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The gut microbiome and metabolic pathways of recurrent kidney stone patients and their non-stone-forming… Choy, Wai Ho 2018

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THE GUT MICROBIOME AND METABOLIC PATHWAYS   OF RECURRENT KIDNEY STONE PATIENTS AND   THEIR NON-STONE-FORMING LIVE-IN PARTNERS   by   WAI HO CHOY  B.Sc., The University of British Columbia, 2014   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF   THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE   in   THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Experimental Medicine)   THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)    August 2018    © Wai Ho Choy, 2018     ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:  The Gut Microbiome and Metabolic Pathways of Recurrent Kidney Stone Patients and their Non-Stone-Forming Live-In Partners  submitted by Wai Ho Choy  in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine  Examining Committee: Dirk Lange, Urological Sciences Supervisor  Ben Chew, Urological Sciences Supervisory Committee Member  Amee Manges, School of Population and Public Health Supervisory Committee Member William Hsiao, Pathology and Laboratory Medicine Additional Examiner     Additional Supervisory Committee Members:  Supervisory Committee Member  Supervisory Committee Member     iii Abstract  Background: Metabolism-associated kidney stones such as oxalate, uric acid and cystine stones are caused by the over-accumulation or under-excretion of their associated metabolites in the human body. Although the kidney is the primary excretion site for these metabolites, the intestine is an important alternative site of excretion. Intestinal bacterial community members contribute to the breakdown, transport and assimilation of stone-associated metabolites including oxalate, uric acid, cystine and butyrate. To better diagnose and prevent the formation of metabolic kidney stones, there is a need to examine the intestinal microbiome not just as individual bacteria or genes but as bacterial communities and interconnected metabolic pathways.  Experimental approach: This thesis examines the differences in bacterial communities and metabolic pathways between the intestinal microbiomes of recurrent kidney stone patients and non-stone-forming controls. Fecal samples were collected from 17 recurrent kidney stone patients and 17 controls with no stone-forming history. Bacterial DNA was then extracted from the fecal samples. To examine bacterial taxonomy, specific variable regions of the 16S rRNA gene were sequenced from the DNA and aligned to a bacterial gene database to identify and quantify the bacteria present. To examine metabolic pathways, metagenomic DNA libraries were sequenced, assembled and aligned to a metabolic gene database to identify and quantify the metabolic genes present in each sample.   Results: Bacterial populations in patient microbiomes appear to be less diverse than those in control microbiomes. At the bacterial species level, we found that patient microbiomes had lower abundance of Oxalobacter formigenes, a well-known oxalate-degrading bacterium. At the metabolic pathway level, patient microbiomes were found to contain a lower abundance of genes important for the production of butyrate, a fatty acid that promotes overall intestinal integrity and has been found to upregulate the expression of oxalate transporters in the gut.   Conclusions: Our study verifies previous findings that a majority of recurrent kidney stone formers lack O. formigenes in their intestinal microbiomes. Additionally, our analysis into metabolic genes in the gut uncovered an additional deficiency in the butyrate metabolism  iv pathway that could influence overall gut homeostasis. Reduced bacterial diversity in recurrent stone formers also suggest that patient microbiomes may be dysbiotic, a state common to many intestinal diseases.    v Lay summary  Kidney stones affect approximately 1 out of 11 people in North America causing extreme pain, long-term renal deterioration and often, the loss of a kidney. Although kidney stones can be removed with a high success rate, they often recur due to an underlying metabolic imbalance in the body. In the case of metabolic stones such as oxalate, uric acid and cystine stones, there is an over-accumulation of metabolites in the body that end up in the kidney and urine. The intestine is as an alternative site for the transport and breakdown of these metabolites in the body. In particular, there are many bacterial community members inside the intestine that can harvest, transport and degrade the metabolites. In this study, we look at the differences in bacterial communities between recurrent kidney stone patients and healthy non-stone-forming controls to understand how intestinal bacteria can help reduce the buildup of metabolic waste.    vi Preface   Wai Ho (David) Choy was involved in designing, conducting and analyzing the research data under the direct guidance of Dr. Dirk Lange and Dr. Ben Chew with assistance from Dr. Amee Manges, Dr. William Hsiao, Dr. Steven Hallam and their respective lab members at the University of British Columbia. Approval for the study was given by the Clinical Research Ethics Board of the University of British Columbia (Ethics application # H10-01195) and Vancouver Coastal Health (Ethics application # V11-01195)  Participant fecal samples and metadata were kindly collected by staff at the Vancouver Stone Centre and brought to the laboratory. Fecal DNA extraction, DNA clean-up and metagenomic library preparation were performed by Wai Ho Choy. 16S rRNA library preparation was performed by Microbiome Insights. High-throughput sequencing of both the metagenomic and 16S rRNA DNA libraries were performed by the UBC Pharmaceutical Sciences laboratory using default Illumina sequencing protocols.  For the 16S rRNA analysis, Microbiome Insights performed the initial round of post-sequencing DNA reads cleanup, annotation and analysis of bacterial taxa. However, for the purpose of standardizing the analysis steps and for Wai Ho Choy’s own personal learning, Wai Ho Choy re-did the cleanup, annotation and analysis of bacterial taxa using the software mothur and custom R scripts.  For the metagenomic metabolic pathways analysis, Wai Ho Choy performed the cleanup of post-sequencing DNA reads. Connor Morgan-Lang from the Hallam lab assembled the cleaned DNA sequencing reads using the sequence assembler MEGAHIT on the WestGrid server and ran Metapathways, a bioinformatics software, on the resulting assemblies to generate annotated counts of metabolic genes. All downstream results were quality-controlled and analyzed by Wai Ho Choy using a combination of R, bash and python scripts.     vii Table of Contents  Abstract………………………………………………...…………………………………………iii  Lay Summary………………………………………….…………………………………………..v  Preface……………………………………………...…………………………………………….vi  Table of Contents.………………………………………...……………………………………...vii  List of Tables……………………………………….……………………………………………..x  List of Figures…………………………………………………………………………………….xi  List of Abbreviations………………………………...…………………………………………..xii  Acknowledgements…………………………………..………………………………………….xiv  Dedication…………………………………………...…………………………………………...xv  Chapter 1: Background………………………………………….………………………………...1  1.1 Kidney stones……………………………………………….…………………………1  1.2 The role of metabolites in kidney stone disease………………………………………2  1.2.1 Oxalate and kidney stones………………………………………………...2  1.2.2 Uric acid and kidney stones……………………………………………….4  1.2.3 Cystine and kidney stones………………………………………………...6  1.3 The human gut microbiome………………………………………….………………..7  1.3.1 Gut bacteria and oxalate………………………………….………………..8  1.3.2 Gut bacteria and uric acid…………………………………………………9  1.3.3 Gut bacteria and cystine………..…………………………...……………10  1.3.4 Gut bacteria and butyrate………………………………...………………10  1.4 Thesis project……………………………………………………………...…………11  1.4.1 Rationale……………………………………………………………...….11   viii 1.4.2 Hypothesis………………………………………………………………..11  1.4.3 Specific objectives……………………………………………...………..11  Chapter 2: Materials & Methods……………………………………………….……..………….13  2.1 Sample collection………………………………………………………...…………..13  2.2 Fecal DNA extraction……………………………………………………..…………16  2.3 16S rRNA sequencing and analysis……………………………………...…………..16  2.3.1 16S rRNA amplicon library preparation………………………...……….16  2.3.2 16S rRNA DNA sequence cleanup…………………………...………….17  2.3.3 Taxonomic analysis…………………………………………..………….18  2.4 Whole-genome shotgun sequencing and analysis…………………………..……….19  2.4.1 Shotgun-sequencing library preparation………………………...……….19  2.4.2 Shotgun sequence cleanup…………………………………...…………..19  2.4.3 Shotgun sequence assembly…………………………………...…………20  2.4.4 ORF prediction and annotation of assembled contigs………...…………20  2.4.5 Metabolic pathway statistical analysis……………………..……………20  2.4.6 Alignment of reads to Oxalate oxidoreductase subunit genes………...…22  Chapter 3: Results…………………………………………………………………..…………...23  3.1 Phyla distribution…………………………………………………..………………..23  3.2 Bacterial diversity………………………………………………………..………….25  3.3 Taxonomic differences between patient and control microbiomes………..………..26  3.4 Examination of oxalate-degrading bacteria……………………………..…………..27  3.5 Overall abundance and presence of three metabolic pathways……………..………28  3.6 Differences in individual gene relative abundances………………………....…….37   ix 3.7 Examination of oxalate-degrading metabolic genes……………………………...…40  3.8 Follow-up analysis of oxalate oxidoreductase…………………………………...…42  Chapter 4: Discussion……………………………………………………………………...…….44  4.1 Summary……………………………………………………………………………..44  4.2 Loss of species diversity in patient microbiomes……………………………………44  4.2.1 Loss of Oxalobacter, an oxalate-degrading bacterial genus,………………44  in patient microbiomes  4.2.2. Higher abundance of unclassified bacteria in control microbiomes………45  4.3 Differences in metabolic pathways of patient microbiomes…………………………45  4.3.1 Possible deficiency in the butanoate biosynthesis pathway………………..45  4.3.1.1 Link between butanoate and oxalate……………………………………..46  4.3.2 No meaningful differences in other metabolic pathways associated………47 with stone metabolites  Chapter 5: Conclusions & Future Directions…………………………………………………….48  5.1 Summary……………………………………………………………………………..48  5.2 Limitations…………………………………………………………………………...48  5.3 Future directions……………………………………………………………………..48  Bibliography……………………………………………………………………...……………...50  Appendix……………………………………………………………………...…………………61  Appendix A: Demographics of recurrent oxalate kidney stone formers and controls…...61  Appendix B: Primer design for PCR amplification and Illumina MiSeq sequencing…...62 as described in the supplementary methods of Kozich et al. 2013  Appendix C: Sequencing read depth and other sequence abundance metrics…………..65  Appendix D: Oxalate metabolism reactions in bacteria…………………………………72   x List of Tables  Table 1. Four major categories of kidney stones………………………………….………………2  Table 2. Inclusion and exclusion criteria for patient and control participants…………………...13  Table 3. Patient and control metadata ……………………………………………………….…..61  Table 4. Summary of patient and control characteristics……………………………………...…14  Table 5. Forward primers for PCR amplification of the bacterial 16S rRNA gene (V4 region)...62  Table 6. Reverse primers for PCR amplification of the bacterial 16S rRNA gene (V4 region)...63  Table 7. Primers for Illumina MiSeq sequencing of 16S rRNA amplicons……………………..64  Table 8. 16S rRNA sequencing read depth and operational taxonomic unit depth…………...…65  Table 9. Top three most abundant OTU in the second 16S rRNA sequencing batch……………68  Table 10. Shotgun-sequencing read depth and abundance of open reading frames (ORFs)….…70  Table 11. Top 5 most abundant bacterial phyla in patient and control microbiomes…….....…...23  Table 12. Differences in bacterial phyla between patient and control microbiomes………...…..23  Table 13. Taxonomic differences between patient and control microbiomes. ………….…...….27  Table 14. Presence and relative abundance of detected oxalate-degrading bacteria……...……..28  Table 15. Relative abundance of bacterial genes assigned to three metabolic pathways;……….30 Glyoxylate & Dicarboxylate metabolism, Ascorbate & Aldarate metabolism and Butanoate metabolism.   Table 16. Percentage of unique genes detected in three metabolic pathways; Glyoxylate &...…30 Dicarboxylate metabolism, Ascorbate & Aldarate metabolism and Butanoate metabolism.   Table 17. Differentially abundant genes within the three metabolic pathways……….……..…..38  Table 18. Abundance and presence of bacterial genes associated with the metabolism ….…….41 of oxalate or its associated substrates; formate, oxalyl-CoA and formyl-CoA    Table 19. Enzymatic reactions of enzymes associated with oxalate metabolism………….…….72  Table 20. Presence and abundance of subunit genes for the oxalate-degrading ………….……..43 enzyme, Oxalate Oxidoreductase   xi List of Figures  Figure 1. Flow of oxalate in the human body……………………………………………..………3  Figure 2. Flow of uric acid in the human body. ……………………………………………..……5  Figure 3. Flow of cystine in the human body………………………………………………….….7  Figure 4. The process of extracting bacterial taxonomy and metabolic pathway…….…...……..15 information from fecal samples of recurrent kidney stone patients and matching controls  Figure 5. Comparison of 16S rRNA sequencing depth between pairs of samples………………66  Figure 6. Comparison of OTU depth between pairs of samples…………………………………66  Figure 7. Plot of sequencing depth against OTU count and number of unique OTUs………..…67  Figure 8. Shotgun-sequencing read depth across pairs of samples.……………………………...71  Figure 9. Shotgun-sequencing read depth across pairs of samples after KneadData’s………….71 human read removal and trimming  Figure 10. Formulas for the calculation of RPKM and TPM ………………………………….21  Figure 11. Distribution of bacterial phyla across microbiome samples…………….…..…..……24  Figure 12. Species richness and Shannon alpha diversity in patient and control microbiomes…25  Figure 13. Heat map of gene abundances within the Butanoate metabolism pathway,.…..……..31 Glyoxylate & Dicarboxylate metabolism pathway and Ascorbate & Aldarate  metabolism pathway.   Figure 14. Heat map of genes detected within the Butanoate metabolism pathway, …….……..34 Glyoxylate & Dicarboxylate metabolism pathway and Ascorbate & Aldarate  metabolism pathway.   Figure 15. Butanoate Synthesis pathways. ………………………………………….……..……39    xii List of Abbreviations   US   United States of America  CaOx   Calcium Oxalate  PH  Primary Hyperoxaluria  DNA  Deoxyribonucleic acid  RNA  Ribonucleic acid  16S rRNA 16S ribosomal RNA  PCR  Polymerase Chain Reaction  SOP  Standard Operating Procedure  OTU  Operation Taxonomic Unit  KEGG  Kyoto Encyclopedia of Genes and Genomes  RPKM  Reads Per Kilobase Per Million  TPM   Transcripts Per Million  KO  KEGG Orthology ID  E.C.  