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/lp/ou_press/a-system-biology-perspective-on-environment-host-microbe-interactions-oh9O1Y4WD7
- Publisher
- Oxford University Press
- Copyright
- © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com
- ISSN
- 0964-6906
- eISSN
- 1460-2083
- DOI
- 10.1093/hmg/ddy137
- Publisher site
- See Article on Publisher Site
Abstract
Abstract A vast, complex and dynamic consortium of microorganisms known as the gut microbiome colonizes the human gut. Over the past few decades, we have developed an increased awareness of its important role in human health. In this review we discuss the role of the gut microbiome in complex diseases and the possible causal scenarios behind its interactions with the host genome and environmental factors. We then propose a new analysis framework that combines a systems biology approach, cross-kingdom integration of multiple levels of omics data, and innovative in vitro models to yield an integrated picture of human host–microbe interactions. This new framework will lay the foundation for the development of the next phase in personalized medicine. Introduction Human bodies harbour a diverse community of microbes that together compose the human microbiome. With the aid of 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 characterized the composition of the human microbiome at 18 different body sites, characterized inter-individual variation in microbiome, provided reference genomes for nearly 3000 microbial strains and constructed a catalogue for ∼10 million microbial genes (1). 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 g in adults (2,3). 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 (4). Given its high diversity in composition and function, the gut microbiome greatly expands our capacity to metabolize food and drugs (5,6) and defence against xenobiotic toxins (7), while also strengthening our immune response (8). 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 (9), type 2 diabetes (T2D) (10), cardiovascular disease (11), inflammatory bowel disease (IBD) (6,12), cancers (13,14), depression (15) and Parkinson disease (16). Moreover, the gut microbiome can also determine individual response to diet (6) and medication (17). 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. 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 itself is 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 3 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 (Fig. 1). In general populations, up to 20% of the inter-individual variation in the gut microbiome can be explained by various intrinsic and exogenous factors (18–21). It is also consistently observed in humans and mice that environmental factors dominate over host genetics in shaping the gut microbiome (18,20,22). Figure 1. View largeDownload slide Development and dynamic changes of gut microbiome during the course of human life. The colonization of the human gut begins at birth. It is 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, 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. Figure 1. View largeDownload slide Development and dynamic changes of gut microbiome during the course of human life. The colonization of the human gut begins at birth. It is 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, 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 gut microbiome (22,23). The gut microbiome of vegans, for example, is rather different from that of people with an omnivorous diet (19) and a western diet (i.e. high calorie and high fat intake) is associated with a less diverse microbial ecology than a diet with high fibre intake (6). Higher consumption of fruit, vegetables, fibre and red wine has been linked to higher abundances of beneficial bacteria including butyrate-producing Faecalibacterium prausnitzii, anti-inflammatory Clostridiales and mucin-degrading Akkermansia muciniphila (6,20). 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 (24). These SCFA-producers can also be promoted by metformin, a common drug in the treatment of T2D (10,25). Many other prescribed medications have also been shown to affect gut microbiome composition (6,18,20,26,27), including antibiotics (28–30), proton pump inhibitors (5,6), statins and laxatives (10,20,31). 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 (21,32) and identified a proportion of gut bacteria that are substantially heritable. The highest estimated heritability for a given species is up to 0.4, 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 (33). These observations strongly suggest that host–microbe interactions have a role in the development of complex traits and diseases. To date, four genome-wide association studies (GWAS) of >1000 samples have identified genetic loci associated to microbial diversity, species abundance and bacterial pathways (34–36). Although there was limited overlap in the associated loci due to the heterogeneity of the statistical methods used and the relatively small samples sizes (37), the identified loci converged towards several common processes, including innate immunity, metabolism and food processing. In particular, consistent associations were found for C-type lectin genes in independent human (34,35) and animal (38–40) studies and for the lactase gene (LCT locus) that affects the abundance of milk fermenting Bifidobacteria (34,36,41), an association found to be dependent on the consumption of milk products (34). 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 harmonized methodology and analytical pipeline (consortium description paper in preparation). 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 (Fig. 