University of Groningen
Understanding the gut ecosystem: bugs, drugs & diseases Vich Vila, Arnau
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Understanding the gut ecosystem:
bugs, drugs & diseases
Arnau Vich Vila
Printing of this thesis was financially supported by:
University Medical Center of Groningen (UMCG), University of Groningen (RUG), Graduate school of medical science, Takeda
ISBN (printed version): 978-94-034-2202-2 ISBN (electronic version): 978-94-034-2201-5
Cover design and layout: Diego Muñoz / milvietnams.com Printing: Ipskamp
Copyright © 2019 by Arnau Vich Vila. All rights reserved. No part of this thesis may be reproduced, stored in a retrieved system, or transmitted in any form or by any means, without prior written permission from the author or from the publisher holding the copyright of the published articles.
Understanding the gut ecosystem:
bugs, drugs & diseases
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnifi cus Prof. C. Wijmenga
and in accordance with the decision by the College of Deans.
This thesis will be defended in public on Monday 2 December 2019 at 12.45 hours
by
Arnau Vich Vila born on 29 August 1988 in Sant Cugat del Vallès, Spain
Supervisors Prof. R.K. Weersma Prof. A.P. Zhernakova
Assessment Committee Prof. J.W.A. Rossen Prof. L. Dijkhuizen Prof. B. Oldenburg
Paranymphs V.Collij T.Sinha
Propositions
The gut microbiota of patients with inflammatory bowel disease (IBD) is characterized by a decreased abundance of obligate anaerobes and a “blooming” of pathobionts. (This thesis)
Intestinal surgeries and disease location greatly influence the microbiome composition in patients with IBD. (This thesis) The fecal microbiome has great potential as a biomarker and could be used to help distinguish patients with IBD from patients with irritable bowel syndrome. (This thesis)
IBD sub-phenotypes are related not only to changes in bacterial composition, but also to changes in absolute bacterial abundance. (This thesis)
The effects of environmental factors on gut microbiota composition dominate over the effects of genetics.
Nonetheless, the study of IBD cohorts can help reveal associations between host genetics and gut microbial composition. (This thesis)
Commonly used medication affects the gut microbiota. (This thesis)
In the future, the combination of sequencing data with quantitative measurements of the gut microbiota will help to improve the comparability of different microbiome studies.
Studies involving the perturbation of the gut microbiota and the estimation of the ecosystem resilience are needed in order to identify the key characteristics of a “healthy” microbiota.
The gut microbiota is not an organ: it is an ecosystem. This notion is essential for designing therapeutic strategies that modify the gut microbiota.
“Science and everyday life cannot and should not be separated.”
Rosalind Franklin
“Words are never ‘only words’; they matter because they define the contours of what we can do.”
Slavoj Žižek
“Sono pessimista con l’intelligenza, ma ottimista per la volontà.”
Pessimism of the intellect, optimism of the will.
Antonio Gramsci
“Jongens, stop met luisteren naar Arnau.”
People, stop listening to Arnau.
Valerie Collij
CHAPTER 1:
INTRODUCTION
1. Gut microbiota
2. How to study the gut microbiota:
methods and cohorts used in this thesis
3.Research goals and outline of the thesis
8
10 14
18
26
58
84
118 CHAPTER 2
Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease CHAPTER 3
Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome
CHAPTER 4
Whole exome sequencing analyses reveal gene-microbiota interactions in the context of inflammatory bowel disease
CHAPTER 5
Combining absolute quantification of faecal bacteria with
metagenomic sequencing data improves characterization of the gut microbiome in patients with Crohn’s disease
PART I:
MICROBIOME IN GASTROINTESTINAL DISORDERS
Table of contents
Understanding the gut ecosystem:
bugs, drugs & diseases
1. Discussion
2. Future perspectives 3. Final remarks
Nederlandse samenvatting Resumen español
Resum català Curriculum vitae Acknowledgments
230
231 238 246
254
256 259 262 266 27o 138
166
188
214 CHAPTER 6
Pharmacomicrobiomics: a novel route towards personalized medicine?