Enzyme Commission  Gut  Intestine  spp.  Specie   SCFA  short-chain fatty acid  IBD   Inflammatory bowel disease  PT  Patient  CTRL  Control      xiii Bacteria  O. formigenes Oxalobacter formigenes  E. coli  Escherichia coli  B. subtilis Bacillus subtilis  B. ovatus Bacteroides ovatus  B. fragilis Bacteroides fragilis  R. flavefaciens Ruminococcus flavefaciens  C. difficile Clostridium difficile    Genes and enzymes  oxc  oxalyl-CoA decarboxylase   frc  formyl-CoA:oxalate CoA-transferase  oxIT  oxalate-formate antiporter  ACSM  Medium-chain acyl-CoA synthetase  AbfD  4-hydroxybutyryl-CoA dehydratase  Hbd  3-hydroxybutyryl-CoA dehydrogenase  PFOR  Pyruvate Ferredoxin Oxidoreductase  OOR  Oxalate Oxidoreductase  SLC26A3 Solute Carrier Family 26 Member 3 (DRA, Down-regulated in adenoma)  SLC26A6 Solute Carrier Family 26 Member 6 (PAT1, Putative anion transporter 1)  SLC7A9  Solute Carrier Family 7 Member 9  SLC3A1 Solute Carrier Family 3 Member 1    xiv Acknowledgments   I would like to first thank my supervisor Dirk Lange for providing me with the mountains of support, patience and guidance throughout my time as a graduate student. I will always be grateful for the opportunity you’ve given me to explore the world of bugs, stones and bioreactors.  Secondly, I would also like to thank Dr. Ben Chew, Olga, Joey, Kristina, Tommy, Adrienne who have been tremendous mentors and colleagues since the early days I started in the lab. You’ve provided me with the team spirit and confidence I needed to pursue new directions with my work and learning. And thanks also to Elliya, Anthony, Karen, Bonnie and Amal for some amazing memories at the lab, I wish you all the best in your lives and careers.  Thirdly, I would like to thank Dr. Amee Manges, Dr. Steven Hallam, Dr. William Hsiao, Connor and all the lovely people at the Jack Bell research centre who have contributed a significant part to my journey and beyond. You have all been an inspiration to me.   Last and most important of all, my deepest and sincerest gratitude goes to my wife, Maggie, my mum, my dad, my brother and my little baby Olive for being there for me every minute of every day. Thanks for making the journey worth every second of it.    xv Dedication                 To my family who have patiently and lovingly supported me in every endeavor.   1 Chapter 1: Introduction  1.1 Kidney stones  An estimated 8.8% of the US population suffer from kidney stones based on 2007-2010 survey data; a percentage that has been increasing consistently from 3.8% in 19801,2. Although kidney stone disease by itself is not strongly associated with patient mortality, it can cause extreme pain, long-term renal deterioration and sometimes, the loss of an entire kidney. Kidney stones are also associated with patient hospitalization, surgery and lost work time accounting for up to $5 billion dollars in cost annually in the US3,4.   Calcium oxalate kidney stones (CaOx stones) are the most common type of kidney stones, representing about 80% of all known kidney stone cases5 (Table 1). Other kidney stones include calcium phosphate stones, struvite stones, cystine stones and uric acid stones that are caused by various health conditions including bacterial infections, metabolic imbalances, lifestyle and genetic disorders5. Kidney stones also often occur as a combination of stone types in patients; examples include calcium oxalate-phosphate stones, uric acid-calcium oxalate stones and calcium-struvite stones6.  Small kidney stones are often passed out in urine without any complications, however, larger stones require medically-assisted removal ranging from non-invasive methods such as shock wave lithotripsy and ureteroscopy to more invasive methods such as percutaneous nephrolithotomy and open surgery7. Despite the high success rate of stone removal techniques, the challenge with treating kidney stone disease is the recurrence of kidney stones after treatment8,9. For infectious stones such as struvite stones, antibiotics are often prescribed to prevent the recolonization of bacteria in the urinary tract. Metabolic stones such as calcium stones, uric acid stones and cystine stones however require a combination of preventative diets, supplements, drugs and changes in lifestyle8. Even then, current prevention methods are ineffective, thus there is a need to explore the origins of metabolic imbalances in kidney stone patients that lead to recurrent stone formation.      2 Crystal shape Stone type Primary risk factors Prevalence      Calcium stones (calcium oxalate, calcium phosphate) Hyperoxaluria, hypercalciuria 80%      Uric acid stones Excess intake of protein in diet, gout, Inflammatory bowel disease (IBD) 3-10%      Cystine stones Genetic defect of amino acid transporter in renal tubules < 2%       Struvite stones Urinary infection by urease-positive bacteria 10-15%  Table 1. Four major categories of kidney stones   1.2 The role of metabolites in kidney stone disease  Kidney stones are mostly made up of minerals, ions and metabolites from the urine including calcium, carbon, oxalate, phosphate, magnesium and ammonium. From a chemical perspective, there needs to be sufficient concentration of these metabolites in urine for the first kidney stone crystals to form. The following sections describe the metabolites commonly associated with kidney stones.   1.2.1 Oxalate and kidney stone disease  Oxalate, or oxalic acid, is the main component of calcium oxalate kidney stones and can be found in most diets derived from plant sources (Figure 1). It is especially abundant in high-oxalate but otherwise, nutritious plants such as rhubarb, spinach, chocolate, nuts and beetroot10. Oxalate is also produced enzymatically from glyoxylate, a metabolite produced by hepatocytes,  3 which are the primary cells of the human liver, as part of normal liver metabolism11,12. Lastly, oxalate is formed from the spontaneous breakdown of ascorbic acid (vitamin C) in the body11,12.  Figure 1. Flow of oxalate in the human body.  Oxalate is produced from the breakdown of glyoxylate in the liver (1), the spontaneous breakdown of ascorbate in the body (2) and from the intake of oxalate-rich diets (3). Oxalate transporters at the intestine (4) transport oxalate between the intestine and circulatory system. Oxalate in the intestine are either degraded by oxalate-degrading bacteria (5), reabsorbed into the bloodstream via oxalate transporters (4) or combines with calcium ions to form insoluble calcium oxalate (6), which is then excreted out of the body. Unabsorbed lipids can sequester free calcium ions, preventing the formation of calcium oxalate in the intestine.    Despite the ubiquity of oxalate, humans and other mammals lack the enzymatic ability to digest and breakdown oxalate, thus relying on urinary excretion, fecal excretion and the breakdown of oxalate by intestinal bacteria to remove oxalate from the body12. When those mechanisms fail to remove oxalate, there is an over accumulation of oxalate in the body which is then excreted into the urine. This excess of oxalate in urine is called hyperoxaluria.   4 Studies in the past have classified hyperoxaluria into two major classes; primary hyperoxaluria and secondary hyperoxaluria. Primary hyperoxaluria occurs in approximately 1-3 cases per million people11,12 and is caused by genetic mutations in enzymes of the liver responsible for the metabolism of glyoxylate. As glyoxylate is a major precursor metabolite for the biosynthesis of oxalate, any increase in its synthesis or defect in its breakdown has a cascading effect on oxalate production. There are three known types of primary hyperoxaluria, primary hyperoxaluria type 1 (PH1), primary hyperoxaluria type 2 (PH2) and primary hyperoxaluria type 3 (PH3) with PH1 being the most common type of primary hyperoxaluria11,12.  Secondary hyperoxaluria is a harder condition to define as it has been characterized by a number of conditions including the consumption of high oxalate or high oxalate precursor foods, malabsorption of fat in the gut, changes in expression of oxalate transporters in the intestine and shifts in the abundance of oxalate-degrading bacteria in the gut microbiome11,12. In general, secondary hyperoxaluria seems to involve deficiencies in the transport and degradation of oxalate at the intestinal level, leading to increased absorption or retention of oxalate.    1.2.2 Uric acid and kidney stone disease  Uric acid is the main component of uric acid stones and is produced from the metabolism of purine nucleotides in the body or from the breakdown of purines from our diet (Figure 2). Foods high in purines include alcohol, seafood and certain meat products13. An overabundance of uric acid in the body also causes gout, a form of inflammatory arthritis that causes swelling at bone joints14.  Purine bases such as hypoxanthine, guanine and adenine are precursors of uric acid are mostly recycled by the body to form purine nucleotides, the building blocks of DNA and RNA. Excess purines in the body are broken down by various enzymes into uric acid and excreted out of the body via the kidneys and urine or via the intestines15,16. Previous studies estimate that 70% of excess uric acid are excreted renally while the remaining 30% are excreted into the intestine and metabolized by resident gut bacteria17. Although past research has mostly focused on uric acid transport in the kidneys (renal tubules), recent genome-wide association studies, gene expression  5 studies and knockout mouse models show that there is significant efflux of uric acid into the intestine facilitated by multiple ionic transporters in the intestine18–20. These findings support the idea that the intestine is an important pathway for uric acid excretion and homeostasis, especially in the event of renal insufficiency.      Figure 2. Flow of uric acid in the human body.  Uric acid is formed from the breakdown of purines in the body as part of normal cell metabolism (1) and from the breakdown of purines from the diet (2). 70% of excreted uric acid is removed via the kidneys, while 30% is removed via the intestines with the help of intestinal transporters (3). Excess uric acid in the kidney promotes the formation of uric acid stones. Excess uric acid in the body also often accumulates in bone joints, causing inflammation and a condition called gout (4).   6 1.2.3 Cystine and kidney stone disease  Cystine is the main component of cystine stones and can be found in most high-protein diets including animal meat, eggs, dairy and plants such as peppers, garlic and onions. Cystine is formed from the combination of two molecules of cysteine, a semi-essential amino acid that is produced enzymatically from the amino acids methionine and serine21–23 (Figure 3).  Patients with cystine stones have an inherited autosomal recessive disease where mutations in two amino acid transporters, SLC7A9 and SLC3A1 prevent the resorption of cystine into the blood from the proximal tubules of the kidney24. Previous studies show that these two cystine transporters are also found on the apical surface of the human intestine along with other intestinal cystine transporters that are distributed on the basolateral surface of the intestinal epithelium25. The abundance and variety of cystine transporters on both the apical and basolateral surfaces of the intestine suggests that the intestine is a site of active cystine regulation.   7  Figure 3. Flow of cystine in the human body Cystine is formed from two molecules of Cysteine, an amino acid that is biosynthesized by the body from methionine and serine (1). It can also be sourced from most protein diets (2). Excess cystine is excreted via amino acid transporters in the kidneys and intestine (3). Patients with cystinuria have a genetic mutation in two amino acid transporters that transport cystine (4). This prevents the resorption of cystine from the proximal tubules of the kidney.   1.3 The human gut microbiome  In urology and the wider field of medicine, bacteria have historically been looked upon as pathogens whose main role in humans is to cause infection and disease26. However, that thinking has changed with studies showing that bacteria play a number of positive roles in the  8 maintenance of human health and metabolism27,28. The key to good health, therefore, is maintaining the right balance of bacteria.  Finding the right balance of bacteria is a non-trivial task due to the sheer number and diversity of bacteria in humans. The human gut, for example, is estimated to contain 10 trillion bacterial cells, belonging to over a thousand known bacterial species and possibly thousands more yet-to-be-discovered bacterial species29–31. Within these thousands of species, there are also hundreds of thousands of bacterial genes, each with unique functions that help the bacterial communities survive, interact and propagate within the gut31. Together, these interconnected communities of bacteria form what is called the human gut microbiome.  Early methods in the identification and characterization of the gut microbiome were performed by simply culturing the bacteria from fecal samples or intestinal sections. However, due to the strict nutritional and environmental requirements for the growth of many bacteria32, culture-based methods could only capture a portion of the bacterial diversity in the gut; up to 20-40% of the bacterial operational taxonomic units (OTUs)33. With the discovery and application of the polymerase chain reaction (PCR) and the subsequent advancements in high-throughput DNA sequencing, researchers are now able to sequence millions of bacterial DNA at affordable costs and faster rates, allowing us to discover and characterize many more novel bacteria and bacterial genes in the gut microbiome32.   The following sections describe the metabolic relationships between the intestinal microbiome and metabolites associated with kidney stone disease.   1.3.1 Gut bacteria and oxalate  One of the earliest relationships between oxalate and the gut microbiome was identified in 1980 when a research group in Iowa isolated and characterized a novel anaerobic bacterium called Oxalobacter formigenes from the rumen of sheep that could degrade oxalate under anaerobic conditions34. More importantly, this bacterium used oxalate as its only source of carbon and could degrade oxalate at high rates in vitro35. The discovery also explained why certain  9 populations of cattle and sheep which harbored the bacteria in their rumen could graze on extremely high-oxalate plants without suffering from calcium oxalate poisoning36.  