diversity prairie), 2) soil pore size (small (4-10 µm Ø) and large (30-150 µm Ø)), and 3) incubation time (24 hr (short-term) and 30 days (long-term)) on the microbial communities involved in the utilization of a newly added carbon (glucose). This is the first work to explore the influence of soil micro-habitat, as presented by pores of different sizes ranges, on the microbial communities’ responses to new carbon inputs. The intact soil cores (5 cm Ø) from the three systems were supplied with either 50 μM C g-1 soil of 13C labeled glucose, unlabeled glucose, or no glucose. Glucose was added to small or large pores based on matrix potential approach. After 24 hr or 30 day incubations stable isotope probing (SIP) was used to identify the phylotypes actively responsible for glucose assimilation in the small and large pore micro- 70 habitats. Both extracted DNA and the fractions separated by SIP were subject to 16S rRNA gene sequencing. PICRUSt2 was used to predict the microbial functions of the sequencing data from KEGG orthologs. The overall microbial communities were affected by multiple years of contrasting vegetation, but not by pore sizes or incubation times. Pseudomonas (Proteobacteria) played an important role in carbon uptake from glucose in all short-term incubations and in the long-term incubations within large pores. In the long-term incubations of both switchgrass and prairie systems’ soils, the community compositions of carbon consumers acting within the small and large pore micro-habitats differed and could be linked to disparate carbon assimilation strategies (r- vs. K-strategists) and to disparate carbon acquisition ecological strategies (plant polymer decomposers, microbial necromass decomposers, predators, and passive consumers). The predicted enriched functional genes indicated the dominance of glucokinase in the soil of the prairie, but not switchgrass system, suggesting a competitive advantage for consuming glucose. 3.2 Introduction Bioenergy crops are important for decreasing the impacts of climate change as they reduce our dependence on fossil fuels, mitigate the emission of atmospheric CO2 and enhance soil carbon sequestration (Follett 2001, West and Post 2002, McLauchlan, Hobbie et al. 2006, Ogle, Swan et al. 2012, Post, Izaurralde et al. 2012, IPCC 2014, Sprunger and Robertson 2018). Much interest has been directed towards switchgrass (Panicum virgatum L.) as a perennial bioenergy feedstock (Sanderson, Adler et al. 2006). Switchgrass can increase carbon sequestration in the long-term (after 10 years) (Ma, Wood et al. 2000) and promote carbon accumulation compared to annual systems due to more root-derived carbon inputs (Adkins, Jastrow et al. 2016). However, there are also reports suggesting switchgrass is slow to accumulate soil organic carbon (Chimento, Almagro et al. 2016, Sprunger and Robertson 2018). 71 Carbon stored in soils is driven by the balance of root litter production, root exudates and the microbial decomposition of these compounds (Jastrow, Amonette et al. 2007). Important knowledge gaps involve the comparison of switchgrass to other crops concerning the impact of soil architecture (soil pores) on soil carbon uptake and the identification of the active microbial communities involved. Plant diversity is known to impact carbon inputs to soils, soil microbial community composition and soil microbial activity (Carney and Matson 2006, Zhang, Wang et al. 2010, Lamb, Kennedy et al. 2011, Ravenek, Bessler et al. 2014, Lange, Eisenhauer et al. 2015). For example, total root biomass and root residue concentrations increased with increased plant species richness (Ravenek, Bessler et al. 2014). Such higher root biomass due to more diverse plants can then result in carbon accrual in soils (Fornara and Tilman 2008). These plant-derived carbon inputs provide more carbon substrates for microbial populations (Eisenhauer, Bessler et al. 2010). Plant diversity can also promote soil microbial activity (Zak, Holmes et al. 2003, Eisenhauer, Bessler et al. 2010, Lange, Eisenhauer et al. 2015). Further, microclimatic conditions, such as increased soil moisture, have been identified as important mechanisms for how plant diversity impacts soil microbial biomass (Lange, Habekost et al. 2014). Soil pore structure has also impacted the activities of soil microbial communities (Chenu, Hassink et al. 2001, Carson Jennifer, Gonzalez-Quiñones et al. 2010, Sleutel, Bouckaert et al. 2012, Kravchenko, Negassa et al. 2014, Negassa, Guber et al. 2015, Kravchenko and Guber 2017, Kravchenko, Guber et al. 2021, Xia, Zheng et al. 2022). Large pores are likely to hold more roots residues (diameters of the finest roots are between 30 and 40 µm) (Kuchenbuch and Jungk 1982). Oxygen supply, dissolved nutrients and carbon carried by water fluxes are also more sufficient in large pores (Or, Smets et al. 2007, Kravchenko, Guber et al. 2021). In 72 comparison to monocultures, diverse plant communities have been linked to the development of 30–150 µm pores, which have been associated with higher enzyme activities, and consequently soil carbon storage capacity (Kravchenko, Guber et al. 2019). Further, large pores promoted the prevalence of r-strategists, 14C-CO2 release and dissolved organic 14C while small pores held more 14C microbial biomass (Kravchenko, Guber et al. 2021). In addition, there were stronger associations between β-glucosidase activity and glucose-derived carbon when glucose was added to the large pores (Kravchenko, Guber et al. 2021). Fine roots are important for the carbon accumulation in soils and diverse biofuel cropping systems are likely to hold more fine roots than monoculture systems (Sprunger, Oates et al. 2017). In fact, plant-stimulated soil pore formation was previously reported to be a major determinant of whether new carbon inputs are stored or lost (Kravchenko, Guber et al. 2019). Researchers have found soil microbial communities differ depending on soil pore size (Kravchenko, Negassa et al. 2014, Negassa, Guber et al. 2015, Xia, Rufty et al. 2020, Xia, Zheng et al. 2022). For example, Betaproteobacteria, Bacteroidetes, and Eurotiales (order of fungi) were more abundant in the large pores of row crop soils (> 30 µm), whereas Alphaproteobacteria, Sordariomycetes (class of fungi), and Chaetothyriales (order of fungi) were more abundant in the small pores of row crop soils (< 30 µm) (Xia, Zheng et al. 2022). Others have also found Betaproteobacteria to be more abundant in large pores (Xia, Rufty et al. 2020). In a study involving the addition of plant residues to different soil pore sizes, the communities in the samples with large pores (> 30 µm) contained more cellulose decomposers, including Bacteroidetes, Proteobacteria, Actinobacteria and Firmicutes, while oligotrophic Acidobacteria were more abundant on the plant residue from the samples with small pores (Negassa, Guber et al. 2015). In research with two contrasting agricultural 73 systems, Actinobacteria, Proteobacteria, and Firmicutes were positively correlated with the presence of large pores (> 110 µm) (Kravchenko, Negassa et al. 2014). Although previous research has discovered differences in microbial communities between pores of different sizes, limited efforts have been made to identify which phylotypes are actively responsible for carbon assimilation in those pores. To address this the unknowns in this research area, the current study applied stable isotope probing (SIP) to determine which microorganisms were responsible for the assimilation of a labile carbon source (glucose) in different sized soil pores in three soils; bare soil and soil under either switchgrass or high diversity prairie crops. Although SIP has been widely used to identify microbial populations involved in carbon metabolism, e.g. glucose (Padmanabhan, Padmanabhan et al. 2003, Lemanski and Scheu 2014, Zhang, Ding et al. 2015, Kong, Zhu et al. 2018), root exudate (Rangel‐Castro, Killham et al. 2005, Haichar, Heulin et al. 2016) and cellulose assimilation (el Zahar Haichar, Achouak et al. 2007, Eichorst Stephanie and Kuske Cheryl 2012, Štursová, Žifčáková et al. 2012), this is the first study to explore the influence of soil pore sizes and plant diversity on the microbial communities responsible for carbon uptake. The rationale behind this work is that root systems of diverse plant communities are likely more effective than monocultures in creating optimal micro-environments and pore structures for microbial functioning. The creation of such optimal micro-environments leads to a greater spread of active microbial communities throughout the soil, and hence, to a greater volume of the soil matrix where root-derived and microbially-processed carbon inputs can be entombed and, subsequently, protected. The objectives of this work were to investigate the effects of 1) cropping system (no plants, switchgrass, high diversity prairie), 2) soil pore size and 3) incubation time (24 hr and 30 days) on the microbial communities involved in the utilization 74 of a newly added carbon (glucose). Also, the impacts of those factors on the overall composition and diversity of the soil microbial communities were also investigated. 3.3 Methods 3.3.1 Soils and Incubations Soils were collected from replicate treatment blocks of the Cellulosic Biofuel Diversity Experiment at the W.K. Kellogg Biological Station Long-Term Ecological Research (KBS LTER), located between Kalamazoo and Battle Creek, Michigan. A map of the experimental design of the KBS LTER can be found at https://lter.kbs.msu.edu/maps/images/current- cellulosic-biofuel-map.pdf. Soil cores were collected from four blocks of CE1 (hereafter called bare soil), three blocks of CE7 (hereafter called switchgrass) and three blocks of CE12 (hereafter called high diversity prairie). Soil cores were incubated with 50 μM C g-1 soil of 13C labeled glucose, unlabeled glucose or no glucose at 20-25 °C for 24 hours or 30 days. Additional details on the blocks and incubation times investigated for each treatment have been summarized (Table 3.1). 75 Table 3.1 Treatment summary of the samples and replicate blocks analyzed. 24 hours only Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil Bare soil 24 hours and 30 days Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass N/A N/A N/A N/A N/A Switchgrass Switchgrass Switchgrass Switchgrass Switchgrass 24 hours and 30 days High Diversity High Diversity High Diversity High Diversity High Diversity High Diversity High Diversity High Diversity High Diversity High Diversity N/A N/A N/A N/A N/A High Diversity High Diversity High Diversity High Diversity High Diversity Block Pore size Small pore Small pore N/A Large pore Large pore Small pore Small pore N/A Large pore Large pore Small pore Small pore N/A Large pore Large pore Small pore Small pore N/A Large pore Large pore 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 Glucose added 13C 12C No glucose 13C 12C 13C 12C No glucose 13C 12C 13C 12C No glucose 13C 12C 13C 12C No glucose 13C 12C 76 3.3.2 DNA Extraction, Ultracentrifugation and Fractioning DNA was extracted using the DNeasy PowerSoil kit or the DNeasy PowerSoil Pro kit (Qiagen, USA) following the manufacturer’s protocols. For this, approximately 1 g of each soil was used for DNA extraction. Carnation instant nonfat dry milk (40 mg, Nestlé, Rosslyn, VA) was added at the beginning of the extraction process to improve the DNA yield (Yuko Takada- Hoshino and Naoyuki 2004). DNA concentrations from each extraction were determined using the Qubit fluorometer (Thermo Fisher, USA) with the ds DNA HS Assay kit. For ultracentrifugation, approximately 10 µg of each DNA extract was mixed with Tris-EDTA buffer (10 mM Tris, 1mM EDTA, pH 8) and cesium chloride (CsCl) solution (1.62 M) and loaded into Quick-Seal Round-Top Polypropylene tubes (13×51 mm, 5 ml; Beckman Coulter, USA). Refractive index (RI) values of each solution were determined using AR200 digital refractometer (Leica Microsystems Inc., Buffalo Grove, IL) and the RI was adjusted to between (1.4069 - 1.4071) by adding small volumes of TE buffer or CsCl solution. The sealed tubes were ultracentrifuged at 178,000 ×g (20 °C) for 46 h in a StepSaver 70 V6 vertical titanium rotor (8 by 5.1 mL capacity) within a Sorvall WX 80 Ultra Series centrifuge (Thermo Scientific, Waltham, MA). Following ultracentrifugation, each tube was placed onto a fraction collection system (Beckman Coulter) to generate ~26 fractions (200 μL). The RI of each fraction was determined, and, from this, buoyant density values were calculated. CsCl in the fractions was removed using linear polyacrylamide (Thermo-Scientific, USA) and a polyethylene glycol solution (1.6M NaCl, 30% PEG solution; Thermo-Scientific, USA). The DNA concentration in each fraction was determined using the ds DNA HS Assay kit to identify the four heaviest fractions with the minimum amount of DNA for high throughput sequencing. For each of the labelled and unlabeled glucose amended samples, sixteen tubes were ultracentrifuged: four replicate blocks 77 (B1, B2, B3, B4) for the bare soil 24 hour incubation; three replicate blocks (B1, B2, B4) for both the switchgrass 24 hour incubation and the high diversity prairie 24 hour incubations; and three replicate blocks (B1, B2, B4) for both the switchgrass 30 day incubation and the high diversity prairie 30 day incubations. As both small pores and large pore incubations were also examined, in total, sixty-four tubes were ultracentrifuged (2 glucose forms [12C and 13C] X 16 treatments/blocks X 2 pore sizes). 3.3.3 Miseq Illumina Sequencing and Mothur Analysis Total genomic DNA extracts (before ultracentrifugation) and ultracentrifugation fractions were submitted to the Research Technology Support Facility (RTSF) at MSU for 16S rRNA gene amplicon sequencing. For each of the sixty-four ultracentrifugation runs (as described above), three heavy fractions (buoyant density 1.73-1.75 g/ml) and one light fraction (~1.70 g/ml) were submitted in triplicate for sequencing. This involved amplification of the V4 region of the 16S rRNA gene using dual indexed Illumina compatible primers 515f/806r, as previously described (Kozich, Westcott et al. 2013). PCR products were batch normalized using Invitrogen SequalPrep DNA Normalization plates and the products recovered from the plates pooled. The pool was cleaned up and concentrated using AmpureXP magnetic beads; it was QC'd and quantified using a combination of Qubit dsDNA HS, Agilent 4200 TapeStation HS DNA1000 and Kapa Illumina Library Quantification qPCR assays. The pool was loaded onto an Illumina MiSeq v2 standard flow cell and sequencing was performed in a 2 × 250 bp paired end format using a MiSeq v2 500 cycle reagent cartridge. Custom sequencing and index primers were added to appropriate wells of the reagent cartridge. Base calling was performed by Illumina Real Time Analysis (RTA) v1.18.54 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.19.1. The amplicon sequencing data in the fastq format was analyzed 78 by Mothur (Schloss, Westcott et al. 2009) using the Mothur MiSeq SOP (accessed August 2021) (Kozich, Westcott et al. 2013). Briefly, the Mothur analysis involved trimming the raw sequences and quality control. The SILVA bacteria database (Release 138) for the V4 region (Pruesse, Quast et al. 2007) was used for the alignment. Chimeras, mitochondrial and chloroplast lineage sequences were removed, then the sequences were classified into operational taxonomic units (OTUs) at 0.03 cutoff. The OTUs were then grouped into taxonomic levels and the downstream analysis was conducted in R (version 4.0.2) (R_Core_Team 2020) with RStudio (version 1.5042) (RStudio_Team 2020). The sequencing data of the total DNA and SIP fractions were submitted to NCBI under Bioproject PRJNA801760 (accession numbers SAMN25378717 to SAMN25378796) and Bioproject PRJNA802612 (accession numbers SAMN25563888 to SAMN25564655), respectively. Sequencing data from the total DNA extracts and the ultracentrifugation fractions were analyzed separately, as described below. 3.3.4 Total DNA Community Analysis For the analysis of the total DNA samples, two Mothur files (shared file and taxonomy file) along with an independently created metafile were used as the input for packages phyloseq (McMurdie and Holmes 2013) (version 1.34.0), file2meco (version 0.1.0) (Liu, Cui et al. 2021) and microeco (version 0.5.1) (Liu, Cui et al. 2021). This resulted in the creation of 1) a phylum level bar chart, 2) a Venn diagram of OTU abundance and 3) two box plots at the genus and order level. The packages phyloseq (McMurdie and Holmes 2013) (version 1.34.0), microbiome (Lahti and Shetty 2012-2019) (version 1.12.0) and ampvis2 (version 2.7.11) (Andersen, Kirkegaard et al. 2018) were used to 1) generate heatmaps of the most abundant genera, 2) perform alpha diversity analysis (Chao1, ACE, Shannon's values, Simpson, Inverse Simpson, and Fisher indices) and 3) create barplots for the most abundant classes. The “adonis” function in 79 the package vegan (version 2.5.7) (Oksanen, Blanchet et al. 2020) was used to test differences between microbial communities in different soil treatments with Permutational Multivariate Analysis of Variance (PERMANOVA). The “pairwise.adonis” function in package pairwiseAdonis (version 0.4) (Martinez 2017) was used for the comparison of significant PERMANOVA results (p<0.05). The “simper” function in the package vegan (version 2.5.7) (Oksanen, Blanchet et al. 2020) was used for dissimilarity analyses for the significant comparison results (p<0.05). The abundance and the classification of top twenty OTUs contributing to the difference between treatments and the connection between the OTUs and different samples was determined using the circlize package (version 0.4.13) (Gu, Gu et al. 2014). 3.3.5 Identification of Enriched Phylotypes Sequencing datasets were compared between the heavy and light fractions of the 13C glucose amended samples and fractions of similar buoyant density from the 12C glucose amended samples to determine which phylotypes were responsible for label uptake. For this, data generated from the packages phyloseq (McMurdie and Holmes 2013) (version 1.34.0), and microbiome (Lahti and Shetty 2012-2019) (version 1.12.0) were analyzed using the packages dplyr (version 1.0.7) (Wickham, François et al. 2021), tidyr (version 1.1.4) (Wickham 2021), ggpubr (version 0.4.0) (Kassambara 2020)and rstatix (version 0.7.0) (Kassambara 2021). Specifically, those enriched in the heavy fractions of the 13C glucose amended samples (compared to the same fractions in the 12C glucose amended samples) were determined using the Wilcoxon test (function wilcox_test) in RStudio (one sided, p < 0.05). From those significantly enriched, the six most abundant were selected for the creation of boxplots using ggplot2 (version 3.3.5) (Wickham 2016). The analysis also included the comparison of phylotypes in the light 80 fractions of the 13C glucose amended fractions compared to the light fractions of the 12C glucose amended samples. Those enriched in the light fractions of the 13C glucose amended samples were removed from the above analysis. 3.3.6 Function Prediction by PICRUSt2 PICRUSt2 (Douglas, Maffei et al. 2020) was used to predict the microbial functions of the sequencing data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO) (Kanehisa, Sato et al. 2016). Biom and fasta files generated by Mothur were used for this analysis. The PICRUSt2 analysis included sequence placement with EPA-NG (Barbera, Kozlov et al. 2019) and gappa (Czech, Barbera et al. 2020), hidden state prediction with castor R package (Louca and Doebeli 2017) and pathway abundance inference with MinPath (Ye and Doak 2009). The KO functions associated with carbohydrate metabolism and energy metabolism were examined, including citrate cycle (TCA cycle) [PATHko00020], glycolysis gluconeogenesis [PATHko00010], methane metabolism [PATHko00680] and nitrogen metabolism [PATHko00910]. The metagenome output files were analyzed with ggplot2 (version 3.3.5) (Wickham 2016) and ggpubr (version 0.4.0) (Kassambara 2020) in RStudio (version 1.5042) (RStudio_Team 2020). The relative abundance of genes associated with carbohydrate metabolism and energy metabolism were also determined for each treatment. The enriched genes in the samples amended with 13C glucose were determined using a similar approach as described above for the enriched phylotypes (Wilcoxon test, p < 0.05). The eight most abundant genes associated with the carbohydrate metabolism and energy metabolism were displayed in barplots. The most abundant phylotypes associated with methane metabolism and nitrogen metabolism for the total DNA samples were determined in RStudio (version 1.5042) (RStudio_Team 2020). The 81 connections between the phylotypes and genes associated methane metabolism and nitrogen metabolism for the total DNA sequences for each treatment were created with the circlize package (version 0.4.13) (Gu, Gu et al. 2014). Genera with the most abundant relative abundance for each set of functional genes were selected for building the chord diagrams for the methane/ammonia monooxygenase genes (taxon_rel_function_abun > 0.008), nitrate and nitrite reductase genes (taxon_rel_function_abun > 0.1) and nitric and nitrous oxide reductase genes (taxon_rel_function_abun > 0.2). 3.4 Results 3.4.1 Microbial Community Analysis The microbial community structure was compared between the three treatments/soils (bare soil, switchgrass, high diversity prairie) and between the two pore sizes (large and small). The basic soil characteristics of the three soils are presented in Table A3.1. Principal Coordinate Analysis (PCoA) indicated a separation between the bare soil community and the two other treatments (Figure 3.1A). Both the switchgrass and high diversity prairie treatments clustered together (Figure 3.1A). Richness (Chao1, ACE) and diversity (Shannon, Simpson, Inverse of Simpson and Fisher) indices were determined and compared between treatments and pore sizes (Figure A3.1 and Tables A3.2 and A3.3). For the 24 hr incubations, there were significant differences (ANOVA, p < 0.05) between the three treatments for all except for Fisher (Table A3.3). Further analysis (24 hr incubations, t-test, two-tailed) revealed the values were higher for the high diversity prairie compared to the bare soil treatment for Chao1 (p < 0.018), ACE (p < 0.015), Shannon (p < 0.017), Simpson (p < 0.034) and Inverse Simpson (p < 0.022). The values were also higher for the switchgrass compared to the bare soil (24 hr incubations, t-test, two- tailed) for Shannon (p < 0.038), Simpson (p < 0.031) and Inverse Simpson (p < 0.022). In 82 contrast to this, there were no significant differences (p > 0.05) for any richness or diversity value between the switchgrass and high diversity prairie treatments for either incubation time. The impacts of pore size on the richness and diversity values were limited (evaluated with t-tests, two-tailed). Specifically, there were no significant differences (p > 0.05) between any richness or diversity value between the large pores and the small pores for the bare soil or the switchgrass treatments (Table A3.2). However, for the high diversity treatment, values for Shannon (p < 0.044), Simpson (p < 0.027), and Inverse Simpson (p < 0.03) were higher for the small pores compared to the large pores (when both incubation times were considered) (Table A3.2). The most abundant phyla were determined for the three treatments (Figure 3.2A). Compared to bare soil, the relative abundance of Latescibacteria, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia appeared greater in the high diversity prairie and switchgrass treatments. In contrast, in many cases, the relative abundance of Firmicutes, Chloroflexi, Bacteroidetes and Actinobacteria appeared greater in bare soil than the other two treatments. As shown in the Venn diagram (Figure 3.2B), the proportion of shared operational taxonomic units (OTU) between the bare soil and high diversity prairie (4.7 %) was higher than the corresponding proportion of shared OTUs between the bare soil and switchgrass (3.9 %) and between the switchgrass and high diversity prairie (1.7 %). The proportions of unique OTUs in the bare soil, switchgrass, and high diversity prairie were 4.7, 0.7, and 0.7 %, respectively. 83 Figure 3.1 Principal Coordinate Analysis (PCoA) plots for both small and large pores (A), large pores only (B) and small pores only (C) for the three treatments and two incubation times. 84 Figure 3.2 A bar chart illustrating relative abundance of the most abundant phyla for each treatment (A) and a Venn diagram showing the number of OTUs and percentage of the total OTUs for each treatment (B). In the Venn diagram, the integers are the OTU number and the percentages are the sequence number/total sequence number. 