CHAPTER 7
Proton pump inhibitors affect the gut microbiome
CHAPTER 8
Impact of 41 commonly used drugs on the composition, metabolic function and resistome of the gut microbiome
CHAPTER 9
Analysis of 1135 gut metagenomes identifies sex-specific resistome profile
PART II:
MEDICATION USE AND MICROBIOME SIGNATURES
CHAPTER 10:
DISCUSSION AND FUTURE PERSPECTIVES
APPENDICES
Introduction
Microorganisms are everywhere, including in our body. One of the most important organs serving as a reservoir for bacteria is the gut. The microorganisms present there, however, are not just passive travelers. Although microbes have traditionally
been associated to infections, it is becoming clear that a balanced collection of these in the gut is a key factor for human health.
To contextualize the content of this thesis, in this introduction I will summarize the main aspects of microbiome research. I first briefly describe the evolution of this research field, then shift my focus to the relevance of gut microbiota in human health, and end by describing the methodology used in this
thesis, highlighting its advantages and its technical challenges and limitations.
Chapter 1
Microbiology research is living a revolution
1. The availability of new high-throughput se- quencing technologies, together with a decrease in sequencing costs
2,3, has enabled the study of complex microbial ecosystems on a large scale
4–7. In consequence, the knowledge gained on microbial communities has increased exponentially over the last 10 years, fol- lowing a trend similar to that seen in the field of human genetics during the genome-wide association study (GWAS) era (fig 1).
Previously in microbiology, the identification and characterization of microbial species was limited by the capacity for isolating and culturing each microorganism in the lab.
Sequencing approaches, however, have allowed researchers to overcome these limitations:
it is now possible to identify multiple members of an ecosystem in a single sequencing experiment, including microbial species that are especially difficult to grow in the lab such as anaerobes or extremophiles (organisms found in habitats with extreme conditions)
8,9. From a clinical perspective, this revolution is improving our knowledge of the microbial communities in the human body and their implications in healthcare. It is, for example, providing us with a better characterization of pathogens and enabling the development of novel therapeutic options that target the microbiota. Moreover, the introduction of genetic sequencing and computational biology into the field of microbiology has established new ways of classifying microorganisms by improving the categorization of multiple microbial groups based on their genomic characteristics
10. It has led to a major expansion and re- structuring of the tree-of-life
11,12.
Technological changes come with a new vocabulary. In microbiology, terms like “micro- biome”, “metagenome” or “metagenomics”, and “microbiota” are being used more common- ly. However, mainly due the speed at which the field is evolving, the definitions of many of these novel terms are still under discussion
13. For example, studies based on amplicon sequencing data are sometimes wrongly referred to as metagenomic studies, and the in- distinct use of “microbiota” and “microbiome” has also been a source of confusion. In this thesis, the “microbiome” is defined as the microbial community as studied through the extraction and sequencing of genetic material in a sample.
1. Gut microbiota
The microbiome revolution
Introduction
Humans, as well as other species, have coevolved with their microbial guests, establishing complex symbiotic relations
14,15. It is estimated that we carry the same numbers of bacterial cells as human cells in our bodies
16, with the colon being one of the most dense microbial communities. Based on our current knowledge, the definition of a human being and the characterization of human health are changing: we are not only our cells and genes, we are also the microorganisms that we carry, which both determine and reflect our wellbeing.
Similar to what is currently known about the human genome, the microbiota community in our gut also show a high degree of individual uniqueness. From a population perspective, the gut microbiota composition shows a large variation between individuals
17,18, with the exception of a reduced group (estimated to be around 4% of the total microbial species detected in the gut) of highly prevalent bacteria that is commonly called “the core micro- biota”
19,20. The source of this variation is not well understood, and it most probably relies on a combination of multiple host and environmental factors
17,21. Some of these factors, such as diet or medication use, are discussed in this thesis, but the impact of many others is yet unknown.
Despite the inter-individual divergence in taxonomic compositions, the functional pro- files obtained through metagenomic sequencing do show similarity between different in- dividuals
17,22,23. This indicates that bacteria in the gut present a certain degree of functional redundancy, and that the role of the microbes is probably more relevant than the presence or absence of specific taxa. To exemplify this functional importance, intestinal microbes contribute to the host metabolism by degrading indigestible dietary compounds and syn- thesizing essential vitamins. Moreover, the gut microbiota enforces host immunity by play- ing a role in preventing enteric infections through regulation of the immune system, by maintaining the mucus barrier in the intestinal epithelium and by directly competing with pathogens for a niche
15,24,25.
