University of Groningen
Understanding the gut ecosystem: bugs, drugs & diseases
Vich Vila, Arnau
DOI:
10.33612/diss.102587978
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Vich Vila, A. (2019). Understanding the gut ecosystem: bugs, drugs & diseases. University of Groningen. https://doi.org/10.33612/diss.102587978
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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
Chapter 1
Microbiology research is living a revolution1. The availability of new high-throughput
se-quencing technologies, together with a decrease in sese-quencing costs2,3, has enabled the
study of complex microbial ecosystems on a large scale4–7. 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 characteristics10. It has led to a major expansion and
re-structuring of the tree-of-life11,12.
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 discussion13. 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 relations14,15. It is estimated that we carry the same numbers of bacterial
cells as human cells in our bodies16, 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 individuals17,18, 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 factors17,21. 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-dividuals17,22,23. 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 preventplay-ing 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 niche15,24,25.
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 phyla26,27. The composition of species is not
The relevance of the gut microbiota in human health
The large inter-individual heterogeneity of the gut microbiota poses a challenge for defin-ing a “normal” or “healthy” microbiota and for determindefin-ing 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 population34, while the incidence of IBD is ~0.3%, with a
higher incidence in the western world35. 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 tract36,37. 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 ecosystem38. 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 concentrations28.
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 pathogens29. To overcome these disruptions, the human gut microbial
ecosystem has resilience properties that help to (partially) restore the original composi-tion30,31. 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 cancer32 to neurological diseases33,
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 microbes39,40. However, these findings are not sufficient
to explain or predict the disease41. 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 medications42. 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 IBS45. Moreover, the heterogeneity of the presentation of IBS patients and the
lack of good classification criteria have hampered the identification of genetic-risk loci46.
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 microbiota47.
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 inde-crease of aerotolerant species at the detriment of anaerobic microbes48–50. 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 processing51,52. 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:
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 composition53–56.
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 used57–60. 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 reads61 and the
sample coverage (number of generated reads)52 determine 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 microbiology62. 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-cies63. 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 species64. Using the diversity of microbial genes
that can be identified through metagenomics studies, the functional potential of the mi-crobial ecosystem can be esitimated65,66 and important features of this ecosystem, such as
antibiotic resistance and virulence mechanisms, can be revealed67. 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 purpose68–70, but there is still no consensus on best practice71,72. 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 database73. In metagenomic studies this can
be done either by identifying subsets of marker genes74,75 or by aligning sequences to
pub-licly available microbial genomes76,77. Finally, shot-gun sequencing enables reference-free
approaches78–80 that 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 Deep81,
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 Center82 and 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
1000IBD cohort (n=544). The 1000IBD cohort is an initiative from University Medical Center Groningen to create a disease-specific biobank for patients with IBD83.
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.
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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