85 Of the most abundant genera (Figure 3.3A), two (Acidothermus and Chujaibacter) illustrated higher relative abundance values in the bare soil compared to the high diversity prairie and switchgrass treatments. For many of the other abundant genera, relative abundance values in the bare soil were lower compared to the other treatments (Figure 3.3A). The most abundant genera were also determined for each treatment, pore size and incubation time (Figure A3.2). The two most abundant genera (Subgroup 6 ge and Candidatus Udaeobacter) for the high diversity prairie and switchgrass treatments were the same for both the large pores (Figure S2A) and small pores (Figure A3.2B) for both incubation times. Whereas the most abundant genera in the bare soils were Acidothermus and uncultured ge in the large pore samples and Chitinophagaceae_unclassified, Acidothermus, and Chujaibacter in the small pore samples (Figure A3.2). At the order level, Gaiellales, Frankiales, Acidobacteriales, Xanthomonadales, Bacillales and Ktedonobacterales were less abundant in the switchgrass and high diversity prairie treatments than in the bare soil samples (Figure 3.3). The relative abundance values of other orders were similar between the switchgrass and high diversity prairie treatments (Figure 3.3B). The differences in the microbial communities at the OTU level between treatments were further investigated using PERMANOVA (Tables A3.5& A3.6). The differences were not significant for soil pore sizes in the bare soil, switchgrass and high diversity prairie treatments and were also not significant for the incubation time in switchgrass and high diversity prairie (p > 0.05). However, the bacterial communities were significantly different between the three treatments incubated for 24 hr and also between the two treatments (switchgrass and high diversity prairie) incubated for 30 days (p < 0.05). These trends are consistent with the chaos clusters between soil pore sizes or incubation time as well as the obvious differentiation between 86 soil treatments in PCoA plots (Figure 3.1). A similarity percentage analysis (simper) revealed the bacteria contributing to 50% dissimilarity at the phylum and class levels between treatments. The two dominant phyla contributing to the 50% difference were Proteobacteria and Actinobacteria between bare soil and high diversity prairie incubated for 24 hr (Table A3.6), between bare soil and switchgrass incubated for 24 hr (Table A3.7), as well as between switchgrass and high diversity prairie incubated for 24 hr (Table A3.8). While Proteobacteria and Acidobacteria were the key contributing phyla for the differences between the switchgrass and high diversity prairie incubated for 30 days (Table A3.7). The top 20 OTUs contributing to the differences of communities between treatments are also shown (Figure A3.3). Most of the contributing OTUs were more abundant in bare soil compared to the switchgrass and high diversity prairie incubated for 24 hr. Some OTUs from Actinobacteria with low abundance illustrated high contributions between the switchgrass and high diversity prairie incubated for 24 hr and 30 days. At the class level, each treatment, pore size and incubation time illustrated a range of different classes (Figure A3.4). 87 Figure 3.3 Box and whisker plots illustrating the relative abundance of the most abundant genera (A) and orders (B) in the three treatments. 88 3.4.2 Enriched Phylotypes for 13C Uptake from Glucose The phylotypes significantly enriched (Wilcoxon test, p < 0.05) in the 13C labelled glucose amended heavy fractions compared to unlabeled glucose amended heavy fractions were determined for the bare soil, switchgrass and high diversity prairie treatments for both pore sizes and incubation times (Figure 3.4). For the bare soil treatment (incubated for 24 hr) Pseudomonas and Planococcus were the dominant phylotypes incorporating the label in the large pores soils (Figure 3.4A). The trend was similar for the equivalent treatment for small pores soils (Figure 3.4F). Additional phylotypes incorporating the label in both the large and small pores of the bare soil treatment included Micrococcaceae and Burkholderia (Figure 3.4A & F). For the switchgrass treatment (24 hr incubation), Burkholderia, Pseudomonas and Duganella were involved in label uptake in the heavy fractions of both the large and small pores (Figure 3.4B & G). For the same treatment, label uptake was also attributed to Burkholderiaceae, Cupriavidus and Azoarcus (Figure 3.4B) in the large pores and Clostridium, Geobacter, and Pseudarthrobacter in the small pores (Figure 3.4G). For the high diversity prairie treatment (24 hr incubation), Pseudomonas, Clostridium, Pseudarthrobacter and Burkholderiaceae were the dominant phylotypes for label uptake in both large and small pores (Figure 3.4C & H). For the same treatment, Allorhizobiume and Dechlorosoma were associated with label uptake in the large and small pores, respectively (Figure 3.4C & H). Overall, the phylotypes enriched after 30 days were different from those enriched after 24 hr, although a number of similar phylotypes were also observed. Similar to the switchgrass 24 hr incubation, Burkholderia and Pseudomonas were also associated with label uptake for the large pores after 30 days (Figure 3.4D). However, for the small pores, in contrast to the shorter incubation, Burkholderia and Pseudomonas were not associated with label uptake (Figure 3.4I). 89 Instead, Spirochaeta, Dechlorosoma, Nakamurella, Geobacter and Burkholderiaceae were dominant phylotypes associated with label uptake in small pores soils after 30 days (Figure 3.4I). Two phylotypes were different between the shorter and the longer switchgrass incubations for the large pores (Duganella and Azoarcus were replaced by Nakamurella and Mycobacterium) (Figure 3.4B and D). Whereas five phylotypes were different between the shorter and the longer switchgrass incubations for the small pores (Geobacter was the only similar phylotype) (Figure 3.4G & D). For the high diversity prairie treatment incubated for 30 days, Pseudomonas only exhibited label uptake for the large pores (Figure 4E). For the large pores, in contrast to the earlier incubation for this treatment (Figure 4C), four phylotypes were different (Nakamurella, Spirochata, Mycobacterium and Bradyhizobium) (Figure 4E). For the small pores high diversity prairie, five phylotypes were different (Nakamurella, Cellulomonus, Candidatus, Solirubrobacter, Mycobacterium) between the shorter and longer incubation times (Burkholderiaceae was the only similar phylotype) (Figure 4H & J). 90 Figure 3.4 Phylotypes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the bare soil, switchgrass and high diversity prairie incubated for 24 hr or 30 days for the large pore (A-E) and the small pore (F-J) samples. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The six most abundant are shown for each treatment (A-J). 91 3.4.3 Functional Genes Predicted and Connections with Phylotypes The KO functions associated with carbohydrate metabolism and energy metabolism (including citrate cycle, glycolysis gluconeogenesis, methane metabolism and nitrogen metabolism) were investigated in the bare soil, switchgrass and high diversity prairie treatments. The most abundant genes significantly enriched in the 13C-glucose amended soils compared to the unlabeled glucose amended soils are shown (Figures A3.5-A3.9). The gene descriptions associated with each pathway are provided (Tables A3.10-A3.13). All genes significantly enriched for each pathway in the soil treatments are also summarized (Tables A3.14-A3.17). Differences were observed in the most enriched predicted genes between large and small pores soils for switchgrass and high diversity prairie incubated for 30 days (Figures 3.5 & Figures A3.10-A3.12). Notable trends included the enrichment of glk (glucokinase [EC:2.7.1.2], responsible for the conversion of glucose into glucose-6 phosphate, first step of glycolysis) in both the large and small pores high diversity prairie, but not in the switchgrass treatments (Figure 3.5). Additionally, the small pores soils were more dominated by genes associated with the later steps in the glycolysis/ gluconeogenesis pathways compared to the large pores soils for both switchgrass and high diversity prairie incubated for 30 days (Figure 3.5). Specifically, genes responsible for the conversion from pyruvate to acetyl-CoA were enriched in the small pores soils (incubated for 30 days) for the switchgrass and high diversity prairie treatments compared to large pores soils (Figure 3.5). The phylotypes predicted to be associated with a subset of genes are shown with chord diagrams (Figures 3.6, 3.7 & Figure A3.13). The phylotypes associated with methane/ammonia monooxygenase are shown for all three treatments and eight, ten and thirteen phylotypes were associated with all three subunits (pmoA-amoA, pmoB-amoB and pmoC-amoC) in the bare soil, 92 switchgrass and high diversity prairie treatments (Figure A3.13). Unclassified Beijerinckiaceae, Bauldia, unclassified Betaproteobacteriales and A21b_ge were associated the methane/ ammonia monooxygenase genes in all the three soils. The dominant phylotypes included Nitrosospira and Bauldia for bare soils, Methylovirgula and unclassified Nitrosomonadaceae for switchgrass and unclassified Nitrosomonadaceae and unclassified Beijerinckiaceae for high diversity prairie. As shown in Figure 6, no phylotype was associated with all the nitrate reductase genes (napA and napB) and nitrite reductase genes (nirK and nirS). One, four and five phylotypes contained napA, napB and nirK for the bare soil, switchgrass and high diversity prairie treatments, respectively. All four phylotypes containing napA, napB and nirK in switchgrass were included in high diversity prairie treatment (Figure 3.6B and C). Unclassified Xanothobacteraceae was the dominant phylotype containing napA, napB and nirK in all three treatments. Zero, two and one phylotypes contained napA, napB and nirS for the bare soil, switchgrass and high diversity prairie treatments, respectively (Figure 3.6). Phylotypes containing napA, napB and nirS were Pseudomonas and unclassified Betaproteobacteriales in the switchgrass treatment and A21b_ge in high diversity prairie treatment (Figure 3.6B and C). For the phylotypes associated with nitric oxide reductase and nitrous oxide reductase genes, two, six and two contained all three (norB, norC and nosZ) in the bare soil, switchgrass and high diversity prairie treatments, respectively (Figure 3.7). Unclassified Xanthobacteraceae contained all three genes (norB, norC and nosZ) for all the three treatments (Figure 3.7). Dominant phylotypes associated with nitric oxide reductase and nitrous oxide reductase genes included Sediminibacterium and Halomonas for the switchgrass treatment. The relative abundance of the phylotypes associated with the key functional genes in the 24 hr incubated and 30 day incubated soils are also shown (Figures A3.14-A3.17). Unclassified Xanthobacteraceae was more 93 abundant in the 24 hr incubated switchgrass treatment and high diversity prairie treatment than in the 30 day incubated soils (Figures A3.15 &A3.16). 94 Figure 3.5 Genes enriched in glycolysis/gluconeogenesis pathway in the high diversity prairie soil (A, B) and in the switchgrass soil (C, D) after 30 days of incubation. 95 Figure 3.5 (cont’d) 96 Figure 3.6 Phylotypes associated with nitrate reductase genes (napA and napB) and nitrite reductase genes (nirK and nirS) in bare soil (A), switchgrass soil (B) and high diversity prairie soil (C) (both 24 hours and 30 days). To limit the number of phylotypes on each figure, the relative abundance threshold was set to > 0.1. One, four and five phylotypes (in orange) contained napA, napB and nirK for the bare soil (A), switchgrass (B) and high diversity prairie (C) samples, respectively. Zero, two and one phylotypes (in yellow) contained napA, napB and nirS for the bare soil (A), switchgrass (B) and high diversity prairie (C) samples, respectively. 97 Figure 3.7 Phylotypes associated with nitric oxide reductase genes (norB and norC) and nitrous oxide reductase gene (nosZ) in the bare soil (A), switchgrass soil (B) and high diversity prairie soil (C) (both 24 hours and 30 days). To limit the number of phylotypes on each figure, the relative abundance threshold was set to > 0.2. Two, six and two phylotypes (in red) contained all three genes for the bare soil (A), switchgrass (B) and high diversity prairie (C) samples, respectively. 98 3.5 Discussion The impacts of soil pore size and treatment (bare soil, switchgrass, high diversity prairie) on the microbial communities involved in the utilization of a newly added carbon source (glucose) were assessed. The temporal fate of the added carbon was also explored by incubating the switchgrass and high diversity prairie soils over two time periods. SIP and high throughput 16S rRNA gene amplicon sequencing were combined to characterize the microbial communities active in carbon uptake. The overall microbial communities were not significantly different between soil pores sizes or incubation times but varied significantly between treatments. In contrast, the active microorganisms capable of utilizing the newly added carbon source (identified via SIP) differed between soil pores sizes, treatments and incubation times. The enriched functional genes associated with carbohydrate metabolism and energy metabolism, as predicted by PICRUSt2 (Douglas, Maffei et al. 2020), also differed depending on the soil pore size and soil treatment. Soil organic matter levels varied between the three soils (greatest in high plant diversity, followed by the switchgrass, then the bare soil). Consistent with this, others have reported high diversity plants exhibited increased root carbon inputs and root biomass concentration (Fornara and Tilman 2008, Ravenek, Bessler et al. 2014). Further, higher soil organic carbon content was observed in more diverse systems (Kravchenko, Guber et al. 2019). High plant diversity also increased the rhizosphere carbon inputs into soil microbial communities (Lange, Eisenhauer et al. 2015). The overall microbial communities were significantly different between the three treatments and differences were also observed for the microbial richness and diversity indices. Specifically, these values were significantly lower in bare soil compared to high diversity prairie 99 or switchgrass treatments. Correlations between plant diversity and microbial activity or diversity have also been reported by others. For example, biomass, carbon use efficiency, microbial respiration and respiratory response to nutrients changed with increasing plant species richness (Eisenhauer, Bessler et al. 2010). The researchers concluded the quality of litter and rhizodeposits were key factors impacting the soil microbial community (Eisenhauer, Bessler et al. 2010). In the current study, considering the overall microbial community, all three treatments were dominated by Proteobacteria and Actinobacteria. Both phyla also accounted for the majority of the community differences between treatments. Both Proteobacteria and Actinobacteria have been closely linked to the metabolism of labile organic substrates (Goldfarb, Karaoz et al. 2011) and were also enriched in other glucose amended soils (Wang, Zhang et al. 2021). There was no significant impact of pore size on the microbial diversity or richness values for the bare soil or switchgrass treatments. However, for the high diversity treatment, the small pores illustrated significantly higher diversity values (Shannon, Simpson, Inverse Simpson) compared to the large pores for the overall microbial community. Previous studies have also documented the impact of pore size and/or pore connectivity on soil microbial communities. It has been suggested that soils with a higher proportion of large pores are more likely to have disconnected water films, leading to a decline in microbial activity and microbial competitive interactions, thus potentially contributing to increased bacterial diversity (Carson, Gonzalez- Quinones et al. 2010, Chau , Bagtzoglou et al. 2011, Xia, Zheng et al. 2022). Bacterial species richness in acidic forest soils significantly increased with soil coarseness (quantified as % sand) (Chau , Bagtzoglou et al. 2011). The authors suggested the increase in species richness is due to the increased number of isolated water films in soils with larger pores. However, no significant 100 relationship was noted between the Shannon index and soil coarseness (% sand). A more recent study on the impact of macropores (>30 μm) and micropores (<30 μm) on soil microbial communities under row crop production reported no significant difference for Shannon diversity values between macropores and micropores for the total microbial community (Xia, Zheng et al. 2022). The range of previous and current results concerning the impact of pore size on microbial diversity and richness may be due to variations in methodologies, soil types, treatments and/or water availability. SIP enabled the identification of microorganisms assimilating carbon from the amended glucose. A notable trend involved the dominance of Pseudomonas (Proteobacteria) in carbon uptake in all short-term incubations and in the large pore, longer incubations (switchgrass and high diversity prairie). Similarly, in field soil SIP biodegradation assays, others also identified Pseudomonas as being important for glucose metabolism (Padmanabhan, Padmanabhan et al. 2003). In another study, Pseudomonas was the most abundant genus utilizing added 13C -glucose in rhizosphere soils after 3, 7 and 15 days (Zhou, Qin et al. 2021). It has been suggested that Pseudomonas species grow faster on labile substrates and are less able to compete for more recalcitrant substrates, thus have a putative role as a copiotroph (Bittman, Forge et al. 2005, Adamczyk, Perez-Mon et al. 2020). Fast-growing (r-strategists) copiotrophic microbes usually respond to labile carbon substrates quickly, whereas slow-growing (K-strategists) oligotrophic microbes grow on more recalcitrant carbon compounds (e.g., lignin) (Fierer, Bradford et al. 2007, Adamczyk, Perez-Mon et al. 2020). In the short-term SIP incubations, dominant carbon assimilators varied between treatments. Pseudarthrobacter (Actinobacteria) was identified as an active assimilator of carbon in the high diversity prairie treatment, short-term incubation, in both of the large and small pores 101 soils. In other research, strains of Pseudarthrobacter (from permafrost soils) were found to be among the fastest growing microorganisms, suggesting they are likely copiotrophic (Adamczyk, Perez-Mon et al. 2020). Pseudarthrobacter has also been associated with plant growth promoting activities (Abadi, Sepehri et al. 2020, Tapia-García, Hernández-Trejo et al. 2020, Ham, Yoon et al. 2021) and the degradation of organic contaminants, such as 2,4,6-trinitrotoluene and phthalic acid esters (Chen, Chen et al. 2021, Lamba, Anand et al. 2021). Duganella (Proteobacteria) was an active assimilator of carbon in the switchgrass treatment, short-term incubation, in both of the large and small pores soils. Duganella was previously linked to the promotion of plant growth (Verma, Yadav et al. 2014) and the suppression of plant pathogens (Haack, Poehlein et al. 2016). Pseudomonas and Planococcus (Firmicutes) were the dominant active assimilators of carbon in the bare soil treatment, short-term incubation, in both of the large and small pores soils. Consistent with this, others have considered Firmicutes to be copiotrophic (Verastegui, Cheng et al. 2014, Pepe-Ranney, Campbell et al. 2016). Planococcus has been associated with the biodegradation of aromatic hydrocarbons as well as the production of biosurfactants, which can make carbon compounds more accessible (Waghmode, Suryavanshi et al. 2020). Other key variables investigated with SIP in the current study involved soil pore size and incubation time. Although the overall microbial communities did not differ significantly based on OTU level between large and small pores sizes for each soil treatment (p > 0.05), the microorganisms actively responsible for carbon uptake (identified with SIP) differed between large and small pores sizes. For the shorter incubations, the numbers of unique phylotypes involved in carbon assimilation between large and small pores were zero, three and one for the bare soil, switchgrass and high diversity prairie treatments, respectively. However, for the longer incubations, the majority of the enriched phylotypes were different between the small and large 102 pore soils. In the large pores of the switchgrass and high diversity prairie treatments, approximately half of the enriched phylotypes were the same between incubation times. Although for the small pores of the same treatments, the phylotypes involved in carbon assimilation were largely different between incubation times, suggesting the active communities were more impacted by time in the small pores compared to the large pores. For the shorter incubation of the switchgrass treatment, except for Pseudomonas, Burkholderia was dominant in the large pores while Clostridium (Firmicutes) was dominant in the small pores. Similarly, others have identified Pseudomonas and Burkholderia as responders to amendments of 14C-glucose in soils (Jenkins, Rushton et al. 2010). This is consistent with their putative roles as r-strategists (Bittman, Forge et al. 2005). Clostridium has been associated with the biodegradation of complex carbohydrates (e.g., glucose, cellulose) (Warnick, Methé et al. 2002, Abd-Alla, Zohri et al. 2015, Figueiredo, Lopes et al. 2020). In flooded paddy soils, Clostridium communities illustrated a higher abundance with the addition of glucose (Li, Qu et al. 2017). In the shorter incubation of the high diversity treatment, the dominant phylotype for carbon assimilation was Pseudomonas in the large pores, while both Pseudomonas and Pseudarthrobacter were dominant in the small pores. However, for the longer incubation, Pseudomonas was only dominant in the large pores, suggesting the longer incubation time impacted the active microbial populations. The results are consistent with the theory of the dominance of r-strategists in large pores compared to small pores (Kravchenko, Guber et al. 2021). Many previous studies have documented the impact of soil pore scale on microbial community structures (Ruamps, Nunan et al. 2013, Kravchenko, Negassa et al. 2014, Negassa, Guber et al. 2015, Kravchenko and Guber 2017, Xia, Zheng et al. 2022). Large pore soils are 103 likely to hold more plant roots due to the typical diameter of roots (30 µm) (Kuchenbuch and Jungk 1982), have better air diffusion, greater O2 availability (Or, Smets et al. 2007), have more dissolved carbon and nutrients carried by the water fluxes (Kravchenko, Guber et al. 2021) as well as higher enzyme activity (Kravchenko, Guber et al. 2019). One conceptual model is that in large pores, r-strategists process labile carbon, enhance CO2 emission and increase concentrations of dissolved organic carbon (Kravchenko, Guber et al. 2021). The necromass of r- strategists is then linked to the stimulation of K-strategists, which respond by enhanced extracellular enzyme production and microbial biomass growth (Kravchenko, Guber et al. 2021). In contrast, in the small pores, K-strategists dominate, as they are adapted to low carbon inputs (Kravchenko, Guber et al. 2021). A key finding concerning the predicted enriched functional genes was the dominance of glucokinase (first step of glycolysis) in both the large and small pores of high diversity prairie treatment, but not in the switchgrass treatment. The trend suggests microorganisms in the high diversity prairie soil have a competitive advantage for consuming glucose. Another key finding concerns the dominance of genes associated with the later steps in the glycolysis/ gluconeogenesis pathways in the small pores compared to the large pores soils for both switchgrass and high diversity prairie. This is consistent with the theory that small pores favor K- strategists that are more likely to consume degradation products. Dominant phylotypes predicted to be associated with the methane/ammonia monooxygenase genes (pmoA-amoA, pmoB-amoB and pmoC-amoC) included Nitrosospira and Bauldia for the bare soils, Methylovirgula and unclassified Nitrosomonadaceae for the switchgrass treatment and unclassified Nitrosomonadaceae and unclassified Beijerinckiaceae for high diversity prairie. Unclassified Beijerinckiaceae and Nitrospira were also predicted to be 104 associated with pmoA/amoA in other research (Cupples and Thelusmond 2022). Beijerinckiaceae is a family known to contain methanotrophs (Knief 2015). Nitrosomonadaceae are lithoautotrophic ammonia oxidizers (Prosser, Head et al. 2014). Nitrospira is a commonly reported ammonia oxidizing bacteria (Norton 2011). More phylotypes contained napA, napB and nirK compared to those containing napA, napB and nirS. This is consistent with previous reports of the greater occurrence of nirK compared to nirS (Zhang, Zeng et al. 2015, Kim, Riggins et al. 2021, Li and Cupples 2021). For example, nirK was up to 3.8 times more abundant than nirS in 35 from 37 soils (Jones, Spor et al. 2014). The two genes are thought to be mutually exclusive, indicative of two ecologically distinct denitrifying communities (Enwall, Throback et al. 2010, Jones and Hallin 2010). Fewer phylotypes contained all three (of either group) in the bare soil compared to the switchgrass or high diversity prairie treatment, suggesting greater functional diversity in the latter two treatments. The switchgrass treatment contained the highest number of phylotypes containing nitric oxide reductase and nitrous oxide reductase genes (norB, norC and nosZ). Unclassified Xanthobacteraceae contained all three genes (norB, norC and nosZ) for all the three treatments. Interestingly, the same phylotype was the dominant phylotype containing napA, napB and nirK in all three treatments. Members of this family grow as aerobic chemoheterotrophs, but facultative chemolithoautotrophy (with hydrogen and/or reduced sulfur compounds) has also been reported (Oren 2014). The current study indicates an important role for an unclassified Xanthobacteraceae phylotype in denitrification for all three treatments. 3.6 Conclusions The overall microbial communities as well as the active microorganisms capable of carbon uptake from glucose were determined in three soils (bare soil, switchgrass and high 105 diversity prairie treatments) for two pore sizes. The overall microbial communities were significantly different between the three treatments, but not between pore sizes or incubation times. Proteobacteria and Actinobacteria accounted for the majority of the community differences between the three treatments. Lower microbial diversity values were observed in the bare soil compared to switchgrass and high prairie diversity soils. Although no significant differences were noted in microbial diversity and richness values between the small and large pore sizes of the bare soil and the switchgrass treatments, the small pores of the high diversity prairie treatment exhibited higher microbial diversity values compared to the large pores. SIP enabled the identification of the microorganisms assimilating carbon from the amended glucose. The active phylotypes responsible for carbon uptake varied between treatments, soil pore sizes and incubation times. Pseudomonas (Proteobacteria) played an important role in carbon uptake from glucose in all short-term incubations and in the large pore long-term incubations. A key finding included the identification of different carbon assimilators in the longer incubations between the small and large pore sizes for both the switchgrass and the high diversity prairie treatments. Such trends may be linked to different carbon assimilation strategies (r- vs. K-strategists) depending on pore size. The predicted enriched functional genes indicated the dominance of glucokinase in the high diversity prairie, but not in the switchgrass treatments, suggesting a competitive advantage for consuming glucose. The genes associated with the later steps in the glycolysis/gluconeogenesis pathways were dominant in the small pores compared to the large pores for both switchgrass and high diversity prairie, consistent with the theory that small pores favor K-strategists. 