The gut, therefore, can be defined as a complex ecosystem in which a constant cross-talk between the host and the microbiota is established. As a consequence, changes in the host can have an impact on the microbiota and, conversely, alterations on the microbial commu- nity can have implications for the host. The understanding of how the microbiota is related to the host, and of which mechanisms are involved in the maintenance and regulation of these relations, will bring a new perspective on human health and provide a better under- standing of diseases that opens up new possibilities for treatments.
The gut microbiota is established in the first years of life. Vertical transmission (from mothers to babies) and environmental exposure determine the composition of the early gut microbiome, which is characterized by a low-richness ecosystem dominated by Bi- fidobacterium species. By the age of 2 years, approximately, the gut microbiota composition becomes richer in species and tends to stabilize coarsely, with the majority of bacteria belonging to Firmicutes and Bacteroidetes phyla
26,27. The composition of species is not The relevance of the gut microbiota in human health
Evolution and dynamics of the human gut microbiota
The large inter-individual heterogeneity of the gut microbiota poses a challenge for defin- ing a “normal” or “healthy” microbiota and for determining the host mechanisms involved in its regulation. By defining which microbial factors are disrupted in disease, particularly in gastrointestinal disorders, we can ultimately define the key characteristics of the “healthy microbiome”. For example, the identification of common patterns of bacterial depletion in disease can pinpoint which species are key in maintaining health.
Irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) are two of the most common chronic gastrointestinal disorders. It is estimated that IBS affects between 10% and 20% of the world-wide population
34, while the incidence of IBD is ~0.3%, with a higher incidence in the western world
35. Although the pathogenesis of these two diseases differs, patients with IBD or IBS present with similar clinical manifestations that include pain, bloating and functional diarrhea. Consequently, invasive measures such as colonosco- py are needed to decide on the final diagnosis.
IBD is an inflammatory disorder of the gastrointestinal tract characterized by inter- mittent periods of relapse. It comprises two main phenotypes: ulcerative colitis (UC) and Crohn’s disease (CD). While patients with UC present with a continuous inflammation in the colon, patients with CD usually present with a patchy inflammation pattern along the gastrointestinal tract
36,37. Although the exact etiology of IBD is still unknown, it is con- sidered an immune-mediated disease in which subjects with a genetic susceptibility have a dysregulated immune response against the gut microbial ecosystem
38. In recent years, mul- tiple factors have been associated with the risk of developing IBD. GWAS have yielded more than 200 loci associated with the disease, and a large proportion of the identified ge- netic variants occur in regions with important immunological functions and those involved Intestinal diseases: a model to understand host-microbiome interactions
homogenous along the gastrointestinal tract: gradients of acidity as well as differences in oxygen concentrations are key determinants of the distribution of the bacterial species. For example, aerotolerant bacteria such as members of the Enterobacteriaceae family are more abundant in the small intestine, while Ruminococcaceae grow better in the colon thanks to its lower acidity and oxygen concentrations
28.
The microbial community in the gut is not fixed however: it experiences (disruptive)
fluctuations in time as a consequence of the host’s lifestyle, including their diet, medication
use and exposure to pathogens
29. To overcome these disruptions, the human gut microbial
ecosystem has resilience properties that help to (partially) restore the original composi-
tion
30,31. However, if the disruptions are severe, the health status of the host can be compro-
mised. The fact that changes in the gut microbiome composition have been associated with
a wide range of disorders and diseases, from colorectal cancer
32to neurological diseases
33,
supports this idea. It is therefore important to investigate how common lifestyle factors
can impact the microbiota and how disruptive patterns are related to the development of
different medical conditions.
in the interaction with commensal microbes
39,40. However, these findings are not sufficient to explain or predict the disease
41. Epidemiological studies have identified a large number of environmental features that increase the risk of IBD, such as Western diet, stress or the use of certain medications
42. Interestingly, these factors have also been associated with changes in gut microbiota composition.
In contrast to IBD, patients with IBS do not present with inflammation in the gut.
