University of Groningen
Environment-host-microbe interactions shape human metabolism
Chen, Lianmin
DOI:
10.33612/diss.171839167
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Chapter 1
Introduction
The introduction is partially based on the review paper:
A system biology perspective on environment-host-microbe interactions
Lianmin Chen, Sanzhima Garmaeva, Alexandra Zhernakova, Jingyuan Fu &
Cisca Wijmenga.
Complex diseases such as cardiovascular disease (CVD), obesity,
inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D)
and cancers have become a major burden to public health and affect more than 20% of the population worldwide. The aetiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g. dietary habits), and by their interactions. Following the completion of human genome sequencing in 2003, numerous genome-wide association studies (GWAS) have generated a wealth of information on the genetic architecture of complex diseases, which has advanced the development of genomics-based medicine. While the success of GWAS has led to the development of “genomic medicine”, it has become increasingly clear that genetics can only explain a limited proportion of an individual’s risk of developing a complex, non-infectious disease. For instance, the heritability for T2D and Crohn’s disease are estimated at
about 6% and 20% 1, respectively, indicating that a large proportion of the
phenotypic variation is explained by environmental or other unidentified factors. In the past few years, the impact of the gut microbiome on the development of complex human diseases has increasingly been recognized. The human intestines are colonized by a vast number of bacteria, archaea, microbial eukaryotes and viruses that are as abundant as our somatic cells and are collectively known as the gut microbiome.
The microbes in our body are relevant for our health and disease
Human bodies harbour a diverse community of microbes that together compose the human microbiome. With the aid of two culture-independent next generation sequencing technologies, 16s rRNA sequencing and shotgun metagenomics sequencing (Box 1), our knowledge of the composition and functional properties of the human microbiome has been increasing rapidly over the past several decades. The Human Microbiome Project, completed in 2013, and other related projects have now characterized the composition of the human microbiome at 18 different body sites, characterized the inter-individual variation in the microbiome, provided reference genomes for nearly 3,000 microbial strains and constructed a catalogue for ~10 million
microbial genes 2. This work has provided a great reference source for human
microbial research.
The majority of human-inhabiting microbes are found in the human gut. This community, known as the gut microbiome, contains up to a 1000 different
species and can weigh as much as 200 grams in adults 3,4. The number of
bacterial cells in the gut microbiome is similar to the number of human cells
in the body, yet it carries 100 times more genes than the human genome 5.
Given its high diversity in composition and function, the gut microbiome
against xenobiotic toxins 8, while also strengthening our immune response
9. In line with this, a large body of evidence now supports a role for the gut
microbiome in our predisposition for various diseases, including obesity 10,
T2D 11, CVD 12, IBD 7,13, cancers 14,15, depression 16 and Parkinson disease
17. Moreover, the gut microbiome can also determine individual response to
diet 7 and medication 18. Given these roles, the gut microbiome has become
known as “the second human genome”. However, in comparison to the human genome, the gut microbiome can be modulated by various means, and this has led to it being considered an important player in personalized medicine. The human gut microbiome is thus emerging as an attractive therapeutic target for disease prevention and treatment.
Box 1. Terminology in human microbiome research
Microbiome:the collection of all genomes of microbes in an ecosystem.
16S rRNA sequencing: an analytical method for characterizing the microbiome based on sequencing the 16S rRNA gene of bacteria and archaea. Shotgun metagenomics sequencing: an analytical method for characterizing the microbiome based on sequencing of all DNA fragments.
Homeostasis: stability of the microbiome maintained via internal mechanisms of self-regulation that is resilient to external perturbation. Dysbiosis: imbalance or maladaptation of the microbiome.
Diversity: variety and variability of a microbial community. The most common measures used to characterize these features are alpha diversity and beta diversity.
Alpha diversity:a measure of the diversity of species or other taxa within a sample. Widely used alpha diversity measures include species richness, the number of taxa present in a sample and the entropy-based Shannon and Simpson indices.
Beta diversity: a measure that describes the difference in taxonomic composition between samples, which can be represented as a square distance matrix. Commonly used beta diversity measures are UniFrac distance and Bray-Curtis dissimilarity.