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Horticultural Plant Journal. 117 APPENDIX Figure A3.1 Alpha diversity assessed by richness (Chao1, ACE) and diversity (Shannon, Simpson, Inverse of Simpson and Fisher) for bare soil, high diversity prairie and monoculture switchgrass. Note: the hollow rhombus represents the means of the indices in control, large pore and small pore soils in each treatments. 118 Figure A3.2 Heatmaps of thirty most abundant OTUs in the bare soil, monoculture switchgrass and high diversity prairie of large pores (A) and small pores (B) over the two incubation times. 119 Figure A3.3 Chord diagrams illustrating the top 20 OTUs contributing to the differences of communities between bare soil and high diversity prairie incubated for 24 hr (A), bare soil and switchgrass incubated for 24 hr (B), switchgrass and high diversity prairie incubated for 24 hr (C) and switchgrass and high diversity prairie incubated for 30 days (D). 120 Figure A3.3 (cont’d) 121 Figure A3.4 Relative abundance by class level for the treatments involving the small pore soils (A) and large pore soils (B) for the three treatments at two different incubation times. 122 Figure A3.5 Genes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the bare soil incubated for 24 hr. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The eight most abundant for each treatment (A-H) are shown. In some cases, less than eight genes were significantly enriched. 123 Figure A3.6 Genes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the monoculture switchgrass soil incubated for 24 hours. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The eight most abundant for each treatment (A-H) are shown. 124 Figure A3.7 Genes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the monoculture switchgrass soil incubated for 30 days. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The eight most abundant for each treatment (A-H) are shown. In some cases, less than eight genes were significantly enriched. 125 Figure A3.8 Genes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the high diversity prairie soil incubated for 24 hours. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The eight most abundant for each treatment (A-H) are shown. In some cases, less than eight genes were significantly enriched. 126 Figure A3.9 Genes statistically significantly enriched (Wilcoxon test, one sided, p < 0.05) in the high diversity prairie soil incubated for 30 days. Wilcoxon test one-sided p values of 0.0001, 0.001, 0.01, and 0.05 are represented by ****, ***, **, *. The eight most abundant for each treatment (A-H) are shown. 127 Figure A3.10 Genes enriched in methane metabolism pathway in the high diversity prairie soil (A, B) and in the switchgrass soil (C, D) after 30 days of incubation. 128 Figure A3.11 Genes enriched in the citrate cycle (TCA cycle) pathway in the high diversity prairie soil (A, B) and in the switchgrass soil (C, D) after 30 days of incubation. 129 Figure A3.12 Genes enriched in nitrogen metabolism pathway in the high diversity prairie soil (A, B) and in the switchgrass soil (C, D) after 30 days of incubation. 130 Figure A3.13 Phylotypes associated with methane/ammonia monooxygenase genes (pmoA-amoA, pmoB-amoB and pmoC-amoC) in the bare soil (A), switchgrass soil (B) and high diversity prairie soil (C) (both 24 hours and 30 days). To limit the number of phylotypes on each figure, the relative abundance threshold was set to > 0.008. Eight, ten and thirteen phylotypes (in red) contained all three genes for the bare soil (A), switchgrass (B) and high diversity prairie (C) samples, respectively. 131 Figure A3.14 Phylotypes associated with methane/ammonia genes pmoA-amoA (A), pmoB- amoB (B) and pmoC-amoC (C) in the bare soil, switchgrass and high diversity prairie soils (incubated for 24 hours or 30 days). The relative abundance values were set to 0.008, 0.008 and 0.2 for pmoA-amoA, pmoB-amoB, and pmoC-amoC, respectively to limit the number of phylotypes shown. 132 Figure A3.15 Phylotypes associated with nitrate reductase genes napA (A) and napB (B) in the bare soil, switchgrass and high diversity prairie soils (incubated for 24 hours or 30 days). The relative abundance values were set to 0.07 for both genes to limit the number of phylotypes shown. 133 Figure A3.16 Phylotypes associated with nitrite reductase genes nirK (A) and nirS (B) in the bare soil, switchgrass and high diversity prairie soils (incubated for 24 hours or 30 days). The relative abundance values were set to 0.3 and 0.02 for nirK and nirS, respectively to limit the number of phylotypes shown. 134 Figure A3.17. Phylotypes associated with nitric oxide reductase genes norB (A) and norC (B) and nitrous oxide reductase gene nosZ (C) in the bare soil, switchgrass and high diversity prairie soils (incubated for 24 hours or 30 days). The relative abundance values were set to 0.8, 0.05, and 0.5 for norB, norC and nosZ, respectively to limit the number of phylotypes 135 Table A3.1. Basic soil characteristics of the bare soil, switchgrass and high diversity prairie. % of Exchangeable Bases (Mg) % of Exchangeable Bases (K) Magnesium (ppm) Phosphorus (ppm) Potassium (ppm) Calcium (ppm) % of Exchangeable Bases (Ca) Organic Matter % 6 6 5 Treatment pH Switchgrass B1 Switchgrass B2 Switchgrass B4 High Div. B1 High Div. B2 High Div. B4 Bare soil B1 6 6 6 5 13 27 25 27 55 46 57 83 107 80 89 154 213 113 157 172 116 163 783 770 775 890 173 1019 177 1060 132 739 3.9 4.9 4.1 3.8 5.7 7.5 5.7 24 26 19 23 21 20 22 72 69 77 74 74 72 73 2.3 2 2.4 2.7 3.4 3.9 2 136 Table A3.2 P-values for statistical tests with the diversity indexes in large and small pores. “-” indicates the data was not collected and p-values in red indicate a significant difference (p ≤ 0.05). Index Incubation time Bare Soil Large Pore Small Pore - - - - 216.588 177.688 - - - - 219.448 244.585 - - 3.629 - - 0.969 - - 33.659 - - - - 3.543 - - 0.961 - - 30.564 - - 132.098 164.032 24 hr & 30 days 30 days 24 hr 24 hr & 30 days 30 days 24 hr 24 hr & 30 days 30 days 24 hr 24 hr & 30 days 30 days 24 hr 24 hr & 30 days 30 days 24 hr 24 hr & 30 days 30 days 24 hr Chao1 ACE Shannon Simpson InvSimpson Fisher Large Pore Small Pore T-test (two- tailed) - - Switchgrass Monoculture T-test (two- tailed) 0.644 0.393 0.69 0.485 0.272 0.675 0.663 0.423 0.626 0.575 0.374 0.548 0.643 0.309 0.488 0.248 0.203 0.43 243.474 282.586 197.832 326.655 0.382 289.117 245.863 338.283 456.402 238.356 585.344 348.949 438.21 3.71 3.679 3.715 3.612 3.706 3.746 0.973 0.972 0.974 0.969 0.973 0.975 38.848 37.384 39.757 34.099 40.669 38.091 270.508 382.604 559.936 148.09 0.649 392.926 234.826 - - 0.657 - - 0.426 - - 0.343 - - 0.54 - - High Diversity Prairie Large Pore Small Pore 317.03 481.863 214.542 486.143 419.517 477.583 439.475 673.704 300.262 710.714 578.688 636.693 3.794 3.67 3.788 3.647 3.801 3.694 0.976 0.971 0.976 0.971 0.976 0.972 42.222 36.262 42.157 35.895 36.629 42.287 271.475 404.614 200.872 463.674 342.078 345.555 T-test (two- tailed) 0.189 0.14 0.762 0.208 0.147 0.834 0.044 0.182 0.167 0.027 0.154 0.127 0.03 0.154 0.147 0.299 0.183 0.986 137 Table A3.3 P-values for statistical tests with the diversity indexes in different soil treatments. “-” indicates the data was not collected and p-values in red indicate a significant difference (p ≤ 0.05). Bare vs High Div. vs Switchgrass Bare vs High Div. Bare vs Switchgrass High Div.vs Switchgrass Switchgrass ANOVA T-test (two-tailed) T-test (two- tailed) T-test (two- tailed) Chao1 ACE Shannon Simpson InvSimpson Fisher Bare Soil 24 hr & 30 days - 30 days 24 hr 24 hr & 30 days 30 days 24 hr - 197.14 - - 232.016 24 hr & 30 days - 30 days 24 hr - 3.586 24 hr & 30 days - 30 days 24 hr - 0.965 24 hr & 30 days - 30 days 24 hr - 32.112 High Diversity Prairie 399.446 350.343 448.55 556.589 505.488 607.691 3.732 3.717 3.747 0.974 0.973 0.974 39.242 39.026 39.458 262.18 256.388 267.49 394.774 396.078 393.58 3.694 3.659 3.726 0.973 0.971 0.974 38.084 36.671 39.38 - - 0.008 - - 0.013 - - 0.025 - - 0.029 - - 0.022 24 hr & 30 days - 338.045 324.119 - 30 days 24 hr - 148.065 332.273 343.817 335.293 313.876 - 0.104 138 - - 0.018 - - 0.015 - - 0.017 - - 0.034 - - 0.022 - - 0.059 - - 0.219 - - 0.14 - - 0.038 - - 0.031 - - 0.022 - - 0.116 0.136 0.401 0.096 0.29 0.57 0.202 0.541 0.465 0.695 0.723 0.463 0.972 0.775 0.49 0.976 0.912 0.985 0.818 Table A3.4 Coefficients from the Permutational Multivariate Analysis of Variance (PERMANOVA) of the microbial communities in different soil treatments. Coefficients for microbial communities based on pore size in bare soil incubated for 24 hr pore Residuals Total Df 2 17 19 SumsOfSqs MeanSqs 0.47 3.43 3.89 0.23 0.20 NA F.Model 1.16 NA NA R2 0.12 0.88 1.00 Pr(>F) 0.34 NA NA Coefficients for microbial communities based on pore size in high diversity prairie incubated for 24 hr pore Residuals Total Df 1 10 11 SumsOfSqs MeanSqs 0.16 1.43 1.59 0.16 0.14 NA F.Model 1.14 NA NA R2 0.10 0.90 1.00 Pr(>F) 0.22 NA NA Coefficients for microbial communities based on pore size in high diversity prairie incubated for 30 days. pore Residuals Total Df 1 10 11 SumsOfSqs MeanSqs 0.21 2.15 2.36 0.21 0.22 NA F.Model 0.95 NA NA R2 0.09 0.91 1.00 Pr(>F) 0.43 NA NA Coefficients for microbial communities based on pore size in switchgrass incubated for 24 hr. pore Residuals Total Df 1 10 11 SumsOfSqs MeanSqs 0.14 2.35 2.49 0.14 0.23 NA F.Model 0.60 NA NA R2 0.06 0.94 1.00 Pr(>F) 0.91 NA NA 139 Table A3.4 (cont’d) Coefficients for microbial communities based on pore size in switchgrass incubated for 30 days. pore Residuals Total Df 1 10 11 SumsOfSqs MeanSqs 0.21 3.41 3.62 0.21 0.34 NA F.Model 0.62 NA NA R2 0.06 0.94 1.00 Pr(>F) 1.00 NA NA Coefficients for microbial communities based on incubation time in high diversity prairie (ignoring pore size). time Residuals Total Df 1 22 23 SumsOfSqs MeanSqs 0.28 3.95 4.22 0.28 0.18 NA F.Model 1.53 NA NA R2 0.07 0.93 1.00 Pr(>F) 0.12 NA NA Coefficients for microbial communities based on soil treatment incubated for 24 hr (ignoring pore size). treatment Residuals Total Df 2 37 39 SumsOfSqs MeanSqs 3.45 6.92 10.37 1.72 0.19 NA F.Model 9.21 NA NA R2 0.33 0.67 1.00 Pr(>F) 0.01 NA NA Coefficients for microbial communities based on soil treatment incubated for 30 days (ignoring pore size). treatment Residuals Total Df 1.00 22.00 23.00 SumsOfSqs MeanSqs 0.57 5.97 6.54 0.57 0.27 NA F.Model 2.09 NA NA R2 0.09 0.91 1.00 Pr(>F) 0.02 NA NA 140 Table A3.5 Coefficients from Pairwise comparison based on soil treatment incubated for 24 hr (ignoring pore size). pairs High Diversity Prairie vs Bare Soil High Diversity Prairie vs Switchgrass Bare Soil vs Switchgrass SumsOfSqs 2.6 0.63 1.78 F.Model 15.26 3.42 8.66 p.value 0.01 0.01 0.01 R2 0.37 0.13 0.25 p.adjusted 0.03 0.03 0.03 Df 1 1 1 sig * * * 141 Table A3.6 Summary of bacteria that contributed to 50% of the dissimilarity between the bare soil and high diversity prairie incubated for 24 hr, revealed by similarity percentage analyses. + represents more abundant, − represents less abundant. Phylum Class taxonomy Proteobacteria Actinobacteria Bacteroidetes Acidobacteria Verrucomicrobia Chloroflexi Firmicutes Latescibacteria Gemmatimonadetes Nitrospirae Planctomycetes Gammaproteobacteria Thermoleophilia Alphaproteobacteria Actinobacteria Bacteroidia Verrucomicrobiae Acidobacteriia Bacilli Deltaproteobacteria Ktedonobacteria Subgroup_6 KD4-96 Blastocatellia_(Subgroup_4) Latescibacteria_cl Gemmatimonadetes AD3 MB-A2-108 Subgroup_22 Subgroup_17 Nitrospira Holophagae Clostridia Phycisphaerae High diversity prairie_24h + - - - + - - + - + + - - + - - + - - + - + + + + - - + + + + - + + contribution Bare_24h - + + + - + + - + - - + + - + + - + + - + - - - - + + - - - - + - - 18.15% 13.08% 4.94% 4.91% 3.53% 2.67% 1.73% 0.43% 0.39% 0.11% 0.07% 8.98% 7.91% 7.60% 5.00% 4.94% 3.53% 2.21% 1.64% 1.56% 1.55% 1.47% 0.87% 0.85% 0.43% 0.39% 0.26% 0.18% 0.15% 0.12% 0.11% 0.11% 0.09% 0.07% 142 Table A3.7 Summary of bacteria that contributed to 50% of the dissimilarity between the bare soil and switchgrass incubated for 24 hr, revealed by similarity percentage analyses. + represents more abundant, − represents less abundant. Phylum Class taxonomy Proteobacteria Actinobacteria Acidobacteria Bacteroidetes Verrucomicrobia Firmicutes Chloroflexi Latescibacteria Gemmatimonadetes Planctomycetes Spirochaetes Gammaproteobacteria Thermoleophilia Alphaproteobacteria Actinobacteria Bacteroidia Verrucomicrobiae Acidobacteriia Deltaproteobacteria Bacilli Ktedonobacteria Subgroup_6 vadinHA49 Blastocatellia_(Subgroup_4) KD4-96 Clostridia Gemmatimonadetes Acidimicrobiia Latescibacteria_cl Holophagae MB-A2-108 AD3 Subgroup_5 Planctomycetacia contribution bare_24h switchgrass_24h + - + - + - - + + + + - - + - - + - + - - + + + + + + + + + + - + + - + - + - + + - - - - + + - + + - + - + + - - - - - - - - - - + - - 17.42% 13.46% 5.34% 4.99% 3.02% 2.15% 2.52% 0.26% 0.57% 0.28% 0.09% 8.17% 7.51% 7.14% 5.48% 4.99% 3.02% 2.70% 2.11% 1.57% 1.47% 1.41% 0.96% 0.92% 0.89% 0.59% 0.57% 0.29% 0.26% 0.21% 0.18% 0.16% 0.10% 0.10% 143 Table A3.8 Summary of bacteria that contributed to 50% of the dissimilarity between the switchgrass and high diversity prairie incubated for 24 hr, revealed by similarity percentage analyses. + represents more abundant, − represents less abundant. Phylum Class taxonomy Proteobacteria Actinobacteria Acidobacteria Verrucomicrobia Bacteroidetes Chloroflexi Gemmatimonadetes Firmicutes Latescibacteria Planctomycetes Bacteria_unclassified Nitrospirae Spirochaetes Fibrobacteres Elusimicrobia Cyanobacteria Gammaproteobacteria Alphaproteobacteria Thermoleophilia Verrucomicrobiae Actinobacteria Deltaproteobacteria Bacteroidia Blastocatellia_(Subgroup_4) Subgroup_6 Acidobacteriia KD4-96 Gemmatimonadetes Latescibacteria_cl Clostridia Acidimicrobiia Bacilli MB-A2-108 Subgroup_22 Bacteria_unclassified high diversity prairie_24h - - - + + - + - + - + + - - - - + - - + - - + - + - + + + - - - + + + contribution switchgrass_24h + + + - - + - + - + - - + + + + - + + - + + - + - + - - - + + + - - - 18.15% 11.60% 6.31% 4.50% 2.75% 1.92% 1.05% 1.02% 0.99% 0.88% 0.28% 0.27% 0.10% 0.08% 0.08% 0.06% 7.84% 6.67% 6.45% 4.50% 4.25% 3.64% 2.67% 1.88% 1.80% 1.70% 1.53% 1.05% 0.99% 0.58% 0.47% 0.44% 0.42% 0.35% 0.28% 144 Table A3.8 (cont’d) taxonomy contribution switchgrass_24h high diversity prairie_24h Class Nitrospira OM190 Pla4_lineage vadinHA49 Planctomycetacia Anaerolineae Ktedonobacteria Subgroup_17 Thermoanaerobaculia Holophagae Spirochaetia Phycisphaerae Subgroup_5 Fibrobacteria Lineage_IIa Subgroup_11 Ignavibacteria Melainabacteria AD3 0.27% 0.24% 0.22% 0.17% 0.16% 0.16% 0.16% 0.16% 0.13% 0.13% 0.10% 0.10% 0.09% 0.08% 0.08% 0.07% 0.07% 0.06% 0.06% - + + + + + + + + + + - + + + - - + + + - - - - - - - - - - + - - - + + - - 145 Table A3.9 Summary of bacteria that contributed to 50% of the dissimilarity between the switchgrass and high diversity prairie incubated for 30 days, revealed by similarity percentage analyses. + represents more abundant, − represents less abundant. Phylum Class taxonomy Proteobacteria Acidobacteria Actinobacteria Verrucomicrobia Bacteroidetes Chloroflexi Gemmatimonadetes Latescibacteria Planctomycetes Bacteria_unclassified Firmicutes Nitrospirae Dependentiae Rokubacteria Entotheonellaeota Fibrobacteres Cyanobacteria Patescibacteria Chlamydiae Alphaproteobacteria Gammaproteobacteria Verrucomicrobiae Deltaproteobacteria Thermoleophilia Bacteroidia Subgroup_6 Actinobacteria Blastocatellia_(Subgroup_4) Gemmatimonadetes KD4-96 Latescibacteria_cl Acidobacteriia Bacteria_unclassified Planctomycetacia Holophagae high diversity prairie_30d - - + + - + - - - - - + + + + - - - - - + + - + - - - - - + - - - - - contribution switchgrass_30d + + - - + - + + + + + - - - - + + + + + - - + - + + + + + - + + + + + 19.74% 7.89% 6.50% 5.77% 3.02% 1.71% 1.64% 1.02% 0.99% 0.49% 0.35% 0.35% 0.29% 0.27% 0.18% 0.15% 0.10% 0.09% 0.09% 8.06% 7.23% 5.77% 4.36% 3.47% 2.92% 2.78% 2.75% 2.73% 1.54% 1.10% 1.02% 0.96% 0.49% 0.47% 0.39% 146 Table A3.9 (cont’d) Class taxonomy Anaerolineae Bacilli Nitrospira Subgroup_17 Subgroup_22 Babeliae NC10 Subgroup_5 Gitt-GS-136 Phycisphaerae OM190 MB-A2-108 Entotheonellia Fibrobacteria Thermoanaerobaculia vadinHA49 Sericytochromatia Ignavibacteria Longimicrobia Parcubacteria Chlamydiae Proteobacteria_unclassified Actinobacteria_unclassified high diversity prairie_30d + - + + + + + + + + + - + - - + - + - - - - - contribution switchgrass_30d - + - - - - - - - - - + - + + - + - + + + + + 0.37% 0.35% 0.35% 0.32% 0.32% 0.29% 0.27% 0.27% 0.24% 0.23% 0.20% 0.19% 0.18% 0.15% 0.12% 0.10% 0.10% 0.09% 0.09% 0.09% 0.09% 0.09% 0.09% 147 Table A3.10 KO numbers and gene descriptions for KEGG glycolysis gluconeogenesis pathway (PATHko00010). Gene Name K00114 exaA; alcohol dehydrogenase (cytochrome c) [EC:1.1.2.8] Gene Description frmA, ADH5, adhC; S-(hydroxymethyl) glutathione dehydrogenase / alcohol dehydrogenase [EC:1.1.1.284 1.1.1.1] K00121 K00128 ALDH; aldehyde dehydrogenase (NAD+) [EC:1.2.1.3] K00131 gapN; glyceraldehyde-3-phosphate dehydrogenase (NADP+) [EC:1.2.1.9] K00134 GAPDH, gapA; glyceraldehyde 3-phosphate dehydrogenase [EC:1.2.1.12] K00161 PDHA, pdhA; pyruvate dehydrogenase E1 component alpha subunit [EC:1.2.4.1] K00162 PDHB, pdhB; pyruvate dehydrogenase E1 component beta subunit [EC:1.2.4.1] K00171 porD; pyruvate ferredoxin oxidoreductase delta subunit [EC:1.2.7.1] K00174 korA, oorA, oforA; 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase subunit alpha [EC:1.2.7.3 1.2.7.11] korB, oorB, oforB; 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase subunit beta [EC:1.2.7.3 1.2.7.11] K00175 K00382 DLD, lpd, pdhD; dihydrolipoamide dehydrogenase [EC:1.8.1.4] DLAT, aceF, pdhC; pyruvate dehydrogenase E2 component (dihydrolipoamide acetyltransferase) [EC:2.3.1.12] K00627 K00845 glk; glucokinase [EC:2.7.1.2] K00850 pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11] K00873 PK, pyk; pyruvate kinase [EC:2.7.1.40] K00886 ppgK; polyphosphate glucokinase [EC:2.7.1.63] K00927 PGK, pgk; phosphoglycerate kinase [EC:2.7.2.3] K01006 ppdK; pyruvate, orthophosphate dikinase [EC:2.7.9.1] K01007 pps, ppsA; pyruvate, water dikinase [EC:2.7.9.2] K01222 E3.2.1.86A, celF; 6-phospho-beta-glucosidase [EC:3.2.1.86] K01223 E3.2.1.86B, bglA; 6-phospho-beta-glucosidase [EC:3.2.1.86] K01596 E4.1.1.32, pckA, PCK; phosphoenolpyruvate carboxykinase (GTP) [EC:4.1.1.32] K01610 E4.1.1.49, pckA; phosphoenolpyruvate carboxykinase (ATP) [EC:4.1.1.49] K01624 FBA, fbaA; fructose-bisphosphate aldolase, class II [EC:4.1.2.13] K01689 ENO, eno; enolase [EC:4.2.1.11] K01785 galM, GALM; aldose 1-epimerase [EC:5.1.3.3] K01792 E5.1.3.15; glucose-6-phosphate 1-epimerase [EC:5.1.3.15] K01803 TPI, tpiA; triosephosphate isomerase (TIM) [EC:5.3.1.1] K01810 GPI, pgi; glucose-6-phosphate isomerase [EC:5.3.1.9] PGAM, gpmA; 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase [EC:5.4.2.11] K01834 K01835 pgm; phosphoglucomutase [EC:5.4.2.2] K01895 ACSS1_2, acs; acetyl-CoA synthetase [EC:6.2.1.1] K01905 acdA; acetate---CoA ligase (ADP-forming) subunit alpha [EC:6.2.1.13] K02446 glpX; fructose-1,6-bisphosphatase II [EC:3.1.3.11] K02777 crr; sugar PTS system EIIA component [EC:2.7.1.-] K02791 malX; maltose/glucose PTS system EIICB component [EC:2.7.1.199 2.7.1.208] K03841 FBP, fbp; fructose-1,6-bisphosphatase I [EC:3.1.3.11] 148 Table A3.10 (cont’d) Gene Name K04072 adhE; acetaldehyde dehydrogenase / alcohol dehydrogenase [EC:1.2.1.10 1.1.1.1] Gene Description glpX-SEBP; fructose-1,6-bisphosphatase II / sedoheptulose-1,7-bisphosphatase [EC:3.1.3.11 3.1.3.37] fbaB; fructose-bisphosphate aldolase, class I [EC:4.1.2.13] tal-pgi; transaldolase / glucose-6-phosphate isomerase [EC:2.2.1.2 5.3.1.9] K11532 K11645 K13810 K13953 adhP; alcohol dehydrogenase, propanol-preferring [EC:1.1.1.1] K13979 yahK; alcohol dehydrogenase (NADP+) [EC:1.1.1.2] K15633 gpmI; 2,3-bisphosphoglycerate-independent phosphoglycerate mutase [EC:5.4.2.12] K15634 gpmB; 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase [EC:5.4.2.11] apgM; 2,3-bisphosphoglycerate-independent phosphoglycerate mutase [EC:5.4.2.12] K15635 K16370 pfkB; 6-phosphofructokinase 2 [EC:2.7.1.11] 149 Table A3.11 KO numbers and gene descriptions for the KEGG methane metabolism pathway (PATHko00680). Gene Name K00018 K00024 Gene Description hprA; glycerate dehydrogenase [EC:1.1.1.29] mdh; malate dehydrogenase [EC:1.1.1.37] serA, PHGDH; D-3-phosphoglycerate dehydrogenase / 2-oxoglutarate reductase [EC:1.1.1.95 1.1.1.399] frmA, ADH5, adhC; S-(hydroxymethyl)glutathione dehydrogenase / alcohol dehydrogenase [EC:1.1.1.284 1.1.1.1] FDH; formate dehydrogenase [EC:1.17.1.9] fdoG, fdhF, fdwA; formate dehydrogenase major subunit [EC:1.17.1.9] fdoH, fdsB; formate dehydrogenase iron-sulfur subunit fdsD; formate dehydrogenase subunit delta [EC:1.17.1.9] fdoI, fdsG; formate dehydrogenase subunit gamma porA; pyruvate ferredoxin oxidoreductase alpha subunit [EC:1.2.7.1] porB; pyruvate ferredoxin oxidoreductase beta subunit [EC:1.2.7.1] porD; pyruvate ferredoxin oxidoreductase delta subunit [EC:1.2.7.1] porC, porG; pyruvate ferredoxin oxidoreductase gamma subunit [EC:1.2.7.1] cdhD, acsD; acetyl-CoA decarbonylase/synthase, CODH/ACS complex subunit delta [EC:2.1.1.245] cooF; anaerobic carbon-monoxide dehydrogenase iron sulfur subunit cdhE, acsC; acetyl-CoA decarbonylase/synthase, CODH/ACS complex subunit gamma [EC:2.1.1.245] cooS, acsA; anaerobic carbon-monoxide dehydrogenase catalytic subunit [EC:1.2.7.4] fwdA, fmdA; formylmethanofuran dehydrogenase subunit A [EC:1.2.7.12] fwdB, fmdB; formylmethanofuran dehydrogenase subunit B [EC:1.2.7.12] fwdC, fmdC; formylmethanofuran dehydrogenase subunit C [EC:1.2.7.12] dmd-tmd; dimethylamine/trimethylamine dehydrogenase [EC:1.5.8.1 1.5.8.2] mer; 5,10-methylenetetrahydromethanopterin reductase [EC:1.5.98.2] frhD; coenzyme F420 hydrogenase subunit delta glyA, SHMT; glycine hydroxymethyltransferase [EC:2.1.2.1] E2.3.1.8, pta; phosphate acetyltransferase [EC:2.3.1.8] ftr; formylmethanofuran--tetrahydromethanopterin N-formyltransferase [EC:2.3.1.101] AGXT; alanine-glyoxylate transaminase / serine-glyoxylate transaminase / serine-pyruvate transaminase [EC:2.6.1.44 2.6.1.45 2.6.1.51] serC, PSAT1; phosphoserine aminotransferase [EC:2.6.1.52] pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11] DAK, TKFC; triose/dihydroxyacetone kinase / FAD-AMP lyase (cyclizing) [EC:2.7.1.28 2.7.1.29 4.6.1.15] pfkC; ADP-dependent phosphofructokinase/glucokinase [EC:2.7.1.146 2.7.1.147] ackA; acetate kinase [EC:2.7.2.1] K00058 K00121 K00122 K00123 K00124 K00126 K00127 K00169 K00170 K00171 K00172 K00194 K00196 K00197 K00198 K00200 K00201 K00202 K00317 K00320 K00442 K00600 K00625 K00672 K00830 K00831 K00850 K00863 K00918 K00925 150 Gene Description Table A3.11 (cont’d) Gene Name K01079 K01007 K01070 K01499 K01595 K01622 K01623 K01624 K01689 serB, PSPH; phosphoserine phosphatase [EC:3.1.3.3] pps, ppsA; pyruvate, water dikinase [EC:2.7.9.2] frmB, ESD, fghA; S-formylglutathione hydrolase [EC:3.1.2.12] mch; methenyltetrahydromethanopterin cyclohydrolase [EC:3.5.4.27] ppc; phosphoenolpyruvate carboxylase [EC:4.1.1.31] K01622; fructose 1,6-bisphosphate aldolase/phosphatase [EC:4.1.2.13 3.1.3.11] ALDO; fructose-bisphosphate aldolase, class I [EC:4.1.2.13] FBA, fbaA; fructose-bisphosphate aldolase, class II [EC:4.1.2.13] ENO, eno; enolase [EC:4.2.1.11] PGAM, gpmA; 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase [EC:5.4.2.11] ACSS1_2, acs; acetyl-CoA synthetase [EC:6.2.1.1] thrH; phosphoserine / homoserine phosphotransferase [EC:3.1.3.3 2.7.1.39] glpX; fructose-1,6-bisphosphatase II [EC:3.1.3.11] hdrA2; heterodisulfide reductase subunit A2 [EC:1.8.7.3 1.8.98.4 1.8.98.5 1.8.98.6] hdrB2; heterodisulfide reductase subunit B2 [EC:1.8.7.3 1.8.98.4 1.8.98.5 1.8.98.6] hdrC2; heterodisulfide reductase subunit C2 [EC:1.8.7.3 1.8.98.4 1.8.98.5 1.8.98.6] gfa; S-(hydroxymethyl)glutathione synthase [EC:4.4.1.22] torC; trimethylamine-N-oxide reductase (cytochrome c), cytochrome c-type subunit TorC torD; TorA specific chaperone FBP, fbp; fructose-1,6-bisphosphatase I [EC:3.1.3.11] fbp3; fructose-1,6-bisphosphatase III [EC:3.1.3.11] fdhA; formate dehydrogenase (NADP+) alpha subunit [EC:1.17.1.10] comC; L-2-hydroxycarboxylate dehydrogenase (NAD+) [EC:1.1.1.337] comB; 2-phosphosulfolactate phosphatase [EC:3.1.3.71] comD; sulfopyruvate decarboxylase subunit alpha [EC:4.1.1.79] mfnD; tyramine---L-glutamate ligase [EC:6.3.4.24] mfnF; (4-(4-[2-(gamma-L-glutamylamino)ethyl]phenoxymethyl)furan-2- yl)methanamine synthase [EC:2.5.1.131] mfnE; 5-(aminomethyl)-3-furanmethanol phosphate kinase [EC:2.7.4.31] torA; trimethylamine-N-oxide reductase (cytochrome c) [EC:1.7.2.3] torZ; trimethylamine-N-oxide reductase (cytochrome c) [EC:1.7.2.3] hxlA; 3-hexulose-6-phosphate synthase [EC:4.1.2.43] hxlB; 6-phospho-3-hexuloisomerase [EC:5.3.1.27] comA; phosphosulfolactate synthase [EC:4.4.1.19] qhpA; quinohemoprotein amine dehydrogenase [EC:1.4.9.1] mcl; malyl-CoA/(S)-citramalyl-CoA lyase [EC:4.1.3.24 4.1.3.25] K01834 K01895 K02203 K02446 K03388 K03389 K03390 K03396 K03532 K03533 K03841 K04041 K05299 K05884 K05979 K06034 K06914 K07072 K07144 K07811 K07812 K08093 K08094 K08097 K08685 K08691 151 Gene Description Table A3.11 (cont’d) Gene Name K08692 K09733 K10713 K10714 mtkB; malate-CoA ligase subunit alpha [EC:6.2.1.9] mfnB; (5-formylfuran-3-yl)methyl phosphate synthase [EC:4.2.3.153] fae; 5,6,7,8-tetrahydromethanopterin hydro-lyase [EC:4.2.1.147] mtdB; methylene-tetrahydromethanopterin dehydrogenase [EC:1.5.1.