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. View largeDownload slide 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. Figure 2. View largeDownload slide 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 of complex diseases, i.e. in addition to the known genetic and environmental factors (Fig. 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 high density lipoprotein cholesterol level (42). 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 (23). 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 has a higher power to predict host phenotypes (18). In a Mediator model, the gut microbiome mediates the effects of genetic and environmental factors on complex diseases (Fig. 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 microbiome 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 of NLRP3 inflammasome deficiency in non-alcoholic steatohepatitis (43), the therapeutic effect of metformin in the treatment of T2D and the protective effects of dietary capsaicin against obesity-associated chronic low-grade inflammation (44). In an Interaction model the impact of genetics and environmental factors on a complex disease will depend on the gut microbiome and on the interactions between all three (Fig. 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 genetic or environmental effects. For instance, individual response to PD-1/PD-L1 blockers or anti-CTLA4 immunotherapy has been found to be dependent on the gut microbiome (13,14), 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 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 (45), illustrating the importance of genetic–microbiome interaction in the pathogenesis of IBD. Moving from Association to Causality and Mechanism Environment–genetics–microbiome interactions in complex diseases could be much more complex than the three simple scenarios discussed above; different causal models can jointly form a complex model. Despite several illustrative examples, the underlying causal inference and mechanisms of environment–genetics–microbiome interactions in the development of complex diseases remain largely unexplored. Recently, we highlighted the importance of cohort studies in studying the aetiology of complex diseases in the post-GWAS era (37). Below we would like to further emphasize how integration of an approach using omics data, systems biology and genetics with a combination of other cutting-edge technologies in bacterial culture-omics and organ-on-chips can accelerate our understanding of causality and the mechanisms involved in host–microbe interactions (Fig. 3). Figure 3. View largeDownload slide An analysis frame that combines a cohort-based systems biology approach with individual-based in vitro models to study host–microbe interactions. Figure 3. View largeDownload slide An analysis frame that combines a cohort-based systems biology approach with individual-based in vitro models to study host–microbe interactions. Moving from metagenome to meta-omics In addition to omics data on the human genome, omics datasets have been emerging from the metagenome, including metatranscriptomics, metaproteomics and metametabolomics. Several decades of using deoxyribonucleic acid (DNA) sequencing to determine the differences in microbial composition between health and disease has produced increasing evidence on the dysregulation of microbial composition in diseases. In one study, a longitudinal analysis of both metagenome and metatranscriptomics in IBD patients showed that certain species pathways exhibit different changes on the transcription level compared to the DNA level (46). Similarly, a comparison between 372 human faecal metatranscriptomes and 929 metagenomes identified both a ‘house-keeping’ core of metatranscriptomes that is universally expressed over time and highly variable metatranscriptional activity that may reflect dynamic regulation of microbial composition in response to environmental perturbations (47). Metaproteomics and metametabolomics have been proposed as complementary approaches to studying the functional properties of the gut microbiome, and these methods combined have revealed species-specific metabolic shifts and variability in the gut microbiome of preterm infants and during the early years of development (48). Studies using mouse and other animal models have reported that early life exposure, host genetics and diet can affect gut microbiome and metabolome (49); diet can impact lipid metabolism in the gut (50); and microbial metabolites can further affect host development, hormonal signalling, behaviour and gut physicochemical conditions (51,52). Despite some technical challenges in data profiling, e.g. problems with the stability and reproducibility of microbial transcriptomics profiling and with the identification of proteomics and metabolomics based on mass-spectrometry data, meta-omics data has the potential to deliver a direct functional readout of the metagenome. Cross-kingdom integration Simultaneously profiling diverse omics data from the host genome and the metagenome, and incorporating these omics data, will substantially aid in our understanding of cross-kingdom regulation and interaction at molecular levels. One of the most outstanding technological challenges here is the statistical complexity of integrating heterogeneous ‘omics’ datasets (53). Commonly used statistical approaches may be applied to integrate host omics and meta-omics data, including co-abundance network analysis, Bayesian network analysis, mediation analysis and causal inference analysis. However, it has been well noted that the distribution of bacterial data often diverges from the normal distribution that many of these statistical methods assume. For instance, some bacterial species can be very abundant in some individuals but completely absent in others. Dealing