Follow up studies over three decades showed that human gut microbiomes also harbored unique strains of this bacterium, making it a promising probiotic candidate for calcium oxalate therapy37,38. Calcium oxalate kidney stone patients also had a lower prevalence of O. formigenes in their intestinal tracts, suggesting that the lack of this bacterium is a strong biomarker for calcium oxalate kidney stone disease39.   Since then, clinical trials have attempted to use O. formigenes as a probiotic therapy to reduce oxalate availability in the gut but have been met with mixed success40–44. Part of the challenge is that O. formigenes does not readily re-colonize patients that don’t already harbor the bacteria with one study showing a transient re-colonization of under 2 weeks41. Additionally, the bacterium has not been able to consistently and significantly decrease oxalate levels in patients41, suggesting that there may be other gut mechanisms or gut bacteria involved in oxalate regulation at the gut. In summary, both observations show that modifying the gut microbiome for therapeutic effect may take more than introducing one bacteria into the ecosystem.   Indeed, a recent review by Miller et al. on oxalate-degrading bacteria suggest that bacteria changes in response to high oxalate often occur in bacterial clusters instead of individual bacterial species45,46. In particular, they proposed that there are 4 groups of bacteria that respond differently to oxalate. The first group are bacteria that utilize oxalate as a resource such as O. formigenes, the second group are bacteria that are inhibited by oxalate but can degrade if it is present, the third group are bacteria that are inhibited by oxalate but indirectly benefit from the presence of other oxalate-degrading bacteria and the last group are bacteria that are unaffected by the presence of oxalate. To date, at least 19 species of bacteria are known to be oxalate-degraders (first and second group) but less is known about bacteria in the third and fourth group, emphasizing the need to study the intestinal microbiome as a whole45.      10 1.3.2 Gut bacteria and uric acid  Uric acid and intestinal bacteria have historically been studied together in the context of hyperuricemia and gout, with past work showing that multiple phyla of bacteria are capable of either degrading, transporting and utilizing uric acid via enzymes such as urate oxidase (also known as uricase), allantoinase and allantoicase18,47. In fact, near complete sets of enzymes for uric acid degradation are common to almost all plant, bacteria and fungi as an essential nitrogen scavenging function; a capability lost to humans and many animals as part of the evolutionary process48. By further examining these enzymes in the gut microbiome of gout patients, one research group found deficiencies in the abundance of uric acid-degrading enzymes along with changes to overall bacterial community structure49.   1.3.3 Gut bacteria and cystine  Although cystine stones are primarily caused by genetic mutations in renal cystine transporters, there is increasing evidence that the intestinal microbiome is an active site for cystine metabolism regulation. Most of the work performed in this area has been focused on enterobacteria and other model bacteria such as E. coli and B. subtilis, showing that multiple bacterial groups are able to transport, synthesize and degrade both cysteine and its dimerized form, cystine50–52. To bacteria, cysteine is both an antioxidant and the primary pathway for incorporating sulfur and disulfide bonds into cellular components. Within the human intestine, cysteine is actively assimilated by the intestinal microbiome, primarily by colonic bacteria, thereby reducing the concentration of free-floating cysteine to undetectable levels53,54.    1.3.4 Gut bacteria and butyrate  Butyrate or butanoate (butanoic/butyric acid is the acidic form) is not a metabolite that is directly related to kidney stone formation. Instead, it is one of a few beneficial short-chain fatty acids (SCFA) produced by commensal intestinal bacteria such as Faecalibacterium prausnitzii, Coprococcus spp. and Roseburia spp.55. Butyrate is the preferred food source for epithelial cells of the human colon and has received significant research interest as it has been shown to promote intestinal barrier integrity and prevent intestinal inflammation in diseases such as inflammatory  11 bowel disease (IBD) and colorectal cancer56. As a healthy and functional intestinal barrier is essential for proper transport of metabolites such as oxalate, uric acid and cystine, butyrate is an important component in the prevention of metabolic stone disease by maintaining gut barrier function. In the specific context of calcium oxalate kidney stone disease, butyrate has also been shown to promote the expression of an intestinal oxalate transporter SLC26A3 (down-regulated in adenoma, DRA) in a human colonic cell line57.   1.4 Thesis project  1.4.1 Rationale  In summary, past research has shown that the intestine is an alternative pathway for the regulation of stone-associated metabolites such as oxalate, uric acid and cystine. This is supported by 1) the discovery of various intestinal transporters capable of transporting these metabolites across the intestinal layer and into the lumen for excretion and 2) the existence of various groups of intestinal bacteria that can produce, transport and degrade the metabolites. The final destination of these metabolites and their associated bacteria is the fecal matter that travels through the intestine and thus, there is research and diagnostic value in studying the fecal samples of kidney stone patients. This is especially true with recurrent kidney stone patients as the chances are higher that the recurrence is due to an underlying metabolic imbalance.   1.4.2 Hypothesis  The hypothesis for this thesis is that there are both compositional and metabolic differences between the intestinal microbiomes of recurrent kidney stone patients and healthy controls.   1.4.3 Specific objectives  The experiments designed to test this hypothesis have the following objectives:  1) To identify differences in bacterial communities between recurrent kidney stone patients and healthy controls:  12 a. By comparing the relative abundance of individual bacterial taxa between patients and controls b. By examining overall bacterial diversity and richness between patients and controls c. By comparing the relative abundance of oxalate-degrading bacteria between patients and controls  2) To identify differences in the metabolic gene profile between kidney stone patients and healthy controls: a. By examining overall abundances of metabolic pathways associated with kidney stone metabolites b. By comparing the relative abundances of individual bacterial metabolic genes between patients and controls c. By comparing the relative abundances of oxalate-degrading genes between patients and controls    13 Chapter 2: Materials and Methods  2.1 Sample collection  Patient and control fecal samples were collected as part of the Urine and Stool Analysis project at the Vancouver Kidney Stone Centre. Approval for the study was given by the Clinical Research Ethics Board of the University of British Columbia (Ethics application # H10-01195) and Vancouver Coastal Health (Ethics application # V11-01195). Informed consent for the collection of fecal samples was obtained from each research participant in writing. 24-hour urine samples and a diet questionnaire were also collected from each participant but were not analyzed in this thesis.   Patients and controls were selected using the criteria in Table 2, which are the same criteria described in the original ethics application with the addition of three criteria; 1) control participants are members of the same household as the patient, 2) patient and control participants have no reported antibiotic use within 1 month prior to sample collection and 3) patient participants had at least one recurrence of kidney stones.    Inclusion criteria Exclusion criteria Patients  Above 19 years old  Radiological evidence indicating presence of a current renal or ureteric stone  At least 1 recurrence of kidney stones++  Pregnancy  Positive urine culture  Active cancer  Recurrent urinary infections  Gross hematuria  Inability to provide informed consent  Controls  Above 19 years old  No history of kidney stone disease  Lives in same household as patient++  Family history of kidney stones  Antibiotic use within 1 month prior to sample collection++  Table 2. Inclusion and exclusion criteria for patient and control participants  ++Additional criteria used to select for patients and controls; not included in original ethics application  14 Fecal samples were collected by participants at their personal residences using a stool collection container and either delivered to the Stone Centre on the day of sample delivery or immediately frozen after collection for delivery on another day. Upon arrival at our facility and within 4 hours of defecation, fecal samples were immediately transferred into pre-labelled microfuge tubes and stored at -80°C until DNA extraction.   In total, fecal samples from 17 recurrent kidney stone formers (patients) and 17 matching controls (controls) were used for analysis (Table 3, Appendix A). Table 4 provides basic details about the subjects included in the study. Figure 4 provides a basic overview of the process of extracting bacterial taxonomy and metabolic pathway information from the fecal samples. These processes are described in more detail in the following subsections.   Patients Matching Controls Number of male participants 12 4 Number of female participants 5 13 Mean age 58 ± 2.9 58 ± 2.7 Primary stone type  (number of patients) Calcium oxalate (10) Cystine (2) Uric acid (2) Struvite (1) Unknown (2)   Table 4. Summary of patient and control characteristics  15   Figure 4. The process of extracting bacterial taxonomy and metabolic pathway information from fecal samples of recurrent kidney stone patients and matching controls  16 2.2 Fecal DNA extraction  Fecal DNA was extracted and purified using the QIAamp DNA Stool Mini Kit (Qiagen, Catalog #51504) according to the manufacturer’s instructions with two modifications. Firstly, the kit’s cell lysis buffer was replaced with an improved cell lysis buffer (4% SDS, 500mM NaCl, 50mM EDTA, 50mM Tris pH 8.0). Secondly, acid-washed glass lysis beads were added to the kit’s cell lysis tubes for a more thorough lysis of Firmicutes bacteria; 0.3 g of 0.1 mm beads and 0.1 g of 0.5 mm beads were added to each lysis tube.    2.3 16S rRNA sequencing and analysis  2.3.1 16S rRNA amplicon library preparation  A 16S rRNA DNA library was prepared from the extracted fecal DNA as described by a protocol by Kozich et al. 201358 using the primers described in Appendix B (Tables 5, 6 & 7). Modifications were made to the PCR amplification cycle, PCR amplicon cleaning and DNA quantification steps in the protocol.   Briefly, the V4 region of the bacterial 16S rRNA gene was amplified from the extracted DNA using the Phusion Hot Start II DNA Polymerase (2U/ul) kit (Thermo Fisher Scientific, Catalog #F549S) in 50 ul reactions according to the manufacturer’s instructions with the following modifications to the PCR amplification cycle; initial denaturation at 98°C for 2 minutes, 30 cycles of 98°C for 20s; 55°C for 15s; and 72°C for 30s extensions; followed by a final extension at 72°C for 10 minutes and holding at 4°C. A list of forward and reverse primers used for PCR amplification are described in Appendix B.  To validate PCR success, a random subset of PCR amplicons was analyzed for visible bands on gel electrophoresis. The PCR amplicons were purified using Agencourt Ampure XP beads (Beckman Coulter, Catalog #A63880) using a 0.8:1 bead to sample ratio. The purified PCR products were normalized using the SequalPrep Normalization Plate kit (Invitrogen, Catalog #A1051001) to a concentration of 1 – 2 ng/ ul. 5 ul from each normalized sample was pooled into a single library and further concentrated using the DNA Clean & Concentrator-5 kit (Zymo  17 Research, Catalog #D4013). The pooled library was analyzed on the Agilent Bioanalyzer using the High Sensitivity DS DNA assay (Agilent, Catalog #5067-4626) to determine approximate library fragment size and to verify library integrity. The QIAquick Gel Extraction kit (Qiagen, Catalog #28704) was used to extract properly-sequenced 16S rRNA amplicons in the pooled library and exclude unintended amplicons.   The concentration of the final pooled library was determined using the KAPA Library Quantification Kit for Illumina (Kapa Biosystems, Catalog #KK4824). The library was then diluted to 4nM and denatured into single strands using 0.2N NaOH. The final library loading concentration was 8pM with an additional 20% PhiX (Illumina, Catalog #FC-110-3001) spike-in for sequencing quality control. The 16S rRNA pooled library was then sequenced on an Illumina MiSeq platform.   2.3.2 16S rRNA DNA sequence cleanup  MiSeq sequencing was performed in two batches yielding between 11,723 and 138,661 paired-end DNA reads per sample (Table 8, Appendix C). All patient and control pairs had similar read depth within pairs (< 5-fold difference in sequencing depth within pairs) except for pairs 7 and 9 (Figure 5, Appendix C). Control sample, “CTRLA10B”, from pair 7 and patient sample, “PT198”, from pair 9 were sequenced twice because they yielded no reads in the first sequencing batch.   The 16S rRNA reads were processed with mothur59 (version 1.35.1), a sequence processing software. Briefly, mothur removes low-quality reads and chimeras and aligns the final reads to a taxonomic database. Mothur was run using the default standard operating protocol for Illumina MiSeq reads (MiSeq SOP). A modification was made to the MiSeq SOP in that the Greengenes60 16S rRNA database (version gg_13_8_99) was used instead of the Silva61 database to assign bacterial taxonomy to each 16S rRNA read. The output of the mothur software yielded between 894 and 2,988 operational taxonomic units (OTUs) for the first batch of reads and between 17,610 and 47,704 OTUs for the second batch of reads (Table 8, Appendix C). All patient and  18 control pairs had similar read depth within pairs (< 5-fold difference within pairs) except for pairs 7 and 9 (Figure 6, Appendix C).   