-] pmoA-amoA; methane/ammonia monooxygenase subunit A [EC:1.14.18.3 1.14.99.39] pmoB-amoB; methane/ammonia monooxygenase subunit B pmoC-amoC; methane/ammonia monooxygenase subunit C cofD; LPPG:FO 2-phospho-L-lactate transferase [EC:2.7.8.28] fwdE, fmdE; formylmethanofuran dehydrogenase subunit E [EC:1.2.7.12] gck, gckA, GLYCTK; glycerate 2-kinase [EC:2.7.1.165] glpX-SEBP; fructose-1,6-bisphosphatase II / sedoheptulose-1,7-bisphosphatase [EC:3.1.3.11 3.1.3.37] fbaB; fructose-bisphosphate aldolase, class I [EC:4.1.2.13] fbiC; FO synthase [EC:2.5.1.147 4.3.1.32] cofG; 7,8-didemethyl-8-hydroxy-5-deazariboflavin synthase [EC:4.3.1.32] cofH; 5-amino-6-(D-ribitylamino)uracil---L-tyrosine 4-hydroxyphenyl transferase [EC:2.5.1.147] cofE; coenzyme F420-0:L-glutamate ligase / coenzyme F420-1:gamma-L- glutamate ligase [EC:6.3.2.31 6.3.2.34] comE; sulfopyruvate decarboxylase subunit beta [EC:4.1.1.79] pta; phosphate acetyltransferase [EC:2.3.1.8] hps-phi; 3-hexulose-6-phosphate synthase / 6-phospho-3-hexuloisomerase [EC:4.1.2.43 5.3.1.27] mtkA; malate-CoA ligase subunit beta [EC:6.2.1.9] mtaA; [methyl-Co(III) methanol/glycine betaine-specific corrinoid protein]:coenzyme M methyltransferase [EC:2.1.1.246 2.1.1.377] mttB; trimethylamine---corrinoid protein Co-methyltransferase [EC:2.1.1.250] mvhA, vhuA, vhcA; F420-non-reducing hydrogenase large subunit [EC:1.12.99.- 1.8.98.5] mvhD, vhuD, vhcD; F420-non-reducing hydrogenase iron-sulfur subunit [EC:1.12.99.- 1.8.98.5 1.8.98.6] mvhG, vhuG, vhcG; F420-non-reducing hydrogenase small subunit [EC:1.12.99.- 1.8.98.5] cofC, fbiD; 2-phospho-L-lactate/phosphoenolpyruvate guanylyltransferase [EC:2.7.7.68 2.7.7.105] fdhB; formate dehydrogenase (NADP+) beta subunit [EC:1.17.1.10] mauA; methylamine dehydrogenase light chain [EC:1.4.9.1] mauB; methylamine dehydrogenase heavy chain [EC:1.4.9.1] K10944 K10945 K10946 K11212 K11261 K11529 K11532 K11645 K11779 K11780 K11781 K12234 K13039 K13788 K13831 K14067 K14080 K14083 K14126 K14127 K14128 K14941 K15022 K15228 K15229 152 Table A3.11 (cont’d) Gene Name Gene Description K15633 K15634 K15635 K16254 K16256 K16257 K16258 K16259 K16260 K16306 K16370 K16792 K16793 K17067 K18277 gpmI; 2,3-bisphosphoglycerate-independent phosphoglycerate mutase [EC:5.4.2.12] gpmB; 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase [EC:5.4.2.11] apgM; 2,3-bisphosphoglycerate-independent phosphoglycerate mutase [EC:5.4.2.12] mxaJ; mxaJ protein mxaA; mxaA protein mxaC; mxaC protein mxaK; mxaK protein mxaL; mxaL protein mxaD; mxaD protein K16306; fructose-bisphosphate aldolase / 2-amino-3,7-dideoxy-D-threo-hept- 6-ulosonate synthase [EC:4.1.2.13 2.2.1.10] pfkB; 6-phosphofructokinase 2 [EC:2.7.1.11] aksD; methanogen homoaconitase large subunit [EC:4.2.1.114] aksE; methanogen homoaconitase small subunit [EC:4.2.1.114] mdo; formaldehyde dismutase / methanol dehydrogenase [EC:1.2.98.1 1.1.99.37] tmm; trimethylamine monooxygenase [EC:1.14.13.148] 153 Table A3.12 KO numbers and gene descriptions for the KEGG nitrogen metabolism pathway (PATHko00680). Gene Name Gene Description K00261 K00262 K00265 K00266 K00284 K00360 K00362 K00367 K00368 K00372 K00376 K00459 K00926 K01673 K01674 K01725 K01915 K02567 K02568 K02586 K02588 K02591 K03385 K05601 K15371 K15579 K15876 GLUD1_2, gdhA; glutamate dehydrogenase (NAD(P)+) [EC:1.4.1.3] E1.4.1.4, gdhA; glutamate dehydrogenase (NADP+) [EC:1.4.1.4] gltB; glutamate synthase (NADPH) large chain [EC:1.4.1.13] gltD; glutamate synthase (NADPH) small chain [EC:1.4.1.13] GLU, gltS; glutamate synthase (ferredoxin) [EC:1.4.7.1] nasB; assimilatory nitrate reductase electron transfer subunit [EC:1.7.99.-] nirB; nitrite reductase (NADH) large subunit [EC:1.7.1.15] narB; ferredoxin-nitrate reductase [EC:1.7.7.2] nirK; nitrite reductase (NO-forming) [EC:1.7.2.1] nasA; assimilatory nitrate reductase catalytic subunit [EC:1.7.99.-] nosZ; nitrous-oxide reductase [EC:1.7.2.4] ncd2, npd; nitronate monooxygenase [EC:1.13.12.16] arcC; carbamate kinase [EC:2.7.2.2] cynT, can; carbonic anhydrase [EC:4.2.1.1] cah; carbonic anhydrase [EC:4.2.1.1] cynS; cyanate lyase [EC:4.2.1.104] glnA, GLUL; glutamine synthetase [EC:6.3.1.2] napA; nitrate reductase (cytochrome) [EC:1.9.6.1] napB; nitrate reductase (cytochrome), electron transfer subunit nifD; nitrogenase molybdenum-iron protein alpha chain [EC:1.18.6.1] nifH; nitrogenase iron protein NifH nifK; nitrogenase molybdenum-iron protein beta chain [EC:1.18.6.1] nrfA; nitrite reductase (cytochrome c-552) [EC:1.7.2.2] hcp; hydroxylamine reductase [EC:1.7.99.1] GDH2; glutamate dehydrogenase [EC:1.4.1.2] nrtD, cynD; nitrate/nitrite transport system ATP-binding protein nrfH; cytochrome c nitrite reductase small subunit 154 Gene Description aceE; pyruvate dehydrogenase E1 component [EC:1.2.4.1] IDH3; isocitrate dehydrogenase (NAD+) [EC:1.1.1.41] IDH1, IDH2, icd; isocitrate dehydrogenase [EC:1.1.1.42] Table A3.13 KO numbers and gene descriptions for the KEGG citrate cycle (TCA cycle) pathway (PATHko00020). Gene Name K00024 mdh; malate dehydrogenase [EC:1.1.1.37] K00025 MDH1; malate dehydrogenase [EC:1.1.1.37] K00030 K00031 K00116 mqo; malate dehydrogenase (quinone) [EC:1.1.5.4] K00161 PDHA, pdhA; pyruvate dehydrogenase E1 component alpha subunit [EC:1.2.4.1] K00162 PDHB, pdhB; pyruvate dehydrogenase E1 component beta subunit [EC:1.2.4.1] K00163 K00164 OGDH, sucA; 2-oxoglutarate dehydrogenase E1 component [EC:1.2.4.2] K00171 K00174 K00175 K00176 K00177 K00239 K00240 K00241 K00245 K00246 K00247 K00382 DLD, lpd, pdhD; dihydrolipoamide dehydrogenase [EC:1.8.1.4] porD; pyruvate ferredoxin oxidoreductase delta subunit [EC:1.2.7.1] korA, oorA, oforA; 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase subunit alpha [EC:1.2.7.3 1.2.7.11] korB, oorB, oforB; 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase subunit beta [EC:1.2.7.3 1.2.7.11] korD, oorD; 2-oxoglutarate ferredoxin oxidoreductase subunit delta [EC:1.2.7.3] korC, oorC; 2-oxoglutarate ferredoxin oxidoreductase subunit gamma [EC:1.2.7.3] sdhA, frdA; succinate dehydrogenase / fumarate reductase, flavoprotein subunit [EC:1.3.5.1 1.3.5.4] sdhB, frdB; succinate dehydrogenase / fumarate reductase, iron-sulfur subunit [EC:1.3.5.1 1.3.5.4] sdhC, frdC; succinate dehydrogenase / fumarate reductase, cytochrome b subunit frdB; fumarate reductase iron-sulfur subunit [EC:1.3.5.4] frdC; fumarate reductase subunit C frdD; fumarate reductase subunit D K00627 DLAT, aceF, pdhC; pyruvate dehydrogenase E2 component (dihydrolipoamide acetyltransferase) [EC:2.3.1.12] DLST, sucB; 2-oxoglutarate dehydrogenase E2 component (dihydrolipoamide succinyltransferase) [EC:2.3.1.61] K00658 K01596 E4.1.1.32, pckA, PCK; phosphoenolpyruvate carboxykinase (GTP) [EC:4.1.1.32] K01610 E4.1.1.49, pckA; phosphoenolpyruvate carboxykinase (ATP) [EC:4.1.1.49] K01616 K01647 CS, gltA; citrate synthase [EC:2.3.3.1] K01676 E4.2.1.2A, fumA, fumB; fumarate hydratase, class I [EC:4.2.1.2] kgd; multifunctional 2-oxoglutarate metabolism enzyme [EC:2.2.1.5 4.1.1.71 1.2.4.2 2.3.1.61] 155 Gene Description Table A3.13 (cont’d) Gene Name K01679 E4.2.1.2B, fumC, FH; fumarate hydratase, class II [EC:4.2.1.2] K01681 ACO, acnA; aconitate hydratase [EC:4.2.1.3] K01682 K01902 K01903 K01958 PC, pyc; pyruvate carboxylase [EC:6.4.1.1] K01960 acnB; aconitate hydratase 2 / 2-methylisocitrate dehydratase [EC:4.2.1.3 4.2.1.99] sucD; succinyl-CoA synthetase alpha subunit [EC:6.2.1.5] sucC; succinyl-CoA synthetase beta subunit [EC:6.2.1.5] pycB; pyruvate carboxylase subunit B [EC:6.4.1.1] 156 Table A3.14 Significantly enriched genes (p < 0.05) associated with the KEGG glycolysis gluconeogenesis pathway (X indicates the presence of the enriched genes). Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores Monocultur e switchgrass, 24 hr, large pores X X Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores Gene Name K00114 K00121 K00128 K00131 K00134 K00161 K00162 K00171 K00174 K00175 K00382 K00627 K00845 K00850 K00873 K00886 K00927 K01006 K01007 K01222 K01223 K01596 K01610 K01624 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 157 X X X X X X X X X X X X X X X X X X Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X Table A3.14 (cont’d) Gene Name K01689 K01785 K01792 K01803 K01810 K01834 K01835 K01895 K01905 K02446 K02777 K02791 K03841 K04072 K11532 K11645 K13810 K13953 K13979 K15633 K15634 K15635 K16370 Bare soil, 24 hr, large pores X Bare soil, 24 hr, small pores X High diversit y prairie, 24 hr, large pores X High diversit y prairie, 24 hr, small pores X High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores X Monocultur e switchgrass, 30 days, large pores X Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 158 X X X X X X X X X X X X X X X X X X X X X Table A3.15 Significantly enriched genes (p < 0.05) associated with the KEGG methane metabolism pathway (X indicates the presence of the enriched genes). Gene Name K00018 K00024 K00058 K00121 K00122 K00123 K00124 K00126 K00127 K00169 K00170 K00171 K00172 K00194 K00196 K00197 K00198 K00200 K00201 K00202 K00317 K00320 K00442 Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores X High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores X X X X X X X X 159 Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores X X X X X X X X X X X X X X X X X X X X X X X X Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X Table A3.15 (cont’d) Gene Name K00600 K00625 K00672 K00830 K00831 K00850 K00863 K00918 K00925 K01007 K01070 K01079 K01086 K01499 K01595 K01622 K01623 K01624 K01689 K01834 K01895 K02203 K02446 K03388 Bare soil, 24 hr, small pores Bare soil, 24 hr, large pores X High diversit y prairie, 24 hr, large pores X High diversit y prairie, 24 hr, small pores X High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores X Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 160 X X X X X Table A3.15 (cont’d) Gene Name K03396 K03532 K03533 K03841 K04041 K05299 K05884 K05979 K06034 K06914 K07072 K07144 K07811 K07812 K08093 K08094 K08097 K08685 K08691 K08692 K09733 K10713 K10714 K10944 Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores Monocultur e switchgrass, 24 hr, large pores X X X X X X X X X X X X X X 161 Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 24 hr, small pores X X X X Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Table A3.15 (cont’d) Gene Name K11212 K11261 K11529 K11532 K11645 K11779 K11780 K11781 K12234 K13039 K13788 K13831 K14067 K14080 K14083 K14126 K14127 K14128 K14941 K15022 K15228 K15229 K15633 Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores X High diversit y prairie, 30 days, small pores X Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores X Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 162 Table A3.15 (cont’d) Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores X High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores X X X X X Gene Name K15634 K15635 K16257 K16258 K16259 K16260 K16306 K16370 K16792 K16793 K17067 K18277 Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X 163 Table A3.16 Significantly enriched genes (p < 0.05) associated nitrogen metabolism (X indicates the presence of the enriched genes). High diversit y prairie, 30 days, small pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 30 days, small pores Monocultur e switchgrass, 30 days, large pores High diversit y prairie, 30 days, large pores High diversit y prairie, 24 hr, large pores X High diversit y prairie, 24 hr, small pores X Bare soil, 24 hr, large pores X Bare soil, 24 hr, small pores X X X X X X X X X X X X X X X X X X X X X Gene Name K00261 K00262 K00265 K00266 K00284 K00360 K00362 K00367 K00368 K00372 K00376 K00459 K00926 K01673 K01674 K01725 K01915 K02567 K02568 K02586 K02588 K02591 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 164 Table A3.16 (cont’d) Bare soil, 24 hr, large pores Bare soil, 24 hr, small pores High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores X Gene Name K03385 K05601 K15371 K15579 K15876 Monocultur e switchgrass, 24 hr, large pores X X Monocultur e switchgrass, 24 hr, small pores X X X Monocultur e switchgrass, 30 days, large pores X Monocultur e switchgrass, 30 days, small pores X X X X X 165 Table A3.17 Significantly enriched genes (p < 0.05) associated with the KEGG citrate cycle (TCA cycle) (X indicates the presence of the enriched genes). Bare soil, 24 hr, large pores X Bare soil, 24 hr, small pores High diversit y prairie, 24 hr, large pores X High diversit y prairie, 24 hr, small pores X High diversit y prairie, 30 days, large pores High diversit y prairie, 30 days, small pores Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 30 days, small pores Gene Name K00024 K00025 K00030 K00031 K00116 K00161 K00162 K00163 K00164 K00171 K00174 K00175 K00176 K00177 K00239 K00240 K00241 K00245 K00246 K00247 K00382 K00627 K00658 K01596 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 166 X X X X X X X X X X X X X X X X X X X Table A3.17 (cont’d) Bare soil, 24 hr, large pores X Bare soil, 24 hr, small pores X High diversit y prairie, 24 hr, large pores High diversit y prairie, 24 hr, small pores High diversit y prairie, 30 days, large pores X High diversit y prairie, 30 days, small pores X Monocultur e switchgrass, 24 hr, large pores Monocultur e switchgrass, 24 hr, small pores Monocultur e switchgrass, 30 days, large pores Monocultur e switchgrass, 30 days, small pores X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Gene Name K01610 K01616 K01647 K01676 K01679 K01681 K01682 K01902 K01903 K01958 K01960 167 CHAPTER 4: IMPACT OF YEAST EXTRACT AND BASAL SALTS MEDIUM ON 1,4- DIOXANE BIODEGRADATION RATES AND THE MICROORGANISMS INVOLVED IN CARBON UPTAKE FROM 1,4-DIOXANE This chapter is a modified version of a published work in Environmental Pollution: Li, Z. and A. M. Cupples (2024). "Impact of Yeast Extract and Basal Salts Medium on 1,4-Dioxane Biodegradation Rates and the Microorganisms Involved in Carbon Uptake from 1,4-Dioxane." Environmental Pollution: 125014 4.1 Abstract Conventional physical and chemical treatment technologies for 1,4-dioxane can be ineffective and consequently attention has focused on bioremediation. Towards this, the current research investigated the impact of basal salts medium (BSM) and yeast extract on 1,4-dioxane biodegradation rates in microcosms with different soil or sediment (agricultural soil, wetland sediment, sediment from an impacted site). Phylotypes responsible for carbon uptake from 1,4- dioxane were determined using stable isotope probing (SIP), both with and without BSM and yeast extract. Further, putative functional genes were investigated using 1) soluble di-iron monooxygenase (SDIMO) based amplicon sequencing, 2) qPCR targeting propane monooxygenase (large subunit, prmA) and 3) a predictive approach (PICRUSt2). The addition of BSM and yeast extract enhanced 1,4-dioxane removal rates in all three inocula, however, the differences were only significant for the agricultural soil and impacted site sediment microcosms. The phylotypes associated with carbon uptake varied across treatments and inocula. Gemmatimonas was important in the heavy SIP fractions of the wetland sediment microcosms. Unclassified Solirubacteraceae, Solirubrobacter, Pseudonocardia and RB4 were dominant in the 168 heavy SIP fractions of the agricultural soil microcosms. The heavy SIP fractions of the impacted site microcosms were dominated by only two phylotypes, unclassified Burkholderiaceae and oc3299. SDIMO based amplicon sequencing detected three genes previously associated with 1,4- dioxane. The predicted functional gene analysis suggested the importance of propane monooxygenases associated with Solirubrobacter and Pseudonocardia. Overall, it is likely that a community of microorganisms were involved in carbon uptake from 1,4-dioxane in both the wetland and agricultural soil microcosms. In contrast, carbon uptake in the impacted site sediment microcosms was largely restricted to two phylotypes. Many of these microorganisms have not previously been associated with 1,4-dioxane removal. The results suggest amending with BSM and yeast extract, even at low levels, could be a promising approach for the enhancement of 1,4-dioxane biodegradation rates. 4.2 Introduction 1,4-Dioxane, a probable human carcinogen, commonly used as a solvent and stabilizer for the chlorinated solvents, has been detected in both surface water and groundwater (Derosa, Wilbur et al. 1996, USEPA 2013, Adamson, Piña et al. 2017, Dang, Kanitkar et al. 2018, Karges, Becker et al. 2018). The characteristics of 1,4-dioxane (high water solubility and low Henry’s Law constant) pose challenges for remediation using conventional treatment techniques, such as air stripping or activated carbon (Zenker, Borden et al. 2003, Steffan, McClay et al. 2007, Zhang, Gedalanga et al. 2017, Godri Pollitt, Kim et al. 2019, Kikani, Satasiya et al. 2022). In the past decade, biologically mediated 1,4-dioxane removal has been used as alternative approach to clean up 1,4-dioxane contaminated sites (Lippincott, Streger et al. 2015, Horst, Bell et al. 2019, Bell, Wong et al. 2022, Divine, Bell et al. 2024). Numerous microorganisms have been associated with metabolic or co-metabolic 1,4- 169 dioxane biodegradation. Pseudonocardia dioxanivorans CB1190 (Parales, Adamus et al. 1994, Mahendra and Alvarez-Cohen 2006), Pseudonocardia sp. D17 (Sei, Oyama et al. 2013), Pseudonocardia sp. N23 (Yamamoto, Saito et al. 2018), Pseudonocardia benzenivorans B5 (Mahendra and Alvarez-Cohen 2006), Xanthobacter flavus DT8 (Chen, Jin et al. 2016), Mycobacterium sp. PH-06 (Kim, Jeon et al. 2009), Acinetobacter baumannii DD1 (Huang et al. 2014) and Rhodanobacter AYS5 (Pugazhendi, Rajesh Banu et al. 2015) utilize 1,4-dioxane as a sole carbon source. Others degrade 1,4-dioxane co-metabolically, including: Pseudonocardia sp. ENV478 (Vainberg, McClay et al. 2006), Pseudonocardia tetrahydrofuranoxydans sp. K1 (Kohlweyer, Thiemer et al. 2000) and Rhodococcus sp. YYL (Yao, Lv et al. 2009) induced by tetrahydrofuran; Rhodococcus sp. RR1, Burkholderia cepacia G4, Ralstonia pickettii PKO1 and Pseudomonas mendocina KR1 (Mahendra and Alvarez-Cohen 2006) induced by toluene; and Rhodococcus ruber ENV425 (Lippincott, Streger et al. 2015), Mycobacterium vaccae JOB5 (Mahendra and Alvarez-Cohen 2006) and Rhodococcus jostii RHA1 (Hand, Wang et al. 2015) induced by propane. The biochemical pathway for 1,4-dioxane biodegradation is initiated by soluble di-iron monooxygenases (SDIMOs). In general, SDIMOs have been classified into seven groups based on their substrate specificity and function (Notomista, Lahm et al. 2003, Coleman, Bui et al. 2006, Yang, Haritos et al. 2024). SDIMOs associated with the co-metabolic and metabolic biodegradation of 1,4-dioxane were previously summarized, being primarily in SDIMO groups 1, 2, 3, 5 and 6 (He, Mathieu et al. 2017). To date, the majority of 1,4-dioxane focused research has involved groups 5 and 6 SDIMOs, such as propane monooxygenase from Mycobacterium dioxanotrophicus PH-06 (group 6) (Deng, Li et al. 2018) and tetrahydrofuran monooxygenase from Pseudonocardia dioxanivorans CB1190 (group 5) (Sales, Mahendra et al. 2011, Sales, 170 Grostern et al. 2013), Pseudonocardia sp. strain ENV478 (group 5) (Masuda, McClay et al. 2012) and Pseudonocardia tetrahydrofuranoxydans K1 (group 5) (Sales, Grostern et al. 2013). Propane monooxygenase subunit sequences similar to Rhodococcus sp. RR1 prmA (group 5) were also linked to 1,4-dioxane biodegradation in mixed microbial communities (Eshghdoostkhatami and Cupples 2024). Although much is known about the enzymes, genes and microorganisms associated with 1,4-dioxane biodegradation in pure culture, less is known about removal mechanisms in mixed microbial cultures. A valuable approach for examining contaminant biodegradation in mixed communities is known as stable isotope probing (SIP). SIP is a cultivation-independent method, tracking the incorporation of a stable isotope from a labeled chemical into DNA or RNA (Radajewski, Ineson et al. 2000, Cupples 2016, Kim, Hwangbo et al. 2023). This approach has been applied to characterize active degraders for various chemicals, such as ethane and propane (Farhan Ul Haque, Hernández et al. 2022), n-hexadecane (Liu, Zhang et al. 2019), phenanthrene (Thomas, Corre et al. 2019, Bao, Li et al. 2022), vinyl chloride (Paes, Liu et al. 2015), hexahydro-1,3,5-trinitro-1,3,5-triazine (Cho, Lee et al. 2013, Jayamani and Cupples 2015), methyl tert-butyl ether (Sun, Sun et al. 2012) and cis-dichloroethene (Dang and Cupples 2021). Previous studies used this approach to identify 1,4-dioxane degraders in sludge (Aoyagi, Morishita et al. 2018), groundwater (Bell, McDonough et al. 2016) and soils (Dang and Cupples 2021). Optimizing bioremediation at contaminated sites often involves the addition of carbon sources to support in situ microbial communities. Various carbon sources have been evaluated as substrates to enhance 1,4-dioxane biodegradation, including tetrahydrofuran, 1,3,5-trioxane, ethylene glycol, diethylene glycol, 1,4-butanediol, butanone, acetone, 1-butanol, 2-butanol, 171 phenol, propanol, acetate, ethane, propane, methane and lactate (Sei, Kakinoki et al. 2010, Hatzinger, Banerjee et al. 2017, Xiong, Mason et al. 2019, Inoue, Hisada et al. 2020, Xiong, Mason et al. 2020, Dang and Cupples 2021, Miao, Heintz et al. 2021, Inoue, Hisada et al. 2022, Tawfik, Al-sayed et al. 2022). The current study examined the impact of adding yeast extract and basal salts medium (BSM) on 1,4-dioxane biodegradation rates. As yeast extract contains multiple growth factors, it has the potential to be beneficial to numerous microorganisms potentially linked to 1,4-dioxane biodegradation. Although yeast extract has previously been shown to enhance 1,4-dioxane biodegradation in pure cultures (Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016), little is known about the impact in mixed communities. Also, in those studies, high yeast extract concentrations were used (20 mg/L and 100 mg/L) (Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016), which is unlikely to be suitable at contaminated sites because of biofouling. Therefore, the current research examined the impact of lower yeast extract concentrations (60 µg/L) on 1,4-dioxane biodegradation rates. The objectives were to 1) examine the impact of yeast extract and BSM on 1,4-dioxane degradation rates in microcosms amended with different inocula (agricultural soil, wetland sediment and impacted site sediments), 2) identify the phylotypes involved in carbon uptake from 1,4-dioxane using SIP, and 3) determine the functional genes putatively associated with 1,4-dioxane biodegradation. 4.3 Methods 4.3.1 Chemicals, Inocula and Microcosm Setup Unlabeled 1,4-dioxane (³99.5%) and 1,4-dioxane-d8 (³99% isotopic purity) were purchased from Sigma-Aldrich (MO, USA). Labeled 13C-1,4-dioxane ((13C)4H8O2, 99% isotopic purity) was purchased from Santa Cruz Biotechnology (TX, USA). The biodegradation of 1,4- dioxane was examined using three inocula, including wetland sediment (Lake Lansing, MI), 172 sediment from an impacted site in California (West Coast Naval Station) and agricultural soil. The agricultural soil was collected from six replicate plots of the Main Cropping System Experiment at the Kellogg Biological Station Long-Term Ecological Research, in southwest Michigan. This treatment (called Treatment 4) receives no chemical inputs, compost or manure. More details of this treatment can be found at https://lter.kbs.msu.edu/research/long-term- experiments/main-cropping-system-experiment/. All soils and sediments were stored at 4 °C in the dark before use. Laboratory microcosms were established with soil or sediment (10 g wet weight) and 30 mL of liquid (water or BSM with yeast extract) in 160 mL serum bottles. The BSM was modified from a previous recipe (Pugazhendi, Rajesh Banu et al. 2015) and contained NH4Cl (1.0 g/L), K2HPO4 (3.24 g/L), NaH2PO4 (1.0 g/L), MgSO4 (0.20 g/L), FeSO4 (0.012 g/L), MnSO4 (0.003 g/L), ZnSO4 (0.003 g/L) and CoCl2 (0.001 g/L). The final pH of the BSM was adjusted to 7.4 with 0.1N NaOH. The media also contained 60 µg/L yeast extract (Sigma- Aldrich, USA). For each inocula type, the experimental design included triplicate live microcosms amended with 13C labeled 1,4-dioxane, triplicate live microcosms amended with 12C 1,4-dioxane and triplicate abiotic microcosms (abiotic controls) amended with 12C 1,4-dioxane. For each inocula type, the nine microcosms were either amended with water or were amended with BSM and yeast extract (eighteen microcosms for each in total). For all treatments, the abiotic controls were autoclaved daily for three consecutive days. All microcosms, sealed with a rubber stopper and aluminum crimp, were incubated at room temperature on a rotary bench-top shaker. The microcosms were opened for 0.5 hr every five days for aeration. The initial concentrations of 1,4-dioxane were ~2 mg/L in the live sample microcosms and abiotic controls. 