Due to the diversity and complexity of presentation of the patients, the diagnosis ‘IBS’
generally relies on the exclusion of other diseases. Thus, IBS can be classified in three pre- dominant subtypes dependent on its phenotype: constipation (IBS-C), diarrhea (IBS-D) and mixed (IBS-M)
43,44. Like in IBD, the risk factors or triggers of this disorder remain unknown, with stress, anxiety, pathogenic infections and diet all suggested as potential causes of IBS
45. Moreover, the heterogeneity of the presentation of IBS patients and the lack of good classification criteria have hampered the identification of genetic-risk loci
46. Recently, the study of targeted genetic regions involved in disaccharide metabolism have provided new evidence for the role of host genetics in the risk of IBS, linking genetics, diet and gut microbiota
47.
Despite the clinical and pathological differences between IBD and IBS, and despite our still limited understanding of their etiology, there is an increasing conviction among researchers and clinicians that the gut microbiota has a central role in the development and progression of these gastrointestinal disorders. For example, studies on the gut microbial community in patients with IBD have identified a common signature that includes a de- crease in the bacterial richness and an increase of aerotolerant species at the detriment of anaerobic microbes
48–50. The in-depth study of these two diseases will bring a better under- standing of their pathology and benefit patients through development of new diagnostics tools and/or treatment options. Furthermore, as explained above, it will help us define the key characteristics of a healthy gut microbiota.
Currently, a typical microbiome experiment can be described in four main steps: sampling, extraction of genetic material, sequencing and bioinformatics processing
51,52. The protocols used in each of these steps determine which parts of the ecosystem will be captured in the posterior prediction (figure 2). The study of microbial ecosystems using culture-free methods comes with new laboratory and computational challenges. The lack of previous knowledge on the content of the sample complicates the assessment of the quality of mi-
2. How to study the gut microbiota:
methods and cohorts used in this thesis
From sampling to ecosystem characterizations:
the challenges and limitations of microbiome protocols
crobiome isolation protocols. Limiting sources of biases is, therefore, essential in every microbiome study.
During sample collection it is important to maintain the microbial environment. With- in a sample, different species have complex relations of mutualism and competition, and disruption of the environment during the sampling will alter the microbial composition.
For example, exposure to oxygen during sampling should be minimal when studying an anaerobic ecosystem. If not, anaerobic species will diminish and aerotolerant microbes will overgrow. Moreover, the use of preservative buffers or the temperature at which the sample will be stored can introduce biases in the microbial community composition
53–56.
Once a sample is collected and correctly stored, the genetic material, either DNA or RNA, needs to be extracted from each cell before sequencing. Currently used extraction protocols show a selection bias towards certain microorganisms. DNA isolation protocols often rely on cell wall characteristics that vary across microorganisms: a virus capsid differs from a eukaryotic membrane, but also among bacteria, species can show gram-positive or gram-negative cell wall characteristics. Moreover, cell lysis is a balanced job: soft protocols will select for certain cell types, while severe lysis protocols will potentially damage the DNA of species with weak cell walls. The optimal protocol for each microbiome study will therefore depend on the microorganisms of interest, the number of samples and the sequencing technology to be used
57–60. When analyzing microbiome data, one should be aware of the selection bias of the isolation method used.
Furthermore, sequence characteristics will have a major impact on the subsequent bio- informatics steps in the microbiome analysis. The lengths of the sequenced reads
61and the sample coverage (number of generated reads)
52determine the capacity to study low abun- dance microbial species and to recover full genomes or the ability to study specific char- acteristics of the ecosystem, such as the presence of antibiotic or virulence mechanisms.
In this thesis, two sequencing approaches were used: 16S ribosomal RNA (rRNA) gene sequencing and shot-gun metagenomic sequencing. The first method is based on the char- acteristics of the ribosomal gene as a universal marker gene for bacteria and has been extensively used in the field of microbiology
62. The 16S rRNA gene encodes for one of the small subunits of the ribosome, which is present in almost all the bacteria and archaea spe- cies. The gene can be divided into conserved regions, which show low mutation rates in the evolutionary tree, and hypervariable regions, nine genetic regions that are species-specific.
