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Longitudinal Analysis Reveals Transition Barriers Between Dominant Ecological States in the Gut Microbiome

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Longitudinal Analysis Reveals Transition Barriers Between Dominant Ecological States in the Gut Microbiome Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome Roie Levya,1, Andrew T. Magisa, John C. Earlsa, Ohad Manorb, Tomasz Wilmanskia, Jennifer Lovejoya, Sean M. Gibbonsa,c, Gilbert S. Omenna,d, Leroy Hooda,2, and Nathan D. Pricea,2 aInstitute for Systems Biology, Seattle, WA 98109; bArivale, Seattle, WA 98104; ceScience Institute, University of Washington, Seattle, WA 98195; and dDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 Contributed by Leroy Hood, March 25, 2020 (sent for review February 13, 2020; reviewed by Eugene B. Chang and Paul Wilmes) The Pioneer 100 Wellness Project involved quantitatively profiling qualified clinician-scientist) who identified actionable opportu- 108 participants’ molecular physiology over time, including genomes, nities and incorporated individuals’ goals to develop personal- gut microbiomes, blood metabolomes, blood proteomes, clinical ized regimens to optimize wellness (5, 7). chemistries, and data from wearable devices. Here, we present a An integral element of the Pioneer 100 study was 16S profiling longitudinal analysis focused specifically around the Pioneer 100 of the bacterial and archaeal component of the intestinal micro- gut microbiomes. We distinguished a subpopulation of individuals biome. The ecology of the gut microbiome directly affects its host with reduced gut diversity, elevated relative abundance of the genus by modulating metabolism (8–12) and influences many diseases, Prevotella, and reduced levels of the genus Bacteroides. We found such as obesity (13), inflammatory bowel disease (14), and di- that the relative abundances of Bacteroides and Prevotella were abetes (11). Microbiome composition may influence how we me- significantly correlated with certain serum metabolites, including tabolize certain foods and has led to calls for personalized diets omega-6 fatty acids. Primary dimensions in distance-based redun- (15). A major determinant of variation in the gut microbiome dancy analysis of clinical chemistries explained 18.5% of the variance across people is the dominance of either Prevotella or Bacteroides Bacteroides/ in bacterial community composition, and revealed a (16), which influences the fermentative output of the microbiome Prevotella dichotomy aligned with inflammation and dietary markers. (17) and can determine the outcome of dietary weight loss inter- SYSTEMS BIOLOGY Finally, longitudinal analysis of gut microbiome dynamics within ventions (18, 19). However, while dietary intervention was able to individuals showed that direct transitions between Bacteroides- Bacteroides Firmicutes Prevotella modulate the abundance of relative to (20), dominated and -dominated communities were rare, sug- specific dietary modulation of the Bacteroides/Prevotella ratio has gesting the presence of a barrier between these states. One implication not been thoroughly demonstrated. Specifically, in at least two is that interventions seeking to transition between Bacteroides-and studies, controlled, short-term dietary interventions were in- Prevotella-dominated communities will need to identify permissible effective in pushing the microbiome between compositional states paths through ecological state-space that circumvent this apparent barrier. Significance microbiome | multiomic | state transition | Prevotella | Bacteroides Deep molecular phenotyping of individuals provides the op- echnological advances in molecular profiling and deep phe- portunity for biological insight into host physiology. As the hu- Tnotyping of individual humans (i.e., measuring thousands of man microbiome is increasingly being recognized as an health-related biomarkers) are poised to transform biomedicine important determinant of host health, understanding the – in coming years. Accordingly, numerous public and private in- host microbiome relationship in a multiomics context may stitutions recently launched initiatives with the aim of de- pave the way forward for targeted interventions. In this termining how to translate deeply characterized phenotypes into study, we analyze gut microbial composition of 101 indi- improvements to health and health care. For example, the Na- viduals over the course of a year, alongside clinical markers tional Institutes of Health launched the Precision Medicine and serum metabolomics. We establish association between Initiative with the goal of creating a voluntary research cohort of specific gut compositional states and host health biomarkers one million individuals to identify genetic drivers of cancers and (e.g., of inflammation). Finally, we provide evidence for an