The gut microbiome is a complex trait
To study the potential of microbiome-modulating approaches in personalized medicine, we first need to bear in mind that the gut microbiome is itself a complex trait that can be affected by host genetics and exogenous factors,
and by their interactions. The colonization of the human gut begins at birth, after which it is shaped to become mature around three years of age, when its composition and diversity are similar to those found in adults. During the course of human life, gut microbiome composition can be affected by many perturbation factors and its status can change from homeostasis to dysbiosis through ageing or the development of disease (Figure 1). In the general population, up to 20% of the inter-individual variation in the gut microbiome
can be explained by various intrinsic and exogenous factors 19-22. It is also
consistently observed in humans and mice that environmental factors
dominate over host genetics in shaping the gut microbiome 19,21,23.
Figure 1. Development and dynamic changes of the gut microbiome over the course of human life. The colonization of the human gut begins at birth. It is then rapidly shaped over the first years of life to reach a mature state at the age of three, when its composition and diversity is close to that of an adult. Over the course of life, gut microbiome composition can be affected by many perturbation factors, including diet, medication use, lifestyle and host physiological status. The gut microbiome can thus show high inter-individual variation. Over a lifetime, the microbiome can preserve its homeostasis, but dysbiosis can also occur with ageing or during the development of disease, when diversity is significantly decreased.
Diet is one dominant environmental factor affecting the gut microbiome
23,24. The gut microbiome of vegans, for example, is rather different from that
of people with an omnivorous diet 20, and a western diet (i.e. one with high
calorie and high fat intake) is associated with a less diverse microbial ecology
than a diet with high fibre intake 7. Higher consumption of fruit, vegetables,
fibre and red wine has been linked to higher abundances of beneficial
Early development Homeostasis Dysbiosis
Div ersit y low high perturbation factors Microbial status Host status
bacteria including the butyrate-producing Faecalibacterium prausnitzii,
anti-inflammatory Clostridiales and mucin-degrading Akkermansia muciniphila
7,21. The impact of diet on the gut microbiome suggests that dietary invention
could be a means of improving microbial composition to improve human health. One recent dietary intervention study showed that a high-fibre diet can promote short-chain fatty acid (SCFA)-producing bacterial species, and these species sequentially exerted a beneficial effect on their host by lowering haemoglobin A1c level and diminishing metabolically detrimental
compounds, thereby alleviating T2D 25. These SCFA-producers can also be
promoted by metformin, a common drug in the treatment of T2D 11,26. Many
other prescribed medications have also been shown to affect gut microbiome
composition 7,19,21,27,28, including antibiotics 29-31, proton pump inhibitors
6,7, statins and laxatives 11,21,32.
The impact of host genetics on the gut microbiome is also emerging. Twin studies have estimated the heritability of individual bacteria and microbial
pathways in humans 22,33 and identified a proportion of gut bacteria
that are substantially heritable. The highest estimated heritability for a given species is up to 0.4, which is comparable to the heritability of many common diseases. Interestingly, several heritable taxa and pathways are also associated with complex traits. For instance, the most heritable bacteria
taxa (Christensenellaceae, Archaea, Tenericutes and Bifidobacteriaceae)
are associated to traits including body mass index (BMI) and bacterial biosynthesis of branched-chain amino acids, which are both also linked to
insulin resistance 34. These observations strongly suggest that host microbe
interactions have a role in the development of complex traits and diseases. Several GWAS of >1000 samples had identified genetic loci associated to
microbial diversity, species abundance and bacterial pathways 35-37. Although
there was limited overlap in the associated loci due to the heterogeneity
of the statistical methods used and the relatively small samples sizes 38,
the identified loci converged toward several common processes, including innate immunity, metabolism and food processing. In particular, consistent
associations were found for C-type lectin genes in independent human 35,36
and animal 39-41 studies and for the lactase gene (LCT locus) that affects
the abundance of milk fermenting Bifidobacteria 35,37,42, an association
found to be dependent on the consumption of milk products 35. To increase
the statistical power to discover more of these relationships, the MiBioGen consortium has now been established to analyse the genetics of microbiome in >19,000 subjects from 18 participating groups using a harmonised
methodology and analytical pipeline 43,44.
