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The Gut Microbiome and Metabolic Pathways of Recurrent Kidney Stone Patients and Their Non-Stone-Forming Live-In Partners

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The Gut Microbiome and Metabolic Pathways of Recurrent Kidney Stone Patients and Their Non-Stone-Forming Live-In Partners 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 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 in partial fulfillment of the requirements submitted by Wai Ho Choy for the degree of Master of Science in Experimental Medicine Examining Committee: Dirk Lange, Urological Sciences Supervisor Ben Chew, Urological Sciences Supervisory Committee Member Amee Manges, School of Population and Public Health Supervisory Committee Member William Hsiao, Pathology and Laboratory Medicine Additional Examiner Additional Supervisory Committee Members: Supervisory Committee Member Supervisory Committee Member ii 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 iii 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. iv 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. v 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. vi 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 vii 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 viii 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
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