ap- other diseases of unknown etiology (1), the Google Baseline parent transition barrier between these compositional states. A study includes developing wearable technologies to profile bio- deeper understanding of microbiome dynamics and the associ- molecules in real time (2), and Human Longevity, Inc., focuses ated variation in host phenotypes furthers our ability to engineer on aging-associated diseases (3). Furthermore, integrating mo- effective interventions that optimize wellness. lecular profiling into ongoing longitudinal cohort studies, such as Author contributions: R.L., A.T.M., J.L., G.S.O., L.H., and N.D.P. designed research; R.L., the Framingham Heart Study, has been successful in identifying A.T.M., O.M., J.L., S.M.G., and N.D.P. performed research; J.C.E. contributed new reagents/ genomic drivers of diseases like obesity (4). analytic tools; R.L., J.C.E., O.M., and T.W. analyzed data; and R.L., T.W., S.M.G., G.S.O., In 2014, the Institute for Systems Biology launched the Pio- L.H., and N.D.P. wrote the paper. neer 100 study (5) as a pilot for the longer-term 100K Wellness Reviewers: E.B.C., The University of Chicago; and P.W., University of Luxembourg. Project (6). As part of the Pioneer 100 study, we densely quan- The authors declare no competing interest. tified the molecular profiles of 108 participants over 9 mo, This open access article is distributed under Creative Commons Attribution-NonCommercial- producing thousands of measurements comprising genome, NoDerivatives License 4.0 (CC BY-NC-ND). blood proteome, blood metabolome, gut microbiome, clinical 1Present address: Verily Life Sciences, South San Francisco, CA 94080. chemistries, and activity monitoring (i.e., deep phenotyping). In 2To whom correspondence may be addressed. Email: [email protected] or contrast to the initiatives described above, we focused on opti- [email protected]. mizing general wellness as opposed to targeting specific disease This article contains supporting information online at https://www.pnas.org/lookup/suppl/ phenotypes. Central to this focus, each participant’s molecular doi:10.1073/pnas.1922498117/-/DCSupplemental. profile was interpreted alongside a wellness coach (i.e., a www.pnas.org/cgi/doi/10.1073/pnas.1922498117 PNAS Latest Articles | 1of7 Downloaded by guest on September 30, 2021 dominated by Bacteroides or Prevotella, despite the demonstrated phylogenetic relationships among taxa. Dimension 1 of the BCD association of these states with long-term diet (16, 21, 22). One NMDS revealed a nonmonotonic association with Bacteroidetes possible explanation is that exclusionary interactions between and Firmicutes (SI Appendix, Fig. S1), possibly indicating two these taxa or interactions with the host immune system establish a different subclasses within the high-Bacteroidetes samples. hysteresis; i.e., the behavior of the system depends not only on its To further characterize these putative subclasses of high- input but also on its current and preceding states. Bacteroidetes samples, we compared an equivalent number of Here, we report a longitudinal analysis of the Pioneer 100 samples from both extremes of BCD NMDS dimension 1 (n = 25 microbiome data and its relationship with metabolomic and per subsample) (Methods). These two subsamples differed sig- clinical chemistries profiles. We identify within our cohort a nificantly in α-diversity (Cohen’s d = −0.28, P < 0.018), and thus subpopulation distinguished by different levels of bacterial were termed low diversity (LO) vs. high diversity (HI). We in- community diversity (i.e., α-diversity, the number of taxa and/or vestigated which, if any, operational taxonomic units (OTUs) the evenness in their abundances within a sample) and by the disproportionately represented LO vs. HI samples. We chose dominance of either Bacteroides or Prevotella genera. The stringent selection criteria to preferentially weight more abun- abundances of these taxa correlate strongly with serum metab- dant representative OTUs (Methods). Using these criteria, we olites, including medium- and long-chain fatty acids. Distance- found that the OTUs resolving to Prevotella best represented the − based redundancy analysis (dbRDA) identified associations be- LO class (d = 4.94, false discovery rate [FDR] < 6.70 × 10 12), tween the Bacteroides/Prevotella ratio and clinical chemistries while those resolving to Bacteroides best represented the HI class − including inflammation markers and cholesterol levels. Finally, (d = −4.37, FDR < 6.70 × 10 12)(SI Appendix, Fig. S2). Notably, longitudinal analysis of microbiome compositional trajectories a single OTU resolving to Prevotella copri dominated the LO indicates that while the microbiota may occasionally transition class, while diversity was more evenly spread among multiple between Bacteroides- and Prevotella-dominated states,
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