In addition to microbial interactions with diet and host genetics, the gut microbiome is an ecosystem in which microbes can exchange or compete for nutrients, signalling molecules, or immune-evasion mechanisms through
Enthusiasm has thus been rising to decipher these microbial interactions in
order to detect key microbes in health and disease 48,49. One way of doing
this is to create co-abundance networks based on correlations, a method that has the potential to study interactions between microbes and thereby to
generate hypotheses for experimental validation 48,49. Microbial taxonomic
interactions have recently been inferred in healthy individuals and in individuals with extreme longevity, gestational diabetes, Crohn’s disease
and colorectal cancer 50-55. These studies have identified microbial genera
that are potentially key in health and disease, e.g. Porphyromonas and
Bacteroides in gestational diabetes 53.
Environment-genetics-microbiome interactions in complex diseases
Increasing evidence of the impact of genetic and environmental factors on the gut microbiome has led to a paradigm shift in our perspective on the complexity of the development of complex diseases. The typical view of complex diseases was that these disorders resulted from multiple genetic factors and their interaction with environmental factors. Now, the gut microbiome has been included as a third factor that can affect susceptibility to complex diseases via its interaction with genetics and environment (Figure 2A). Understanding the causal role of the microbiome in this complex interaction is essential for the development of microbiome-targeting therapy for the prevention and treatment of complex diseases. Such complex interactions can fall into three global scenarios: an additive model, a mediator model, or an interaction model.
Figure 2. Complex environment-genetics-microbiome interactions in complex diseases. A. Genetics, environment, gut microbiome and their interactions can contribute to individual susceptibility to complex diseases. B. Additive model that assumes genetics, environment and the gut microbiome exert independent and additive effects on the susceptibility to complex diseases. C. Mediator model in which the gut microbiome mediates the effects of genetics and environment on complex disease susceptibility. D. Interaction model in which the impact of genetics and environmental factors on a complex disease may not occur via their direct effects on the gut microbiota but may depend on the gut microbiome and their interactions. In an additive model, the gut microbiome exerts an additive effect on the susceptibility to complex diseases, i.e. in addition to the known genetic and environmental factors (Figure 2B). The gut microbiome can explain extra inter-individual variation of a trait, suggesting that microbiome-targeting approaches may have a better control on a complex trait on top of other approaches of modulating genetic and environmental effects. For example,
one population-based microbiome study showed that gut microbiota explains an extra 4.5-6% of the variance in BMI and blood lipid levels and an additive model with age, sex and genetic risk factors can explain up to 25.9% of the
variation in HDL cholesterol level 56. Similar findings have been observed in
other studies in mouse and humans. In mice, the combined and integrated effects of diet, host background and gut microbiome drive dynamic changes
in faecal and plasma metabolites 24. In Israeli and Dutch human cohorts, the
gut microbiome and host genetics have been shown to be largely independent and a combination of both factors had a higher power to predict host
phenotypes 19.
In a mediator model, the gut microbiome mediates the effects of genetic and environmental factors on complex diseases (Figure 2C). Here, adding the gut microbiome to the prediction model may not explain extra inter-individual variation, but microbiome-targeting approaches can alleviate the impact of genetics and environmental factors on the susceptibility of complex diseases. This kind of causal model can be tested by transferring genetic- or diet-modulated faecal microbiomes from donors to receivers and assessing whether the corresponding phenotype was transmitted or not. Using this approach, the mediating role of the gut microbiome has been reported for the
increased susceptibility to NLRP3 inflammasome deficiency in non-alcoholic
steatohepatitis 57, the therapeutic effect of metformin in the treatment of
T2D and the protective effects of dietary capsaicin against obesity-associated
chronic low-grade inflammation 58.