2.3.2.1 Correlation between 16S rRNA sequencing depth and OTU counts   There was a strong correlation between sequencing depth and OTU counts (Pearson’s r = 0.995, p < 0.001), and a moderate correlation between sequencing depth and total number of unique OTUs (Pearson’s r = 0.801, p < 0.001) (Figure 7, Appendix C). The average OTU count per sample for the second sequencing batch was ~35,000 OTU counts/sample, which is surprisingly high in contrast to the average OTU count for the first sequencing batch, ~1700 OTU counts/sample. This suggested a strong batch difference between the two sequencing rounds. Further examination of individual OTUs counts in the second sequencing batch show that the extreme OTU counts are caused by higher than normal OTU counts for certain bacterial OTUs ranging up to ~37,000 OTU counts (Table 9, Appendix C).   2.3.3 Taxonomic analysis  To account for uneven sequencing depth, bacterial counts were normalized to simple percentages by dividing each taxa’s count by the total counts per sample and then multiplying the result by a hundred. This normalization method is commonly called Total Sum Scaling (TSS).  Two diversity metrics were used to evaluate the bacterial diversity of the patient and control microbiomes. Firstly, species richness was measured as the total number of unique operational taxonomic units (OTUs); OTU is a species-like classification system that groups closely-related bacteria based on the similarity of their 16S rRNA sequences. Secondly, the alpha diversity of bacterial OTUs was measured using the Shannon diversity index62.  The equation used for calculating Shannon diversity is “H=EH x lnS”, where H is the Shannon diversity index, EH is the evenness and S is the richness.   The Wilcoxon signed-rank test63, which is a non-parametric paired test, was used to compare the species richness metric and the relative abundances of bacterial taxa between matched patients  19 and controls as Shapiro-Wilk tests indicated that those values had a non-normal distribution. The paired t-test was used to compare the Shannon diversity index between matched patients and controls as those values were found to be normally-distributed. All statistical analyses and diversity calculations were performed using custom scripts written in R64 (version 3.4.1) using statistical functions from the R packages “coin”65 (version 1.1-3) and “vegan”66 (version 2.4-3). A p-value cut-off of 0.05 was used to evaluate the statistical significance of the paired tests. No multiple-testing correction was performed on the tests due to the small sample size and the decision to explore minor bacterial differences within subgroups of paired samples.  2.4 Whole-genome shotgun sequencing and analysis  2.4.1 Shotgun-sequencing library preparation  The Nextera XT DNA library preparation kit (Illumina, #FC-131-1096) was used to construct a shotgun-sequencing DNA library from the extracted fecal DNA from each sample (from section 2.2.2) according to the manufacturer’s instructions. The shotgun-sequencing library was then sequenced on an Illumina HiSeq platform.   2.4.2 Shotgun sequence cleanup   The HiSeq sequencing was performed in three batches yielding between 20 million and 174 million DNA reads per sample (Table 10, Appendix C). KneadData, a sequence processing tool was used to clean up the raw sequencing reads. Briefly, KneadData uses Bowtie67, a sequence aligner and a reference human gene database (GRCh37/hg19) to remove human DNA reads. Then, it runs Trimmomatic68 (version 0.32) with the parameters “ILLUMINACLIP:NexteraPE-PE.fa:2:3:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36” to trim Illumina adaptors and remove low quality reads. The final number of trimmed reads per sample ranged from 14 million to 142 million reads. All patient and control pairs had similar raw and trimmed read depths within pairs (< 3-fold difference in sequencing depth within pairs) except for pair 1 (Figure 8 and 9, Appendix C).      20 2.4.3 Shotgun sequence assembly    The remaining reads were then assembled into contigs using MEGAHIT69 (version 1.0.3-7) with parameters “--k-min 27, --k-max 97, --k-step 10, --merge-level 10,0.99”.    2.4.4 ORF prediction and annotation of assembled contigs   MetaPathways70,71 (version 2.5), a metagenomics analysis software, was used to annotate the assembled contigs using default parameters. Briefly, it predicts open reading frames (ORFs) from assembled contigs using Prodigal72 and aligns the predicted ORFs to a gene database using the LAST sequence aligner73. The ORFs in this study were aligned to the Kyoto Encyclopedia of Genes and Genomes74 (KEGG) gene database (version 2011-06-18); yielding a KEGG ID and annotation for each ORF if a match was detected. Table 10 in Appendix C describes the total number of predicted ORFs per sample, the total number of ORFs with valid KEGG IDs.   2.4.5 Metabolic pathway statistical analysis  To calculate the relative abundance of each ORF, the DIAMOND sequence aligner75 (version 0.9.10) was used to map the cleaned (pre-assembly) DNA sequencing reads to the ORFs. The abundance of reads that mapped to each ORF was normalized by the ORF length and total number of reads per sample to calculate a gene abundance measure called Reads per Kilobase per Million76 (RPKM). RPKM was further converted to Transcripts Per Million77 (TPM) to account for inter-sample differences in average read length and read depth. Figure 10 shows the formulas used to calculate RPKM and TPM. Table 10 in Appendix C describes the total TPM assigned to valid ORFs for each sample.  21  Figure 10. Formulas for the calculation of RPKM and TPM   Only genes from three oxalate-associated metabolic pathways of the KEGG database were compared in this study. This included Ascorbate and Aldarate metabolism (ko00053), Butanoate metabolism (ko00650) and Glyoxylate and Dicarboxylate metabolism (ko00630).   Wilcoxon signed-rank test, which is a non-parametric paired test, was used to compare the abundances of bacterial genes between the matched patients and controls as the genes exhibited a non-normal distribution. The paired t-test was used to compare the pathway abundance (relative abundance of genes assigned to each pathway) and pathway presence (percentage of genes detected from each pathway) between matched patents and controls. All statistical analyses and diversity calculations were performed using custom scripts written in R (version 3.4.1) using statistical functions from the R packages “coin” (version 1.1-3) and “vegan” (version 2.4-3). A p-value cut-off of 0.05 was used to evaluate the statistical significance of pathway abundance and presence paired tests. A larger p-value cut-off of 0.1 was used to evaluate the statistical significance of paired tests on individual genes so that minor differences in relative abundance could still be detected. No multiple-testing correction was performed on the analyses due to the  22 small sample size and the decision to explore minor metabolic differences within subgroups of paired samples.   2.4.6 Alignment of metagenomic reads to Oxalate oxidoreductase subunit genes  Oxalate oxidoreductase (OOR) subunit protein sequences were downloaded in fasta format from UniProt78 under the accession numbers Q2RI41 (subunit alpha), Q2RI40 (subunit delta) and Q2R142 (subunit beta) and formatted into a DIAMOND database via the DIAMOND ‘makedb’ command. Cleaned metagenomic DNA sequences from methods section 2.4.2 were then aligned via DIAMOND’s ‘blastx’ command to the protein sequences. Alignments were filtered using the default e-value cutoff of 0.001 and minimum 30% percent identity. Successful alignments were tallied up and normalized by the overall read depth to calculate counts per million reads (CPM) for each subunit gene.     23 Chapter 3: Results  3.1 Phyla distribution   Figure 11 shows the relative abundance of bacterial phyla across patient and control microbiomes. The top 5 most abundant phyla in each sample group were determined by calculating the median percentage of each phyla across samples (Table 11). In the patient group, the most abundant phyla (in order of decreasing relative abundance) are Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria and Verrucomicrobia. In the control group, the same 5 phyla are also in the top 5 phyla, however the order is different in the lower 3 phyla; with Verrucomicrobia and Actinobacteria being more abundant than Proteobacteria.  No statistically significant differences were found between the abundances of the top 5 phyla. Instead, significant differences were found in the less abundant Tenericutes and Lentisphaerae phyla with the control group having a higher percentage of the two bacterial phyla than the patient group (Table 12). Controls were also found to have a higher percentage of bacteria belonging to unclassified phyla.  Rank Most abundant phyla in patient microbiomes  (Median percentage of abundance across samples) Most abundant phyla in control microbiomes  (Median percentage of abundance across samples) 1 Firmicutes (60.7%) Firmicutes (58.7%) 2 Bacteroidetes (21.3%) Bacteroidetes (23.7%) 3 Proteobacteria (2.8%) Verrucomicrobia (4.4%) 4 Actinobacteria (2.1%) Actinobacteria (2.7%) 5 Verrucomicrobia (0.5%) Proteobacteria (2.4%)  Table 11. Top 5 most abundant bacterial phyla in patient and control microbiomes  Bacterial phyla Mean percentage abundance of phyla in patient microbiomes (%) Mean percentage abundance of phyla in control microbiomes (%) P value of Wilcoxon signed-rank test More abundant in Tenericutes 0.028 ± 0.012 0.918 ± 0.334 0.012 Controls Lentisphaerae 0 ± 0 0.019 ± 0.012 0.046 Controls Unclassified 0.06 ± 0.015 0.165 ± 0.053 0.02 Controls  Table 12. Differences in bacterial phyla between patient and control microbiomes  24 a) Distribution of bacterial phyla grouped by patient vs. control group  b) Distribution of bacterial phyla grouped by matched pairs  Figure 11. Distribution of bacterial phyla across microbiome samples. a) grouped into patient versus control groups and b) grouped by matched pairs of patients and controls.    25 3.2 Bacterial diversity  The alpha diversity of patient and control microbiomes was measured using the Species richness and Shannon Alpha diversity measures. To calculate the two measures, Operational Taxonomic Units (OTUs) were used as a proxy for bacterial species as it represented more accurately the evolutionary relationships between different bacterial groups within a microbiome. Control microbiomes had both higher Species richness (p = 0.022) and Shannon alpha diversity (p = 0.010) than patient microbiomes (Figure 12).   Figure 12. Species richness and Shannon alpha diversity in patient and control microbiomes.  Patients were found to have an average of 345.1 ± 93.1 unique OTUs and an average value of 3.1 ± 0.1 for Shannon alpha diversity. Controls were found to have an average of 409.1 ± 85.2 unique OTUs and an average value of 3.6 ± 0.2 for Shannon alpha diversity.   *  p = 0.022 * p = 0.01  26 3.3 Taxonomic differences between patients and controls   Bacterial OTUs were assigned to their closest bacterial taxa, counted and normalized as the percentage of total bacterial counts in each sample. The relative abundance of each bacteria taxa was then compared between patient and control microbiomes using a Wilcoxon signed-ranked test as a Shapiro-Wilk test indicated that the taxa abundances belonged to a non-normal distribution.   Table 13 shows taxa that differed in relative abundance between patients and controls. At a p value cut-off of 0.05 with no multiple-testing correction, 6 specific taxa were found to be more abundant in the patient group (Table 13a) including the bacterial order Myxococcales, bacterial family Carnobacteriaceae, bacterial genera Paludibacter and Geobacter and bacterial species B. ovatus and B. fragilis. 7 specific taxa were found to be more abundant in the control group (Table 13b) including the bacterial class Mollicutes, bacterial order RF39, bacterial families Porphyromonadaceae and Victivallaceae, bacterial genera 02d06 and Oxalobacter and bacterial species R. flavefaciens. Interestingly, control microbiomes have a higher abundance of unclassified bacteria at the phyla, class and family taxonomic levels.    27 13 a. Bacterial taxa more abundant in patients       13 b. Bacterial taxa more abundant in controls Bacterial taxonomy p value  Bacterial taxonomy p value Firmicutes   Firmicutes        Bacilli         Clostridia              Lactobacillales               Clostridiales                    Carnobacteriaceae * 0.03                    Clostridiaceae                             02d06 * 0.048 Bacteroidetes                     Ruminococcaceae        Bacteroidia                           Ruminococcus              Bacteroidales                                 R. flavefaciens * 0.046                   Porphyromonadaceae                             Paludibacter * 0.046  Bacteroidetes                    Bacteroidaceae         Bacteroidia                          Bacteroides               Bacteroidales                                B. ovatus * 0.044                    Porphyromonadaceae * 0.044                               B. fragilis * 0.046       Proteobacteria  Proteobacteria         Betaproteobacteria        Deltaproteobacteria               Burkholderiales              Desulfuromonadales                     Oxalobacteraceae                    Geobacteraceae                           Oxalobacter * 0.005                         Geobacter * 0.046                 Myxococcales * 0.046  Lentisphaerae           Lentisphaeria                 Victivallales                       Victivallaceae * 0.046         Tenericutes           Mollicutes * 0.012                RF39 * 0.025         Unclassified phyla 0.02    Unclassified class 0.016    Unclassified family 0.049       Table 13. Taxonomic differences between patient and control microbiomes.  a) Bacterial taxa higher in controls b) Bacterial taxa higher in patients. * denotes the bacterial taxa at the lowest taxonomic level for each significant result.     28 3.4 Examination of oxalate-degrading bacteria  The presence and distribution of oxalate-degrading bacteria was also analyzed based on a list of oxalate-degrading bacteria from a 2013 review by Miller et al. 45. Results of this analysis show that 2 out of 17 patients (11%) had detectable levels of Oxalobacter compared to 8 out of 17 of controls (47%). Besides Oxalobacter, 3 other bacterial species and 7 other bacterial genera associated with oxalate degradation were detected in our microbiome samples (Table 14). However, there were no significant differences in their abundances between patient and control microbiomes. Similarly, there was no significant difference in the total abundance of all detected oxalate-degrading bacteria species and genera between patient and control microbiomes.   