The Human Gut Microbiome: From Association to Modulation

Our understanding of the human gut microbiome continues to evolve at a rapid pace, but practical application of thisknowledge is still in its infancy. This review discusses the type of studies that will be essential for translating microbiome research into targeted modulations with dedicated benefits for the human host. Our understanding of the human gut microbiome continues to evolve at a rapid pace, but practical application of thisknowledge is still in its infancy. This review discusses the type of studies that will be essential for translating microbiome research into targeted modulations with dedicated benefits for the human host. The human microbiota is the focus of one of the most dynamic research fields of our time, and most efforts are directed at the gastrointestinal tract, which harbors most of our microbes. In the past decade, our understanding of the organisms inhabiting our gut, their functionality, and their roles in human health and disease has advanced greatly, facilitated by fast technological development. Research on the gut microbiome is progressing through several steps that mirror those of other fields on other biological systems: (1) compilation of parts lists, (2) association of the system or its components to external factors, (3) establishment of functional knowledge, and (4) translation of that knowledge into applications. For the gut microbiome, this is reflected in the following developments. (1) The compilation of gut microbiome “parts lists” has been in full swing for more than a decade and is now almost complete, for the dominating prokaryotic domains, and at the resolution of genera and species. Several studies established the baseline structure and function of the microbiome—that is, lists of species and their genes—with major contributions from two large collaborative efforts of the MetaHIT (Li et al., 2014Li J. Jia H. Cai X. Zhong H. Feng Q. Sunagawa S. Arumugam M. Kultima J.R. Prifti E. Nielsen T. et al.MetaHIT ConsortiumAn integrated catalog of reference genes in the human gut microbiome.Nat. Biotechnol. 2014; 32: 834-841Crossref PubMed Scopus (423) Google Scholar, Qin et al., 2010Qin J. Li R. Raes J. Arumugam M. Burgdorf K.S. Manichanh C. Nielsen T. Pons N. Levenez F. Yamada T. et al.MetaHIT ConsortiumA human gut microbial gene catalogue established by metagenomic sequencing.Nature. 2010; 464: 59-65Crossref PubMed Scopus (4267) Google Scholar) and Human Microbiome Project (HMP) (Nelson et al., 2010Nelson K.E. Weinstock G.M. Highlander S.K. Worley K.C. Creasy H.H. Wortman J.R. Rusch D.B. Mitreva M. Sodergren E. Chinwalla A.T. et al.Human Microbiome Jumpstart Reference Strains ConsortiumA catalog of reference genomes from the human microbiome.Science. 2010; 328: 994-999Crossref PubMed Scopus (375) Google Scholar, Human Microbiome Project Consortium, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (3391) Google Scholar) consortia. Although novel diversity continues to be discovered, in particular at subspecies and strain level, and although a large fraction of microbial genes remains functionally uncharacterized, the census of the most dominant lineages in industrialized populations is arguably approaching completion (e.g., Zhou et al., 2018Zhou W. Gay N. Oh J. ReprDB and panDB: minimalist databases with maximal microbial representation.Microbiome. 2018; 6: 15Crossref PubMed Scopus (0) Google Scholar). (2) Using these parts list, a wealth of studies has probed for associations of the gut microbiome to disease, host factors, or the wider environment. As coverage and scope increase, these have been collectively referred to as metagenome-wide association studies (MWASs) (Wang and Jia, 2016Wang J. Jia H. Metagenome-wide association studies: fine-mining the microbiome.Nat. Rev. Microbiol. 2016; 14: 508-522Crossref PubMed Scopus (228) Google Scholar), in analogy to genome-wide association studies (GWASs). Recently, MWASs have reached population level, as large-scale cross-sectional studies (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (295) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.LifeLines cohort studyPopulation-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (0) Google Scholar) started to provide an integrated view of the relative impact of various host and environmental factors on microbiome composition (see Box 1).Box 1Why Can We Explain So Little of Observed Microbiome Variation?It has been a sobering observation that the combined effect size of different microbiome co-variates (both technical and biological) appears to be intriguingly low: in the Flemish Gut Flora Project and LifeLines-DEEP cohorts, the total non-redundant compositional variation explained was in the single digit percent range (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (295) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.LifeLines cohort studyPopulation-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (0) Google Scholar), the influence of host genetics has been reported in a similar range (Bonder et al., 2016Bonder M.J. Kurilshikov A. Tigchelaar E.F. Mujagic Z. Imhann F. Vila A.V. Deelen P. Vatanen T. Schirmer M. Smeekens S.P. et al.The effect of host genetics on the gut microbiome.Nat. Genet. 