with those ‘zeros’ may require a two-part model to deal with presence/absence and abundance levels separately (42) or the use of zero-inflated models (54,55). This complexity in distribution greatly increases the complexity of data analysis, particularly in complex multi-omics models. Secondly, given that the gut microbiome harbours 100 times more bacterial genes than the human genome, the number of factors under study exponentially increases when integrating meta-omics data with host omics data. The power issue thus becomes a major burden in minimizing the false discovery rate. Although we can increase sample size and conduct meta-analyses to combine association signals across multiple cohorts, for the time being the number of samples will continue to be far lower than the number of factors under study. Furthermore, additional microbiome features will soon add to the complexity of cross-kingdom interaction studies. The gut virome and phageome compositions, for example, are important regulators of bacterial abundance and function that have not yet properly investigated in the majority of microbiome studies (56). It is therefore essential to develop more advanced statistical algorithms and to take advantage of newly developed machine-learning algorithms and artificial intelligence methods to build models that can dissect the complexity of big data. Even with these challenges, integrating diverse omics data from the genome and metagenome offers a great opportunity to study the underlying causality. For example, each kind of meta-omics data can be treated as a complex trait and subjected to genetic analysis. We can then use genetic variants as the instrumental variables and apply a Mendelian randomization approach to investigate the causal relationship between the genome and metagenome. Innovative in vitro models for host–microbe interactions For obvious ethical and practical reasons, human omics data is largely based on blood samples. This has, however, greatly limited our mechanistic understanding on the interaction of gut microbiome with other human organs, including the intestine, the liver and the brain. Although mouse and other animal models have been used for these tasks, these animal models cannot fully mimic biological processes in humans. Nor can they take human genetic make-up into account, and thus cannot be used to study human genetics–microbe interactions. Over the past few years, organ-on-chip technology is emerging as a next-generation disease and drug model (57,58). In this new technology, human induced pluripotent stem cells (e.g. developed from urine renal tubular cells that can be collected non-invasively) can be further differentiated to different tissue cell types that can be used to construct organs-on-chips. One can imagine an analysis frame that combines (1) cohort-based systems biology studies using well-characterized human cohorts with (2) individual-based studies using innovative in vitro model systems to investigate host–microbe interactions in health and disease (Fig. 3). Two types of organs-on-chips, in particular, would be very interesting in this respect due to their direct interactions with the gut microbiome: gut-on-a-chip to study microbe–intestine interactions and liver-on-a-chip to study host–microbe metabolic interactions. With the recent advance of bacterial culturomics, around 80% of gut microbes can be cultured (59), enabling functional studies at both whole-composition level and single strain level. This will allow for the whole gut microbiome, a specific strain or its metabolic product to be applied to the organs-on-chips in order to assess the immune or metabolic response of human cells. Moreover, in such systems the genetic background (e.g. via CRISPR-CAS9) and/or gut microbiome (e.g. specific strains) can be modified to test causality and genetics–microbe interactions. In conclusion, the past several decades have witnessed an increased awareness and understanding of the role of the gut microbiome in human health and of its interactions with host genome and environmental factors. The gut microbiome is now emerging as an important player in personalized medicine. With the aid of well-characterized human cohorts and cutting-edge technologies, we are now on the verge of a major breakthrough in our understanding of host–microbe interactions that will lay the foundation for the development of the next phase of personalized medicine, a phase that coordinates and encompasses both the human genome and metagenome. 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 BrayCurtis dissimilarity. Acknowledgements We thank Kate Mc Intyre for editing the manuscript. Conflict of Interest statement. None declared. Funding The authors are supported by a joint PhD fellowship from China Scholarship Council (CSC 201708320268) and University of Groningen to L.C.; a PhD scholarship from the Graduate School of Medical Sciences, University of Groningen to S.G.; a Netherlands Organization for Scientific Research (NWO) Vidi grant (NWO-VIDI 016.178.056), a European Research Council (ERC) starting grant (ERC Starting Grant 715772) and a Rosalind Franklin Fellowship (University of Groningen) to A.Z.; an NWO-Vidi (NWO-VIDI 864.13.013) grant to J.F.; a CardioVasculair Onderzoek Nederland (CVON 2012-03) grant to A.Z. and J.F.; an ERC advanced grant (FP/2007-2013/ ERC grant 2012-322698), an NWO Spinoza prize (NWO SPI 92-266), the NWO Gravitation Netherlands Organ-on-Chip Initiative (024.003.001), the Stiftelsen Kristian Gerhard Jebsen foundation (Norway) and the RuG investment agenda grant Personalized Health to C.W. 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Journal
Human Molecular Genetics – Oxford University Press
Published: Aug 1, 2018