In an interaction model, the impact of genetics and environmental factors
Genetics Environment Microbiome Complex diseases Genetics Environment Microbiome Complex diseases Genetics Environment Microbiome Complex diseases Genetics Environment Microbiome Complex diseases A B C D
on a complex disease will depend on the gut microbiome and on the interactions between all three (Figure 2D). Adding an interaction term with the gut microbiome to a prediction model could explain more inter-individual variation and improve power for phenotype prediction. Moreover, modulating the gut microbiome, or its products, could alleviate or enhance the genetic or environmental effects. For instance, individual response to PD-1/PD-L1 blockers or anti-CTLA4 immunotherapy have been found to
be dependent on the gut microbiome 14,15, very likely through its anabolic
functions, which can enhance systemic and anti-tumour immune response by increasing antigen presentation and improving effector T cell function. Moreover, the genetic susceptibility for IBD at the autophagy-related 16–like 1 (ATG16L1) and nucleotide-binding oligomerization domain–containing
protein 2 (NOD2) genes requires microbial triggers, e.g. the microbial
secreted outer membrane vesicles of the human commensal Bacteroides
fragilis 59, illustrating the importance of genetic-microbiome interaction in
the pathogenesis of IBD.
Metabolites are at the centre of environment-genetics microbiome interactions
Environment-genetics-microbiome interaction effects on complex disease are largely mediated through metabolites. There are thousands of metabolites within the human body, some of which are endogenously produced, e.g. bile acids and lipids, and some that are taken up from the environment, e.g. vitamins and SCFAs. Many of these molecules can affect our health by modulating processes like metabolism and immune responses that provide useful indicators of our health status. However, our knowledge about the origin of most of these molecules and why they vary in concentration between individuals is still very limited. Potential determinants of these molecules include, but are not limited to, the environment (e.g. diet), genetic variants and the gut microbiome. GWAS have identified over 150 loci related
to abnormal lipid levels, explaining around 40% of the total variation 60. Part
of the unexplained variance has been attributed to environmental factors
including diet, as foods act as a direct source of lipids 61,62. Other factors
that influence lipid metabolism include the gut microbiome 56, which plays
an important role in the digestion of different dietary components including fats and polysaccharides. Furthermore, genetics, environment and the gut microbiome can also interact with each other in metabolite regulation, as is the case for trimetlylamine oxide (TMAO). This molecule, which promotes the artery-narrowing disease atherosclerosis, is generated as a result of the metabolism - by both microbes and their host - of certain dietary compounds
that are abundant in red meat 63. However, the key determinants and
molecules that directly affect human health, investigating their determinants and how environment-genetics-microbiome interactions regulate them may help us to understand the aetiology of complex diseases and yield new clinical strategies for complex disease prevention and treatment.
Research aims and outline of this thesis
The overall aim of the studies presented in this thesis is to obtain a mechanistic understanding of the potential roles of the gut microbiome, in interaction with host genetics, environmental factors and with itself, to human metabolic health. For this purpose, I have made use of various layers of “omics” datasets (metagenomics, metabolomics and genetics) in combination with the extensive phenotypic information that has been generated for a unique series of population-based prospective cohorts and patient cohorts.
In Chapter 1, I introduce the state of knowledge regarding the roles of the gut microbiome and its interactions with host genetics and environmental factors in human health and disease at the onset of the project. We then realized that description of differential microbial taxonomies between healthy subjects and patients failed to capture the functional potential of the gut microbiome. In addition, the gut microbiome itself is a complex trait that can interact with host genetics, various exogenous factors (including diet components and the use of certain drugs) and interactions between microbes. Thus, we pinpointed the importance of integrating multiple layers of functional omics information generated for several prospective human cohorts in order to understand how the gut microbiome interacts with the host, e.g. which signalling molecules are involved and how environmental factors modulate this interaction in human health and disease. With this purpose in mind, the five research chapters were designed to investigate the functional roles of the gut microbiome in humans, particularly in relation to CVD, obesity and its co-morbidities, and IBD.
Chapter 2 assesses microbial contributions to plasma lipid concentrations and composition in relation to CVD. Altered gut microbial composition has been linked to CVD, but its functional links to lipid metabolism of the host in relation to CVD development have remained unclear. To systematically assess functional links between the microbiome and plasma lipid profiles, we determined metagenomics-based microbial associations between more than 200 plasma lipidomic traits and microbial species and pathways in the population-based Lifelines-DEEP cohort and a clinical obesity cohort (300OB). This study represents the largest metagenome-based association study on plasma lipids and microbiome relevant to CVD risk and cardiometabolic phenotypes in humans to date. This work identified
novel bacterial species and pathways that associate to specific lipoprotein subclasses and revealed functional links between the gut microbiome and cardiovascular health of the host.