The conserved regions are used for the design of sequencing primers, while the hypervar- iable regions allow for the classification and identification of archaeal and bacterial spe- cies
63. The second method, shot-gun metagenomic sequencing, is based on the untargeted sequencing of all the genetic content present in a sample. Consequently, as compared to 16S rRNA gene sequencing, a higher resolution of microbial profiles can be achieved.
Metagenomic sequencing allows users to not only identify taxa, as with 16S sequencing,
but also strains from the same microbial species
64. Using the diversity of microbial genes
that can be identified through metagenomics studies, the functional potential of the mi-
crobial ecosystem can be esitimated
65,66and important features of this ecosystem, such as
antibiotic resistance and virulence mechanisms, can be revealed
67. In comparison with 16S
sequencing, however, this technique is more expensive and requires more complex post-se-
quencing computational analyses.
The final step of the microbiome workflow, the computational part, is meant to translate the sequencing output to biology. Several bioinformatic tools and pipelines are currently available for this purpose
68–70, but there is still no consensus on best practice
71,72. An es- sential step is quality control of the sequencing data by removing low quality sequences, sequencing artifacts or unwanted sequences such as reads originating from the host ge- nome. Subsequently, microbiome characterization can be done in multiple ways, which will depend heavily on the known features of each microbial species. For microbial iden- tification, 16S sequencing studies usually rely on the homology between sequencing reads and the hypervariable regions in a reference database
73. In metagenomic studies this can be done either by identifying subsets of marker genes
74,75or by aligning sequences to pub- licly available microbial genomes
76,77. Finally, shot-gun sequencing enables reference-free approaches
78–80that can potentially lead to the discovery of new species and microbial features. However, these tend to be computationally intensive and are still not optimized for large cohorts.
To conclude, the discovery of relevant microbial features is highly dependent on the quality of the data. Thus, accurate and standardized protocols are necessary in order to study the gut microbiota.
As highlighted in the previous sections, both the study of complex diseases and the study of the gut microbiota require well-characterized cohorts. The availability of large popula- tion-based databases, such as the UK Biobank and LifeLines, have boosted research by providing a resource for generating and testing new hypotheses and, no less important, have facilitated the replication of findings in independent cohorts. Population cohorts, however, are of limited use in the study of specific health disorders. The size of the bio- bank and the prevalence of each disorder within it will determine the number of samples available for the study of each condition. Moreover, every disease has phenotypical char- acteristics that are rarely captured in standard questionnaires. In the case of the study of the gut microbiota in the context of intestinal disorders, disease-specific factors like use of medication, disease behavior or surgery are key factors to consider in the analysis. Thus, disease-specific cohorts are needed that connect different types of biomaterials and bio- logical measurements for each participant with a detailed information of their lifestyle and their clinical records.
In this thesis, three cohorts were used for the study of the gut microbiota: a popula-
tion-based cohort, a clinical IBS cohort and a cohort of patients with IBD. The population
cohort consisted of a subset of participants of the LifeLines biobank (LifeLines Deep
81,
n=1539) in which extensive phenotype information and different layers of molecular and
genetic data were measured for each participant. The clinical IBS cohort was assembled
by our collaborators at Maastricht University Medical Center
82and consists of patients
with IBS diagnosed by a gastroenterologist and a group of matched controls without gas-
trointestinal disorders (n=336). The cohort of patients with IBD was obtained from the
Biobanks are an essential resource for biomedical research
1000IBD cohort (n=544). The 1000IBD cohort is an initiative from University Medical Center Groningen to create a disease-specific biobank for patients with IBD
83. Informa- tion about lifestyle, IBD-relevant clinical parameters, genetics and microbiota measure- ments is being collected for more than 1000 different participants. Importantly, in order to reduce batch effects, samples for microbial profiling in all the three cohorts were collected and processed using the same protocols.
The work presented in this thesis aims to provide a characterization of the changes in the gut microbiota of patients with IBD that leads to a better understanding of the disease.
In addition, I focus especially on the impact of IBD clinical sub-phenotypes and medi- cation use on the gut microbial ecosystem.
