Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome

Significance Deep molecular phenotyping of individuals provides the opportunity for biological insight into host physiology. As the human microbiome is increasingly being recognized as an important determinant of host health, understanding the host–microbiome relationship in a multiomics context may pave the way forward for targeted interventions. In this study, we analyze gut microbial composition of 101 individuals over the course of a year, alongside clinical markers and serum metabolomics. We establish association between specific gut compositional states and host health biomarkers (e.g., of inflammation). Finally, we provide evidence for an apparent transition barrier between these compositional states. A deeper understanding of microbiome dynamics and the associated variation in host phenotypes furthers our ability to engineer effective interventions that optimize wellness. The Pioneer 100 Wellness Project involved quantitatively profiling 108 participants’ molecular physiology over time, including genomes, gut microbiomes, blood metabolomes, blood proteomes, clinical chemistries, and data from wearable devices. Here, we present a longitudinal analysis focused specifically around the Pioneer 100 gut microbiomes. We distinguished a subpopulation of individuals with reduced gut diversity, elevated relative abundance of the genus Prevotella, and reduced levels of the genus Bacteroides. We found that the relative abundances of Bacteroides and Prevotella were significantly correlated with certain serum metabolites, including omega-6 fatty acids. Primary dimensions in distance-based redundancy analysis of clinical chemistries explained 18.5% of the variance in bacterial community composition, and revealed a Bacteroides/Prevotella dichotomy aligned with inflammation and dietary markers. Finally, longitudinal analysis of gut microbiome dynamics within individuals showed that direct transitions between Bacteroides-dominated and Prevotella-dominated communities were rare, suggesting the presence of a barrier between these states. One implication is that interventions seeking to transition between Bacteroides- and Prevotella-dominated communities will need to identify permissible paths through ecological state-space that circumvent this apparent barrier.

[1]  John C. Earls,et al.  Blood metabolome predicts gut microbiome α-diversity in humans , 2019, Nature Biotechnology.

[2]  L. Hood,et al.  Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program , 2019, Scientific Reports.

[3]  P. Legendre.  Numerical Ecology , 2019, Encyclopedia of Ecology.

[4]  Benjamin M Hillmann,et al.  US Immigration Westernizes the Human Gut Microbiome , 2018, Cell.

[5]  G. Bergström,et al.  Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1 , 2018, Cell.

[6]  Kassem M. Makki,et al.  The Impact of Dietary Fiber on Gut Microbiota in Host Health and Disease. , 2018, Cell host & microbe.

[7]  K. Kristiansen,et al.  Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis , 2018, International Journal of Obesity.

[8]  Vishal Singh,et al.  Fiber-Mediated Nourishment of Gut Microbiota Protects against Diet-Induced Obesity by Restoring IL-22-Mediated Colonic Health. , 2018, Cell host & microbe.

[9]  Sean M. Kearney,et al.  Editorial : The microbiome as a source of new enterprises and job creation Designing synbiotics for improved human health , 2017 .

[10]  T. R. Licht,et al.  Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention , 2017, International Journal of Obesity.

[11]  John C. Earls,et al.  A wellness study of 108 individuals using personal, dense, dynamic data clouds , 2017, Nature Biotechnology.

[12]  B. Hamaker,et al.  Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota , 2017, Scientific Reports.

[13]  Eric J. Alm,et al.  Two dynamic regimes in the human gut microbiome , 2017, PLoS Comput. Biol..

[14]  B. Erdoğan,et al.  Associations of Trauma Severity with Mean Platelet Volume and Levels of Systemic Inflammatory Markers (IL1β, IL6, TNFα, and CRP) , 2016, Mediators of inflammation.

[15]  Johan E T van Hylckama Vlieg,et al.  Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes , 2016, The ISME Journal.

[16]  S. Gilbert,et al.  Getting the Hologenome Concept Right: an Eco-Evolutionary Framework for Hosts and Their Microbiomes , 2016, mSystems.

[17]  E. Segal,et al.  Personalized Nutrition by Prediction of Glycemic Responses , 2015, Cell.

[18]  Jens Nielsen,et al.  The gut microbiota modulates host amino acid and glutathione metabolism in mice , 2015 .

[19]  Partho Sen,et al.  Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome. , 2015, Cell metabolism.

[20]  N. Rifai,et al.  Association of C-reactive protein, tumor necrosis factor-alpha, and interleukin-6 with chronic kidney disease , 2015, BMC Nephrology.

[21]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[22]  Leroy Hood,et al.  Integrating big data and actionable health coaching to optimize wellness , 2015, BMC Medicine.

[23]  R. Vanholder,et al.  The uremic toxicity of indoxyl sulfate and p-cresyl sulfate: a systematic review. , 2014, Journal of the American Society of Nephrology : JASN.