2016; 48: 1407-1412Crossref PubMed Scopus (114) Google Scholar, Turpin et al., 2016Turpin W. Espin-Garcia O. Xu W. Silverberg M.S. Kevans D. Smith M.I. Guttman D.S. Griffiths A. Panaccione R. Otley A. et al.GEM Project Research ConsortiumAssociation of host genome with intestinal microbial composition in a large healthy cohort.Nat. Genet. 2016; 48: 1413-1417Crossref PubMed Scopus (63) Google Scholar, Wang et al., 2016aWang J. Thingholm L.B. Skiecevičienė J. Rausch P. Kummen M. Hov J.R. Degenhardt F. Heinsen F.-A. Rühlemann M.C. Szymczak S. et al.Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota.Nat. Genet. 2016; 48: 1396-1406Crossref PubMed Scopus (103) Google Scholar) or below (Rothschild et al., 2018Rothschild D. Weissbrod O. Barkan E. Korem T. Zeevi D. Costea P.I. Godneva A. Kalka I.N. Bar N. Zmora N. et al.Environmental factors dominate over host genetics in shaping human gut microbiota composition.Nature. 2018; https://doi.org/10.1038/nature25973Crossref PubMed Scopus (101) Google Scholar), as have disease associations (Duvallet et al., 2017Duvallet C. Gibbons S.M. Gurry T. Irizarry R.A. Alm E.J. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.Nat. Commun. 2017; 8: 1784Crossref PubMed Scopus (2) Google Scholar). This could be due to the fact that (1) there are further important uncharacterized co-variates or the current ones are not measured accurately enough, that (2) associations of individual taxa are more relevant than global compositional shifts, that (3) intrinsic compositional constellations or stable states are resilient, that (4) true effects can only be detected at higher taxonomic resolution (Costea et al., 2017aCostea P.I. Coelho L.P. Sunagawa S. Munch R. Huerta-Cepas J. Forslund K. Hildebrand F. Kushugulova A. Zeller G. Bork P. Subspecies in the global human gut microbiome.Mol. Syst. Biol. 2017; 13: 960Crossref PubMed Scopus (0) Google Scholar), or that (5) neutral or stochastic processes (drift) have a stronger impact than previously appreciated. Moreover, (6) the gut microbiome’s intrinsic ecological dynamics and interactions, ecological succession, and ecosystem maturation (G. Falony, S. Viera-Silva, and J.R., unpublished data) are possible factors that have so far remained understudied, in part due to a lack of longitudinal data.Nevertheless, the current total quantification of external factors to microbiome variation is probably in the range of 10%–15%, and thus of significant enough effect size to be considered in clinical studies, as even some individual factors can confound associations. This likely remains true even if one extends the definition of MWAS to “microbiome-wide association studies” by also taking into account other data types, such as metatranscriptomic or metabolomic readouts, as recently suggested (Gilbert et al., 2016Gilbert J.A. Quinn R.A. Debelius J. Xu Z.Z. Morton J. Garg N. Jansson J.K. Dorrestein P.C. Knight R. Microbiome-wide association studies link dynamic microbial consortia to disease.Nature. 2016; 535: 94-103Crossref PubMed Scopus (146) Google Scholar). Therefore, the proper consideration of and stratification for known microbiome covariates as potential confounders will greatly improve the accuracy of MWASs but can also inform the interpretation of longitudinal and interventional datasets. It has been a sobering observation that the combined effect size of different microbiome co-variates (both technical and biological) appears to be intriguingly low: in the Flemish Gut Flora Project and LifeLines-DEEP cohorts, the total non-redundant compositional variation explained was in the single digit percent range (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (295) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.LifeLines cohort studyPopulation-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (0) Google Scholar), the influence of host genetics has been reported in a similar range (Bonder et al., 2016Bonder M.J. Kurilshikov A. Tigchelaar E.F. Mujagic Z. Imhann F. Vila A.V. Deelen P. Vatanen T. Schirmer M. Smeekens S.P. et al.The effect of host genetics on the gut microbiome.Nat. Genet. 2016; 48: 1407-1412Crossref PubMed Scopus (114) Google Scholar, Turpin et al., 2016Turpin W. Espin-Garcia O. Xu W. Silverberg M.S. Kevans D. Smith M.I. Guttman D.S. Griffiths A. Panaccione R. Otley A. et al.GEM Project Research ConsortiumAssociation of host genome with intestinal microbial composition in a large healthy cohort.Nat. Genet. 2016; 48: 1413-1417Crossref PubMed Scopus (63) Google Scholar, Wang et al., 2016aWang J. Thingholm L.B. Skiecevičienė J. Rausch P. Kummen M. Hov J.R. Degenhardt F. Heinsen F.-A. Rühlemann M.C. Szymczak S. et al.Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota.Nat. Genet. 2016; 48: 1396-1406Crossref PubMed Scopus (103) Google Scholar) or below (Rothschild et al., 2018Rothschild D. Weissbrod O. Barkan E. Korem T. Zeevi D. Costea P.I. Godneva A. Kalka I.N. Bar N. Zmora N. et al.Environmental factors dominate over host genetics in shaping human gut microbiota composition.Nature. 2018; https://doi.org/10.1038/nature25973Crossref PubMed Scopus (101) Google Scholar), as have disease associations (Duvallet et al., 2017Duvallet C. Gibbons S.M. Gurry T. Irizarry R.A. Alm E.J. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.Nat. Commun. 2017; 8: 1784Crossref PubMed Scopus (2) Google Scholar). This could be due to the fact that (1) there are further important uncharacterized co-variates or the current ones are not measured accurately enough, that (2) associations of individual taxa are more relevant than global compositional shifts, that (3) intrinsic compositional constellations or stable states are resilient, that (4) true effects can only be detected at higher taxonomic resolution (Costea et al., 2017aCostea P.I. Coelho L.P. Sunagawa S. Munch R. Huerta-Cepas J. Forslund K. Hildebrand F. Kushugulova A. Zeller G. Bork P. Subspecies in the global human gut microbiome.Mol. Syst. Biol. 2017; 13: 960Crossref PubMed Scopus (0) Google Scholar), or that (5) neutral or stochastic processes (drift) have a stronger impact than previously appreciated. Moreover, (6) the gut microbiome’s intrinsic ecological dynamics and interactions, ecological succession, and ecosystem maturation (G. Falony, S. Viera-Silva, and J.R., unpublished data) are possible factors that have so far remained understudied, in part due to a lack of longitudinal data. Nevertheless, the current total quantification of external factors to microbiome variation is probably in the range of 10%–15%, and thus of significant enough effect size to be considered in clinical studies, as even some individual factors can confound associations. This likely remains true even if one extends the definition of MWAS to “microbiome-wide association studies” by also taking into account other data types, such as metatranscriptomic or metabolomic readouts, as recently suggested (Gilbert et al., 2016Gilbert J.A. Quinn R.A. Debelius J. Xu Z.Z. Morton J. Garg N. Jansson J.K. Dorrestein P.C. Knight R. Microbiome-wide association studies link dynamic microbial consortia to disease.Nature. 2016; 535: 94-103Crossref PubMed Scopus (146) Google Scholar). Therefore, the proper consideration of and stratification for known microbiome covariates as potential confounders will greatly improve the accuracy of MWASs but can also inform the interpretation of longitudinal and interventional datasets. (3) Associations identified by MWASs are observational, can be indirect or confounded by underlying factors, and do not easily translate into causal links. However, for a functional understanding of a complex system such as the gut microbiome, it is necessary to connect parts lists (1D) to networks (2D) in a spatial (3D) and temporal (4D) context (Raes and Bork, 2008Raes J. Bork P. Molecular eco-systems biology: towards an understanding of community function.Nat. Rev. Microbiol. 2008; 6: 693-699Crossref PubMed Scopus (0) Google Scholar), and this requires adapted concepts (see below) and methodological approaches (see Box 2). Although the study of the microbiome’s taxa interaction networks (2D), i.e., the interactions between its parts (1D), is ongoing, the inference of species interactions from cross-sectional data remains challenging (Weiss et al., 2016Weiss S. Van Treuren W. Lozupone C. Faust K. Friedman J. Deng Y. Xia L.C. Xu Z.Z. Ursell L. Alm E.J. et al.Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.ISME J. 2016; 10: 1669-1681Crossref PubMed Google Scholar). This is in part because current readouts (fecal samples) are still mostly non-quantitative (Vandeputte et al., 2017cVandeputte D. Kathagen G. D’hoe K. Vieira-Silva S. Valles-Colomer M. Sabino J. Wang J. Tito R.Y. De Commer L. Darzi Y. et al.Quantitative microbiome profiling links gut community variation to microbial load.Nature. 2017; 551: 507-511Crossref PubMed Scopus (0) Google Scholar) and poorly reflect the spatial organization of the intestinal tract (3D). Moreover, interactions and microbiome function are dynamic, and in consequence, individual gut microbes and entire communities need to be studied in the context of time (4D), though longitudinal studies so far remain scarce. Perturbation experiments, in particular, enable the study of a system’s dynamics, both at the level of individual parts and the entire system. An increasing number of intervention studies adds to our functional understanding of the gut microbiome, but it remains unclear whether observed responses are generic, stratified, or indeed personal (see Box 3).Box 2Methodological Advances to Boost Microbiome ResearchMicrobiomics, as a research field, evolves at