In chapter 2 we describe the gut microbial signatures associated to IBD. We characterize the microbial composition using 16S rRNA gene sequencing in a group of patients with IBD from the 1000IBD cohort and compare it to the composition in a subset of healthy individuals of the Dutch population cohort (LifeLines Deep). We then explore the genetic susceptibility to IBD and its relation to microbial features.
To understand the consequences of microbial changes in the context of IBD, in chapter 3 we make use of metagenomic shot-gun sequencing. Here we describe the changes in the metabolic potential and explore important microbial characteristics such as virulence and antibiotic resistance. We also catalogue changes in the gut ecosystem in patients with IBS and show, as a proof-of-concept, that the microbiome can be used as a diagnostic tool to discriminate patients with IBD and IBS.
The combination of metagenomic profiles of the gut microbiota with whole exome se- quencing was used to determine the host-microbiota interplay. Under the hypothesis that host genetics can influence the microbial composition in the gut, in chapter 4 we correlate microbial features to the genetic variants, including rare variants and polymorphisms that effect the structure of proteins.
In chapter 5 we combine metagenomic sequencing with quantitative measures of the gut microbiota. In this chapter we link changes in the number of bacteria in a sample to dif- ferent phenotypic characteristics of patients with Crohn’s disease. Moreover, we argue that the integration of quantitative measurements with sequencing data can benefit the analysis of the human microbiome.
In the second part of this thesis I focus on the relation between medication use and the gut microbiota. In chapter 6 we present a literature review on this topic that introduces the bidirectionality of the relation: microbiota can impact the efficiency and toxicity of certain drugs, but microbiota can also be influenced by chemical compounds derived from medication.
3. Research goals and outline of the thesis
We report a strong association between the microbial composition in the gut and the use of proton-pump inhibitors (PPIs). In chapter 7 we investigate this relation by performing a meta-analysis on the three cohorts described earlier. With the aim of expanding the re- search done on PPIs to other medication categories, in chapter 8 we present the results of the association analyses between almost 40 medication categories and the gut microbiota composition.
Although the biological sex of the participants is added as a correcting covariate in all the analyses in the previous chapters, few studies have highlighted the differences between the gut microbiota in males and females. Chapter 9 includes an in-depth description of the sex-microbiota associations found in a Dutch population cohort and reveals interest- ing links between microbiome signatures and sex-specific patterns in the prescription of medication.
In the concluding chapter, I provide a broader perspective on the main topics presented in this thesis and discuss the impact and limitations of our research in the context of the evolution of the microbiome field during the time period of the realization of this thesis.
As last, I describe my personal point of view on the future directions in the study of the
gut microbiota and its future implications in the diagnosis and treatment of inflammatory
bowel diseases and irritable bowel syndrome.
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Interplay of host genetics and the gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease
Floris Imhann*, Arnau Vich Vila*, Marc Jan Bonder, Jingyuan Fu, Dirk Gevers, Marijn C. Visschedijk, Lieke M. Spekhorst, Rudi Alberts, Lude Franke, Hendrik M. van Dullemen, Rinze W.F. Ter Steege,
Curtis Huttenhower, Gerard Dijkstra, Ramnik J. Xavier, Eleonora A.M. Festen, Cisca Wijmenga, Alexandra Zhernakova
#, Rinse K. Weersma
#*Shared first authors / #Shared last authors.
Adapted version of:
Imhann F, Vich Vila A, Bonder MJ, et al Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel
disease Gut 2018;67:108-119.
Chapter 2
Abstract
Patients with inflammatory bowel disease (IBD) display substantial heterogeneity in clinical characteristics. We hypothesize that individual differences in the complex interaction of the host genome and the gut microbiota can explain the onset and the heterogeneous presentation of IBD. Therefore, we performed a case-control analysis of the gut microbiota, the host genome and the clinical phenotypes of IBD.
Stool samples, peripheral blood and extensive phenotype data were collected from 313 patients with IBD and 582 truly healthy controls, selected from a population cohort. The gut microbiota composition was assessed by tag- sequencing the 16S rRNA gene. All participants were genotyped. We composed genetic risk scores from 11 functional genetic variants proven to be associated with IBD in genes that are directly involved in the bacterial handling in the gut:
NOD2, CARD9, ATG16L1, IRGM and FUT2.
