THE GUT MICROBIOME AND METABOLIC PATHWAYS OF ...

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


William Hsiao, Pathology and Laboratory Medicine

Additional Examiner

Additional Supervisory Committee Members:



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

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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

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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

5

0

10

0

15

0

-4 -2 0 2 4

Color Key and Histogram Heatmap of Butanoate pathway gene abundance

PT:

CTRL:

KEGG Orthology IDs

Sam

ple n

ames

32

Figure 13b. Heat map of gene abundances within the Glyoxylate & Dicarboxylate metabolism pathway




Column Z-Score

Count

0

20

40

6

0

80

100

120

-4 -2 0 2 4

Color Key and Histogram Heatmap of Glyoxylate & Dicarboxylate pathway gene abundance

PT:

CTRL:

KEGG Orthology IDs

Sam

ple n

ames

33

Figure 13c. Heat map of gene abundances within the Ascorbate & Aldarate metabolism pathway.




Column Z-Score

Count

0

10

20

30

4

0

50

60

-4 -2 0 2 4

Color Key and Histogram Heatmap of Ascorbate & Aldarate pathway gene abundance

PT:

CTRL:

KEGG Orthology IDs

Sam

ple n

ames

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

80

0

100

0

15

00

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

Sam

ple n

ames

35

Figure 14b. Heat map of genes detected within the Glyoxylate & Dicarboxylate metabolism pathway.




Column Z-Score

Count

10

80

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

Sam

ple n

ames

36

Figure 14c. Heat map of genes detected within the Ascorbate & Aldarate metabolism pathway.




.

Column Z-Score

Count

400

500

60

0 7

00

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

Sam

ple n

ames

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


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



delta subunit

14 16 6.3 ± 1.3

10.4 ± 2.3


Medium-chain acyl-CoA synthetase/

Butyryl-CoA synthetase

17 17 13 ± 2.3

24.1 ± 5.1


Succinate dehydrogenase

cytochrome b-556 subunit

17 17 67.1 ± 8.6

47.6 ± 5.4


Succinate-semialdehyde

dehydrogenase

0 3 0 ± 0

0.2 ± 0.1


4-hydroxybutyryl-CoA dehydratase 16 17 22.2 ± 5.2

30.7 ± 4.7


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


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.

50

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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



PT157 patient CTRLA16 70 Female


PT178 patient CTRLA10B 58 Male











CTRLA6 control PT12 54 Female

CTRLA10A control PT13 64 Female



CTRLA16 control PT157 72 Male


CTRLA10B control PT178 54 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.SA50

1

AATGATACGGCGACCACCGAGATCTACACATCGTACGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

2

AATGATACGGCGACCACCGAGATCTACACACTATCTGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

3

AATGATACGGCGACCACCGAGATCTACACTAGCGAGTTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

4

AATGATACGGCGACCACCGAGATCTACACCTGCGTGTTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

5

AATGATACGGCGACCACCGAGATCTACACTCATCGAGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

6

AATGATACGGCGACCACCGAGATCTACACCGTGAGTGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

7

AATGATACGGCGACCACCGAGATCTACACGGATATCTTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SA50

8

AATGATACGGCGACCACCGAGATCTACACGACACCGTTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

1

AATGATACGGCGACCACCGAGATCTACACCTACTATATATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

2

AATGATACGGCGACCACCGAGATCTACACCGTTACTATATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

3

AATGATACGGCGACCACCGAGATCTACACAGAGTCACTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

4

AATGATACGGCGACCACCGAGATCTACACTACGAGACTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

5

AATGATACGGCGACCACCGAGATCTACACACGTCTCGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

6

AATGATACGGCGACCACCGAGATCTACACTCGACGAGTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

7

AATGATACGGCGACCACCGAGATCTACACGATCGTGTTATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

v4.SB50

8

AATGATACGGCGACCACCGAGATCTACACGTCAGATATATGGTAATTGTGTGCCAGCMGCC

GCGGTAA

Table 5. Forward primers for PCR amplification of the bacterial 16S rRNA gene (V4 region)

63

Reverse

Primer

ID

Primer sequence

v4.SA70

1

CAAGCAGAAGACGGCATACGAGATAACTCTCGAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

2

CAAGCAGAAGACGGCATACGAGATACTATGTCAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

3

CAAGCAGAAGACGGCATACGAGATAGTAGCGTAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

4

CAAGCAGAAGACGGCATACGAGATCAGTGAGTAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

5

CAAGCAGAAGACGGCATACGAGATCGTACTCAAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

6

CAAGCAGAAGACGGCATACGAGATCTACGCAGAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

7

CAAGCAGAAGACGGCATACGAGATGGAGACTAAGTCAGTCAGCCGGACTACHVGGGTWT

CTAAT

v4.SA70

8

CAAGCAGAAGACGGCATACGAGATGTCGCTCGAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA70

9

CAAGCAGAAGACGGCATACGAGATGTCGTAGTAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA71

0

CAAGCAGAAGACGGCATACGAGATTAGCAGACAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA71

1

CAAGCAGAAGACGGCATACGAGATTCATAGACAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SA71

2

CAAGCAGAAGACGGCATACGAGATTCGCTATAAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

1

CAAGCAGAAGACGGCATACGAGATAAGTCGAGAGTCAGTCAGCCGGACTACHVGGGTWT

CTAAT

v4.SB70

2

CAAGCAGAAGACGGCATACGAGATATACTTCGAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

3

CAAGCAGAAGACGGCATACGAGATAGCTGCTAAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

4

CAAGCAGAAGACGGCATACGAGATCATAGAGAAGTCAGTCAGCCGGACTACHVGGGTWT

CTAAT

v4.SB70

5

CAAGCAGAAGACGGCATACGAGATCGTAGATCAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

6

CAAGCAGAAGACGGCATACGAGATCTCGTTACAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

7

CAAGCAGAAGACGGCATACGAGATGCGCACGTAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

8

CAAGCAGAAGACGGCATACGAGATGGTACTATAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB70

9

CAAGCAGAAGACGGCATACGAGATGTATACGCAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB71

0

CAAGCAGAAGACGGCATACGAGATTACGAGCAAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB71

1

CAAGCAGAAGACGGCATACGAGATTCAGCGTTAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

v4.SB71

2

CAAGCAGAAGACGGCATACGAGATTCGCTACGAGTCAGTCAGCCGGACTACHVGGGTWTC

TAAT

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 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 00016 f__Enterobacteriaceae_unclassified(100); 4455

CTRLA33 00009 f__Enterobacteriaceae(100);g__(98);s__(98); 4410


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


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


PT207 00004 f__Lachnospiraceae(100);g__Roseburia(99);g__Roseburia_unclassified(76); 9126




PT209 00014 f__Coriobacteriaceae(100);g__Collinsella(100);s__aerofaciens(99); 2705


PT220 00007 f__Prevotellaceae(100);g__Prevotella(100);s__copri(100); 12711


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


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.

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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.

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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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