Species Bacteria detected in n patients (n) Bacteria detected in n controls (n) Relative abundance in patients (%) Relative abundance in controls (%) p value More abundant in Eggerthella lenta 11 8 0.175 ± 0.43 0.034 ± 0.063 0.101 Patient Bifidobacterium animalis 0 1 0 ± 0 0.009 ± 0.038 0.317 Control Leuconostoc mesenteroides 1 0 0.000 ± 0.001 0 ± 0 0.317 Patient Total abundance of all detected oxalate-degrading species   0.175 ± 0.43 0.043 ± 0.069 0.244 Patient  Genus Bacteria detected in n patients (n) Bacteria detected in n controls (n) Relative abundance in patients (%) Relative abundance in controls (%) p value More abundant in Oxalobacter 2 8 0.005 ± 0.02 0.034 ± 0.059 0.005 Control Eggerthella 11 8 0.176 ± 0.43 0.034 ± 0.064 0.101 Patient Enterococcus 2 3 0.004 ±  0.016 0.072 ± 0.27 0.274 Control Bifidobacterium 11 16 1.87 ± 4.395 1.526 ± 2.143 0.309 Control Clostridium 13 13 0.338 ± 0.451 0.368 ± 0.47 0.538 Control Streptococcus 16 16 1.065 ± 1.728 0.54 ± 0.96 0.653 Patient Lactobacillus 7 7 0.030 ± 0.061 0.029 ± 0.067 0.821 Patient Leuconostoc 2 3 0.001 ± 0.004 0.006 ± 0.02 1.000 Control Total abundance of all detected oxalate-degrading species   3.489 ± 5.561 2.61 ± 2.955 0.619 Patient  Table 14. Presence and relative abundance of detected oxalate-degrading bacteria     29 3.5 Overall abundance and presence of three metabolic pathways  Open Reading Frame (ORF) sequences were extracted from assemblies of each sample’s whole shotgun metagenomic DNA sequences. The ORFs were then aligned to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to produce metabolic pathway gene annotations and also aligned back to the raw DNA sequences to calculate a relative abundance measure called Transcripts Per Million (TPM) for each gene.   Table 15 shows the relative abundance of DNA sequences assigned to three key metabolic pathways (pathway abundance). A paired t-test comparison of pathway abundance between patient and control microbiomes showed no significant differences in pathway abundance between patient and control groups. In both groups, the highest percentage of DNA sequences were assigned to the Glyoxylate & Dicarboxylate pathway, followed by the Butanoate pathway and lastly, the Ascorbate & Aldarate pathway.  Table 16 shows the percentage of unique genes detected within each pathway (pathway gene presence). A paired t-test comparison of gene presence between patient and control microbiomes showed no significant differences in pathway gene presence. In both groups, the highest percentage of DNA sequences were assigned to the Butanoate pathway, followed by the Glyoxylate & Dicarboxylate pathway and lastly, the Ascorbate & Aldarate pathway.  The heat maps in Figure 13 illustrate the distribution of relative gene abundance within the three metabolism pathways. Patient and control samples were hierarchically clustered by first calculating the Euclidean distance between samples across the relative abundances of different genes and then clustering the samples via complete linkage clustering. As seen in the y-axis of the dendrogram, samples do not appear to cluster according to their stone-forming status.   The heat maps in Figure 14 illustrate the detection of genes (presence as opposed to abundance) within the three pathways. Because of the binary nature of gene presence data, samples were hierarchically clustered by first calculating the Jaccard distance between samples across genes and then clustering the samples via complete linkage clustering. Similar to the abundance heat maps in figure 13, samples do not appear to cluster according to their stone-forming status.   30   Pathway abundance (Total TPM assigned to a pathway)   Metabolic pathway Patients (% of total TPM) Controls (% of total TPM) p value Group with higher pathway abundance Glyoxylate & Dicarboxylate metabolism 0.230 ± 0.011  0.213 ± 0.01  0.07  Patient Butanoate metabolism 0.214 ± 0.01  0.202 ± 0.009  0.084  Patient Ascorbate & Aldarate metabolism 0.047 ± 0.003   0.043 ± 0.004  0.394  Patient  Table 15. Relative abundance of bacterial genes assigned to three metabolic pathways; Glyoxylate & Dicarboxylate metabolism, Ascorbate & Aldarate metabolism and Butanoate metabolism.    Pathway presence (Unique genes detected within a pathway)   Metabolic pathway Patients (% of total unique genes) Controls (% of total unique genes) p value Group with higher pathway presence Butanoate metabolism (69 genes total) 62.5 ± 2.5 (~43/69 genes)  62.2 ± 1.4 (~43/69  genes)  0.906  Patient Glyoxylate & Dicarboxylate metabolism (67 genes total) 48.4 ± 2.1 (~32/67 genes) 47.9 ± 1.5 (~32/67 genes) 0.842  Patient Ascorbate & Aldarate metabolism (35 genes total) 35.8 ± 1.7 (~12/35 genes) 37.8 ± 1 (~13/35 genes) 0.308  Control     Table 16. Percentage of unique genes detected in three metabolic pathways; Glyoxylate & Dicarboxylate metabolism, Ascorbate & Aldarate metabolism and Butanoate metabolism.   31     Figure 13a. Heat map of gene abundances within the Butanoate metabolism pathway  Red-colored cells on the heatmap represent higher gene abundances, blue-colored cells represent lower gene abundances and white-colored cells represent undetected genes for each gene. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL”.     Column Z-Score  Count 0      50     100     150 -4            -2             0             2            4 Color Key and Histogram Heatmap of Butanoate pathway gene abundance PT: CTRL: KEGG Orthology IDs Sample names  32     Figure 13b. Heat map of gene abundances within the Glyoxylate & Dicarboxylate metabolism pathway  Red-colored cells on the heatmap represent higher gene abundances, blue-colored cells represent lower gene abundances and white-colored cells represent undetected genes for each gene. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL”.      Column Z-Score  Count 0     20    40    60    80   100   120 -4            -2             0             2            4 Color Key and Histogram Heatmap of Glyoxylate & Dicarboxylate pathway gene abundance PT: CTRL: KEGG Orthology IDs Sample names  33     Figure 13c. Heat map of gene abundances within the Ascorbate & Aldarate metabolism pathway.  Red-colored cells on the heatmap represent higher gene abundances, blue-colored cells represent lower gene abundances and white-colored cells represent undetected genes for each gene. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL”.   Column Z-Score  Count 0     10    20   30    40    50   60 -4            -2             0             2            4 Color Key and Histogram Heatmap of Ascorbate & Aldarate pathway gene abundance PT: CTRL: KEGG Orthology IDs Sample names  34  Figure 14a. Heat map of genes detected within the Butanoate metabolism pathway.  The light-blue-colored cells represent detected genes and the white-colored cells represent undetected genes for each microbiome sample. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL” Column Z-Score  Count 800   1000    1500 0             0.2            0.4           0.6           0.8           1.0 Color Key and Histogram Heatmap of Butanoate pathway gene presence PT: CTRL: KEGG Orthology IDs Sample names  35  Figure 14b. Heat map of genes detected within the Glyoxylate & Dicarboxylate metabolism pathway. The light-blue-colored cells represent detected genes and the white-colored cells represent undetected genes for each microbiome sample. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL” Column Z-Score  Count 1080 0             0.2            0.4           0.6           0.8           1.0 Color Key and Histogram Heatmap of Glyoxylate & Dicarboxylate pathway gene presence PT: CTRL: KEGG Orthology IDs Sample names  36  Figure 14c. Heat map of genes detected within the Ascorbate & Aldarate metabolism pathway.  The light-blue-colored cells represent detected genes and the white-colored cells represent undetected genes for each microbiome sample. Pathway genes are encoded as KEGG Orthology IDs on the x-axis columns while sample IDs are described on the y-axis rows with patient IDs beginning with the acronym “PT” and control IDs with the acronym “CTRL”  .   Column Z-Score  Count 400  500  600  700 0             0.2            0.4           0.6           0.8           1.0 Color Key and Histogram Heatmap of Ascorbate & Aldarate pathway gene presence PT: CTRL: KEGG Orthology IDs Sample names  37 3.6 Differences in individual gene relative abundances  Table 17 summarizes the differentially abundant genes within the three metabolic pathways. At the p-value cut-off of 0.1 with no multiple-testing correction, 9 genes were found to be differentially abundant in the Butanoate metabolism pathway. 4 genes were found to be differentially abundant in the Glyoxylate & Dicarboxylate metabolism pathway. No genes were found to be differentially-abundant in the Ascorbate & Aldarate metabolism pathway. A larger p-value cut-off of 0.1 was chosen to account for the diversity and variability of enzymatic reactions that lead to the same metabolite within a KEGG pathway.  Of the 9 differentially-abundant Butanoate metabolism genes, 6 were found to be in higher abundance in control microbiomes and to have relevance to butanoate metabolism and synthesis in the human gut microbiome. Specifically, control microbiomes were found to have a higher abundance (p = 0.068) of the gene for medium-chain acyl-CoA synthetase (ACSM), 4-hydroxybutyryl-CoA dehydratase (AbfD) (p = 0.039) and 3-hydroxybutyryl-CoA dehydrogenase (Hbd) (p = 0.092). Control microbiomes were also found to have higher abundances of the genes porB, porD and porG, which encode for three out of four possible subunits of pyruvate ferredoxin oxidoreductase. Figure 15 illustrates the metabolic relationship between the genes and the synthesis of butanoate.  None of the differentially-abundant Glyoxylate & Dicarboxylate metabolism genes in Table 17 were found to be associated with metabolic outcomes in the human gut microbiome.   38 Gene name Genes detected in n patients (n) Genes detected in n controls (n) Average gene abundance in patients  (Mean TPM) Average gene abundance in controls  (Mean TPM) p value Group with higher gene abundance KEGG Orthology ID (KO) Butanoate metabolism        Pyruvate ferredoxin oxidoreductase,  gamma subunit 16 16 9.2 ± 1.6 20.3 ± 3.3 0.005 Control K00172 Pyruvate ferredoxin oxidoreductase,  beta subunit 15 17 15.6 ± 2.8  26.6 ± 4  0.007 Control K00170 Acetolactate synthase I/II/III large  subunit 17 17 312.9 ± 19.2  274.2 ± 16.4  0.028 Patient K01652 3-hydroxybutyryl-CoA  dehydrogenase 17 17 59.8 ± 14.6  81.3 ± 9.1  0.039 Control K00074 Pyruvate ferredoxin oxidoreductase,  delta subunit 14 16 6.3 ± 1.3  10.4 ± 2.3  0.068 Control K00171 Medium-chain acyl-CoA synthetase/  Butyryl-CoA synthetase 17 17 13 ± 2.3  24.1 ± 5.1  0.068 Control K01896 Succinate dehydrogenase  cytochrome b-556 subunit 17 17 67.1 ± 8.6  47.6 ± 5.4  0.076 Patient K00241 Succinate-semialdehyde  dehydrogenase 0 3 0 ± 0  0.2 ± 0.1  0.084 Control K00139       4-hydroxybutyryl-CoA dehydratase 16 17 22.2 ± 5.2  30.7 ± 4.7  0.093 Control K14534 Glyoxylate & Dicarboxylate metabolism              2-hydroxy-3-oxopropionate reductase 17 17 52.4 ± 15.3 20.6 ± 2.8 0.039 Patient K00042       Formamidase 7 4 0.6 ± 0.2 0.1 ± 0 0.047 Patient K01455 Ribulose-bisphosphate carboxylase  large chain 6 1 1.2 ± 0.8 0 ± 0  0.078 Patient K01601       Crotonyl-CoA carboxylase/reductase 0 3 0 ± 0 0.8 ± 0.4 0.084 Control K14446  Table 17. Differentially abundant genes within the three metabolic pathways  39  Figure 15. Butanoate Synthesis pathways. Red circles denote genes that are more abundant in controls than patients. Figure modified from “Comparative In silico Analysis of Butyrate Production Pathways in Gut Commensals and Pathogens” by Anand et al. 201679.    POR Pyruvate ferredoxin oxidoreductase  (E.C. 1.2.7.1) 3-hydroxybutyryl-CoA dehydrogenase (E.C. 1.1.1.157) 4-hydroxybutyryl-CoA dehydratase  (E.C. 4.2.1.120) Medium-chain Acyl-CoA synthetase (E.C. 6.2.1.2) ACSM  40 3.7 Examination of oxalate-degrading metabolic genes  An online search of the KEGG database yielded a total of 13 bacterial enzymes that participate in the metabolism of oxalate or its associated substrates; formate, oxalyl-CoA and formyl-CoA (Table 18). Table 19 in Appendix C describes the metabolic reactions of these 13 enzymes in further detail. 5 out of the 13 enzymes were detected in our samples using the 2011 publicly-available version of the KEGG database. The gene for the oxalate-formate antiporter, oxIT, was the most commonly detected across all samples (detected in 16/17 patients and 16/17 controls). Similarly, the gene for formyl-CoA transferase, frc, was detected in almost all samples (15/17 patients and 16/17 controls). The gene for oxalyl-CoA decarboxylase, oxc, was detected in a higher number of control microbiomes (9 controls) than patient microbiomes (4 patients). Conversely, the NAD-dependent formate dehydrogenase gene, fdnH, was detected in a higher number of patient microbiomes than controls microbiomes (6/10 patients and 2/10 controls). 2 subunit genes for 7,8-didemethyl-8-hydroxy-5-deazariboflavin synthase (FO synthase) were also detected in a few patient and control samples. None of the oxalate metabolism genes were differentially abundant between patient and control groups.    41 Gene name Genes detected in n patients (n) Genes detected in n controls (n) Average gene abundance in patients  (Mean TPM) Average gene abundance in controls  (Mean TPM) p value Group with higher gene abundance KO (E.C. Number) Oxalyl-CoA decarboxylase 4 9 2.9 ± 3.2 3.5 ± 6.4 0.466 Control K01577 Formyl-CoA transferase 15 16 9.6 ± 14.1 5.2 ± 5.3 0.523 Patient K07749 Oxalate-formate antiporter 16 16 21.5 ± 37.6 11.1 ± 9.0 0.586 Patient K08177 Oxalate oxidoreductase              alpha subunit - - - - - - K19070       beta subunit - - - - - - K19071       delta subunit - - - - - - K19072 Oxalate decarboxylase - - - - - - K01569 Formate dehydrogenase  6 2 1 ± 2.3 1 ± 3 0.336 Patient K08349 Glyoxylate oxidase - - - - - - (1.2.3.5) FO synthase - - - - - - K11779       subunit 1 1 3 0.1 ± 0.3 0.6 ± 2.1 0.334 Control K11780       Subunit 2 1 2 0.2 ± 0.8 0.1 ± 0.2 0.614 Control K11781 CoA:oxalate CoA transferase - - - - - - K18702 Oxalate CoA transferase - - - - - - (2.8.3.2) Oxamate amidohydrolase - - - - - - (3.5.1.126) Glyoxylate dehydrogenase - - - - - - (1.2.1.17) Formyl-CoA hydrolase - - - - - - (3.1.2.10)  Table 18. Abundance and presence of bacterial genes associated with the metabolism of oxalate or its associated substrates; formate, oxalyl-CoA and formyl-CoA. The dash indicates that the gene was not detected.     