Strikingly, we observed significant alterations of the gut microbiota of healthy individuals with a high genetic risk for IBD: the IBD-genetic risk score was significantly associated with a decrease in the genus Roseburia in healthy controls (FDR 0.017). Moreover, disease location was a major determinant of the gut microbiota: the gut microbiota of patients with colonic Crohn’s disease (CD) is different from that of ileal patients with ileal CD, with a decrease in alpha diversity associated to ileal disease
(p = 3.28 x 10
-13).
We show for the first time that genetic risk variants associated with IBD influence
the gut microbiota in healthy individuals. Roseburia spp are acetate-to-butyrate
converters and a decrease has already been observed in patients with IBD.
Inflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder of the gastrointestinal tract. In CD, inflammation can occur throughout the gastrointestinal tract whereas, in UC, inflammation is confined to the mucosal layer of the colon. The clinical characteristics of IBD vary greatly between individuals with respect to disease location, disease activity and disease behaviour. The origin of this heterogeneous clinical presentation remains poorly understood
1,2.
The pathogenesis of IBD consists of an exaggerated immune response in a genetically susceptible host to the luminal microbial content of the gut. Driven by rapidly evolving genotyping and next generation sequencing technologies, tremendous progress has been made in deciphering the host genomic landscape of IBD
3,4. Systems biology approaches to genomic and biological data clearly show the importance of the interaction between the host genome and the microbial exposure in the gut
5. Moreover, known and presumed epidemiological risk factors for developing IBD such as mode of birth (vaginal vs. caesarean section), breast feeding, smoking, hygiene, infections, antibiotics, diet, stress and sleep pattern are all known to cause microbial perturbations, suggesting a key role for the gut microbiota in the pathogenesis of IBD
6–9.
Previous studies have shown a reduced biodiversity in the gut microbial composition of patients with IBD, characterized by a reduction of known beneficial bacteria, such as Faecalibacterium prausnitzii, Roseburia intestinalis and other butyrate-producers, and an increase of pathogens or pathobionts, e.g. adherent-invasive Escherichia coli and Shigella species of the Enterobacteriaceae family. However, these studies used a relatively small number of controls, who were usually selected from the patient population of the gastroenterology department after excluding those with IBD
10. Because recent gut microbiome research has shown significant effects of stool consistency and functional complaints on the gut microbiota
11–13, previous results could have been influenced by their method of selection of controls.
While the main composition of the gut microbiota in CD has been studied extensively, the composition of the gut microbiota in patients with UC has received less attention
10,14,15. Furthermore, the relationship between the gut microbiota and the clinical characteristics of IBD, including disease activity, disease duration and disease behaviour has only been studied in an exploratory manner.
Recent studies have begun to unravel the complex interaction of host genetics and the gut microbiota. These links between specific genetic variants and the abundance of specific bacteria are called microbiota quantitative trait loci (microbiotaQTLs).
Twin studies show that the abundances of bacterial families Ruminococcaceae and
Background and aims
Lachnospiraceae containing butyrate-producers and acetate-to-butyrate converters are, to a certain degree, heritable
16–18. Animal studies in mice specifically designed to discover microbiotaQTLs show the influence of genomic loci on several microbial genera
19. Moreover, gut microbiota similarities in twins both concordant and discordant for IBD have been shown in several studies, further suggesting host genetics can influence the gut microbiota
20–22. Furthermore, preliminary data show that specific variants of the NOD2 gene are associated with changes in the abundance of the Enterobacteriaceae family in patients with IBD
23.
We hypothesize that the large heterogeneity between patients with IBD is likely to result from individual differences in the complex interaction between the host genome and the gut microbiota. Therefore, improving our knowledge of this interaction is crucial for our understanding of the pathogenesis of IBD
14. So far, very few studies have been able to elucidate this interaction in an integrated manner.
Here, we present a large single-centre case-control analysis of the luminal gut microbiota, the host genetics and clinical phenotypes of both CD and UC. To ensure optimal data quality, we adopted a rigorously standardized approach to collect and process fresh frozen faecal samples of 313 IBD patients from a single hospital in the North of the Netherlands and 582 truly healthy controls from the same geographical area. For all individuals, extensive clinical data, laboratory and endoscopic findings were collected. In addition, host genomic risk variants and risk scores were obtained in both the patients with IBD and the healthy controls to analyse host genomic influences on the gut microbial composition.