[24]  Antonio Gonzalez,et al.  Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences , 2014, PeerJ.

[25]  Elhanan Borenstein,et al.  Metagenomic systems biology and metabolic modeling of the human microbiome , 2014, Gut microbes.

[26]  Lawrence A. David,et al.  Diet rapidly and reproducibly alters the human gut microbiome , 2013, Nature.

[27]  Christopher E. McKinlay,et al.  Rethinking "enterotypes". , 2014, Cell host & microbe.

[28]  T. R. Licht,et al.  Microbial Enterotypes, Inferred by the Prevotella-to-Bacteroides Ratio, Remained Stable during a 6-Month Randomized Controlled Diet Intervention with the New Nordic Diet , 2013, Applied and Environmental Microbiology.

[29]  E. Borenstein,et al.  Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules , 2013, Proceedings of the National Academy of Sciences.

[30]  A. Konopka,et al.  Quantifying community assembly processes and identifying features that impose them , 2013, The ISME Journal.

[31]  Susan Holmes,et al.  phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data , 2013, PloS one.

[32]  S. Tannenbaum,et al.  Serum metabolomics in a Helicobacter hepaticus mouse model of inflammatory bowel disease reveal important changes in the microbiome, serum peptides, and intermediary metabolism. , 2012, Journal of proteome research.

[33]  Gail M. Sullivan,et al.  Using Effect Size-or Why the P Value Is Not Enough. , 2012, Journal of graduate medical education.

[34]  Jonathan Friedman,et al.  Inferring Correlation Networks from Genomic Survey Data , 2012, PLoS Comput. Biol..

[35]  Curtis Huttenhower,et al.  Microbial Co-occurrence Relationships in the Human Microbiome , 2012, PLoS Comput. Biol..

[36]  D. Relman,et al.  The Application of Ecological Theory Toward an Understanding of the Human Microbiome , 2012, Science.

[37]  Peter Bühlmann,et al.  MissForest - non-parametric missing value imputation for mixed-type data , 2011, Bioinform..

[38]  Sharon I. Greenblum,et al.  Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease , 2011, Proceedings of the National Academy of Sciences.

[39]  F. Bushman,et al.  Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes , 2011, Science.

[40]  R. Knight,et al.  Moving pictures of the human microbiome , 2011, Genome Biology.

[41]  P. Bork,et al.  Enterotypes of the human gut microbiome , 2011, Nature.

[42]  D. Relman,et al.  Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation , 2010, Proceedings of the National Academy of Sciences.

[43]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[44]  Laxman Yetukuri,et al.  The gut microbiota modulates host energy and lipid metabolism in mice[S] , 2010, Journal of Lipid Research.

[45]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[46]  B. Roe,et al.  A core gut microbiome in obese and lean twins , 2008, Nature.

[47]  W. R. Wikoff,et al.  Metabolomics identifies perturbations in human disorders of propionate metabolism. , 2007, Clinical chemistry.

[48]  D. Relman,et al.  An ecological and evolutionary perspective on human–microbe mutualism and disease , 2007, Nature.

[49]  R. Knight,et al.  Quantitative and Qualitative β Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities , 2007, Applied and Environmental Microbiology.

[50]  P. Legendre,et al.  vegan : Community Ecology Package. R package version 1.8-5 , 2007 .

[51]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[52]  F. Budak,et al.  Evaluation of serum C-reactive protein, procalcitonin, tumor necrosis factor alpha, and interleukin-10 levels as diagnostic and prognostic parameters in patients with community-acquired sepsis, severe sepsis, and septic shock , 2006, European Journal of Clinical Microbiology and Infectious Diseases.

[53]  F. Shanahan,et al.  The gut flora as a forgotten organ , 2006, EMBO reports.

[54]  M. Pop,et al.  Metagenomic Analysis of the Human Distal Gut Microbiome , 2006, Science.

[55]  L. Jost.  Entropy and diversity , 2006 .

[56]  F. Hu,et al.  A Common Genetic Variant Is Associated with Adult and Childhood Obesity , 2006, Science.

[57]  Eoin L. Brodie,et al.  Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB , 2006, Applied and Environmental Microbiology.

[58]  Peter R. Minchin,et al.  An evaluation of the relative robustness of techniques for ecological ordination , 1987, Vegetatio.

[59]  D. Haydon,et al.  Alternative stable states in ecology , 2003 .

[60]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[61]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[62]  Pierre Legendre,et al.  DISTANCE‐BASED REDUNDANCY ANALYSIS: TESTING MULTISPECIES RESPONSES IN MULTIFACTORIAL ECOLOGICAL EXPERIMENTS , 1999 .

[63]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[64]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[65]  J. T. Curtis,et al.  An Ordination of the Upland Forest Communities of Southern Wisconsin , 1957 .