42 3.8 Follow-up analysis on oxalate oxidoreductase  A follow-up analysis showed that the protein sequences for the enzyme oxalate oxidoreductase (OOR) were not included in the 2011 version of the KEGG database. Thus, an additional DNA-protein alignment using DIAMOND’s blastx function was used to detect and measure the abundance of the OOR subunit genes in the microbiome. Results show that the alpha and beta subunit genes were detected in all microbiome samples while the delta subunit gene was detected in 15/17 patients and 16/17 controls (Table 20). The genes for the beta subunit of OOR were found to be more abundant in control microbiomes than patient microbiomes (p = 0.011).    43 Gene name Genes detected in n patients (n) Genes detected in n controls (n) Average gene abundance in patients  (Mean CPM) Average gene abundance in controls  (Mean CPM) p value Group with higher gene abundance KEGG Orthology ID Oxalate oxidoreductase subunit alpha 17 17 9.5 ± 3.9 9.1 ± 3.2 0.523 Patient K19070 Oxalate oxidoreductase subunit beta 17 17 2.4 ± 1.1 3.5 ± 1.6 0.011 Control K19071 Oxalate oxidoreductase subunit delta 15 16 0.1 ± 0.1 0.2 ± 0.1 0.266 Control K19072  Table 20. Presence and abundance of subunit genes for the oxalate-degrading enzyme, Oxalate Oxidoreductase   44 Chapter 4: Discussion  4.1 Summary   In this study, we compared bacterial groups and metabolic pathways between the intestinal microbiomes of recurrent kidney stone patients and their non-stone-forming spouses to look for differences that could affect the regulation of stone-associated metabolites in the intestine. In brief, we found that the community of bacteria in patient microbiomes had lower alpha diversity and species richness, lower presence of Oxalobacter (a key oxalate-degrading bacterium), lower presence of oxc (a key oxalate-degrading gene) and had deficiencies in the metabolism of butyrate, a short-chain fatty acid that has been associated with a wide range of benefits to gastrointestinal health.   4.2 Loss of species diversity in patient microbiomes  Loss of species diversity in a natural environment is often an unfavorable situation as it can be a sign of dysbiosis, a drastic change in the environment itself or the invasion of a new dominant species. For example, in obesity80, Type 2 Diabetes81, C. difficile infection82 and Crohn’s disease83,84, the diseases are often accompanied by a reduction in bacterial diversity in the intestinal microbiome. In the case of our kidney stone patients, the lower alpha diversity and OTU richness may be a sign that their intestinal environment is in a state of dysbiosis or could be the result of lasting impact on the patient microbiome due to kidney stone treatment.    4.2.1 Loss of Oxalobacter, an oxalate-degrading bacterial genus, in patient microbiomes  To investigate this state of dysbiosis, we looked for bacterial groups that were more or less abundant in patients. We found that there was a lower prevalence of the Oxalobacter genus in the patient group, of which the most well-known oxalate-degrading bacteria species, O. formigenes, belongs to. This is an interesting finding as multiple studies have supported the lack of O. formigenes colonization as a biomarker for calcium oxalate kidney stones39,85–90. More importantly, the bacterium is known to break down oxalate as its main energy source and has also recently been shown to stimulate oxalate excretion in the intestine37,91. Thus, a lack of this bacteria in our patient microbiomes suggests that our patient group is unable to degrade and  45 transport intestinal oxalate as effectively as their O. formigenes-colonized partners. Unfortunately, the use of O. formigenes as a human probiotic historically has not been effective as its effects on oxalate levels have been inconsistent and the re-colonization of this bacteria in the human intestinal tract is transient40–42,44. Additionally, as some healthy non-stone-formers seem to not need the bacteria, we speculated that there were other bacteria that may have influenced oxalate regulation in the intestine. A comparison of other known oxalate-degrading bacteria45 did not however uncover significant differences in abundance in those bacteria between the groups.   4.2.2. Higher abundance of unclassified bacteria in control microbiomes  We did, however, observe that controls had a higher abundance of unclassified bacteria at the phylum, class, family and species taxonomic levels than patients; an observation that aligns with our previous observation that control microbiomes have an increased bacterial diversity. Although the functional roles of these unclassified bacteria remain to be elucidated, they represent a large repertoire of bacterial enzymes and bacterial-driven processes that could potentially affect stone-related metabolite regulation in the intestine.    4.3 Differences in metabolic pathways of patient microbiomes   The lack of functional (metabolic) descriptions for bacteria is a common limitation for most 16S rRNA studies as the majority of intestinal bacteria have never been cultured and examined metabolically. Thus, using the same samples, we performed an additional shotgun-sequencing study specifically looking at bacterial metabolic pathways in the intestinal microbiome.    4.3.1 Possible deficiency in the butanoate biosynthesis pathway  After mapping the shotgun-sequencing reads to the KEGG gene database, we found that patients had a lower abundance of 3 butanoate metabolism genes, Hbd, AbfD and ACSM. The first two are involved in the synthesis of butanoate from pyruvate and 4-aminobutyrate (Figure 15) while the latter is involved in the interconversion of butanoate with butryl-CoA79,92. We also found that patients had a lower abundance of 3 genes, porB, porD and porG, that translate into subunits for  46 Pyruvate Ferredoxin Oxidoreductase93–95 (PFOR); an enzyme that is known for its breakdown of pyruvate into Acetyl-CoA96,97, a major precursor for butanoate synthesis via the pyruvate-butanoate pathway (Figure 15).  While a lower abundance in butyrate metabolism genes does not necessarily equate to decreased butyrate levels, it suggests that the metabolism and production of butyrate in patient microbiomes may be less active and less robust to metabolic changes in the intestine. Butyrate has been known to have positive benefits to intestinal epithelial health including anti-carcinogenic effects on human colonic cells and improvements to the integrity of gut epithelial cell tight junctions, transepithelial ion transport, intestinal inflammation and intestinal motility56; all of which could affect metabolite transport across the intestinal epithelial layer.    4.3.1.1 Link between butanoate and oxalate  A direct link between butanoate and oxalate regulation has also relatively recently been made, showing that butanoate upregulates the expression of 2 oxalate transporters of the SLC26A gene family in the gut57,98. One of the oxalate transporters, SLC26A3 (DRA), was shown to contribute to significant transcellular (active) oxalate absorption in the ileum, cecum and distal colon of mice99 while the other, SLC26A6 (PAT1) was shown to contribute to transcellular oxalate secretion in the ileum100 and duodenum101 of mice. It remains to be seen, however, whether butyrate-induced expression of oxalate transporters pushes the balance of oxalate transport towards net absorption or net secretion in the gut. Additionally, butyrate’s ability to facilitate intestinal cell tight junction assembly may also play a role in restricting (passive) paracellular oxalate transport; a transport mechanism that has been attributed to significant oxalate absorption in the gut102,103. In summary, as patient microbiomes have lower abundances of butyrate metabolism genes, we speculate that they may have a lesser capability for butyrate production which may affect both the expression of oxalate transporters and the permeability of the gut epithelial layer of patients.        47 4.3.2 No meaningful differences in other metabolic pathways associated with stone metabolites  Unlike in the butanoate pathway, we found no differentially-abundant genes in the ascorbate or the glyoxylate pathways. This is not surprising as there have been no prior studies demonstrating the conversion of ascorbate or glyoxylate into oxalate by intestinal bacteria. Additionally, further review of the literature shows that the effect of ascorbate intake on hyperoxaluria and kidney stone risk is controversial, due to conflicting oxalate results across studies and the fact that ascorbate can degrade spontaneously into oxalate in alkaline conditions12,104. Thus, in this study, we did not find enough evidence to show that there is bacterial involvement in the conversion of ascorbate and glyoxylate into oxalate in the gut.    4.3.3 Low presence of the oxc gene in patient microbiomes but high presence of frc and oxIT across all samples  To further explore the state of oxalate metabolism in the metagenome, we compiled a list of 17 genes associated with the metabolism of oxalate or its intermediate metabolites; formate, Oxalyl-CoA and Formyl-CoA. Here, we found that the oxc gene was detected in fewer patient microbiomes (n=4) than control microbiomes (n=9) although the difference in their abundances did not reach statistical significance. This observation aligns with our previous taxonomic analysis result that there is lower presence of Oxalobacter in patient stone microbiomes. In contrast to this result, we found that three other well-known oxalate degradation genes, frc, oxIT and OOR are present in almost all samples, both patient and control. These observations suggest that frc, oxIT, OOR are genes that are more widely distributed across bacterial taxa. Indeed, past research has shown frc is widely distributed across various bacterial taxa, making it a more comprehensive biomarker for oxalate degradation activity than oxc105. On the other hand, the gene oxc may be a more conservative biomarker for identifying Oxalobacter and its closely-related bacteria. We did not find past studies that examined the use of oxIT and OOR in identifying oxalate-degrading bacteria.     48 Chapter 5: Conclusions & Future Directions  5.1 Summary   In summary, our approach of combining 16S rRNA analysis with whole microbiome shotgun sequencing has allowed us to examine differences in bacterial taxonomy and metabolic processes in the microbiomes of kidney stone patients and controls, yielding new observations that link recurrent kidney stone disease with decreased bacterial diversity, a decrease in Oxalobacter populations, decreased oxalate-degrading enzyme and deficiencies in the butyrate metabolic pathway that may affect oxalate regulation in the gut.   Although these observations are strong biomarkers on their own, when combined, they provide a more comprehensive picture of the intestinal ecosystem in relation to propensity to form kidney stones.    5.2 Limitations  As with any attempt to study a dynamic, natural environment, this analysis has its limitations. The metabolic analyses depend highly on existing bacterial annotations, which are often unavailable as many bacteria have yet to be cultured and studied metabolically. For example, in one past metagenomic study of fecal samples from 124 European participants, it was found that only 47% of all detected genes had been assigned a gene identification number (KEGG Orthology ID) and only 18.7% were assigned to a KEGG metabolic pathway31. Additionally, our limited sample size reduces our ability to tease out meaningful differences that contribute to oxalate regulation in the gut. In particular, the low sample size of our uric acid (n=2) and cystine (n=2) stone patients made it impossible for us to conduct reasonable multi-class analyses.   5.3 Future directions  Moving forward, there is the potential to increase the sample size and conduct similar studies, which will yield greater statistical power to address our primary hypothesis. There is also the potential to integrate bacterial transcriptomics, metabolomics and ex-vivo intestinal studies to validate our gene measurement findings and provide a more comprehensive view into the role of the intestinal microbiome in recurrent kidney stone disease.  49                                 50 Bibliography  1. Scales, C. D., Smith, A. C., Hanley, J. M. & Saigal, C. S. Prevalence of Kidney Stones in the United States. Eur. Urol. 62, 160–165 (2012). 2. Stamatelou, K. K., Francis, M. E., Jones, C. A., Nyberg, L. M. & Curhan, G. C. Time trends in reported prevalence of kidney stones in the United States: 1976–199411.See Editorial by Goldfarb, p. 1951. Kidney Int. 63, 1817–1823 (2003). 3. Worcester, E. M. & Coe, F. L. Calcium Kidney Stones. N. Engl. J. Med. 363, 954–963 (2010). 4. Saigal, C. S., Joyce, G., Timilsina, A. R. & the Urologic Diseases in America Project. Direct and indirect costs of nephrolithiasis in an employed population: Opportunity for disease management? Kidney Int. 68, 1808–1814 (2005). 5. Alelign, T. & Petros, B. Kidney Stone Disease: An Update on Current Concepts. Advances in Urology (2018). doi:10.1155/2018/3068365 6. Spivacow, F. R., Valle, D., E, E., Lores, E. & Rey, P. G. Kidney stones: composition, frequency and relation to metabolic diagnosis. Med. B. Aires 76, 343–348 (2016). 7. Wiesenthal, J. D., Ghiculete, D., Honey, R. J. D. & Pace, K. T. A Comparison of Treatment Modalities for Renal Calculi Between 100 and 300 mm2: Are Shockwave Lithotripsy, Ureteroscopy, and Percutaneous Nephrolithotomy Equivalent? J. Endourol. 25, 481–485 (2011). 8. Dion, M. et al. CUA guideline on the evaluation and medical management of the kidney stone patient – 2016 update. Can. Urol. Assoc. J. 10, E347–E358 (2016). 9. Kuzgunbay, B. et al. Long-Term Renal Function and Stone Recurrence After Percutaneous Nephrolithotomy in Patients with Renal Insufficiency. J. Endourol. 24, 305–308 (2009).  51 10. Brinkley, L. R. D., MgGuire, J. M. D., Gregory, J. M. D. & Pak, C. Y. C. Bioavailability of oxalate in foods. Urology 17, 534–538 (1981). 11. Bhasin, B., Ürekli, H. M. & Atta, M. G. Primary and secondary hyperoxaluria: Understanding the enigma. World J. Nephrol. 4, 235–244 (2015). 12. Robijn, S., Hoppe, B., Vervaet, B. A., D’Haese, P. C. & Verhulst, A. Hyperoxaluria: a gut–kidney axis? Kidney Int. 80, 1146–1158 (2011). 13. Zhang, Y. et al. Purine-rich foods intake and recurrent gout attacks. Ann. Rheum. Dis. 71, 1448–1453 (2012). 14. So, A. & Thorens, B. Uric acid transport and disease. J. Clin. Invest. 120, 1791–1799 (2010). 15. Jin, M. et al. Uric Acid, Hyperuricemia and Vascular Diseases. Front. Biosci. J. Virtual Libr. 17, 656–669 (2012). 16. Maiuolo, J., Oppedisano, F., Gratteri, S., Muscoli, C. & Mollace, V. Regulation of uric acid metabolism and excretion. Int. J. Cardiol. 213, 8–14 (2016). 17. Sorensen, L. B. & Levinson, D. J. Origin and extrarenal elimination of uric acid in man. Nephron 14, 7–20 (1975). 18. Xu, X., Li, C., Zhou, P. & Jiang, T. Uric acid transporters hiding in the intestine. Pharm. Biol. 54, 3151–3155 (2016). 