a breakneck pace, and this is certainly true with regard to methodological advances (see Mallick et al., 2017Mallick H. Ma S. Franzosa E.A. Vatanen T. Morgan X.C. Huttenhower C. Experimental design and quantitative analysis of microbial community multiomics.Genome Biol. 2017; 18: 228Crossref PubMed Scopus (6) Google Scholar for a recent review). Here we highlight recent developments that we expect to make a strong impact in the near future, enabling us to tackle new questions and further complementing the transition from observational to interventional study designs.Multi-omicsHigh-throughput 16S rRNA amplicon and whole-genome shotgun (WGS) metagenomic sequencing have boosted microbiome research for more than a decade, and these technologies continue to dominate the field. More recently, however, the taxonomic and functional census provided by metagenomics is increasingly complemented by readouts on activity, provided by metatranscriptomics, metaproteomics, and metabolomics (reviewed by Franzosa et al., 2015Franzosa E.A. Hsu T. Sirota-Madi A. Shafquat A. Abu-Ali G. Morgan X.C. Huttenhower C. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling.Nat. Rev. Microbiol. 2015; 13: 360-372Crossref PubMed Scopus (156) Google Scholar, Mallick et al., 2017Mallick H. Ma S. Franzosa E.A. Vatanen T. Morgan X.C. Huttenhower C. Experimental design and quantitative analysis of microbial community multiomics.Genome Biol. 2017; 18: 228Crossref PubMed Scopus (6) Google Scholar). Metabolomic analyses, in particular, have served as independent lines of evidence to confirm hypotheses generated in MWASs, for example confirming a link of microbial metabolism to cardiovascular disease (Wang et al., 2011Wang Z. Klipfell E. Bennett B.J. Koeth R. Levison B.S. Dugar B. Feldstein A.E. Britt E.B. Fu X. Chung Y.-M. et al.Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease.Nature. 2011; 472: 57-63Crossref PubMed Scopus (1708) Google Scholar) or the impact of gut microbiome metabolism on insulin sensitivity (Pedersen et al., 2016Pedersen H.K. Gudmundsdottir V. Nielsen H.B. Hyotylainen T. Nielsen T. Jensen B.A.H. Forslund K. Hildebrand F. Prifti E. Falony G. et al.MetaHIT ConsortiumHuman gut microbes impact host serum metabolome and insulin sensitivity.Nature. 2016; 535: 376-381Crossref PubMed Scopus (927) Google Scholar).Metatranscriptomic analyses provide a more direct readout on microbial gene expression profiles, and relating this information to baseline microbiome functional potential can reveal novel insights (see Abu-Ali et al., 2018Abu-Ali G.S. Mehta R.S. Lloyd-Price J. Mallick H. Branck T. Ivey K.L. Drew D.A. DuLong C. Rimm E. Izard J. et al.Metatranscriptome of human faecal microbial communities in a cohort of adult men.Nat. Microbiol. 2018; 106: 1Google Scholar, Schirmer et al., 2018Schirmer M. Franzosa E.A. Lloyd-Price J. McIver L.J. Schwager R. Poon T.W. Ananthakrishnan A.N. Andrews E. Barron G. Lake K. et al.Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.Nat. Microbiol. 2018; 7: 1Google Scholar for recent examples). The gut metaproteome, in contrast, has not been analyzed on a large scale, although a few pilot-sized studies exist (Erickson et al., 2012Erickson A.R. Cantarel B.L. Lamendella R. Darzi Y. Mongodin E.F. Pan C. Shah M. Halfvarson J. Tysk C. Henrissat B. et al.Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn’s disease.PLoS ONE. 2012; 7: e49138Crossref PubMed Scopus (0) Google Scholar, Heintz-Buschart et al., 2016Heintz-Buschart A. May P. Laczny C.C. Lebrun L.A. Bellora C. Krishna A. Wampach L. Schneider J.G. Hogan A. de Beaufort C. Wilmes P. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.Nat. Microbiol. 2016; 2: 16180Crossref PubMed Scopus (41) Google Scholar, Kolmeder and de Vos, 2014Kolmeder C.A. de Vos W.M. Metaproteomics of our microbiome - developing insight in function and activity in man and model systems.J. 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Experimental design and quantitative analysis of microbial community multiomics.Genome Biol. 2017; 18: 228Crossref PubMed Scopus (6) Google Scholar) start challenging common conceptions on the microbiome, e.g., on the relative importance of functional plasticity (Heintz-Buschart and Wilmes, 2017Heintz-Buschart A. Wilmes P. Human gut microbiome: function matters.Trends Microbiol. 2017; (Published online November 22, 2017. S0966-842X(17)30251-2)Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar).Quantitative Microbiome Profiling (QMP)Most microbiome studies rely on compositional data—relative abundances of taxa or genes are scaled by non-informative total library sizes, and compositionality effects may introduce false positive taxa-taxa or taxa-covariate associations (Faust and Raes, 2012Faust K. Raes J. Microbial interactions: from networks to models.Nat. Rev. Microbiol. 2012; 10: 538-550Crossref PubMed Scopus (554) Google Scholar, Friedman and Alm, 2012Friedman J. 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