4.3.2 1,4-Dioxane Analysis A triple quadrupole Agilent 7010B GC/MS system (Agilent Technologies, CA, USA) 173 equipped with a VF-5ms column (length 30 m, inner diameter 0.25 mm, film thickness 0.25 µm) and solid phase micro extraction (SPME) (Sigma-Aldrich, MO, USA) was used to measure 1,4- dioxane concentrations in the liquid phase of the microcosms. The SPME fiber was assembled with 30 µm carboxen/polydimethylsiloxane layer, 50 µm divinylbenzene layer and a 24 Ga needle. At each sampling time-point, 1 mL of each sample was collected using a sterile syringe (3 mL) and needle (22 Ga 1.5 in.) and then filtered (0.22 μm nylon filter) (Biomed Scientific). An aliquot (500 µL) of the filtered samples or series of diluted external standards were added into amber glass vials (40 mL). Also, 500 µL of 200 µg/L 1,4-dioxane-d8 was added into each vial as an internal standard. The vials were maintained at 40°C before the measurement. The SPME fiber was conditioned at 270 °C for 30 mins before each sequence run. For each sample, the fiber was inserted into the vials and exposed to the analytes for 2 mins. The analytes in the headspace adsorbed onto the fiber and then the fiber was exposed to the inlet. The initial oven temperature time was 40 °C and this was maintained for 4 mins. The oven temperature was then programmed to increase to 250 °C at a rate of 40 °C/min. The flow rate of carrier gas (helium) was 1.2 mL/min in constant flow mode. 4.3.3 DNA Extraction, Fractioning and MiSeq Illumina Sequencing DNA was extracted, in triplicate, from the live 12C 1,4-dioxane and 13C labeled 1,4- dioxane amendment microcosms using the DNA extraction kit (DNeasy PowerLyzer PowerSoil Kit, Mo Bio, USA) according to the manual protocol. The concentration of DNA in each extract was quantified using the Quant-iT™ dsDNA High-Sensitivity Assay Kit. Ultracentrifugation and fractioning were performed as previously described (Dang and Cupples 2021, Li, Kravchenko et al. 2024). For each of the labeled and unlabeled 1,4-dioxane DNA extracts, twelve tubes were ultracentrifuged, including DNA from duplicate microcosms for both the H2O treatment and the 174 BSM with yeast treatment, for impacted site, agricultural soil, and wetland sediment. In total, twenty-four tubes were ultracentrifuged (2 isotopes [12C and 13C] ´ 2 microcosms replicates ´ 2 treatments ´ 3 soil/sediment types). For each of the twenty-four ultracentrifugation runs, three heavy fractions (buoyant density ~1.73 to ~1.75 g/mL) and one light fraction (buoyant density ~1.7 g/mL) were selected. Although fractions of heavier buoyant density were collected, they did not meet the minimum DNA concentration required for 16S rRNA gene amplicon sequencing. In total, three 96-well plates (4 fractions, 3 replicates for each fraction, 2 isotopes, 2 microcosms replicates, 2 treatments, 3 soil types) were submitted to the Genomic Cores at the Research Technology Support Facility (RTSF) at Michigan State University (MSU). The V4 region of 16S rRNA gene was targeted for amplification using primers 515f (5’- GTGCCAGCMGCCGCGGTAA-3’) and 806r (5’-GGACTACHVGGGTWTCTAAT-3’) following a previously described protocol (Kozich, Westcott et al. 2013). PCR products were batch normalized using Invitrogen SequalPrep DNA Normalization plates and the products recovered from the plates pooled. The pool was cleaned and concentrated using AmpureXP magnetic beads; then QC'd and quantified using a combination of Qubit dsDNA HS, Agilent 4200 TapeStation HS DNA1000, and Kapa Illumina Library Quantification qPCR assays. The pool was loaded onto an Illumina MiSeq v2 standard flow cell and sequencing was performed in a 2 × 250 bp paired end format using a MiSeq v2 500 cycle reagent cartridge. Custom sequencing and index primers were added to appropriate wells of the reagent cartridge. Base calling was performed by Illumina Real Time Analysis (RTA) v1.18.54 and RTA output demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.19.1. The raw sequences were summitted to NCBI under Bioproject PRJNA1073031 (accession numbers SAMN39784393 to SAMN39784676). 175 4.3.4 Microbial Community Analyses & the Identification of Phylotypes Incorporating 13C Raw amplicon sequences in the fastq format were combined, trimmed, aligned and quality controlled using Mothur (Schloss, Westcott et al. 2009) on the High Performance Computing Cluster (HPCC) at MSU. The SILVA bacteria database (Release 138) for the V4 region (Pruesse, Quast et al. 2007) was used for the alignments and the sequences were then classified into operational taxonomic units (OTUs) at 97% similarity. The classification of OTUs into taxonomic levels and downstream analysis were conducted with two Mothur files (shared file and taxonomy file) with R (Version 4.2.1) (R Core Team 2018) in RStudio (Version 2022.12.0) (RStudio_Team 2020). The packages phyloseq (version 1.34.0) (McMurdie and Holmes 2013) and microbiome (version 1.12.0) (Lahti and Shetty 2012-2019) were used to 1) determine the relative abundance at the phyla level in the fractions, 2) generate barcharts for the most abundant families in the three soil samples, 3) perform the alpha diversity analysis (Chao1, ACE, Shannon's values, Simpson, Inverse of Simpson, and Fisher indices), and 4) conduct the Principal Coordinate Analysis (PCoA). The statistically enriched phylotypes in the heavy fractions of the 13C 1,4-dioxane amended samples (those responsible for carbon uptake from 1,4-dioxane) were determined using the R packages dplyr (version 1.1.3) (Wickham, Francois et al. 2023), tidyr (version 1.3.0) (Wickham, Vaughan et al. 2023), ggpubr (version 0.6.0) (Kassambara 2023) and rstatix (version 0.7.2) (Kassambara 2023). For this, the Wilcoxon Test (function wilcox_test) (one sided, p < 0.05) was used to determine which phylotypes exhibited a greater relative abundance in the 13C 1,4-dioxane amended heavy fractions compared to the corresponding 12C 1,4-dioxane amended heavy fractions. Following this, phylotypes statistically enriched in the light 13C 1,4-dioxane amended fractions compared to the corresponding 12C 1,4-dioxane amended light fractions were 176 removed from the list generated above to avoid possible false positives. The R packages ggplot2 (version 3.3.5) (Wickham 2016) and forcats (version 1.0.0) (Wickham 2023) were used to generate the boxplots for the top ten most abundant statistically enriched phylotypes. The packages dplyr (version 1.1.3) (Wickham, Francois et al. 2023) and ggplot2 (version 3.3.5) (Wickham 2016) were used to illustrate the number of enriched OTUs and families in the three soil types. 4.3.5 PICRUSt2 Monooxygenase Gene Predictions PICRUSt2 (Douglas, Maffei et al. 2020) was utilized to analyze the Mothur generated files on the HPCC at MSU. The inputs to PICRUSt2 involved a fasta file and a biom file. PICRUSt2 predicts the functional potential of microbial communities based on marker gene (16S rRNA gene) sequencing profiles. PICRUSt2 was applied with EPA-NG (Barbera, Kozlov et al. 2019) and gappa (Czech, Barbera et al. 2020) for phylogenetic placement of reads, castor (Louca and Doebeli 2018) for hidden state prediction and MinPath (Ye and Doak 2009) for pathway inference. The PICRUSt2 generated files (pred_metagenome_contrib.tsv and pred_metagenome_contrib.tsv) were investigated (primarily using the R packages tidyr and dplyr) for the presence of genes associated with monooxygenases (from the KEGG database (Kanehisa 2002)) as well as the phylotypes associated with each. More information on the data within each file can be found in the following tutorial (https://github.com/picrust/picrust2/wiki/PICRUSt2-Tutorial-(v2.5.0)). Functional genes investigated (KEGG number in parenthesis) included: prmA propane 2-monooxygenase large subunit (K18223), prmB propane monooxygenase reductase component (K18225), prmC propane 2-monooxygenase small subunit (K18224), prmD (K18226) propane monooxygenase coupling protein, pmoA-amoA methane/ammonia monooxygenase subunit A (K10944), pmoB- 177 amoB methane/ammonia monooxygenase subunit B (K10945), pmoC-amoC methane/ammonia monooxygenase subunit C (K10946), mmoX methane monooxygenase component A alpha chain (K16157), mmoY methane monooxygenase component A beta chain (K16158), mmoZ methane monooxygenase component A gamma chain (K16159), mmoB methane monooxygenase regulatory protein B (K16160), mmoC methane monooxygenase component C (K16161), mmoD methane monooxygenase component D (K16162), tmoA, tbuA1, touA toluene monooxygenase system protein A (K15760), tmoB, tbuU, touB toluene monooxygenase system protein B (K15761), tmoC, tbuB, touC toluene monooxygenase system ferredoxin subunit (K15762), tmoD, tbuV, touD toluene monooxygenase system protein D (K15763), tmoE, tbuA2, touE toluene monooxygenase system protein E (K15764), tmoF, tbuC, touF toluene monooxygenase electron transfer component (K15765), dmpK/poxA/tomA0 phenol/toluene 2-monooxygenase (NADH) P0/A0 (K16249), dmpL/poxB/tomA1 phenol/toluene 2-monooxygenase (NADH) P1/A1 (K16243), dmpM/ poxC/tomA2 phenol/toluene 2-monooxygenase (NADH) P2/A2 (K16244), dmpN/poxD/tomA3 phenol/toluene 2-monooxygenase (NADH) P3/A3 (K16242), dmpO/poxE/ tomA4 phenol/toluene 2-monooxygenase (NADH) P4/A4 (K16245) and dmpP/poxF/tomA5 phenol/toluene 2-monooxygenase (NADH) P5/A5 (K16246). RStudio on the HPCC at MSU was used to generate a file that contained which gene subunits and phylotypes were present using the PICRUSt2 output file pred_metagenome_contrib.tsv (unzipped). The approach involved combining this file with 1) a file containing gene numbers and descriptions and 2) a taxonomy file (from Mothur), using the R packages data.table (version 1.14.8) (Dowle and Srinivasan 2023), dplyr (version 1.1.3) (Wickham, Francois et al. 2023), tidyr (version 1.3.0) (Wickham, Vaughan et al. 2023), ggplot2 (Wickham 2016) and patchwork (version 1.1.3) (Pedersen 2023). Bar charts were generated for 178 each monooxygenase, faceted by the sample type and the gene subunits. 4.3.6 SDIMO Gene Amplicon Sequencing A two-step library preparation was completed for sequencing, first involving PCR with target-specific primers with tags on the 5 prime ends (Fluidigm common oligos CS1/CS2) to facilitate the second PCR for barcoding. The target-specific primers included two degenerate primers previously designed to target conserved regions in the SDIMO alpha subunit gene (called NVC57 and NVC66, target size 420 bp, Table A4.1) (Coleman, Bui et al. 2006). The following steps were performed by the Genomics Core at RTSF at MSU. PCR amplicons were batch-normalized using Invitrogen SequalPrep DNA Normalization plates and the recovered product was pooled. The pool was QC’d and quantified using a combination of Qubit dsDNA HS, Agilent 4200 TapeStation HS DNA1000 and Invitrogen Collibri Library Quantification qPCR assays. This pool was loaded onto one (1) Illumina MiSeq v2 Standard flow cell and sequencing was carried out in a 2x250bp paired end format using a MiSeq v2 500 cycle reagent cartridge. Custom sequencing and index primers complementary to the Fluidigm CS1 and CS2 oligomers were added to appropriate wells of the reagent cartridge. Base calling was done by Illumina Real Time Analysis (RTA) v1.18.54 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.20.0. The raw sequences were summitted to NCBI under Bioproject PRJNA1073036 (accession numbers SAMN39784693 to SAMN39784716). 4.3.7 SDIMO Sequences Processing and Analysis The amplicon sequencing files were processed on the HPCC at MSU using usearchv11 (Edgar 2010). The steps included an inspection of data quality and using the commands - fastx_info and fastq_eestats2. Sequences were then pooled using -fastq_mergepairs. Quality 179 filtering was achieved using -fastq_filter, with a maximum expected error threshold set to 1.0. Following this, sequences were dereplicated using -fastx_uniques. The command cluster_otus was used to complete 97% operational taxonomic units (OTU) clustering using the UPARSE- OTU (Edgar 2013) algorithm and to filter chimeras. The -otutab command was used to generate OTU tables with OTU abundance values. To enable a comparison of the OTUs to genes previously associated with 1,4-dioxane metabolism and co-metabolism, twelve gene sequences previously associated with 1,4-dioxane biodegradation as summarized in (He, Mathieu et al. 2017) were obtained from NCBI. Each of the twelve gene sequence were then uploaded for a nucleotide-nucleotide blastn search to find highly similar sequences to create a blast database for each (Altschul, Gish et al. 1990). To ensure only highly similar sequences were selected, the resulting databases were filtered using a percent identity and query length threshold of greater than or equal to 95%. The occurrence of the gene sequences in each database was investigated in the usearch files generated by using blastn (BLAST/2.10.0-Linux_x86_64 on HPCC). The results from the blastn search were downloaded from HPCC and were examined using R (Version 4.2.1) (R Core Team 2018) in RStudio (Version 2022.12.0) (RStudio_Team 2020). Specifically, the results were filtered to include matches of > 90% sequence identity (the sequence identity was reduced to capture a wide diversity of gene matches) and alignment length of more than 400 bps. The numbers of OTUs aligning to each gene database for each sample were determined and the datasets were used to construct phylogenetic trees (as described below). As only three (Rhodococcus jostii RHA1 prmA and Rhodococcus sp. RR1 prmA, Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976) of the twelve genes were detected, only three trees were generated. Data manipulation, data analysis and the generation of 180 figures was completed with R (Version 4.2.1) (R Core Team 2018) in RStudio (Version 2022.12.0) (RStudio_Team 2020). For this, the following R packages were utilized: tidyverse (Version 1.3.1) (Wickham, Averick et al. 2019), ampir (Version 1.1.0) (Fingerhut L. and I. 2021), writexl (Version 1.4.2) (Ooms 2023), readxl (Version 1.4.2) (Wickham and Bryan 2023), writexl (Ooms 2023), ggplot2 (Wickham 2016) and phylotools (Version 0.2.2) (Zhang 2017). 4.3.8 Phylogenetic Trees Sequences were first submitted for MAFFT (multiple alignment using fast Fourier transform) alignment using an online server (https://mafft.cbrc.jp/alignment/server/) (Katoh, Rozewicki et al. 2019) (Version 7). The alignments generated (using the Neighbor-Joining method and Jukes-Cantor model) were then exported in Newick format and uploaded to the Interactive Tree of Life (https://itol.embl.de) (Letunic and Bork 2021) (Version 6.7.2). The OTU abundance values for each sample were added using the datasets function called multi value bar chart. 4.3.9 Quantitative PCR on SIP Fractions Gene copies of Rhodococcus sp. RR1 prmA were determined the SIP fractions using a previously developed qPCR assay (Eshghdoostkhatami and Cupples 2024) (Table A4.1). Quantitative PCR was performed with the CFX96TM Real-Time PCR System (Bio-Rad, Hercules, CA), using 20 μL total volume containing 10 μL PrimeTime™ Gene Expression Master Mix, 0.3 μM of each primer (IDT Integrated DNA Technologies, Coralville, IA), 0.2 μg/mL bovine serum albumin (Thermo Fisher Scientific), 0.15 μM of the probe (IDT Integrated Technologies), 6.4 μL of PCR grade water (IDT Integrated DNA Technologies), and 2 μL DNA extract or PCR grade water (for the negative controls). BSA was added as it has been shown to limit inhibition in environmental samples (Kreader 1996, Wang, Olson et al. 2007, Gedalanga, 181 Pornwongthong et al. 2014). The thermal cycler program involved an initial activation at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds, and annealing at 60°C for 1 minute. The target gene (prmA) was incorporated into a plasmid for use as qPCR standards (GenScript Biotech Corporation). Each qPCR assay was performed in triplicate with DNA templates, no template controls (NTCs), and 5-fold serial dilutions of the standards to create calibration curves. DNA extract concentrations (Table A4.2), as well as data concerning the qPCR assays (as suggested by MIQE guidelines) (Bustin, Benes et al. 2009) (Table A4.3) has been summarized. 4.3.10 Statistical Analysis Differences in the 1,4-dioxane degradation rates and microbial alpha diversity values between inocula and treatments were investigated using one-way analysis of variance (ANOVA) and Welch’s two-sided t-tests. If the p-value from the one-way ANOVA was smaller than 0.05, t-tests were used to compare the differences between inocula or treatments. The results of ANOVA and t-tests are provided (Tables A4.4-A4.11). 4.4 Results 4.4.1 1,4-Dioxane Biodegradation Rates 1,4-Dioxane concentrations in all live and control microcosms for all treatments were monitored over 50 days (Figure 4.1). For all live microcosms, the 95% confidence intervals (CIs) for the regression lines of 12C 1,4-dioxane and 13C 1,4-dioxane amended live samples overlapped entirely over the incubation, indicating the label did not impact removal rates. In contrast, the 95% CIs differed between the live samples and abiotic controls. The removal slopes were also significantly different between the live samples and corresponding abiotic controls (p < 0.05) (Tables A4.4-A4.7), indicating decreases in 1,4-dioxane concentrations were due to 182 biodegradation. 1,4-Dioxane removal rates were significantly different between the microcosms with the three inocula types (one-way ANOVA, p < 0.05) (Table A4.4). Treatment (water vs. yeast extract and BSM) differences between 1,4-dioxane removal rates also varied between the three inocula. Although the 1,4-dioxane removal rate was higher in the BSM and yeast extract treatment compared to the water treatment in the wetland sediment microcosms, the difference was not significant (t-tests, p > 0.05) (Table A4.5). However, the addition of BSM and yeast extract significantly increased 1,4-dioxane removal rates, compared to the water treatments, in both the agricultural soil and impacted site sediment microcosms (t-tests, p <0.05) (Tables A4.6 & A4.7). 4.4.2 Microbial Community Analyses PCoA analysis indicated the microbial communities differed between the agricultural soils, wetland sediments and site sediments microcosms (Figure A4.1). Greater differentiations were observed between heavy and light fractions for the soil and wetland sediments microcosms (Figure A4.1B & C), compared to the site sediments microcosms (Figure A4.1D). The alpha diversity indices in the microcosms were significantly different (one-way ANOVA and t-tests) between the three soil/sediment types (Tables A4.8-4.10). For both the water and BSM and yeast treatments, the soil microcosms illustrated the highest alpha diversity and richness indices, followed by wetland sediment microcosms, then the impacted site sediment microcosms (Figure 4.2). For the soil microcosms, all the richness (Chao1, ACE) and diversity (Shannon, Simpson, Inverse of Simpson and Fisher) indices were significantly higher in the no yeast (water only) compared to the yeast with BSM treatment (p < 0.05; Table A4.11). Four indices (Chao1, ACE, Inverse of Simpson and Fisher) and three indices (Chao1, ACE and Fisher) were significantly 183 higher in the treatments with no yeast compared to those with yeast and BSM the for wetlandand impacted site sediment microcosms, respectively (p < 0.05; TableAA4.11). 184 Figure 4.1 1,4-dioxane removal in triplicates of three different sample types (A, B for the site, C, D for the soil and E, F for the wetland microcosms) and triplicates of abiotic controls with a starting 1,4-dioxane concentration of approximately 2 mg/L. Graphs on the left represent microcosms amended with 1,4-dioxane and water, while graphs on the right represent those amended with basal salts media (BSM) and yeast extract. The grey areas represent 95% confidence intervals for the regression lines. 185 Figure 4.2 Alpha diversity analysis for all three sample types without yeast and with yeast and basal salts media. Note: the hollow diamonds represent the means of the indices, averaged across all the fractions. 186 4.4.3 Phyla and Phylotypes Responsible for Carbon Uptake from 1,4-Dioxane The relative abundance of phyla in the 12C and 13C amended heavy and lights fractions were identified and compared (Figure 4.3). For the soil and wetland sediment microcosms, different phyla dominated in the heavy fractions compared to the light fractions. Specifically, Firmicutes and Bacteroides were dominant in the light fractions, while Actinobacteria and Proteobacteria were dominant in the heavy fractions. For impacted site sediment microcosms, the light and heavy fractions illustrated similar trends at the phylum level, with both being dominated by Proteobacteria. The ten most abundant phylotypes statistically enriched in the heavy fractions of 13C 1,4- dioxane amended samples compared to the 12C 1,4-dioxane amended samples were determined using the Wilcoxon Rank test (p < 0.05) (Figure 4.4). The phylotypes associated with carbon uptake from 1,4-dioxane varied both across treatments (water vs. BSM and yeast) and inocula types (Figure 4.4). In the wetland microcosms with water only, the dominant phylotypes included an uncultured strain, Gemmatimonas, Gemmata and an unclassified Alphaproteobacteria. In the wetland microcosms with yeast and BSM, dominant phylotypes included Massilia, unclassified Rhizobiales as well as two Gemmatimonas strains. In the soil microcosms with water, the enriched phylotypes were dominated by RB4, Udaeobacter, Subgroup 6 and Ellin. Whereas, in the soil microcosms with yeast and BSM, Solirubacteraceae, Pseudonocardia, Solirubrobacter, Acidothermus and Gaiella were primarily associated with label uptake. In contrast, the enriched phylotypes in the site microcosms were dominated by only one phylotype in each treatment, an unclassified Burkholderiaceae (water treatment) and oc3299 (yeast and BSM treatment). The datasets were also summarized to illustrated enrichment patterns for all statistically 187 enriched phylotypes across treatments and sample types (Figure 4.5). The largest number of OTUs and families were enriched in the wetland communities, followed by the agricultural soil, then the impacted site sediment (Figure 4.5A & B). In the comparison between yeast and no yeast treatments, the number of statistically enriched OTUs and families were similar for the impacted site sediments. However, for both soil and wetland sediments, the numbers of enriched families and OTUs were greater in the yeast treatment compared to the no yeast treatment. 188 Figure 4.3 The relative abundance (%) of phyla in the light and heavy, 12C 1,4-dioxane and 13C 1,4-dioxane, amended fractions of both treatments (water only and basal salts media/yeast) and three sample types. Each column represents average values for three sequencing replicates. 189 Figure 4.4 Relative abundance (%) of significantly enriched phylotypes in the heavy fractions of 12C and 13C 1,4-dioxane amended microcosms inoculated with wetland sediment, agricultural soil and an impacted site sediment, without yeast and with yeast and basal salts media. 190 Figure 4.5 The number of statistically significantly enriched OTUs (A) and families (B) in the site sediment, soil and wetland sediment in the yeast and basal salts media amended samples, in the no yeast treatments and in both. 191 4.4.4 SDIMO Amplicon Sequencing and prmA Quantitative PCR Assay The BLAST analysis compared the SDIMO amplicon sequencing OTUs to genes previously associated with 1,4-dioxane metabolism or co-metabolism. Three genes (Rhodococcus jostii RHA1 prmA and Rhodococcus sp. RR1 prmA, Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976) were detected and aligned in the three soil/sediment types (Figure 4.6). The majority of alignments to both Rhodococcus prmA databases involved methane monooxygenases or propane monooxygenases from other Rhodococcus species. The numbers of alignments to both databases were the greatest for the wetland sediments, followed by the soil, then the impacted site sediments. The majority of the alignments to the Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976 database were from the impacted site sediment microcosms (Figure 4.6C). The alignments were associated with genes encoding for tetrahydrofuran monooxygenase alpha subunit (thmA) from Pseudonocardia, Rhodococcus, Arthrobacter and Acinetobacter. Gene copies of Rhodococcus sp. RR1 prmA were further investigated using qPCR in the 12C and 13C gradient fractions for the wetland sediments, soil and impacted site sediment microcosms. Only the fractions from the wetland microcosms illustrated an increase in buoyant density in the heavy fractions of the 13C amended samples compared to the heavy fractions of the 12C controls (Figure 4.7). The trends were similar for both replicates of both treatments (with and without yeast). In the no yeast treatment (Figure 4.7A), 13C-labeled prmA genes peaked at heavier buoyant densities (BDs) (1.7382 and 1.7371 g/mL) compared to those of 12C-labeled fractions (1.7360 and 1.7306 g/mL). In the yeast treatment (Figure 4.7B), 13C-labeled prmA genes also peaked at heavier BDs (1.7393 and 1.7349 g/mL) compared to those of 12C-labeled fractions (1.7328 and 1.7306 g/mL). 192 4.4.5 Prediction of 13C Enriched Monooxygenase Genes PICRUSt2 predicted the phylotypes associated with monooxygenase genes for the three soil/sediment types (Figure 4.8, Figures A4.2-4.5). A number of microorganisms were associated with propane monooxygenase in the three sample types, however, only a small number were linked to all four subunits (prmA, prmB, prmC and prmD) (Figure 4.8). Specifically, in all three sample types, Pseudonocardia, unclassified Pseudonocardiaceae, Solirubrobacter and unclassified Solirubrobacteraceae were primarily associated with all four subunits. A number of phylotypes were linked to all six subunits of phenol/toluene 2-monooxygenase in all three sample types (Figure A4.2). The most dominant for the impacted site samples included unclassified Burkholderiaceae. The most dominant for the soil samples included IS-44, oc32 (Nitrosomonadaceae), Pseudomonas and SC-I_84. For the wetland samples, Acinetobacter was associated with all six subunits (although the levels for three subunits were lower), as was IS-44, MND1, oc32 and SC-I_84. Dominant patterns for the other functional genes included Labrys (Rhizobiales) for the six subunits of toluene monooxygenase in the impacted site sediments (Figure A4.3), unclassified Rhizobiales for the three subunits of ammonia/particulate methane monooxygenase for all three sample types (Figure A4.4) and Mycobacterium for the five subunits of soluble methane monooxygenase for all three sample types (Figure A4.5). 193 Figure 4.6 Number of OTUs aligning to sequences similar to Rhodococcus sp. RR1 prmA (A), Rhodococcus jostii RHA1 prmA (B) and Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976 (dioxane monooxygenase) (C) in the heavy fractions of each sample type. The numbers at the end of each represent protein IDs. 194 Figure 4.7 Gene copies of RR1 prmA in the SIP fractions from no yeast (water only) treatment (A) and the basal salts media and yeast treatment (B) of wetland sediments. 