The cohort consists of 313 patients with IBD (188 patients with CD, 107 patients with UC and 18 patients with IBD intermediate/IBD undetermined (IBDI/IBDU) and 582 healthy controls selected from the population cohort LifeLines-DEEP (selection criteria can be found in the supplementary appendix)
24. CD patients were younger than healthy controls (41.3 versus 45.9 years; p = 1 x 10
-4, WMW-test) while patients with UC were not older than healthy controls (p = 0.32, WMW-test). At the time of sampling, 81 patients with IBD (25.8%) had active disease, defined as an HBI of higher than 4 in patients with CD or an SCCAI-score higher than 2.5 in UC patients.
Of the IBD patients, 23.7% had used antibiotics within the last 3 months. PPI use was more frequent in patients with IBD (24.5%) than in healthy controls (4.7%) (p <
0.001, X
2-test). Extensive information on all clinical characteristics and medication use is presented in Table 1.
Results
The clinical characteristics of patients with IBD and the selection
of healthy controls
The predominant phyla in both patients with IBD and healthy controls were Firmicutes (73% in patients with IBD, 75% in healthy controls), Actinobacteria (9% in patients with IBD, 13% in healthy controls) and Bacterioidetes (14% in patients with IBD, 8%
in healthy controls). Clostridia was the most abundant class (64% in patients with IBD, 68% in healthy controls). An overview of the abundances at all taxonomic levels can be found in Supplementary Table S1.
Alpha diversity
A statistically significant decrease in the Shannon Index was observed in patients with IBD compared to healthy controls as depicted in Supplementary Figure S1 (p = 5.61 x 10
-14, Wilcoxon test and figure 1).
Principal Coordinate Analysis
The differences in gut microbial composition between patients with IBD and healthy controls were also observed in the PCoA-analysis. Statistically significant differences were found in the first three components (PCoA1 p = 2.62 x 10
-68, PCoA2 p = 0.033, PCoA3 p = 1.50 x 10
-10, Wilcoxon test). The gut microbiota of healthy controls clustered together, while the gut microbiota of patients with IBD were more heterogeneous, partially overlapping the healthy controls. The shape of the PCoA-plot is mainly explained by disease location and the Shannon Index (see results below) as depicted in Figure 2A-2D.
The role of 11 functional genomic variants associated to IBD in the genes NOD2, CARD9, ATG16L1, IRGM and FUT2 was investigated. In the unweighted analysis in healthy controls, a higher number IBD risk alleles was associated with a decrease in the abundance of the genus Roseburia of the phylum Firmicutes (FDR = 0.017) as depicted in Figure 3. In patients with IBD as well as subsets of patients with IBD (patients with CD, patients with UC, patients with ileal CD, patients with ileocolonic CD and patients with colonic CD) neither the single genetic risk variants, the HLA-DRB1*01:03 haplotype nor the weighted or unweighted composite scores of genetic risk alleles showed any statistically significant effect on the gut microbiota composition. All results of the analyses with the risk scores of 11 SNPs can be found in Supplementary Table S3. Risk scores including all 200 IBD risk SNPs did not show any significant relations with the gut microbiota composition.
Overall composition of the gut microbiota in patients with IBD and healthy controls
IBD genetic risk variants are associated to unfavourable gut
microbiota changes in healthy controls
Tab. 1
Clinical characteristics of patients with IBD and healthy controls (I)
Tab. 1
Clinical characteristics of IBD patients and healthy controls (II)
ANCA, Anti-neutrophil cytoplasmic antibodies; ASCA, Anti-Saccharomyces cerevisiae antibodies; BMI, Body Mass Index; CRP, C-reactive protein; CD, Crohn’s Disease; IBD, Infl ammatory Bowel Disease; IBDI, Infl ammatory Bowel Disease Intermediate; IBDU, Infl ammatory Bowel Disease Undetermined; SD, standard deviation; UC, Ulcerative Colitis.