19. Yun, Y. et al. Intestinal tract is an important organ for lowering serum uric acid in rats. PloS One 12, e0190194 (2017). 20. Torralba, K. D., De Jesus, E. & Rachabattula, S. The interplay between diet, urate transporters and the risk for gout and hyperuricemia: current and future directions. Int. J. Rheum. Dis. 15, 499–506 (2012).  52 21. Kalhan, S. C. & Hanson, R. W. Resurgence of Serine: An Often Neglected but Indispensable Amino Acid. J. Biol. Chem. 287, 19786–19791 (2012). 22. Kitabatake, M., Wah So, M., L. Tumbula, D. & Soll, D. Cysteine Biosynthesis Pathway in the Archaeon Methanosarcina barkeri Encoded by Acquired Bacterial Genes? J. Bacteriol. 182, 143–5 (2000). 23. Griffith, O. W. Mammalian sulfur amino acid metabolism: an overview. Methods Enzymol. 143, 366–376 (1987). 24. Claes, D. J. & Jackson, E. Cystinuria: mechanisms and management. Pediatr. Nephrol. 27, 2031–2038 (2012). 25. Steffansen, B. et al. Intestinal solute carriers: an overview of trends and strategies for improving oral drug absorption. Eur. J. Pharm. Sci. 21, 3–16 (2004). 26. Vouga, M. & Greub, G. Emerging bacterial pathogens: the past and beyond. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 22, 12–21 (2016). 27. Lebeer, S., Vanderleyden, J. & Keersmaecker, S. C. J. D. Host interactions of probiotic bacterial surface molecules: comparison with commensals and pathogens. Nat. Rev. Microbiol. 8, 171–184 (2010). 28. Isolauri, E. Probiotics in human disease. Am. J. Clin. Nutr. 73, 1142S-1146S (2001). 29. Sender, R., Fuchs, S. & Milo, R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 14, (2016). 30. Schloss, P. D., Girard, R. A., Martin, T., Edwards, J. & Thrash, J. C. Status of the Archaeal and Bacterial Census: an Update. mBio 7, e00201-16 (2016). 31. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).  53 32. Escobar-Zepeda, A., Vera-Ponce de León, A. & Sanchez-Flores, A. The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics. Front. Genet. 6, (2015). 33. Walker, A. W., Duncan, S. H., Louis, P. & Flint, H. J. Phylogeny, culturing, and metagenomics of the human gut microbiota. Trends Microbiol. 22, 267–274 (2014). 34. Dawson, K. A., Allison, M. J. & Hartman, P. A. Isolation and some characteristics of anaerobic oxalate-degrading bacteria from the rumen. Appl. Environ. Microbiol. 40, 833–839 (1980). 35. Allison, M. J., Littledike, E. T. & James, L. F. Changes in Ruminal Oxalate Degradation Rates Associated with Adaptation to Oxalate Ingestion. J. Anim. Sci. 45, 1173–1179 (1977). 36. Rahman, M. M., Abdullah, R. B. & Wan Khadijah, W. E. A review of oxalate poisoning in domestic animals: tolerance and performance aspects. J. Anim. Physiol. Anim. Nutr. 97, 605–614 (2013). 37. Allison, M. J., Cook, H. M., Milne, D. B., Gallagher, S. & Clayman, R. V. Oxalate Degradation by Gastrointestinal Bacteria from Humans. J. Nutr. 116, 455–460 (1986). 38. Sidhu, H., Allison, M. & Peck, A. B. Identification and classification of Oxalobacter formigenes strains by using oligonucleotide probes and primers. J. Clin. Microbiol. 35, 350–353 (1997). 39. Kaufman, D. W. et al. Oxalobacter formigenes May Reduce the Risk of Calcium Oxalate Kidney Stones. J. Am. Soc. Nephrol. 19, 1197–1203 (2008). 40. Hoppe, B., Unruh, G. von, Laube, N., Hesse, A. & Sidhu, H. Oxalate degrading bacteria: new treatment option for patients with primary and secondary hyperoxaluria? Urol. Res. 33, 372–375 (2005).  54 41. Hoppe, B. et al. Oxalobacter formigenes: a potential tool for the treatment of primary hyperoxaluria type 1. Kidney Int. 70, 1305–1311 (2006). 42. Hoppe, B. et al. Efficacy and safety of Oxalobacter formigenes to reduce urinary oxalate in primary hyperoxaluria. Nephrol. Dial. Transplant. 26, 3609–3615 (2011). 43. Abratt, V. R. & Reid, S. J. Oxalate-degrading bacteria of the human gut as probiotics in the management of kidney stone disease. Adv. Appl. Microbiol. 72, 63–87 (2010). 44. Knight, J. & Holmes, R. P. Role of Oxalobacter formigenes Colonization in Calcium Oxalate Kidney Stone Disease. in The Role of Bacteria in Urology 77–84 (Springer, Cham, 2016). doi:10.1007/978-3-319-17732-8_8 45. Miller, A. W. & Dearing, D. The Metabolic and Ecological Interactions of Oxalate-Degrading Bacteria in the Mammalian Gut. Pathogens 2, 636–652 (2013). 46. Miller, A. W., Dale, C. & Dearing, M. D. The Induction of Oxalate Metabolism In Vivo Is More Effective with Functional Microbial Communities than with Functional Microbial Species. mSystems 2, (2017). 47. Vogels, G. D. & Van der Drift, C. Degradation of purines and pyrimidines by microorganisms. Bacteriol. Rev. 40, 403–468 (1976). 48. Lee, I. R. et al. Characterization of the Complete Uric Acid Degradation Pathway in the Fungal Pathogen Cryptococcus neoformans. PLoS ONE 8, (2013). 49. Guo, Z. et al. Intestinal Microbiota Distinguish Gout Patients from Healthy Humans. Sci. Rep. 6, 20602 (2016). 50. Guédon, E. & Martin-Verstraete, I. Cysteine Metabolism and Its Regulation in Bacteria. in Amino Acid Biosynthesis ~ Pathways, Regulation and Metabolic Engineering 195–218 (Springer, Berlin, Heidelberg, 2006). doi:10.1007/7171_2006_060  55 51. Burguière, P., Auger, S., Hullo, M.-F., Danchin, A. & Martin-Verstraete, I. Three Different Systems Participate in l-Cystine Uptake in Bacillus subtilis. J. Bacteriol. 186, 4875–4884 (2004). 52. Ohtsu, I. et al. The l-Cysteine/l-Cystine Shuttle System Provides Reducing Equivalents to the Periplasm in Escherichia coli. J. Biol. Chem. 285, 17479–17487 (2010). 53. Dai, Z.-L., Wu, G. & Zhu, W.-Y. Amino acid metabolism in intestinal bacteria: links between gut ecology and host health. Front. Biosci. Landmark Ed. 16, 1768–1786 (2011). 54. Smith, E. A. & Macfarlane, G. T. Enumeration of amino acid fermenting bacteria in the human large intestine: effects of pH and starch on peptide metabolism and dissimilation of amino acids. FEMS Microbiol. Ecol. 25, 355–368 (1998). 55. Rivière, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and Butyrate-Producing Colon Bacteria: Importance and Strategies for Their Stimulation in the Human Gut. Front. Microbiol. 7, (2016). 56. Canani, R. B. et al. Potential beneficial effects of butyrate in intestinal and extraintestinal diseases. World J. Gastroenterol. WJG 17, 1519–1528 (2011). 57. Alrefai, W. A. et al. Molecular cloning and promoter analysis of downregulated in adenoma (DRA). Am. J. Physiol. - Gastrointest. Liver Physiol. 293, G923–G934 (2007). 58. Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).  56 59. Schloss, P. D. et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009). 60. DeSantis, T. Z. et al. Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006). 61. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013). 62. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948). 63. Wilcoxon, F. Individual Comparisons by Ranking Methods. Biom. Bull. 1, 80–83 (1945). 64. R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2017). 65. Hothorn, T., Hornik, K., van de Wiel, M. & Zeileis, A. Implementing a Class of Permutation Tests: The coin Package. J. Stat. Softw. 28, (2008). 66. Oksanen, J. et al. vegan: Community Ecology Package. (2017). 67. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009). 68. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). 69. Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016). 70. Hanson, N. W., Konwar, K. M., Wu, S. J. & Hallam, S. J. MetaPathways v2.0: A master-worker model for environmental Pathway/Genome Database construction on grids and  57 clouds. in 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology 1–7 (2014). doi:10.1109/CIBCB.2014.6845516 71. Konwar, K. M. et al. MetaPathways v2.5: quantitative functional, taxonomic and usability improvements. Bioinformatics 31, 3345–3347 (2015). 72. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). 73. Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011). 74. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000). 75. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods N. Y. 12, 59–60 (2015). 76. Mortazavi, A., Williams, B. A., Mccue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods N. Y. 5, 621–8 (2008). 77. Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A. & Dewey, C. N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010). 78. The Universal Protein Resource (UniProt). Nucleic Acids Res. 36, D190–D195 (2008). 79. Anand, S., Kaur, H. & Mande, S. S. Comparative In silico Analysis of Butyrate Production Pathways in Gut Commensals and Pathogens. Front. Microbiol. 7, (2016). 80. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).  58 81. Mrozinska, S. et al. Qualitative Parameters of the Colonic Flora in Patients with HNF1A-MODY Are Different from Those Observed in Type 2 Diabetes Mellitus. Journal of Diabetes Research (2016). doi:10.1155/2016/3876764 82. Chang, J. Y. et al. Decreased Diversity of the Fecal Microbiome in Recurrent Clostridium difficile—Associated Diarrhea. J. Infect. Dis. 197, 435–438 (2008). 83. Ott, S. J. et al. Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease. Gut 53, 685–693 (2004). 84. Pascal, V. et al. A microbial signature for Crohn’s disease. Gut gutjnl-2016-313235 (2017). doi:10.1136/gutjnl-2016-313235 85. Sidhu, H. et al. Direct correlation between hyperoxaluria/oxalate stone disease and the absence of the gastrointestinal tract-dwelling bacterium Oxalobacter formigenes: possible prevention by gut recolonization or enzyme replacement therapy. J. Am. Soc. Nephrol. JASN 10 Suppl 14, S334-340 (1999). 86. Troxel, S. A., Sidhu, H., Kaul, P. & Low, R. K. Intestinal Oxalobacter formigenes Colonization in Calcium Oxalate Stone Formers and Its Relation to Urinary Oxalate. J. Endourol. 17, 173–176 (2003). 87. Neuhaus, T. et al. Urinary oxalate excretion in urolithiasis and nephrocalcinosis. Arch. Dis. Child. 82, 322–326 (2000). 88. Kwak, C., Kim, H. K., Kim, E. C., Choi, M. S. & Kim, H. H. Urinary Oxalate Levels and the Enteric Bacterium Oxalobacter formigenes in Patients with Calcium Oxalate Urolithiasis. Eur. Urol. 44, 475–481 (2003). 89. Mikami, K. et al. Association of absence of intestinal oxalate degrading bacteria with urinary calcium oxalate stone formation. Int. J. Urol. 10, 293–296 (2003).  59 90. Kumar, R. et al. Role of Oxalobacter formigenes in Calcium Oxalate Stone Disease: A Study from North India. Eur. Urol. 41, 318–322 (2002). 91. Hatch, M. Gut microbiota and oxalate homeostasis. Ann. Transl. Med. 5, (2017). 92. Shimizu, S., Inoue, K., Tani, Y. & Yamada, H. Butyryl-CoA synthetase of pseudomonas aeruginosa —Purification and characterization. Biochem. Biophys. Res. Commun. 103, 1231–1237 (1981). 93. Tersteegen, A., Linder, D., Thauer, R. K. & Hedderich, R. Structures and Functions of Four Anabolic 2-Oxoacid Oxidoreductases in Methanobacterium Thermoautotrophicum. Eur. J. Biochem. 244, 862–868 (1997). 94. Kletzin, A. & Adams, M. W. Molecular and phylogenetic characterization of pyruvate and 2-ketoisovalerate ferredoxin oxidoreductases from Pyrococcus furiosus and pyruvate ferredoxin oxidoreductase from Thermotoga maritima. J. Bacteriol. 178, 248–257 (1996). 95. Chabrière, E. et al. Crystal structures of the key anaerobic enzyme pyruvate:ferredoxin oxidoreductase, free and in complex with pyruvate. Nat. Struct. Mol. Biol. 6, 182–190 (1999). 96. Furdui, C. & Ragsdale, S. W. The Role of Pyruvate Ferredoxin Oxidoreductase in Pyruvate Synthesis during Autotrophic Growth by the Wood-Ljungdahl Pathway. J. Biol. Chem. 275, 28494–28499 (2000). 97. Gibson, M. I. et al. The Structure of an Oxalate Oxidoreductase Provides Insight into Microbial 2-Oxoacid Metabolism. Biochemistry (Mosc.) 54, 4112–4120 (2015). 98. Canani, R. B. et al. Genotype-dependency of butyrate efficacy in children with congenital chloride diarrhea. Orphanet J. Rare Dis. 8, 194 (2013).  60 99. Freel, R. W., Whittamore, J. M. & Hatch, M. Transcellular oxalate and Cl− absorption in mouse intestine is mediated by the DRA anion exchanger Slc26a3, and DRA deletion decreases urinary oxalate. Am. J. Physiol. - Gastrointest. Liver Physiol. 305, G520–G527 (2013). 100. Freel, R. W., Hatch, M., Green, M. & Soleimani, M. Ileal oxalate absorption and urinary oxalate excretion are enhanced in Slc26a6 null mice. Am. J. Physiol. - Gastrointest. Liver Physiol. 290, G719–G728 (2006). 101. Jiang, Z. et al. Calcium oxalate urolithiasis in mice lacking anion transporter Slc26a6. Nat. Genet. 38, 474–478 (2006). 102. Knauf, F. et al. Net Intestinal Transport of Oxalate Reflects Passive Absorption and SLC26A6-mediated Secretion. J. Am. Soc. Nephrol. 22, 2247–2255 (2011). 103. Whittamore, J. M. & Hatch, M. The role of intestinal oxalate transport in hyperoxaluria and the formation of kidney stones in animals and man. Urolithiasis 45, 89–108 (2017). 104. Baxmann, A. C., de O.G. Mendonça, C. & Heilberg, I. P. Effect of vitamin C supplements on urinary oxalate and pH in calcium stone-forming patients. Kidney Int. 63, 1066–1071 (2003). 105. Khammar, N. et al. Use of the frc gene as a molecular marker to characterize oxalate-oxidizing bacterial abundance and diversity structure in soil. J. Microbiol. Methods 76, 120–127 (2009).    