195 Figure 4.8 Relative abundance of taxa associated with all four subunits of propane monooxygenase, prmA (K18223), prmB (K18225), prmC (K18224) and prmD (K18226). Taxa without all four subunits were not included. 196 4.5 Discussion This study examined the phylotypes and functional genes associated with 1,4-dioxane biodegradation in three mixed microbial communities. The impact of BSM and yeast extract on 1,4-dioxane biodegradation was also investigated as an easily available and non-hazardous substrate to potentially enhance removal rates in situ. Multiple molecular methods were utilized to ascertain the key biomarkers. The phylotypes responsible for the carbon uptake from 1,4- dioxane were identified using DNA-based SIP. The genes encoding for putative 1,4-dioxane degradative enzymes were investigated using 1) SDIMO based amplicon sequencing, 2) qPCR targeting Rhodococcus sp. RR1 prmA in the SIP fractions and 3) a predictive method (PICRUSt2) for the occurrence of oxygenase genes (Douglas, Maffei et al. 2020). The impact of BSM and yeast extract on 1,4-dioxane biodegradation rates differed between the three microbial communities. The addition of BSM and yeast extract enhanced removal rates in all three inocula types, however, differences were only significant for the agricultural soil and impacted site sediment microcosms. A number of previous studies have added yeast extract while examining 1,4-dioxane biodegradation. One group added yeast extract to laboratory incubations with four river water samples, however, no 1,4-dioxane biodegradation was observed within the study period (29 days) (Sei, Kakinoki et al. 2010). Others have reported yeast extract accelerates 1,4-dioxane degradation rates by pure cultures (Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016). Rhodanobacter AYS5 completely degraded 100 mg/L 1,4-dioxane in 4 days with 20 mg/L of yeast extract as an additional substrate (Pugazhendi, Rajesh Banu et al. 2015). Xanthobacter flavus DT8 degraded 100 mg/L 1,4-dioxane in less than 25 h with 100 mg/L of yeast extract (Chen, Jin et al. 2016). The biodegradation of tetrahydrofuran (a structural analog of 1,4-dioxane) by Rhodococcus strain YYL was also 197 improved by the addition of yeast extract (Yao, Lv et al. 2009). Rhodococcus ruber 219 sustained the degradation of low concentrations of 1,4-dioxane (<100 μg/L) to below health advisory levels (0.35 μg/L) when supplied with thiamine (Simmer, Richards et al. 2021). The researchers suggest that in situ biostimulation with growth supplements might result in efficient removal of 1,4-dioxane (Simmer, Richards et al. 2021). As yeast extract contains multiple growth factors, it has the potential to be beneficial to numerous microorganisms potentially linked to 1,4-dioxane biodegradation. The results of the current study are not surprising as yeast extract has been associated with enhanced biodegradation of a large number of other contaminants, such as pyrene (by a bacterial consortium) (Wang, Qin et al. 2021), nonylphenol (in sediment and media) (Changa, Liua et al. 2008), methyl tert-butyl ether (by Methylibium petroleiphilum PM1) (Chen, Chen et al. 2009), petroleum hydrocarbons (by a bacterial consortium) (Al-Mur, Pugazhendi et al. 2021) and hexadecane (by Burkholderia cepacia GS3C) (Wu, Dang et al. 2011). For B. cepacia GS3C, researchers concluded amino acids were the active ingredients in yeast extract that caused the higher biodegradation rates and the trend was related to a greater level of cytochrome P450 activity (Wu, Dang et al. 2011). In the current study, the different trends between the three microbial communities may be related to the nutritional requirements of the degradative microorganisms involved and/or the nutritional resources already present in the wetland sediment compared to the other two sample types. Given the practical implications, the most important trend is the enhancement of 1,4- dioxane biodegradation rates in the impacted site microcosms due to the addition of yeast extract and BSM. It is notable the effect was significant at an order of magnitude lower yeast extract concentration (60 μg/L) compared to the concentrations used in the pure culture studies. The 198 enhancement at low yeast extract concentrations is important because of the potential for biofouling in situ when biostimulation is practiced with high substrate concentrations. SIP revealed different phylotypes were responsible for carbon uptake from 1,4-dioxane between the three mixed communities. Gemmatimonas was notably enriched in both wetland treatments. This genus belongs to the phylum Gemmatimonadetes and members of this phylum are widely distributed across various natural environments (Zhang, Sekiguchi et al. 2003, DeBruyn, Nixon et al. 2011, Hanada and Sekiguchi 2014). However, the physiology and environmental role of the members are largely unknown due to the limited number of cultivated species (Zeng, Selyanin et al. 2015). To our knowledge, members of this genus have not been previously associated with carbon uptake from 1,4-dioxane. However, Gemmatimonas was previously associated with benzoate biodegradation (Zhang, Sekiguchi et al. 2003) and was dominant in hydrocarbon-polluted soil (Sampaio, Almeida et al. 2017). Gemmatimonas was also associated with pyrene (Wang, Teng et al. 2018) and phenanthrene degradation in soil (Elyamine and Hu 2020, Dou, Sun et al. 2021, Wang, Wang et al. 2021). Xanthobacteraceae (Rhizobiales order) and unclassified Rhizobiales were also responsible for carbon uptake from 1,4-dioxane in the wetland sediment microcosms. Xanthobacteraceae was previously linked to carbon uptake from 1,4-dioxane in soil microcosms (Dang and Cupples 2021). Xanthobacteraceae has also been associated with 1,4-dioxane biodegradation in activated sludge (Chen, Miao et al. 2021, Samadi, Kermanshahi-pour et al. 2023). Further, the 1,4-dioxane degraders Xanthobacter flavus DT8 (Chen, Jin et al. 2016) and Xanthobacter sp. YN2 (Ma, Wang et al. 2021) classify within the same family. Also consistent with the current study, genera classifying within the order Rhizobiales (Hyphomicrobium and Chelativorans) were enriched following 1,4-dioxane biodegradation compared to control 199 microcosms (no 1,4-dioxane) in agricultural soil microcosms (Ramalingam and Cupples 2020). In the agricultural soil microcosms amended with BSM and yeast extract, both the genus Solirubrobacter and the family Solirubacteraceae were linked to carbon uptake from 1,4- dioxane. Solirubrobacter (Solirubacteraceae family) commonly exists in agricultural soil rhizospheres (Aguiar, Souza et al. 2020, Cordero Elvia, de Freitas et al. 2021, Lee, Kim et al. 2021). Members of this genus are difficult to cultivate and isolate due to their slow growth and the lack of specific media (Seki, Matsumoto et al. 2015). Solirubrobacter has previously been associated with the degradation of various substrates, such as 4-nonylphenol (Hung, Chen et al. 2022), coal (Wang, Wang et al. 2019), organic matter (Bukin, Pavlova et al. 2016) and petroleum hydrocarbons (Peng, Zi et al. 2015). To date, no 1,4-dioxane degrading Solirubrobacter isolate has been reported. Pseudonocardia was also associated with carbon uptake from 1,4-dioxane in the agricultural soil microcosms amended with BSM and yeast extract. This genus contains many well-known 1,4-dioxane degraders. For example, Pseudonocardia dioxanivorans CB1190 (Parales, Adamus et al. 1994, Mahendra and Alvarez-Cohen 2006), Pseudonocardia sp. D17 (Sei, Oyama et al. 2013), Pseudonocardia sp. N23 (Yamamoto, Saito et al. 2018) and Pseudonocardia benzenivorans B5 (Mahendra and Alvarez-Cohen 2006) can degrade 1,4- dioxane metabolically. Pseudonocardia asaccharolytica JCM 14343 (Inoue, Tsunoda et al. 2016), Pseudonocardia sp. ENV478 (Vainberg, McClay et al. 2006) and Pseudonocardia tetrahydrofuranoxydans sp. K1 (Kohlweyer, Thiemer et al. 2000) can degrade 1,4-dioxane co- metabolically when induced with tetrahydrofuran. RB4 (Pyrinomonadaceae family) was notably enriched in the water only treatment of the agricultural soil microcosms. This is the first report of carbon uptake from 1,4-dioxane by this phylotype. Members of the same family were linked to phenanthrene degradation in oil field soil 200 with ryegrass root exudates (Li, Luo et al. 2019), with the degradation of cellulose, starch and xylan (Wüst, Foesel et al. 2016) and with the degradation of benzo [α] pyrene in soil (Lu, Sun et al. 2022). Carbon uptake in the impacted site microcosms was dominated by two phylotypes, oc3299 in the BSM and yeast extract treatment and Burkholderiacae in the water only treatment. oc3299 classifies within a family (Nitrosomonadaceae) known to contain microorganisms with ammonia monooxygenases (Clark, Hughes et al. 2021, Cupples and Thelusmond 2022). This enzyme has been linked to the biodegradation of many environmental contaminants, such as 17 alpha-ethinylestradiol (Wang and Li 2023), 2-chlorophenol (Perez-Alfaro, Villaseca et al. 2023), micropollutants (Yu, Han et al. 2018) and trichloroethene (Alpaslan Kocamemi and Cecen 2007). Similar to the wetland sediment microcosms, Gemmatimonas was also responsible for carbon uptake in the impacted site microcosms amended with BSM and yeast extract. The family Burkholderiacae contains the genus Burkholderia which been associated with the biodegradation of many chemicals (Morya, Salvachua et al. 2020), such as hexadecane (Wu, Dang et al. 2011), phenol (Huang, Shao et al. 2022), naphthalene and phenanthrene (Kim, Lee et al. 2003), methyl parathion (Fernández‐López, Popoca‐Ursino et al. 2017, Castrejón-Godínez, Tovar-Sánchez et al. 2022) and polychlorinated biphenyls (Tillmann, Strömpl et al. 2005, Ponce, Latorre et al. 2011). Further, Burkholderia cepacia G4 degrades 1,4-dioxane co-metabolically when induced by toluene (Mahendra and Alvarez-Cohen 2006). The current work investigated SDIMOs via amplicon-based sequencing. When the OTUs generated in the current work were compared to twelve genes previously associated with 1,4- dioxane metabolism and co-metabolism (as summarized (He, Mathieu et al. 2017)), three genes (Rhodococcus jostii RHA1 prmA and Rhodococcus sp. RR1 prmA, Pseudonocardia 201 dioxanivorans CB1190 plasmid pPSED02 Psed_6976) were detected in the three soil/sediment types. A similar trend of the dominance of the two prmA sequences in mixed microbial communities was also observed in previous work (Eshghdoostkhatami and Cupples 2024). Notably, in the current study, in all three mixed communities, the SIP results did not associate Rhodococcus with carbon uptake from 1,4-dioxane. Carbon uptake from microorganisms harboring Rhodococcus sp. RR1 prmA-like genes only occurred in the wetland sediments microcosms. The lack of Rhodococcus in the wetland SIP results could suggest other microorganisms may harbor similar genes. The current study also revealed genes encoding for tetrahydrofuran monooxygenase alpha subunit thmA from Pseudonocardia were present in the impacted site sediments. However, SIP did not identify Pseudonocardia as a carbon consumer in the impacted site microcosms. The pattern suggests either these genes were not active, or biodegradation was co-metabolic and did not involve carbon uptake. The biomarker thmA has been associated with cometabolic 1,4- dioxane degradation by Pseudonocardia tetrahydrofuran K1, Pseudonocardia sp. ENV478 and Rhodococcus sp. YYL (Thiemer, Andreesen et al. 2003, Mahendra and Alvarez-Cohen 2006, Yao, Lv et al. 2009, Masuda, McClay et al. 2012). The current research suggests BSM and yeast extract could stimulate the co-metabolism of 1,4-dioxane in the impacted site sediments via tetrahydrofuran monooxygenase. PICRUSt2 predicted the phylotypes and the functional genes associated with the 1,4- dioxane degradation. The identified degraders Solirubrobacter and Pseudonocardia (as discussed above) were predicted to be associated with all four subunits of propane monooxygenase (prmA, prmB, prmC and prmD) in all three soil types. Consistent with this, a NCBI search indicated Solirubrobacter pauli strain DSM 14954 contained the four propane 202 monooxygenase subunits (all located together and with the correct predicted length for each subunit). Labrys (Rhizobiales) and unclassified Rhizobiales were predicted to be associated with toluene monooxygenase (tmo/tbu/tou) and ammonia/particulate methane monooxygenase genes (pmo/amo) in all three samples. This order was also predicted to be a major phylotype associated with pmo/amo KEGG group in other soils (Cupples, Li et al. 2022). PICRUSt2 predicted Mycobacterium was associated with soluble methane monooxygenase genes in three soil types, however, this genus was not associated with carbon uptake from 1,4-dioxane. The trend indicates either sMMO was not involved in 1,4-dioxane degradation in the current study or the transformation did not result in any carbon uptake. 4.6 Conclusions This research provides insight into the impact of BSM and yeast extract on 1,4-dioxane biodegradation rates as well as the microorganisms involved in carbon uptake during biodegradation. The addition of BSM and yeast extract enhanced removal rates in all three inocula types, however, differences were only significant for the agricultural soil and impacted site sediment microcosms. Numerous phylotypes were associated with carbon uptake across the three communities and two treatments. Gemmatimonas was particularly important in the heavy fractions of both treatments of the wetland sediment microcosms. Unclassified Solirubacteraceae, Solirubrobacter, Pseudonocardia and RB4 were the dominant enriched phylotypes in the agricultural soil microcosms. The impacted site microcosms were dominated by only two phylotypes, unclassified Burkholderiaceae (water treatment) and oc3299 (yeast and BSM treatment). To our knowledge, Gemmatimonas, Solirubacteraceae, Solirubrobacter, RB4 and oc3299 have not previously been linked to carbon uptake from 1,4-dioxane. The SDIMO based amplicon sequencing detected three genes (Rhodococcus jostii RHA1 203 prmA and Rhodococcus sp. RR1 prmA, Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976) in the mixed microbial communities. Although the genes were present, prmA was only linked to 1,4-dioxane biodegradation in one set of samples. The predicted functional gene analysis suggested the importance of propane monooxygenases associated with Solirubrobacter and Pseudonocardia. Overall, it is likely that a community of microorganisms is involved in 1,4-dioxane biodegradation in both the wetland and agricultural soil microcosms. 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Journal of Environmental Management 204: 765-774. 216 APPENDIX Figure A4.1 Principal coordinate analysis for all three sample types together (A), for the soil only (B), wetland sediments only (C) and site sediments only (D), separated by treatment and fraction type. 217 Figure A4.2 Relative abundance of taxa associated with the six subunits of phenol/toluene 2-monooxygenase, dmpK/poxA/tomA0 (K16249), dmpL/poxB/tomA1 (K16243), dmpM/ poxC/tomA2 (K16244), dmpN/poxD/tomA3 (K16242), dmpO/poxE/ tomA4 (K16245) and dmpP/poxF/tomA5 (K16246). Taxa without all six subunits were removed. 218 Figure A4.3 Relative abundance of taxa associated with the six subunits of toluene monooxygenase, tmoA/tbuA1/touA (K15760), tmoB/tbuU/touB (K15761), tmoC/tbuB/touC (K15762), tmoD/tbuV/touD (K15763), tmoE/tbuA2/touE (K15764) and tmoF/tbuC/touF (K15765). Taxa without all six subunits were removed. 219 Figure A4.4 Relative abundance of taxa associated with the three subunits of ammonia/particulate methane monooxygenase, pmoA/amoA (K10944), pmoB/amoB (K10945), pmoC/amoC (K10946). Taxa without all three subunits were removed. 220 Figure A4.5 Relative abundance of taxa associated with the six subunits of soluble methane monooxygenase. Taxa without five (except mmoD) or six subunits were removed. 221 Table A4.1 Target genes, primer names, primer/probe sequences, product sizes and references for the primers used in this study. Target gene Rhodococcus sp. RR1 prmA Primer name RR1 prmA forward RR1 prmA reverse RR1 Sequences (5’-3’) Amplicon size (bp) Reference TTCCCGGAGATCTCGGCGGCC 141 (Eshghdoostkhatami and Cupples 2024) GAGCTTCTTGAGGTTCATCTGGATCGT 141 As above prmA FAM/TGGATCTCG/ZEN/GGGTTGGGCACC/ABkFQ/ As above Monooxygenase Genes probe CS1- TS-F- NV57 CS2- TS-R- NVC66 CAGTCNGAYGARKCSCGN CAYAT 420 (Coleman, Bui et al. 2006) CCANCCNGGRTAYTTRTTYTCRAACCA 420 As above 222 Table A4.2 DNA concentration in DNA extracts, as measured with Qubit. Sample type wetland H2O 12C replicate 1 heavy fraction 1 wetland H2O 12C replicate 1 heavy fraction 2 wetland H2O 12C replicate 1 heavy fraction 3 wetland H2O 12C replicate 1 fraction 4 wetland H2O 12C replicate 1 fraction 5 wetland H2O 12C replicate 1 fraction 6 wetland H2O 12C replicate 1 fraction 7 wetland H2O 12C replicate 1 fraction 8 wetland H2O 12C replicate 1 fraction 9 wetland H2O 12C replicate 1 light fraction 1 wetland BSM yeast 12C replicate 1 heavy fraction 1 wetland BSM yeast 12C replicate 1 heavy fraction 2 wetland BSM yeast 12C replicate 1 heavy fraction 3 wetland BSM yeast 12C replicate 1 fraction 4 wetland BSM yeast 12C replicate 1 fraction 5 wetland BSM yeast 12C replicate 1 fraction 6 wetland BSM yeast 12C replicate 1 fraction 7 wetland BSM yeast 12C replicate 1 fraction 8 wetland BSM yeast 12C replicate 1 light fraction 1 wetland H2O 12C replicate 2 heavy fraction 1 wetland H2O 12C replicate 2 heavy fraction 2 wetland H2O 12C replicate 2 heavy fraction 3 wetland H2O 12C replicate 2 fraction 4 wetland H2O 12C replicate 2 fraction 5 wetland H2O 12C replicate 2 fraction 6 wetland H2O 12C replicate 2 fraction 7 wetland H2O 12C replicate 2 fraction 8 wetland H2O 12C replicate 2 light fraction 1 wetland BSM yeast 12C replicate 2 heavy fraction 1 wetland BSM yeast 12C replicate 2 fraction wetland BSM yeast 12C replicate 2 heavy fraction 2 wetland BSM yeast 12C replicate 2 heavy fraction 3 wetland BSM yeast 12C replicate 2 fraction 4 wetland BSM yeast 12C replicate 2 fraction 5 wetland BSM yeast 12C replicate 2 fraction 6 wetland BSM yeast 12C replicate 2 fraction 7 wetland BSM yeast 12C replicate 2 light fraction 1 DNA concentration (ng/µL) 4.68 21.00 51.40 65.00 2.46 38.20 12.60 12.00 11.44 5.14 7.96 17.68 23.40 12.12 9.50 6.68 6.72 8.12 3.04 4.92 15.44 23.20 25.20 16.20 7.24 5.96 4.98 3.20 3.96 0.91 7.16 16.74 4.32 0.53 5.86 0.66 2.26 223 Table A4.2 (cont’d) Sample type wetland H2O 13C replicate 1 heavy fraction 1 wetland H2O 13C replicate 1 heavy fraction 2 wetland H2O 13C replicate 1 heavy fraction 3 wetland H2O 13C replicate 1 fraction 4 wetland H2O 13C replicate 1 fraction 5 wetland H2O 13C replicate 1 fraction 6 wetland H2O 13C replicate 1 fraction 7 wetland H2O 13C replicate 1 fraction 8 wetland H2O 13C replicate 1 light fraction 1 wetland BSM yeast 13C replicate 2 heavy fraction 1 wetland BSM yeast 13C replicate 2 heavy fraction 2 wetland BSM yeast 13C replicate 2 heavy fraction 3 wetland BSM yeast 13C replicate 2 fraction 4 wetland BSM yeast 13C replicate 2 fraction 5 wetland BSM yeast 13C replicate 2 fraction 6 wetland BSM yeast 13C replicate 2 fraction 7 wetland BSM yeast 13C replicate 2 fraction 8 wetland BSM yeast 13C replicate 2 light fraction 1 wetland H2O 13C replicate 2 heavy fraction 1 wetland H2O 13C replicate 2 heavy fraction 2 wetland H2O 13C replicate 2 heavy fraction 3 wetland H2O 13C replicate 2 fraction 4 wetland H2O 13C replicate 2 fraction 5 wetland H2O 13C replicate 2 fraction 6 wetland H2O 13C replicate 2 fraction 7 wetland H2O 13C replicate 2 fraction 8 wetland H2O 13C replicate 2 light fraction 1 wetland BSM yeast 13C replicate 2 heavy fraction 1 wetland BSM yeast 13C replicate 2 heavy fraction 2 wetland BSM yeast 13C replicate 2 heavy fraction 3 wetland BSM yeast 13C replicate 2 fraction 4 wetland BSM yeast 13C replicate 2 fraction 5 wetland BSM yeast 13C replicate 2 fraction 6 wetland BSM yeast 13C replicate 2 fraction 7 wetland BSM yeast 13C replicate 2 fraction 8 wetland BSM yeast 13C replicate 2 fraction 9 wetland BSM yeast 13C replicate 2 light fraction 1 DNA concentration (ng/µL) 15.48 55.4 4.76 43.8 28.8 61.6 11.62 4.46 5.88 8.52 15.24 21.00 19.74 8.04 6.06 6.98 4.74 2.36 2.08 6.94 23.00 19.42 11.96 6.64 6.26 8.14 4.32 1.96 11.60 13.88 45.80 67.80 46.40 22.60 6.26 16.96 4.10 224 Table A4.3 QPCR parameters based on MIQE guidelines (Bustin, Benes et al. 2009). Target gene Experiment Primer concentration (µM) QPCR linear range (gene copies/reaction) QPCR efficiency Y-intercept Rhodococcus sp. RR1 prmA Rhodococcus sp. RR1 prmA Rhodococcus sp. RR1 prmA Rhodococcus sp. RR1 prmA Wetland 12C H2O and 12C BSM & yeast replicate 1 Wetland 12C H2O and 12C BSM & yeast replicate 2 Wetland 13C H2O and 13C BSM & yeast replicate 1 Wetland 13C H2O and 13C BSM & yeast replicate 2 0.3 98 - 98×105 73.32 ± 2.18 47.04 ± 0.62 0.3 98 - 98×105 76.69 ± 7.90 45.32 ± 1.56 0.3 98 - 98×105 74.98 ± 1.75 45.80 ± 0.36 0.3 98 - 98×105 77.91 ± 4.66 45.24 ± 0.77 Table A4.4 P-values for one-way ANOVA with the slopes of 1,4-dioxane degradation plots between different treatments for each soil/sediment types. P-values in bold indicate a significant difference (p ≤ 0.05). Soil types P-values Wetland sediments Agricultural soil Site sediments 8.64E-07 7.91E-12 1.32E-04 Table A4.5 P-values for the t-test with the slopes of 1,4-dioxane degradation plots between different treatments for wetland sediment microcosms. P-values in bold indicate a significant difference (p ≤ 0.05). 12C H2O 13C H2O 12C BSM& yeast 13C BSM& yeast 12C BSM& yeast 1.02E-01 - 13C BSM& yeast - 4.59E-01 Abiotic 12C H2O 2.23E-03 4.74E-03 Abiotic 12C BSM& yeast - - - - - - - - 8.07E-05 3.00E-04 225 Table A4.6 P-values for the t-test with the slopes of 1,4-dioxane degradation plots between different treatments for agricultural soil microcosms. P-values in bold indicate a significant difference (p ≤ 0.05). 12C H2O 13C H2O 12C BSM& yeast 13C BSM& yeast 12C BSM& yeast 4.17E-03 - 13C BSM& yeast - 6.01E-05 Abiotic 12C H2O 3.20E-06 5.10E-05 Abiotic 12C BSM& yeast - - - - - - - - 6.76E-04 9.08E-04 Table A4.7 P-values for the t-test with the slopes of 1,4-dioxane degradation plots between different treatments for the impacted site sediment microcosms. P-values in bold indicate a significant difference (p ≤ 0.05). 12C H2O 13C H2O 12C BSM& yeast 13C BSM& yeast 12C BSM& yeast 1.80E-02 - - - 13C BSM& yeast - 1.57E-02 - - Abiotic 12C H2O 7.32E-03 7.94E-03 - - Abiotic 12C BSM& yeast - - 1.11E-03 5.79E-04 Table A4.8 P-values for one-way ANOVA with alpha diversity indices between three soil/sediment types of the water or BSM and yeast treatments. P-values in bold indicate a significant difference (p ≤ 0.05). Chao1 ACE Shannon Simpson Inverse of Simpson Water BSM and yeast 1.14E-30 5.74E-30 4.52E-56 1.87E-25 2.82E-37 1.16E-50 8.79E-50 9.20E-35 3.19E-20 2.38E-09 Fisher 2.52E- 42 1.08E- 53 Table A4.9 P-values for the t test with alpha diversity indices between three soil/sediment types of the water treatment. P-values in bold indicate a significant difference (p ≤ 0.05). Soil vs Site Soil vs Wetland Site vs Wetland Chao1 2.02E- 21 4.32E- 16 6.59E- 11 ACE 6.02E- 21 1.10E- 15 7.33E- 11 Shannon Simpson 1.72E- 44 2.26E- 33 2.29E- 20 5.61E- 17 1.03E- 09 6.34E- 14 Inverse of Simpson 4.34E-25 6.15E-20 1.53E-19 Fisher 6.37E- 29 1.48E- 22 5.38E- 18 226 Table A4.10 P-values for the t test with alpha diversity indices between three soil/sediment types of the BSM and yeast treatment. P-values in bold indicate a significant difference (p ≤ 0.05). Chao1 ACE Shannon Simpson Inverse of Simpson Soil vs Site Soil vs Wetland Site vs Wetland 4.13E-39 2.27E-38 8.99E-26 2.52E-12 8.12E-08 2.34E-26 8.77E-26 3.50E-06 2.32E-01 9.10E-04 4.81E-15 5.65E-15 3.77E-23 7.32E-13 4.86E-15 Fisher 1.67E- 40 7.36E- 26 9.00E- 24 Table A4.11 P-values for the t test with the alpha diversity indices between water and yeast treatments for the three soil/sediment types. P-values in bold indicate a significant difference (p ≤ 0.05). Chao1 ACE Shannon Simpson Inverse of Simpson Soil Site 1.79E-03 2.38E-03 7.35E-17 2.89E-10 9.98E-14 2.69E-06 2.39E-06 4.56E-01 2.46E-01 3.00E-01 Wetland 2.28E-02 2.35E-02 1.39E-01 1.28E-01 2.37E-03 Fisher 2.87E- 08 1.47E- 05 3.04E- 02 227 CHAPTER 5: BIODEGRADATION OF 1,4-DIOXANE AT LOW CONCENTRATIONS IN SITE SEDIMENTS USING YEAST AMENDMENT AND BIOAUGMENTATION 5.1 Abstract 1,4-Dioxane was commonly used as a stabilizer in 1,1,1-trichloroethane formulations and is now frequently detected at sites where chlorinated solvents are present. A major challenge in addressing 1,4-dioxane contamination concerns chemical characteristics that result in migration and persistence. The objectives of this study were to 1) examine the effectiveness of yeast extract in supporting growth and prompting 1,4-dioxane biodegradation in mixed microbial communities at low 1,4-dioxane concentrations, 2) identify phylotypes that may utilize 1,4- dioxane and/or metabolites to support growth and 3) evaluate the impact of bioaugmentation with agricultural soil microorganisms on 1,4-dioxane removal rates in impacted site sediments. For this, 1,4-dioxane biodegradation was examined in microcosms amended with sediment from three impacted sites and four agricultural soils. 1,4-Dioxane biodegradation trends differed between inocula sources and treatments. Amending microcosms with basal salts medium (BSM) and yeast extract improved 1,4-dioxane biodegradation rates in sediment with indigenous 1,4- dioxane degraders. 1,4-Dioxane biodegradation did not occur in two of the three impacted sites studied, suggesting an absence of degraders at these sites. Bioaugmentation with agricultural soils led to 1,4-dioxane biodegradation in the impacted site sediments lacking 1,4-dioxane degraders. Differentially abundant phylotypes were determined via DESeq2 analysis by comparing live sample microcosms and live controls (no 1,4-dioxane) and included those previously associated with 1,4-dioxane biodegradation (Rhodococcus, Rhizobiales, Gemmatimonas, Flavobacterium and Chitinophagaceae). PICRUSt2 analysis indicated a group 228 of enriched phylotypes were also associated with ammonia/particulate monooxygenase and propane monooxygenase subunits. Notably, Rhodococcus was associated with methane/ammonia degradation exclusively in the three treatments containing BSM, and with propane monooxygenase subunits only in the treatments containing both BSM and yeast extract in Site 1 sediment microcosms. These findings suggest that Rhodococcus may contribute to 1,4-dioxane degradation via cometabolism. Overall, the results indicate yeast extract and BSM may be beneficial for promoting 1,4-dioxane biodegradation at sites with indigenous 1,4-dioxane degradations. Further, at sites without 1,4-dioxane degraders, bioaugmentation with agricultural soil microorganisms may represent a feasible remediation strategy. 