Chapter 3 goes beyond microbial composition-lipid associations by linking bile acids (BAs) to host lipid metabolism. BAs are amphipathic steroids that are exclusively synthesized in the liver (primary BAs) that can be structurally modified by the gut microbes (secondary BAs) and have been recognized as signalling molecules that act in the aetiologies of several components of metabolic syndrome, including dyslipidaemia. We profiled plasma and faecal BAs in the 300OB cohort and linked BAs with liver fat and plasma lipid parameters. We also characterized potential genetic and microbial determinants of BAs. Our study reveals novel genetic and microbial determinants of BAs in obesity and their relationship to disease-relevant lipid parameters that are important for the design of personalized therapies targeting BA signalling pathways.
Chapter 4 extends insights in microbial impact on plasma metabolite levels to various categories of (biologically-active) compounds generated by untargeted metabolomics platforms to allow for a broader understanding of the functional roles of the microbiome in host metabolic health. We generated profiles of 1,183 plasma metabolites from 1,368 Lifelines-DEEP participants for whom there is extensive information about dietary habits, gut microbiome and genetic background. We assessed metabolite associations to diet, genetics and the gut microbiome and evaluated the relative importance of diet, genetics and the gut microbiome to inter-individual metabolite variations. Additionally, we used Mendelian randomization and mediation
analysis to infer in silico causal relationships in diet-genetics-microbiome
interactions and putative mechanisms underlying complex diseases. Moreover, we assessed the potential of using diet, genetics and microbiome to predict an individual’s future metabolic health using a machine learning algorithm. Our results provide a systematic underpinning of the importance of diet-microbiome-genetics interactions in human metabolism that may act as a basis for developing strategies for the prevention or treatment of metabolic disorders.
Chapter 5 moves forward from cross-sectional to longitudinal analyses to assess whether alterations of the gut microbiome are related to changes in host health status. We present a long-term follow-up analysis of the gut microbiome in 338 participants of the Lifelines-DEEP cohort in which we compare microbiome composition in faecal samples taken 4 years apart. We characterized microbial stability and variations in gut microbial composition and genetic makeup. For temporally stable and individual-specific microbial features, we developed a method to use these microbial features as a “fingerprint” to distinguish samples taken from the same individual.
For microbial features showing large temporal variations, we linked their temporal variation to changes in the host’s clinical phenotypes and lifestyle. To gain further biological insights, we profiled plasma levels of 1,183
metabolites at both time points and used mediation analysis as an in silico
method to infer whether specific metabolites mediate the microbial impact on host health. Finally, we assessed the changes in antibiotic resistance and virulence factors in the human gut microbiome. This study highlights the importance of longitudinal sampling and of integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets for human metabolic disease.
Chapter 6 delves into microbial interactions and compares differential microbial co-abundances between healthy subjects and those with specific diseases. Although the gut microbiome is an ecosystem that involves complex interactions, our current knowledge of the potential roles of the gut microbiome in health and disease relies mainly on description of differential microbial abundances. However, little is known about the role of microbial interactions in the context of human disease. Here, we constructed and compared microbial co-abundance networks using 2,379 metagenomes from four human cohorts: an inflammatory bowel disease (1000IBD) cohort, an obese cohort (300OB) and two population-based cohorts (Lifelines-DEEP and 500FG). We compared the microbial taxonomic and functional networks under different host health conditions and, by doing so, identified potential key species and pathways that shape host-associated microbial networks. Our study provides evidence that altered microbial abundances in a specific disease can reflect their co-abundance relationship, which expands our current knowledge regarding microbial dysbiosis in disease.
Finally, Chapter 7 summarizes the main findings of this thesis and
provides my personal perspectives on how the booming field of “microbiome functional omics” should move forward.
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