61 Appendix  Appendix A: Demographics of recurrent oxalate kidney stone formers and controls  Sample Name Sample Group Matched with Age Gender PT12 patient CTRLA6 56 Male PT13 patient CTRLA10A 64 Male PT136 patient CTRLA7 77 Male PT145 patient CTRLA9 70 Male PT157 patient CTRLA16 70 Female PT177 patient CTRLA19 35 Male PT178 patient CTRLA10B 58 Male PT182 patient CTRLA11 63 Male PT198 patient CTRLA23 54 Female PT199 patient CTRLA15 59 Female PT207 patient CTRLA28 43 Male PT209 patient CTRLA29 57 Male PT211 patient CTRLA36 37 Female PT213 patient CTRLA34 71 Male PT220 patient CTRLA32 59 Male PT223 patient CTRLA33 68 Female PT231 patient CTRLA38 45 Male CTRLA6 control PT12 54 Female CTRLA10A control PT13 64 Female CTRLA7 control PT136 71 Female CTRLA9 control PT145 70 Female CTRLA16 control PT157 72 Male CTRLA19 control PT177 34 Female CTRLA10B control PT178 54 Female CTRLA11 control PT182 56 Female CTRLA23 control PT198 61 Male CTRLA15 control PT199 62 Male CTRLA28 control PT207 48 Female CTRLA29 control PT209 62 Female CTRLA36 control PT211 42 Male CTRLA34 control PT213 65 Female CTRLA32 control PT220 63 Female CTRLA33 control PT223 69 Female CTRLA38 control PT231 42 Female  Table 3. Patient and control metadata   62 Appendix B: Primer design for PCR amplification and Illumina MiSeq sequencing as described in the supplementary methods of Kozich et al. 2013  Forward Primer ID Primer sequence v4.SA501  AATGATACGGCGACCACCGAGATCTACACATCGTACGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA502  AATGATACGGCGACCACCGAGATCTACACACTATCTGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA503  AATGATACGGCGACCACCGAGATCTACACTAGCGAGTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA504  AATGATACGGCGACCACCGAGATCTACACCTGCGTGTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA505  AATGATACGGCGACCACCGAGATCTACACTCATCGAGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA506  AATGATACGGCGACCACCGAGATCTACACCGTGAGTGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA507  AATGATACGGCGACCACCGAGATCTACACGGATATCTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SA508 AATGATACGGCGACCACCGAGATCTACACGACACCGTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB501  AATGATACGGCGACCACCGAGATCTACACCTACTATATATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB502  AATGATACGGCGACCACCGAGATCTACACCGTTACTATATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB503  AATGATACGGCGACCACCGAGATCTACACAGAGTCACTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB504  AATGATACGGCGACCACCGAGATCTACACTACGAGACTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB505  AATGATACGGCGACCACCGAGATCTACACACGTCTCGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB506  AATGATACGGCGACCACCGAGATCTACACTCGACGAGTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB507  AATGATACGGCGACCACCGAGATCTACACGATCGTGTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA v4.SB508  AATGATACGGCGACCACCGAGATCTACACGTCAGATATATGGTAATTGTGTGCCAGCMGCCGCGGTAA  Table 5. Forward primers for PCR amplification of the bacterial 16S rRNA gene (V4 region)               63 Reverse Primer ID Primer sequence v4.SA701  CAAGCAGAAGACGGCATACGAGATAACTCTCGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA702  CAAGCAGAAGACGGCATACGAGATACTATGTCAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA703  CAAGCAGAAGACGGCATACGAGATAGTAGCGTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA704  CAAGCAGAAGACGGCATACGAGATCAGTGAGTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA705  CAAGCAGAAGACGGCATACGAGATCGTACTCAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA706  CAAGCAGAAGACGGCATACGAGATCTACGCAGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA707  CAAGCAGAAGACGGCATACGAGATGGAGACTAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA708  CAAGCAGAAGACGGCATACGAGATGTCGCTCGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA709  CAAGCAGAAGACGGCATACGAGATGTCGTAGTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA710  CAAGCAGAAGACGGCATACGAGATTAGCAGACAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA711  CAAGCAGAAGACGGCATACGAGATTCATAGACAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SA712  CAAGCAGAAGACGGCATACGAGATTCGCTATAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB701  CAAGCAGAAGACGGCATACGAGATAAGTCGAGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB702  CAAGCAGAAGACGGCATACGAGATATACTTCGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB703  CAAGCAGAAGACGGCATACGAGATAGCTGCTAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB704  CAAGCAGAAGACGGCATACGAGATCATAGAGAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB705  CAAGCAGAAGACGGCATACGAGATCGTAGATCAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB706  CAAGCAGAAGACGGCATACGAGATCTCGTTACAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB707  CAAGCAGAAGACGGCATACGAGATGCGCACGTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB708  CAAGCAGAAGACGGCATACGAGATGGTACTATAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB709  CAAGCAGAAGACGGCATACGAGATGTATACGCAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB710  CAAGCAGAAGACGGCATACGAGATTACGAGCAAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB711  CAAGCAGAAGACGGCATACGAGATTCAGCGTTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT v4.SB712  CAAGCAGAAGACGGCATACGAGATTCGCTACGAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT  Table 6. Reverse primers for PCR amplification of the bacterial 16S rRNA gene (V4 region)   64  Sequencing primer ID Primer sequence Read 1 primer for V4 region  TATGGTAATTGTGTGCCAGCMGCCGCGGTAA  Read 2 primer for V4 region  AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT  Index primer for V4 region ATTAGAWACCCBDGTAGTCCGGCTGACTGACT    Table 7. Primers for Illumina MiSeq sequencing of 16S rRNA amplicons    65 Appendix C: Sequencing read depth and other sequence abundance metrics  Sample name Sample group Pair  Sequencing batch Total sequencing reads Total OTU counts  Total unique OTUs PT12 patient 1 1 38,928 2,988 45 CTRLA6 control 1 1 11,723 1,078 88 PT13 patient 2 1 24,162 2,212 55 CTRLA10A control 2 1 14,257 1,274 94 PT136 patient 3 1 21,361 1,981 115 CTRLA7 control 3 1 15,806 1,491 162 PT145 patient 4 2 63,880 18,493 998 CTRLA9 control 4 2 124,205 38,112 1476 PT157 patient 5 1 13,973 894 87 CTRLA16 control 5 1 17,783 1,521 141 PT177 patient 6 1 17,156 1,655 73 CTRLA19 control 6 1 13,097 1,238 116 PT178 patient 7 1 16,676 2,242 81 CTRLA10B control 7 2 97,412 28,780 657 PT182 patient 8 1 20,610 2,233 127 CTRLA11 control 8 1 15,599 1,269 71 PT198 patient 9 2 120,335 37,847 1012 CTRLA23 control 9 1 14,842 1,561 125 PT199 patient 10 1 17,715 1,800 138 CTRLA15 control 10 1 17,968 1,506 158 PT207 patient 11 2 132,335 43,158 619 CTRLA28 control 11 2 135,589 47,601 678 PT209 patient 12 2 103,970 33,095 593 CTRLA29 control 12 2 123,206 41,952 681 PT211 patient 13 1 15,126 1,566 72 CTRLA36 control 13 1 22,698 2,160 164 PT213 patient 14 1 14,601 1,224 120 CTRLA34 control 14 1 19,285 1,804 268 PT220 patient 15 2 131,817 40,953 796 CTRLA32 control 15 2 60,099 17,610 766 PT223 patient 16 2 138,661 47,704 223 CTRLA33 control 16 2 94,005 32,763 604 PT231 patient 17 2 101,293 32,299 652 CTRLA38 control 17 2 81,819 25,921 706  Table 8. 16S rRNA sequencing read depth and operational taxonomic unit depth Red-colored text shows patient and control pairs where there is more than a 5-fold difference in sequencing depth and/or OTU depth between the patient sample and control sample.  66  Figure 5. Comparison of 16S rRNA sequencing depth between pairs of samples For sample pair 7, the control sample had approximately 5.8 times more 16S rRNA sequencing reads than patient sample. For sample pair 9, the patient sample had approximately 8.1 times more 16S rRNA sequencing reads than the control sample.   Figure 6. Comparison of OTU depth between pairs of samples. For sample pair 7, the control sample had approximately 12.8 times more OTU counts than the patient sample. For sample pair 9, the patient sample had approximately 24.2 times more OTU counts than the control sample.  67   Figure 7. Plot of sequencing depth against OTU count and number of unique OTUs. Blue-colored points represent samples from the first 16S rRNA sequencing batch while red-colored points represent samples from the second sequencing batch. The first plot a) shows the relationship between sequencing depth (the number of sequencing reads) and total OTU count per sample. The second plot b) shows the relationship between sequencing depth and the number of unique OTUs. The dotted line in both plots represents the linear regression line of best fit for the data points with dark grey zones representing the 95% confidence interval.   a) b)  68 Sample name OTU Taxonomy OTU count CTRLA10B 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 6345 CTRLA10B 00010 f__Bacteroidaceae(100);g__Bacteroides(100);s__ovatus(66); 2670 CTRLA10B 00006 f__Ruminococcaceae(100);g__Faecalibacterium(100);s__prausnitzii(100); 2580 CTRLA28 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100); 12645 CTRLA28 00005 f__Veillonellaceae(100);g__Dialister(100);s__(100); 6158 CTRLA28 00004 f__Lachnospiraceae(100);g__Roseburia(99);g__Roseburia_unclassified(76); 5170 CTRLA29 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100); 15973 CTRLA29 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 4344 CTRLA29 00004 f__Lachnospiraceae(100);g__Roseburia(99);g__Roseburia_unclassified(76); 2280 CTRLA32 00009 f__Enterobacteriaceae(100);g__(98);s__(98); 3169 CTRLA32 00028 f__Prevotellaceae(100);g__Prevotella(100);s__(100); 1941 CTRLA32 00006 f__Ruminococcaceae(100);g__Faecalibacterium(100);s__prausnitzii(100); 1014 CTRLA33 00005 f__Veillonellaceae(100);g__Dialister(100);s__(100); 7538 CTRLA33 00016 f__Enterobacteriaceae_unclassified(100); 4455 CTRLA33 00009 f__Enterobacteriaceae(100);g__(98);s__(98); 4410 CTRLA38 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100); 2940 CTRLA38 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 2791 CTRLA38 00013 f__Ruminococcaceae(100);g__Oscillospira(98);s__(98); 2393 CTRLA9 00006 f__Ruminococcaceae(100);g__Faecalibacterium(100);s__prausnitzii(100); 2503 CTRLA9 00038 f__Rikenellaceae(100);g__(100);s__(100); 2503 CTRLA9 00005 f__Veillonellaceae(100);g__Dialister(100);s__(100); 2014 PT145 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 1994 PT145 00006 f__Ruminococcaceae(100);g__Faecalibacterium(100);s__prausnitzii(100); 1416 PT145 00054 f__Rikenellaceae(100);g__(100);s__(100); 1394 PT198 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100); 7105 PT198 00003 f__Ruminococcaceae_unclassified(100); 2086 PT198 00008 f__Ruminococcaceae(100);g__Faecalibacterium(99);s__prausnitzii(98); 1848 PT207 00004 f__Lachnospiraceae(100);g__Roseburia(99);g__Roseburia_unclassified(76); 9126 PT207 00003 f__Ruminococcaceae_unclassified(100); 7288 PT207 00008 f__Ruminococcaceae(100);g__Faecalibacterium(99);s__prausnitzii(98); 3907 PT209 00003 f__Ruminococcaceae_unclassified(100); 6871 PT209 00014 f__Coriobacteriaceae(100);g__Collinsella(100);s__aerofaciens(99); 2705 PT209 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 2296 PT220 00007 f__Prevotellaceae(100);g__Prevotella(100);s__copri(100); 12711 PT220 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 2434 PT220 00042 f__Bacteroidaceae(100);g__Bacteroides(100);s__eggerthii(100); 1882   …table continues to next page   69 Sample name OTU Taxonomy OTU count PT223 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100) 37013 PT223 00011 f__Lachnospiraceae(100);g__Blautia(100);s__(100); 1107 PT223 00009 f__Enterobacteriaceae(100);g__(98);s__(98); 1013 PT231 00001 f__Verrucomicrobiaceae(100);g__Akkermansia(100);s__muciniphila(100) 11216 PT231 00013 f__Ruminococcaceae(100);g__Oscillospira(98);s__(98); 2074 PT231 00002 f__Bacteroidaceae(100);g__Bacteroides(100);s__(100); 1809  Table 9. Top three most abundant OTUs for each sample in the second 16S rRNA sequencing batch Bolded rows in red indicate OTUs with more than 5000 counts.            70   Sample name Sample group Pair Sequencing batch Total sequencing reads Trimmed reads Total ORFs  (predicted by Prodigal) Total ORFs  (with valid KEGG IDs) Total TPM for ORFs with valid KEGG IDs CTRLA6 control 1 2 40,533,892 33,377,271 154,682 44,709 219,041 PT12 patient 1 2 149,843,768 126,196,606 140,186 46,699 292,098 CTRLA10A control 2 2 100,779,466 83,816,465 278,277 77,331 197,354 PT13 patient 2 2 174,051,374 141,553,316 194,343 53,352 218,727 CTRLA7 control 3 2 69,951,144 53,027,605 263,603 71,194 220,886 PT136 patient 3 2 79,571,098 63,108,026 270,001 72,448 224,352 CTRLA9 control 4 1 32,996,414 26,687,345 298,337 73,770 195,197 PT145 patient 4 1 27,350,984 21,791,608 206,092 51,514 211,274 CTRLA16 control 5 2 73,512,224 56,030,800 293,400 84,915 235,366 PT157 patient 5 2 88,813,514 71,636,652 253,659 67,148 209,741 CTRLA19 control 6 2 76,180,278 62,308,642 308,044 82,608 225,286 PT177 patient 6 2 58,299,252 48,208,955 171,218 51,723 263,922 CTRLA10B control 7 1 38,662,290 30,158,549 172,674 43,681 201,582 PT178 patient 7 1 31,746,034 24,318,512 144,701 39,495 208,887 CTRLA11 control 8 1 36,040,524 28,264,250 134,918 38,931 214,109 PT182 patient 8 1 29,520,332 22,274,295 186,944 47,885 226,253 CTRLA23 control 9 2 87,636,370 69,525,447 371,208 99,711 214,663 PT198 patient 9 2 123,368,288 100,044,397 503,664 136,543 218,014 CTRLA15 control 10 2 162,317,478 126,822,156 551,930 143,135 206,777 PT199 patient 10 2 54,638,564 45,687,178 352,449 100,103 243,508 CTRLA28 control 11 3 43,296,964 28,829,235 229,204 42,223 143,167 PT207 patient 11 3 40,385,320 27,582,615 198,640 38,328 150,392 CTRLA29 control 12 3 48,125,716 34,764,144 258,835 53,284 158,721 PT209 patient 12 3 35,883,878 24,710,150 242,946 49,059 153,717 CTRLA36 control 13 2 74,018,868 56,853,858 294,149 76,687 205,424 PT211 patient 13 2 66,615,266 54,113,576 163,707 44,746 220,211 CTRLA34 control 14 2 65,524,408 52,600,391 471,637 122,418 217,889 PT213 patient 14 2 51,214,608 41,596,235 322,159 88,908 234,378 CTRLA32 control 15 3 44,978,784 34,155,029 296,671 57,954 166,497 PT220 patient 15 3 49,809,972 34,293,343 234,794 44,538 160,337 CTRLA33 control 16 3 23,505,982 16,503,996 191,474 33,724 133,868 PT223 patient 16 3 29,980,894 20,929,746 74,638 12,026 156,765 CTRLA38 control 17 3 20,616,176 14,474,176 170,943 29,961 138,285 PT231 patient 17 3 47,835,580 32,741,879 243,168 42,209 142,466  Table 10. Shotgun-sequencing read depth and abundance of open reading frames (ORFs)  Red-colored text shows the only patient and control pair where there is more than a 3-fold difference in sequencing depth between the patient sample and control sample.        71  Figure 8. Shotgun-sequencing read depth across pairs of samples For sample pair 1, the patient sample had approximately 3.7 times more sequencing reads than the patient sample.    Figure 9. Shotgun-sequencing read depth across pairs of samples after removal of human sequences and trimming via the KneadData software For sample pair 1, the patient sample had approximately 3.8 times more trimmed sequencing reads than the patient sample.   72 Appendix D: Oxalate metabolism reactions in bacteria  Enzyme name Enzymatic reaction EC    Oxalyl-CoA decarboxylase Oxalyl-CoA <=> Formyl-CoA + CO2 4.1.1.8 Formyl-CoA transferase Formyl-CoA + Oxalate <=> Formate + Oxalyl-CoA 2.8.3.16 Oxalate-formate antiporter Oxalate-Formate exchange 2.A.1.11 Oxalate oxidoreductase  Oxalate + 2 Oxidized ferredoxin <=> 2 CO2 + 2 Reduced ferredoxin + 2 H+ 1.2.7.10 Oxalate decarboxylase Oxalate <=> Formate + CO2 4.1.1.2 Formate dehydrogenase (NAD-dependent) Formate + NAD+ <=> H+ + CO2 + NADH  1.2.1.2  Glyoxylate oxidase Glyoxylate + Oxygen + H2O <=> Oxalate + Hydrogen peroxide 1.2.3.5 FO synthase  3-(4-Hydroxyphenyl)pyruvate + 5-Amino-6-(1-D-ribitylamino)uracil + 2 S-Adenosyl-L-methionine + H2O <=> 7,8-Didemethyl-8-hydroxy-5-deazariboflavin + 2 L-Methionine + 2 5'-Deoxyadenosine + Oxalate + Ammonia 2.5.1.77   CoA:oxalate CoA transferase Acetyl-CoA + Oxalate <=> Acetate + Oxalyl-CoA 2.8.3.19 Oxalate CoA transferase Succinyl-CoA + Oxalate <=> Succinate + Oxalyl-CoA 2.8.3.2 Oxamate amidohydrolase Oxamate + H2O <=> Oxalate + Ammonia 3.5.1.126 Glyoxylate dehydrogenase Glyoxylate + CoA + NADP+ <=> Oxalyl-CoA + NADPH + H+ 1.2.1.17 Formyl-CoA hydrolase Formyl-CoA + H2O <=> CoA + Formate 3.1.2.10     Table 19. Enzymatic reactions of enzymes associated with oxalate metabolism 

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