5.2 Introduction 1,4-Dioxane, a synthetic cyclic ether, was utilized as a stabilizer and is commonly detected at sites contaminated with chlorinated solvents (Derosa, Wilbur et al. 1996, USEPA 2013, Dang, Kanitkar et al. 2018, Mohr, DiGuiseppi et al. 2020). The US Environmental Protection Agency (USEPA) has classified 1,4-dioxane as a likely human carcinogen and established a health advisory level of 0.35 µg/L in drinking water based on a one-in-one million cancer risk assessment (USEPA 2013). Due to its high solubility and mobility in water (low Henry's law constant, low Kow and low Koc) (USEPA 2013), 1,4-dioxane is widespread in surface water and groundwater worldwide (USEPA 2013, Stepien, Diehl et al. 2014, Sun, Lopez- Velandia et al. 2016, Adamson, Piña et al. 2017), making it a challenging contaminant to remediate. Traditional technologies such as air stripping, thermal desorption, and soil vapor extraction have been less effective for 1,4-dioxane remediation (Chiang, Anderson et al. 2016, Mohr, DiGuiseppi et al. 2020) Alternative strategies for remediating 1,4-dioxane-impacted sites include advanced oxidation processes (Barndõk, Merayo et al. 2016, Ikehata, Wang-Staley et al. 229 2016), phytoremediation (Aitchison, Kelley et al. 2000, Kelley, Aitchison et al. 2001) and microbial remediation (Mahendra and Alvarez-Cohen 2006, Sei, Oyama et al. 2013). Both metabolic and co-metabolic 1,4-dioxane biodegradation have been reported. Numerous metabolic 1,4-dioxane degraders can utilize 1,4-dioxane as a sole source of carbon and energy to support growth (Mahendra and Alvarez-Cohen 2006, Kim, Jeon et al. 2009, Huang, Shen et al. 2014, Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016, Yamamoto, Saito et al. 2018). Metabolic strains, such as Pseudonocardia dioxanivorans CB1190 and Mycobacterium sp. PH-06, are known for their ability to mineralize 1,4-dioxane and avoid the accumulation of harmful byproducts (Mahendra, Petzold et al. 2007, Kim, Jeon et al. 2009). However, half-saturation Monod constants (Ks) of metabolic degraders are typically high and their growth can be slow unless at very high 1,4-dioxane concentrations (>100 mg/L) (Mahendra and Alvarez-Cohen 2006, Kim, Jeon et al. 2009, Chen, Jin et al. 2016). Consequently, metabolic degraders may not be able to grow effectively or degrade 1,4-dioxane at environmental relevant low concentrations, as the available carbon substrates may be insufficient for growth and enzyme induction (Barajas-Rodriguez and Freedman 2018). Other strains can degrade 1,4-dioxane co-metabolically while growing on other carbon substrates (e.g., propane, toluene and tetrahydrofuran) to induce enzymes (Kohlweyer, Thiemer et al. 2000, Vainberg, McClay et al. 2006, Yao, Lv et al. 2009, Hand, Wang et al. 2015, Lippincott, Streger et al. 2015). Co-metabolic degraders, which generate metabolic energy from other primary substrates (i.e., propane), may perform better at low 1,4-dioxane concentrations. Most 1,4-dioxane-contaminated plumes are below 1000 µg/L (Adamson, Mahendra et al. 2014, Gedalanga, Madison et al. 2016). However, the majority of research on 1,4-dioxane biodegradation by metabolic strains has been conducted at higher concentrations (Huang, Shen 230 et al. 2014, Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016, Inoue, Tsunoda et al. 2016, Yamamoto, Saito et al. 2018). There are limited metabolic studies conducted at µg/L concentrations (He, Mathieu et al. 2018, Simmer, Richards et al. 2021). Mycobacterium sp. PH- 06, Pseudonocardia sp. CB1190 and two microbial consortia (with Mycobacterium as the dominant genus) degraded 300 µg/L 1,4-dioxane to below 5 µg/L when bioaugmented into groundwater from a dioxane-impacted site (He, Mathieu et al. 2018). In another study, rapid metabolic 1,4-dioxane degradation was observed for Rhodococcus ruber strain 219, which degraded ~100 µg/L 1,4-dioxane to < 0.35 µg/L when supplied with thiamine (vitamin B1) (Simmer, Richards et al. 2021). Several studies reported co-metabolic 1,4-dioxane degradation at environmentally relevant concentrations (Lippincott, Streger et al. 2015, Chu, Bennett et al. 2018, Li, Deng et al. 2021). Azoarcus sp. DD4 co-metabolically degraded 1,4-dioxane from 20 µg/L to below 0.4 µg/L when supplemented with propane (Li, Deng et al. 2021). One field study reported indigenous microbial populations in the groundwater achieved 1,4-dioxane removal from 60 µg/L to below 3 µg/L when stimulated with propane and oxygen (Chu, Bennett et al. 2018). Another field study demonstrated that bioaugmentation with propanotroph Rhodococcus ruber ENV425, induced by propane amendment, can reduce low levels of 1,4-dioxane in a deep aquifer (Lippincott, Streger et al. 2015). However, some inducing substrate (i.e., tetrahydrofuran) at high concentrations may increase the lag period for 1,4-dioxane removal due to competitive inhibition (Li, Liu et al. 2017). Contaminated sites have demonstrated intrinsic 1,4-dioxane biodegradation potential (Chiang, Mora et al. 2012, Li, Mathieu et al. 2013, Li, Mathieu et al. 2014, Li, Van Orden et al. 2015, Gedalanga, Madison et al. 2016, Li, Liu et al. 2017). Various 1,4-dioxane-degrading 231 monooxygenase genes were detected in Arctic groundwater impacted by 1,4-dioxane (Li, Mathieu et al. 2013, Li, Mathieu et al. 2014). Moreover, microcosms prepared with the source zone groundwater demonstrated 1,4-dioxane degradation at a rate of 13 µg/L per week with initial concentrations of approximately 250 µg/L (Li, Mathieu et al. 2013). In comparison, unamended microcosms prepared with soils and groundwater samples from the same site were able to degrade 1,4-dioxane at a rate of 9.8 µg/L per week with an initial concentration of 500 µg/L (Li, Fiorenza et al. 2010). Even at an initial concentration as low as 7.5 µg/L, complete 1,4-dioxane degradation was achieved at a rate of 0.3 µg/L per week in the microcosms inoculated with contaminated groundwater and aquifer materials (Li, Van Orden et al. 2015). 1,4-Dioxane-degrading bacteria with natural attenuation capacities widely exist in uncontaminated soils and thus offer potential for 1,4-dioxane remediation (He, Mathieu et al. 2018, Ramalingam and Cupples 2020, Dang and Cupples 2021, Tang, Wang et al. 2023). For instance, two 1,4-dioxane-degrading bacteria consortia enriched from uncontaminated garden soils removed 1,4-dioxane from 300 µg/L to below 5 µg/L within three days in impacted groundwater without known intrinsic biodegradation potential (He, Mathieu et al. 2018). In another enrichment study using uncontaminated garden soils, three consecutive doses of 300 μg/L of 1,4-dioxane were quickly degraded to below 80 μg/L within three days (Tang, Wang et al. 2023). Metabolic degradation of 1,4-dioxane at low concentrations by thiamine-grown Rhodococcus ruber 219 suggests that in situ biostimulation with growth supplements might reduce 1,4-dioxane to health advisory levels (Simmer, Richards et al. 2021). As yeast extract contains multiple growth factors (amino acids, peptides, and water-soluble vitamins, and carbohydrates), various microorganisms involved in 1,4-dioxane biodegradation could 232 potentially benefit. Previous studies applied yeast extract to enhance 1,4-dioxane degradation at high concentrations (100 mg/L) by pure cultures of metabolic strains Rhodanobacter AYS5 and Xanthobacter flavus DT8 (Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016). Another study discovered that the amendment of basal salts medium (BSM) and yeast extract significantly enhanced the 1,4-dioxane degradation (2 mg/L) in the mixed communities from impacted site sediments and agricultural soils (manuscript in progress, Li et al.). However, little is known about the impact of yeast on the 1,4-dioxane degradation at low concentrations (<500 µg/L), especially in mixed microbial communities. To fill the knowledge gap, the objectives of this study were to 1) examine the impact of varying concentrations of yeast extract on biodegradation kinetics of 1,4-dioxane at low concentrations in mixed microbial communities; 2) investigate the phylotypes deriving a growth benefit from the 1,4-dioxane of low concentration of 1,4-dioxane and 3) examine the impact of bioaugmentation with agricultural soil microorganisms on 1,4-dioxane removal rates in site sediments. 5.3 Methods 5.3.1 Chemicals and Inocula 1,4-Dioxane (³99.5%) and 1,4-dioxane-d8 (³99% isotopic purity) were purchased from Sigma-Aldrich (MO, USA) and Santa Cruz Biotechnology (TX, USA), respectively. Sediments from three contaminated sites and soils from four agricultural systems were utilized to inoculate microcosms. This included sediments from an impacted site at a West Coast Naval Station in California (herein called Site 1), sediments from a site contaminated with tetrachloroethene, trichloroethylene, 1,4-dioxane and hexavalent chromium from a former latex product manufacturing facility in California (herein called Site 2), sediments from a low pH site in New Jersey (herein called Site 3) and agricultural soils from Main Cropping System Experiment at the 233 Kellogg Biological Station Long-Term Ecological Research (KBS LTER) in southwest Michigan. The agricultural soils were collected from six replicate plots for four treatments (referred to as Soil 1, Soil 2, Soil 3 and Soil 4). The Soil 1 and Soil 2 receive conventional levels of chemical inputs, but with chisel plow and no-till management, respectively. The Soil 3 receives reduced chemical input and the Soil 4 receives no chemical inputs. More details of the agricultural soils can be found at https://lter.kbs.msu.edu/research/long-term-experiments/main- cropping-system-experiment/. Remediation of Site 2 included In Situ Chemical Oxidation followed by Enhanced Reductive Dechlorination (ERD), using molasses and emulsified vegetable oil. As a result of ERD, it was likely the sediments from Site 2 were anaerobic. Due to privacy concerns, no other information is available on the other sites. 5.3.2 Microcosms Setup To investigate the impact of yeast extract on 1,4-dioxane degradation, a series of experiments were performed as summarized in Table 5.1. The initial experiment involved a higher concentration of 1,4-dioxane (2 mg/L), 10 g wet-weight sediments from site 1 and 30 mL of liquid (160 mL serum bottles). The liquid amendments included water with 300 µg/L yeast extract (Sigma-Aldrich, USA), BSM only and BSM with 300 µg/L yeast extract. The BSM contained NH4Cl (1.0 g/L), K2HPO4 (3.24 g/L), NaH2PO4 (1.0 g/L), MgSO4 (0.20 g/L), FeSO4 (0.012 g/L), MnSO4 (0.003 g/L), ZnSO4 (0.003 g/L) and CoCl2 (0.001 g/L) and was adjusted to 7.4 with 0.1N NaOH, modified from a previous recipe (Pugazhendi, Rajesh Banu et al. 2015). All other microcosm studies were established to evaluate 1,4-dioxane degradation with low initial concentrations. For each inoculum, the experimental design included triplicate live microcosms, triplicate abiotic control microcosms and triplicate live control microcosms. The abiotic controls were autoclaved daily for three consecutive days. Live controls were also 234 included to evaluate changes in microbial communities with and without 1,4-dioxane. The live controls were treated in the same manner as live microcosms except no 1,4-dioxane was added. The first experiment involved 10 g wet-weight sediments from Site 1 and Site 2 suspended in 30 mL of water or media in 160 mL serum bottles. The amendments examined included water, BSM, BSM with 100 µg/L yeast extract and BSM with 1000 µg/L yeast extract (Sigma-Aldrich, USA) with approximately 50 µg/L, 100 µg/L or 500 µg/L 1,4-dioxane. The second experiment involved soil microcosms with 10 g wet-weight sediments from site 2 and site 3 as well as 30 mL of liquid in 160 mL serum bottles. The amendments included water, BSM with 100 µg/L yeast extract and BSM with 1000 µg/L yeast extract with approximately 50 µg/L 1,4-dioxane. The third low 1,4-dioxane concentration experiment was designed to evaluate the impact of bioaugmentation with agricultural soil microorganisms on 1,4-dioxane biodegradation in site sediments. Sediment from Site 2 was selected for this because no removal was noted in the Site 2 microcosms described above. This involved soil microcosms with 5 g wet-weight sediments from Site 2 inoculated with 5 g wet-weight soils from Soil 1, Soil 2 or Soil 3, and 30 mL of water or media in 160 mL serum bottles. Two amendments examined included water and BSM with 1000 µg/L yeast extract with approximately 50 µg/L 1,4-dioxane. 5.3.3 1,4-Dioxane Analysis 1,4-Dioxane concentrations were measured by a triple quadrupole Agilent 7010B GC/MS system (Agilent Technologies, CA, USA) equipped with solid phase micro extraction (SPME) (Sigma-Aldrich, MO, USA). Each sample was collected by extracting 1 mL from the liquid phase using a 3 mL sterile syringe and 22 Ga 1.5 in. needle. The extracted samples were then filtered through 0.22 μm nylon filters (Biomed Scientific). An aliquot (500 µL) of the filtered 235 samples or series of diluted external standards were injected into 40 mL amber glass vials. Also, 500 µL of 200 µg/L 1,4-dioxane-d8 was added to each vial as an internal standard for subsequent GC/MS analysis. The vials were kept at 40°C before the analysis, with the SPME fiber conditioned at 270 °C for 30 mins before each sequence run. For each sample, the SPME fiber was inserted into the vials and exposed to the analytes for 2 mins. The analytes in the headspace adsorbed onto the fiber and then the fiber was exposed to the inlet. Initially, the oven temperature time was set at 40 °C for 4 mins, followed by a programmed increase to 250 °C at a rate of 40 °C/min. The detection limit was approximately 1 µg/L. 5.3.4 DNA Extraction, 16S rRNA Gene Sequencing and Microbial Analysis DNA was extracted from the live sample microcosms and live control microcosms using the DNA extraction kit (DNeasy PowerLyzer PowerSoil Kit, Qiagen, USA) as per the manual protocol. The DNA concentrations were quantified using the Quant-iT™ dsDNA High- Sensitivity Assay Kit. Then DNA extracts were submitted for 16S rRNA gene amplicon sequencing to the Genomic Cores at the Research Technology Support Facility (RTSF) at Michigan State University (MSU). The V4 region of the 16S rRNA gene was targeted for amplification using primers 515f (5’ GTGCCAGCMGCCGCGGTAA-3’) and 806r (5’- GGACTACHVGGGTWTCTAAT-3’) following a previous protocol (Kozich, Westcott et al. 2013). The PCR products were normalized using Invitrogen SequalPrep DNA Normalization plates, followed by pooling of the normalized products. The pool was then cleaned and concentrated using AmpureXP magnetic beads. Quality control (QC) and quantification were performed using a combination of Qubit dsDNA HS for concentration, Agilent 4200 TapeStation HS DNA1000 for sizing, and Kapa Illumina Library Quantification qPCR assays. The pooled library was loaded onto an Illumina MiSeq v2 standard flow cell for sequencing, using a 2 × 250 236 bp paired-end format with a MiSeq v2 500-cycle reagent cartridge. Custom sequencing and index primers were added to the appropriate wells of the reagent cartridge. Base calling was carried out with Illumina Real Time Analysis (RTA) v1.18.54, and the RTA output was demultiplexed and converted into FastQ format using Illumina Bcl2fastq v2.19.1. The raw sequences were submitted to NCBI under Bioproject PRJNA1183415, with accession numbers SAMN44626197 to SAMN44626290. Raw amplicon sequences in fastq format were processed using Mothur (Schloss, Westcott et al. 2009) using the Mothur Miseq SOP (accessed July 2024) on the High-Performance Computing Cluster (HPCC) at MSU. This process included combining, trimming, aligning, and quality-controlling the sequences. For alignments, the SILVA bacteria database (Release 138.2) specific to the V4 region was used (Pruesse, Quast et al. 2007). Sequences was then be classified into operational taxonomic units (OTUs) at 97% similarity. The classification of OTUs into taxonomic levels and downstream analysis were performed using the shared and taxonomy files generated by Mothur, with R (Version 4.4.1) (R Core Team 2018) in RStudio (Version 2024.09.0) (RStudio_Team 2020). The differentially abundant taxa between the samples and live controls were determined by the Wald test coupled with a parametric model using the R packages DESeq2 (Love, Huber et al. 2014) and microbiome (version 1.26.0) (Lahti and Shetty 2012-2019). The packages phyloseq (version 1.48.0) (McMurdie and Holmes 2013) and microbiome (version 1.26.0) (Lahti and Shetty 2012-2019) were used to determine the relative abundance at the phyla level in the soil microcosms. 5.3.5 PICRUSt2 Monooxygenase Gene Predictions PICRUSt2 (Douglas, Maffei et al. 2020) was employed to predict the functional potential of microbial communities from 16S rRNA gene sequencing data. Input files for PICRUSt2 237 included a fasta file and a biom file. The analysis incorporated EPA-NG (Barbera, Kozlov et al. 2019) and gappa (Czech, Barbera et al. 2020) for the phylogenetic placement of reads, castor (Louca and Doebeli 2018) for hidden state prediction and MinPath (Ye and Doak 2009) for pathway inference. The PICRUSt2 generated files (pred_metagenome_contrib.tsv and pred_metagenome_contrib.tsv) were investigated (primarily using the R packages tidyr and dplyr) to identify the genes associated with monooxygenases (from the KEGG database (Kanehisa 2002)) as well as the phylotypes associated with each. More information on the data within each file can be found in the following tutorial (https://github.com/picrust/picrust2/wiki/PICRUSt2-Tutorial-(v2.5.0)). Functional genes investigated (KEGG number in parenthesis) included: prmA propane 2-monooxygenase large subunit (K18223), prmB propane monooxygenase reductase component (K18225), prmC propane 2-monooxygenase small subunit (K18224), prmD (K18226) propane monooxygenase coupling protein, pmoA-amoA methane/ammonia monooxygenase subunit A (K10944), pmoB- amoB methane/ammonia monooxygenase subunit B (K10945), pmoC-amoC methane/ammonia monooxygenase subunit C (K10946), tmoA, tbuA1, touA toluene monooxygenase system protein A (K15760), tmoB, tbuU, touB toluene monooxygenase system protein B (K15761), tmoC, tbuB, touC toluene monooxygenase system ferredoxin subunit (K15762), tmoD, tbuV, touD toluene monooxygenase system protein D (K15763), tmoE, tbuA2, touE toluene monooxygenase system protein E (K15764) and tmoF, tbuC, touF toluene monooxygenase electron transfer component (K15765). RStudio on the HPCC at MSU was used to generate a file that contained which gene subunits and phylotypes were present using the PICRUSt2 output file pred_metagenome_contrib.tsv (unzipped). The approach involved combining this file with 1) a 238 file containing gene numbers and descriptions and 2) a taxonomy file (from Mothur), using the R packages data.table (version 1.16.2) (Dowle and Srinivasan 2023), dplyr (version 1.1.4) (Wickham, Francois et al. 2023), tidyr (version 1.3.1) (Wickham, Vaughan et al. 2023), ggplot2 (version 3.5.1) (Wickham 2016) and patchwork (version 1.3.0) (Pedersen 2023). Bar charts were generated for each monooxygenase, faceted by the sample type and the gene subunits. 5.4 Results 5.4.1 1,4-Dioxane Biodegradation rates 1,4-Dioxane degradation rates with an initial concentration of 2 mg/L were investigated in the Site 1 microcosms (Figure A5.1). A two-phase degradation pattern was observed in all three treatments, with an initial lag phase followed by a rapid decline. During the lag phase, the 1,4-dioxane removal rates were notably faster in the treatment of BSM and yeast extract compared to treatments of water and yeast extract or BSM only. This trend suggests the amendment of BSM and yeast extract may provide sufficient growth substrates for the microbial population, thus initiating the 1,4-dioxane degradation in a shorter time. The potential of 1,4-dioxane degradation at low concentrations (100 μg/L) was further evaluated with microcosms inoculated with Site 1 sediment (Figure 5.1). 1,4-Dioxane removal rates have been summarized in Table 5.2. The BSM and yeast extract treatments resulted in higher 1,4-dioxane biodegradation rates (0.23 ± 0.10 μg/L per day for BSM/100 μg/L yeast extract, 0.32 ± 0.02 μg/L per day for BSM/1000 yeast extract μg/L) (Figure 5.2C and D), compared to the water only (0.13 ± 0.03 μg/L per day) treatment (Figure 5.2A) or the BSM only treatment (0.14 ± 0.01 μg/L per day) (Figure 5.2B). Also, there was no significant difference in biodegradation rates between the water treatment and the BSM only treatment, indicating that BSM alone does not improve 1,4-dioxane removal. 239 1,4-Dioxane biodegradation was also evaluated at two other concentrations (~50 μg/L and ~500 μg/L) for Site 1 sediment inoculated microcosms (Figures 5.3 & 5.4). At ~50 μg/L, the BSM and yeast extract treatments resulted in higher 1,4-dioxane biodegradation rates (0.22 ± 0.003 μg/L per day for BSM/100 μg/L yeast extract, 0.2 ± 0.02 μg/L per day for BSM/1000 yeast extract μg/L) (Figure 5.3B and C), compared to the water only (0.08 ± 0.03 μg/L per day) (Figure 3A). At ~500 μg/L, the BSM and yeast extract treatments resulted in higher 1,4-dioxane biodegradation rates (1.1 ± 0.16 μg/L per day for BSM/100 μg/L yeast extract, 1.16 ± 0.34 μg/L per day for BSM/1000 yeast extract μg/L) (Figure 5.3B and C), compared to the water only (0.19 ± 0.13 μg/L per day) (Figure 5.3A). The amendment of BSM and yeast extract was also reported to significantly accelerate the 1,4-dioxane degradation rates in the microcosms inoculated with Site 1 sediments and Soil 4 at ~2 mg/L 1,4-dioxane (Li and Cupples 2024). As shown in Table 5.2, 1,4-dioxane degradation rates greatly increased with increasing initial concentrations for site 1 sediments. The 1,4-dioxane degradation rates at ~2 mg/L were more than one order of magnitude lower than those observed at low concentrations for Site 1 sediments and Soil 4. The biodegradation of 1,4-dioxane was also evaluated at concentrations of ~50 µg/L, ~100 µg/L, and ~500 µg/L in Site 2 microcosms (Figures A5.2 & A5.3) and at ~50 µg/L in Site 3 microcosms (Figure A5.4). No degradation was observed in any of the treatments for these sites, indicating a lack of intrinsic degradation capabilities. In contrast to the results from the microcosms inoculated with Site 2 and Site 3 sediments, the microcosms inoculated with Soil 4 illustrated 1,4-dioxane biodegradation. In the Soil 4 microcosms, 1,4-dioxane at ~50 µg/L degraded in all treatments (Figure A5.5), As shown in Table 5.2, Soil 4 exhibited higher degradation rates than those noted in the Site 1 microcosms with the same 1,4-dioxane concentration and amendment. 240 Table 5.1 Soil microcosm treatments spiked with dioxane of different concentrations. Experiment Soil type Initial 1,4- dioxane concentrations (µg/L) Initial Site 1 2000 1st experiment Site 1 50, 100, 500 Site 2 50, 100, 500 2nd experiment 3rd experiment Site 3 T4 Site 2/T1 Site 2/T2 Site 2/T3 50 50 50 50 50 Treatments Water/yeast extract 300 µg/L BSM/yeast extract 300 µg/L Only BSM Water BSM BSM/yeast extract 100 µg/L BSM/yeast extract 1000 µg/L Water BSM BSM/yeast extract 100 µg/L BSM/yeast extract 1000 µg/L Water BSM/yeast extract 1000 µg/L 241 Figure 5.1 1,4-Dioxane concentrations over time in microcosms inoculated with Site 1 sediments with four treatments. The initial concentration was approximately 100 µg/L 1,4- dioxane. 242 Sediments from Site 2 were used to investigate the feasibility of 1,4-dioxane remediation by bioaugmentation with agricultural soils. As discussed above, no 1,4-dioxane biodegradation was observed in microcosms amended with Site 2 sediment. In contrast, when Site 2 sediment was mixed with different agricultural soils, biodegradation was observed. At ~50 μg/L, Site 2 sediments demonstrated 1,4-dioxane biodegradation inoculated with Soil 1, Soil 2 and Soil 3 (Figure 5.3). The mixed soil microcosms resulted in approximately half of the degradation rates compared to microcosms with Soil 4 (Table 5.2). This suggests that the 1,4-dioxane degraders in the agricultural soils can adapt to the mixed soil environments and further enhance the 1,4- dioxane degradation at low levels. 243 A B Figure 5.2 1,4-Dioxane concentrations over time (C/C0) in microcosms inoculated with Site 1 sediments, three treatments and two initial 1,4-dioxane concentrations (approximately 50 µg/L (A) and approximately 500 µg/L 1,4-dioxane (B)). 244 BSM/yeast extract 1000 μg/L BSM/yeast extract 60 μg/L 0.2± 0.02 0.32± 0.02 1.16± 0.34 0.78± 0.05 0.49± 0.02 0.45± 0.02 0.51± 0.07 - - - - - - - - - - 14.91± 2.87 15.76± 1.27 20.4± 2.52 References This study This study This study This study This study This study This study (Li and Cupples 2024) (Li and Cupples 2024) (Li and Cupples 2024) Table 5.2 Removal rates (μg/L per day) for 1,4-dioxane biodegradation. Initial 1,4- dioxane concentration (μg/L) Water BSM Treatments Soil type Site 1 Site 1 Site 1 T4 Site 2/T1 Site 2/T2 Site 2/T3 Site 1 T4 Wetland 50 100 500 50 50 50 50 2000 2000 2000 BSM/yeast extract 100 μg/L 0.22± 0.003 0.23± 0.08 1.1± 0.16 0.87± 0.02 - - - - - 0.08± 0.03 0.13± 0.03 0.14± 0.01 0.19± 0.13 0.93± 0.04 0.84± 0.04 0.44± 0.01 0.46± 0.03 0.5± 0.09 - - - 8.42± 1.68 12.69± 0.12 13.83± 2.33 - - - - - - 245 Figure 5.3 1,4-Dioxane concentrations over time in microcosms inoculated with agricultural soils T1 (A, B), T2 (C, D) and T3 (E, F) with two treatments. The initial concentration was approximately 50 µg/L 1,4-dioxane. 246 5.4.2 Microbial Community Analyses The composition of microbial communities in soil microcosms was characterized at the phylum level across various treatment conditions (Figure A5.6). For Site 1 sediment microcosms, Proteobacteria represented over 75% of the community in the water treatment, while alternative phyla were more prominent in treatments with BSM (Figure A5.6A). Notably, Proteobacteria and Nitrospira dominated in the treatment with BSM and 1000 μg/L yeast extract. For the soil microcosms, Proteobacteria was more enriched in the treatment with BSM and yeast extract compared to that in water treatment (Figure A5.6B). For the agricultural soil microcosms mixed with Site 2 sediments, Acidobacteria and Verrucomicrobia were more abundant in the water treatment compared to the treatment with BSM and yeast extract. To further elucidate differential abundances between samples and live controls (without 1,4-dioxane), DESeq analysis was conducted to determine significantly differentiated phylotypes (p<0.05) (Figures 5.4–5.6, Figures A5.7–A5.11). A group of phylotypes were enriched in Site 1 microcosms amended with 1,4-dioxane compared to the live controls (no 1,4-dioxane). At a concentration of ~100 μg/L, samples of Site 1 sediment microcosms displayed a significant enrichment of phylotypes from Proteobacteria, Actinobacteria, and Gemmatimonadetes, including genera such as Gemmatimonas, Hydrogenophaga, Phyllobacterium, Dietzia, unclassified Rhizobiales, and Rhodococcus (Figure 5.4). At ~50 μg/L, enriched phylotypes in the samples included representatives from Gemmatimonadetes, Actinobacteria, Firmicutes, Proteobacteria, and Bacteroidetes, specifically Gemmatimonas, Rhodococcus, Geobacillus, Streptococcus, unclassified Rhizobiales, and Flavobacterium (Figure 5.5). At ~500 μg/L, Geobacillus (phylum Firmicutes) was significantly more abundant in Site 1 sediment sample microcosms compared to the live controls (Figure 5.6). 247 Figure 5.4 The top 50 differentially abundant taxa between the samples amended with 1,4- dioxane (100 µg/L) and the live controls (as determined by DESeq2 analysis) for Site 1 sediments microcosms. 248 Figure 5.5 Differentially abundant taxa (46 in total) between the samples amended with 1,4- dioxane (50 µg/L) and the live controls (as determined by DESeq2 analysis) for Site 1 sediments microcosms. 249 Figure 5.6 Differentially abundant taxa (27 in total) between the samples amended with 1,4- dioxane (500 µg/L) and the live controls (as determined by DESeq2 analysis) for Site 1 sediments microcosms. 250 In the agricultural soil microcosms, a limited number of phylotypes were significantly enriched in the samples compared to live controls (Figures A5.7-A5.11). Specifically, in Soil 2 microcosms inoculated with Site 2 sediments, the water treatment led to significant enrichment of unclassified Chitinophagaceae and unclassified Bacteroidetes in the samples compared to live controls (Figure A5.8). The treatment with BSM and yeast extract resulted in the enrichment of unclassified Bacteria, unclassified Ktedonobacterales, unclassified Myxococcales and Gp. For the Soil 3 microcosms inoculated with Site 2 sediments, unclassified Chitinophagaceae, unclassified Bacteroidetes, unclassified Myxococcales and unclassified Bacteria were differentially abundant in the water treatment compared to live controls (Figure A5.9). Due to the unsuccessful sequencing of one replicate, DESeq analysis was not performed for the samples treated with BSM and yeast extract in Soil 3 microcosms. For Soil 4 microcosms, enriched phylotypes in water treatment included unclassified Alphaproteobacteria, unclassified Betaproteobacteria, unclassified Myxococcales, unclassified Chitinophagaceae, Nitrospira, unclassified Bacteroidetes, unclassified Firmicutes, 3_genus_incertae and unclassified Bacteria (Figure A5.10). In contrast, only unclassified Bacteria and unclassified Gammaproteobacteria were differentially abundant in the treatment of BSM and yeast extract (Figure A5.11). 5.4.3 Prediction of Monooxygenase Genes PICRUSt2 predicted the enriched phylotypes (identified by DESeq analysis) associated with monooxygenase genes for the Site 1 and agricultural soil microcosms (Figure 5.7 and Figures A5.12-A5.16). A number of microorganisms were associated with monooxygenase genes in the different sample types. In the Site 1 sediment microcosms, dominant phylotypes associated with methane/ammonia monooxygenase subunits included unclassified Rhizobiales, unclassified Betaproteobacteria and unclassified Bacteria for all four treatments (Figure 5.7). 251 Notably, Rhodococcus was associated with methane/ammonia only in the three treatments with BSM. In the Site 1 sediment microcosms, unclassified Acetobacteraceae was the most abundant phylotype associated with propane monooxygenase subunits in all four treatments. Interestingly, Rhodococcus was found to be associated with propane subunits only in the treatments with BSM and yeast extract. The main phylotypes predicted to be associated with methane/ammonia monooxygenase subunits, including unclassified Rhizobiales, unclassified Betaproteobacteria, and unclassified Bacteria, showed higher abundance in samples compared to live controls (Figure A5.12). In the Site 2 sediment and agricultural soil microcosms combined, most of the enriched phylotypes identified by DESeq analysis were predicted to be associated with methane/ammonia and propane monooxygenase subunits (Figures A5.13 &A5.14). In particular, Nitrospira exhibited higher abundances in water treatment compared to the treatment with BSM and yeast extract (Figures A5.13). Unclassified Ktedonobacterales were associated three subunits of propane monooxygenase (prmA, prmB and prmC) only in live samples compared to live controls. In addition, the relative abundance of enriched phylotypes associated with toluene monooxygenase subunits is also presented (Figures A5.15 &A5.16). Rare phylotypes were predicted to be associated with the monooxygenase genes in the untreated Site 2 sediment (Figures A5.12- A5.16). 252 Figure 5.7 Relative abundance of enriched taxa (identified by DESeq analysis) associated with methane/ammonia monooxygenase subunits pmoA-amoA (K10944), pmoB-amoB (K10945) and pmoC-amoC (K10946) as well as taxa associated with all four propane monooxygenase subunits, prmA (K18223), prmB (K18225), prmC (K18224) and prmD (K18226) in the Site 1 sediment microcosms. 253 5.5 Discussion This study examined the effectiveness of yeast extract for sustaining microbial growth and facilitating 1,4-dioxane biodegradation within mixed microbial communities at low 1,4- dioxane concentrations. Differentially enriched phylotypes that benefited from low 1,4-dioxane concentrations were identified using DESeq analysis. Functional monooxygenase genes involved in the 1,4-dioxane degradation were predicted by PICRUSt2 (Douglas, Maffei et al. 2020). The impact of bioaugmentation with agricultural soil microorganisms on 1,4-dioxane removal rates in site sediments were also evaluated. Amending microcosms with BSM and yeast extract improved 1,4-dioxane biodegradation rates in sediment with indigenous 1,4-dioxane degraders. Yeast extract was previously found to accelerate the degradation rates by pure cultures of Rhodanobacter AYS5 and Xanthobacter flavus DT8 at a high 1,4-dioxane concentration (100 mg/L) (Pugazhendi, Rajesh Banu et al. 2015, Chen, Jin et al. 2016). Rhodococcus ruber 219 demonstrated the ability to effectively degrade 1,4-dioxane to levels below the health advisory threshold (0.35 μg/L) when supplied with thiamine at low 1,4-dioxane concentrations (below 100 μg/L), (Simmer, Richards et al. 2021). Due to the multiple growth factors contained in yeast extract, it is reasonable that yeast extract can enhance the 1,4-dioxane degradation at low 1,4-dioxane concentrations. No degradation was observed in any of the treatments for Site 2 and Site 3, indicating a lack of intrinsic degradation capabilities. A consistent lack of 1,4-dioxane degradation activity at low concentrations was observed in site samples down gradient from the source zone (Li, Van Orden et al. 2015). This could be due to the absence or low population of 1,4-dioxane degraders in these site samples. In addition, the low 1,4-dioxane concentrations may be insufficient to induce the activity of degrading populations (Adamson, Mahendra et al. 2014). These findings 254 suggest the need for bioaugmentation to enhance the degradation of 1,4-dioxane at low concentrations in these site samples. Uncontaminated agricultural soils have demonstrated 1,4-dioxane degradation potential (Ramalingam and Cupples 2020, Dang and Cupples 2021, Li and Cupples 2024). In microcosms amended with four different agricultural soils, over 50% 1,4-dioxane was removed within 40 days (Ramalingam and Cupples 2020). Concurrent degradation of 1,4-dioxane and cis- dichloroethene was also observed in agricultural soil microcosms (Dang and Cupples 2021). Functional propane monooxygenase genes were detected in agricultural soils (Eshghdoostkhatami and Cupples 2024, Li and Cupples 2024). In addition, genes potentially associated with 1,4-dioxane degradation were predicted to exist in agricultural soils through PICRUSt2 analysis (Cupples, Li et al. 2022). Other researchers have also highlighted the 1,4-dioxane degradation potential at environmental relevant concentrations by bacteria consortia enriched from uncontaminated garden soils (He, Mathieu et al. 2018, Tang, Wang et al. 2023). In one study, bacteria consortia dominated by Mycobacterium degraded 300 μg/L 1,4-dioxane to below 5 μg/L within three days (He, Mathieu et al. 2018). In another study, bacteria consortia dominated by Pseudonocardia degraded three consecutive doses of 300 μg/L of 1,4-dioxane to below 80 μg/L within 3 days (Tang, Wang et al. 2023). Both Pseudonocardia and Mycobacterium are well-known 1,4- dioxane degraders (Parales, Adamus et al. 1994, Mahendra and Alvarez-Cohen 2006, Kim, Jeon et al. 2009). In addition, the 1,4-dioxane degrading consortia in the above two studies were enriched by amending 100 mg/L 1,4-dioxane to the soil suspension mixture. Differentially abundant phylotypes identified via DESeq2 analysis between the samples and live controls provide insights into microbial taxa potentially involved in 1,4-dioxane 255 degradation. These phylotypes may contribute to the initial breakdown of 1,4-dioxane and/or benefit from the consumption of its biodegradation products (Ramalingam and Cupples 2020). In the current study, Rhodococcus was differentially enriched in the Site 1 sediment microcosms. However, A prior study using yeast extract amendments did not associate Rhodococcus with carbon uptake from 1,4-dioxane in the sediment microcosms from same site (Li and Cupples 2024). This suggests that Rhodococcus may be involved in 1,4-dioxane degradation without directly utilizing it as a carbon source. Within the Rhodococcus genus, strains such as Rhodococcus ruber 219 and Rhodococcus aetherivorans JCM 14343 have been reported to utilize 1,4-dioxane as the sole carbon and energy source (Bernhardt and Diekmann 1991, Inoue, Tsunoda et al. 2016). Additionally, several members of this genus, such as Rhodococcus sp. YYL, Rhodococcus sp. RR1, Rhodococcus jostii RHA1, Rhodococcus ENV425, Rhodococcus rhodochrous strain ATCC 21198, Rhodococcus. ruber T1 and T5, and Rhodococcus aetherivorans JCM 14343, are capable of degrading 1,4-dioxane co-metabolically in the presence of tetrahydrofuran, propane, toluene, isobutane or 1,4-butanediol (Mahendra and Alvarez-Cohen 2006, Vainberg, McClay et al. 2006, Yao, Lv et al. 2009, Sei, Oyama et al. 2013, Hand, Wang et al. 2015, Inoue, Tsunoda et al. 2018, Rolston, Hyman et al. 2019). Gemmatimonas benefited from 1,4-dioxane degradation in the Site 1 sediment microcosms. This genus was first reported to be responsible for carbon uptake in the contaminated Site 1 sediment and lake wetland microcosms (Li and Cupples 2024). In addition, Gemmatimonas was previously associated with benzoate, pyrene and phenanthrene degradation (Zhang, Sekiguchi et al. 2003, Wang, Teng et al. 2018, Wang, Wang et al. 2021). Rhizobiales also benefited from the presence of 1,4-dioxane in Site 1 sediments. 256 Consistent with the current study, members of the Rhizobiales order (Hyphomicrobium, Bartonella and Chelativorans) have been shown to thrive in response to 1,4-dioxane degradation in agricultural soil microcosms (Ramalingam and Cupples 2020). This order has been associated with carbon uptake from 1,4-dioxane in the lake wetland microcosms (Li and Cupples 2024) and was dominant in microbial consortia across successive 1,4-dioxane degradation cycles (Tian, Zhang et al. 2024). Furthermore, the family Xanthobacteraceae, which belongs to the Rhizobiales order, as well as Xanthobacter flavus DT8 and Xanthobacter sp., which are classified within the Xanthobacteraceae family, are known 1,4-dioxane degraders (Chen, Jin et al. 2016, Chen, Miao et al. 2021, Ma, Wang et al. 2021, Samadi, Kermanshahi-pour et al. 2023). Other enriched phylotypes include Flavobacterium in Site 1 sediments. Flavobacterium is known to co-metabolically degrades 1,4-dioxane in the presence of tetrahydrofuran (Sun, Ko et al. 2011) and has been detected in contaminated site sediments during the 1,4-dioxane degradation period (Ramalingam and Cupples 2020). This genus is also recognized for its ability to degrade a range of compounds, including diesel, polysaccharide, cellulose, dichlorvos, and paracetamol (Ning, Gang et al. 2012, Nedashkovskaya, Balabanova et al. 2014, Palma, Donaldben et al. 2018, Chaudhary, Kim et al. 2019, Kim and Yu 2020). Chitinophagaceae was enriched in the water treatment of agricultural soil microcosms. This family was also notably enriched in the uncontaminated garden soil (Tang, Wang et al. 2023) and activated sludge (Chen, Miao et al. 2021), which exhibited 1,4-dioxane degradation capacity. Additionally, the Chitinophagales order was consistently increased during the 1,4- dioxane degradation process in sludge (Samadi, Kermanshahi-pour et al. 2023). The Chitinophagaceae family has also been suggested to play an role in the biodegradation of other compounds, such as 1,1,1-trichloroethane, lindane, anthracene, hydrocarbon, 2-methylisoborneol 257 and benzo[a]pyrene (Zhang, Wang et al. 2011, Aburto-Medina, Adetutu et al. 2012, Song, Luo et al. 2015, Du, Zhou et al. 2017, Tusher, Inoue et al. 2022, Wu, Chang et al. 2022). PICRUSt2 analysis revealed a subset of the enriched phylotypes associated with monooxygenase subunits. Rhodococcus was associated with methane/ammonia degradation exclusively in the three treatments containing BSM and be linked to propane monooxygenase subunits only in the treatments containing both BSM and yeast extract in Site 1 sediment microcosms. Rhodococcus sp. RR1 is known for its ability to catabolize a wide range of compounds due to its diverse metabolic pathways (McLeod, Warren et al. 2006). In particular, the initiation of 1,4-dioxane biodegradation by Rhodococcus species has been associated with various groups of soluble di-iron monooxygenases (SDIMOs), including Rhodococcus sp. strain YYL thmA (group 5) (Yao, Lv et al. 2009), Rhodococcus jostii RHA1 prmA (group 5) (Sharp, Sales et al. 2007, Hand, Wang et al. 2015), Rhodococcus sp. RR1 prmA (group 5) (Sharp, Sales et al. 2007). Despite the absence of propane, Rhodococcus community may survive by extracting energy from co-metabolic intermediates of 1,4-dioxane (Miao, Heintz et al. 2021). Further, Rhodococcus may acquire dioxane degradation gene via horizontal transfer, as indicated by its significant correlation with prmA gene, which encodes the alpha subunit of propane monooxygenase (Li, Deng et al. 2023). SDIMO analysis also detected that Rhodococcus species were detected in relation to Rhodococcus jostii RHA1 prmA and Rhodococcus sp. RR1 prmA in the Site 1 sediments (Li and Cupples 2024). Overall, these findings suggest that Rhodococcus may participate in 1,4-dioxane degradation in Site 1 sediments through cometabolism involving multiple monooxygenases. 5.6 Conclusions This study demonstrates the potential for 1,4-dioxane biodegradation in contaminated 258 sediments and highlights the efficacy of agricultural soils for bioaugmentation. While yeast extract enhanced biodegradation rates in certain treatments, particularly in sediments containing indigenous degraders, its impact was limited in two of the three impacted sites (which lacked indigenous degraders). Bioaugmentation with agricultural soils successfully facilitated 1,4- dioxane removal in sediments that lacked native degraders. These results suggest that agricultural soils could provide a promising strategy for the bioremediation of 1,4-dioxane, particularly at low concentrations. Furthermore, a subset of the differentially abundant phylotypes (determined via DESeq analysis) between the samples and the live controls were previously associated with 1,4-dioxane biodegradation (Rhodococcus, Rhizobiales, Gemmatimonas, Flavobacterium, and Chitinophagaceae). PICRUSt2 analysis indicated some of the enriched phylotypes were associated with ammonia/particulate monooxygenase and propane monooxygenase subunits. Acknowledgment Thanks to Stacey VanderWulp (MSU) for providing the agricultural soil samples from Kellogg Biological Station (KBS, MSU). Thanks to Anthony Danko and Michael Pound (NAVFAC), Dr. Vidhya Ramalingam (Tetra Tech) as well as Paul Hatzinger (APTIM) for providing the sediment samples from the impacted site. Also, thanks to James O’Keefe, Dr. Casey Johnny, and Dr. Tony Schilmiller at the Mass Spectrometry Laboratory at the RTSF (MSU) for 1,4-dioxane analytical method support. 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"Changes in bacterial community of anthracene bioremediation in municipal solid waste composting soil." Journal of Zhejiang University SCIENCE B 12: 760-768. 267 APPENDIX Figure A5.1 1,4-Dioxane concentrations over time in microcosms inoculated with Site 1 sediments with three treatments. The initial concentration was approximately 2 mg/L 1,4- dioxane. Dioxane concentrations lower than the detection limit (i.e., 1 µg/L) were denoted as 0 in the graphs. 268 Figure A5.2 1,4-Dioxane concentrations over time (C/C0) in microcosms inoculated with Site 2 sediments with four treatments. The initial concentration was approximately 100 µg/L 1,4- dioxane. 269 A B Figure A5.3 1,4-Dioxane concentrations over time (C/C0) in microcosms inoculated with Site 2 sediments with three treatments. The initial concentration was approximately (A) 50 µg/L and (B) 500 µg/L 1,4-dioxane. 270 Figure A5.4 1,4-Dioxane concentrations over time (C/C0) in microcosms inoculated with Site 3 sediments with four treatments. The initial concentration was approximately 50 µg/L 1,4- dioxane. Dioxane concentrations lower than the detection limit (1.72 μg/L) were denoted as 0 in the graphs. 271 Figure A5.5 1,4-Dioxane concentrations over time (C/C0) in microcosms inoculated with agricultural Soil 4 with four treatments. The initial concentration was approximately 50 µg/L 1,4-dioxane. 272 A B C Figure A5.6 The relative abundance (%) of phyla in the (A) Site 1 sediments microcosms, (B) Agricultural soil microcosms and (C) untreated Site 2 microcosms. Each column represents average values for three sequencing replicates. 273 A B Figure A5.7 Top 50 differentially abundant taxa between the samples and live controls using DESeq2 analysis for the water treatment (A) and the BSM and yeast treatment (B) in Site 2 sediments microcosms inoculated with agricultural Soil 1. Note: "Soil 2_Site2_Yeast" is an abbreviation for "Soil2_Site2_BSMYeast1000." The column names were abbreviated to fit the visual constraints of the heatmap. 274 A B Figure A5.8 Top 50 differentially abundant taxa between the live samples and live controls using DESeq2 analysis for the water treatment (A) and the BSM and yeast treatment (B) in Site 2 sediments microcosms inoculated with agricultural Soil 2. Note: "Soil 2_Site2_Yeast" is an abbreviation for "Soil2_Site2_BSMYeast1000." The column names were abbreviated to fit the visual constraints of the heatmap. 275 Figure A5.9 Top 50 differentially abundant taxa between the live samples and live controls using DESeq2 analysis for the water treatment in Site 2 sediments microcosms inoculated with agricultural Soil 3. There was no differentially expressed abundant taxa for the BSM and yeast treatment. 276 Figure A5.10. Top 50 differentially abundant taxa between the live samples and live controls using DESeq2 analysis for water treatment in agricultural Soil 4 microcosms. 277 Figure A5.11 Top 50 differentially abundant taxa between the live samples and live controls using DESeq2 analysis for BSM and yeast treatment in agricultural Soil 4 microcosms. 278 Figure A5.12 Relative abundance of enriched taxa (identified by DESeq analysis) associated with methane/ammonia monooxygenase subunits pmoA-amoA (K10944), pmoB-amoB (K10945) and pmoC-amoC (K10946) as well as taxa associated with all four subunits of propane monooxygenase prmA (K18223), prmB (K18225), prmC (K18224) and prmD (K18226) in the Site 1 microcosms. 279 Figure A5.13 Relative abundance of enriched taxa (identified by DESeq analysis) associated with methane/ammonia monooxygenase subunits pmoA-amoA (K10944), pmoB-amoB (K10945) and pmoC-amoC (K10946) as well as taxa associated with all four subunits of propane monooxygenase prmA (K18223), prmB (K18225), prmC (K18224) and prmD (K18226) in the Site 2 sediment and agricultural soil microcosms combined. 280 Figure A5.14 Relative abundance of enriched taxa (identified by DESeq analysis) associated with methane/ammonia monooxygenase subunits pmoA-amoA (K10944), pmoB-amoB (K10945) and pmoC-amoC (K10946) as well as taxa associated with all four subunits of propane monooxygenase prmA (K18223), prmB (K18225), prmC (K18224) and prmD (K18226) in the Site 2 sediment and agricultural soil microcosms combined. 281 Figure A5.15. Relative abundance of enriched taxa (identified by DESeq analysis) associated with the six subunits of toluene monooxygenase, tmoA/tbuA1/touA (K15760), tmoB/tbuU/touB (K15761), tmoC/tbuB/touC (K15762), tmoD/tbuV/touD (K15763), tmoE/tbuA2/touE (K15764) and tmoF/tbuC/touF (K15765) in the Site 2 sediment and agricultural soil microcosms combined. 282 Figure A5.16 Relative abundance of enriched taxa (identified by DESeq analysis) associated with the six subunits of toluene monooxygenase, tmoA/tbuA1/touA (K15760), tmoB/tbuU/touB (K15761), tmoC/tbuB/touC (K15762), tmoD/tbuV/touD (K15763), tmoE/tbuA2/touE (K15764) and tmoF/tbuC/touF (K15765) in the Site 2 sediment and agricultural soil microcosms combined. 283 CHAPTER 6: CONCLUSIONS AND PERSPECTIVES This dissertation advances the understanding of microbial processes including nitrogen and carbon cycling and the biodegradation of 1,4-dioxane in complex soil and sediment communities. Chapter 2: This study investigated the taxonomic and functional profiles of the soil microbial communities associated with nitrogen metabolism, primarily denitrification, under different management practices by the analysis of shotgun sequences. This successfully links both taxonomic and functional data for the microbes associated with nitrogen cycling processes. Chapter 3: This study identified the phylotypes related to the carbon uptake from added glucose using 16S rRNA gene sequencing coupled with SIP. The active phylotypes responsible for carbon uptake varied between treatments, soil pore sizes and incubation times. Pseudomonas (Proteobacteria) played an important role in carbon uptake from glucose in all short-term incubations and in the large pore long-term incubations. The enriched functional genes were also predicted. This is the first study to examine carbon assimilation from glucose as a function of pore architecture in these agricultural systems. Chapter 4: This study demonstrated that the amendment of BSM and yeast extract significantly enhanced the 1,4-dioxane degradation at 2 mg/L for the impacted site sediments and agricultural soils. Gemmatimonas, Solirubacteraceae, Solirubrobacter, RB4 and oc3299 were identified as novel phylotypes linked to carbon uptake from 1,4-dioxane. Three genes (Rhodococcus jostii RHA1 prmA and Rhodococcus sp. RR1 prmA, Pseudonocardia dioxanivorans CB1190 plasmid pPSED02 Psed_6976) were detected in the mixed microbial communities. Overall, a variety of microbial communities were involved in the 1,4-dioxane 284 degradation. The amendment of BSM and yeast extract offers the potential to enhance 1,4- dioxane biodegradation. Chapter 5: This study examines the impact of BSM and yeast extract on 1,4-dioxane degradation at low levels (< 500 µg/L). The amendment significantly accelerated the 1,4-dioxane degradation for one impacted site sediment but not for the other two site sediments. The rapid 1,4-dioxane degradation for the agricultural soils suggests higher levels of 1,4-dioxane degraders in these soils. The bioaugmentation with agricultural soils enhanced 1,4-dioxane degradation for the site sediments that previously showed no degradation. Overall, the amendment of BSM and yeast extract can enhance the 1,4-dioxane degradation in some cases. Agricultural soils provide options for bioaugmentation to enhance the 1,4-dioxane degradation at low levels. Future research will apply quantitative SIP can be applied to quantify the variations in the growth of functional microbes and their isotope assimilation rates by coupling 13C-labeled carbon substrates with qPCR. This approach will provide deeper insights into microbial activity and their specific roles in biogeochemical processes. Additionally, geochemical properties of different soil types may be explored in the future research, as these properties may have crucial influence on the microbial communities. Correlating geochemical data with molecular data could help identify factors that influence functional genera, enabling the identification of soil characteristics that promote the growth of beneficial microbes. Instead of inoculating agricultural soils, future work could involve enriching 1,4-dioxane degrading consortia from uncontaminated sols for in situ 1,4-dioxane degradation. This could offer a sustainable and cost-effective approach for groundwater bioremediation. 285