Abstract
The gut microbiota influences development1,2,3 and homeostasis4,5,6,7 of the mammalian immune system, and is associated with human inflammatory8 and immune diseases9,10 as well as responses to immunotherapy11,12,13,14. Nevertheless, our understanding of how gut bacteria modulate the immune system remains limited, particularly in humans, where the difficulty of direct experimentation makes inference challenging. Here we study hundreds of hospitalized—and closely monitored—patients with cancer receiving haematopoietic cell transplantation as they recover from chemotherapy and stem-cell engraftment. This aggressive treatment causes large shifts in both circulatory immune cell and microbiota populations, enabling the relationships between the two to be studied simultaneously. Analysis of observed daily changes in circulating neutrophil, lymphocyte and monocyte counts and more than 10,000 longitudinal microbiota samples revealed consistent associations between gut bacteria and immune cell dynamics. High-resolution clinical metadata and Bayesian inference allowed us to compare the effects of bacterial genera in relation to those of immunomodulatory medications, revealing a considerable influence of the gut microbiota—together and over time—on systemic immune cell dynamics. Our analysis establishes and quantifies the link between the gut microbiota and the human immune system, with implications for microbiota-driven modulation of immunity.
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Data availability
All data supporting the findings of this study are available within the paper and its Supplementary Information files. The data used in our study are organized in Excel-compatible comma-separated value files as Supplementary Tables (data-tables.zip). All sequencing data have been made available publicly, and the NCBI SRA accession numbers are listed in the Supplementary Tables. Metadata and processed sequencing data are made available on a public repository via Figshare: meta data, https://doi.org/10.6084/m9.figshare.12016986.v4; samples, https://doi.org/10.6084/m9.figshare.12016983.v4; 16S counts, https://doi.org/10.6084/m9.figshare.12016989.v3; and 16S taxonomy, https://doi.org/10.6084/m9.figshare.12016992.v1.
Code availability
All of the steps of the analyses that were performed in this study are described in detail to allow reproduction of the results. Relevant analysis code is available publicly at https://github.com/jsevo/wbcdynamics_microbiome.
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Acknowledgements
We thank M. Lipsitch, S. B. Andersen, K. R. Foster, J. K. Sia, E. G. Pamer, K. Coyte, S. Mitschka and the members of the Xavier lab for helpful discussion and comments on the manuscript. This work was supported by the National Institutes of Health (NIH) grants U01 AI124275, R01 AI137269 and U54 CA209975 to JBX, by the MSKCC Cancer Center Core Grant P30 CA008748, the Parker Institute for Cancer Immunotherapy at Memorial Sloan Kettering Cancer Center, the Sawiris Foundation, the Society of Memorial Sloan Kettering Cancer Center, MSKCC Cancer Systems Immunology Pilot Grant and Empire Clinical Research Investigator Program. M.S. received funding from the Burroughs Wellcome Fund Postdoctoral Enrichment Program, the Damon Runyon Physician-Scientist Award, and the Robert Wood Johnson Foundation. T.M.H. is investigator in the Pathogenesis of Infectious Diseases from the Burroughs Wellcome Fund, and funded via an award from Geoffrey Beene Foundation, and NIH RO1 AI093808. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Contributions
J.S. and J.B.X. wrote the manuscript. J.S. and J.B.X. designed the analyses with expert help from R.N. J.U.P. and Y.T. contributed to the clinical data preparation, B.P.T. provided the 16S data-processing pipelines, K.A.M., M.S., A.S., S.M., M.F., M.S.P., T.M.H., M.-A.P. and M.R.M.v.d.B. provided clinical context and helped with variable selection, N.J.C., M.L., L.B., A.B. and A.D.S. provided clinical and other data from Duke, A.D. provided the shotgun processing pipelines. E.F., L.A.A. and R.J.W. processed patients’ stool samples, including for 16S sequencing, shotgun metagenomics and qPCR quantification of total 16S rRNA gene. All authors contributed to the writing and interpretation of the results.
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Competing interests
M.R.M.v.d.B. and J.U.P. received financial support from Seres Therapeutics. M.-A.P. has received honoraria from AbbVie, Bellicum, Bristol-Myers Squibb, Incyte, Merck, Novartis, Nektar Therapeutics, and Takeda, research support for clinical trials from Incyte, Kite (Gilead) and Miltenyi Biotec, and serves on data and safety monitoring boards for Servier and Medigene and scientific advisory boards for MolMed and NexImmune.
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Extended data figures and tables
Extended Data Fig. 1 Blood cell counts over time.
a, WBC counts and platelet counts per graft source over the first 100 days post HCT per day relative to HCT from N = 2,235 adult patients (detailed demographics in supplementary Table 1); lines: mean, shaded: ± standard deviations. b, Data exclusion diagram.
Extended Data Fig. 2 FMT increases WBC counts.
a, HCT patient who received an autologous faecal microbiota transplant (auto-FMT, dashed red line) that restored commensal microbial families and ecological diversity in the gut microbiota, with concurrent cell counts of peripheral neutrophils, lymphocytes and monocytes and immunomodulatory drug administrations. b, Total WBC counts in 24 enrolled patients (10 control, 14 treated) post-neutrophil engraftment; vertical lines indicate randomization dates. c, Weekly mean WBC counts aligned to the randomization date (FMT-treated: red, control: black). Line: mean per week, shaded region: 95% CI. d, Coefficient estimates (mean vs. mean + FMT effect) from linear mixed effects models of total WBC counts over time indicate an auto-FMT-induced increase of WBCs (βFMT: P = 7 × 10−14). e–g, Respectively: neutrophil, lymphocyte and monocyte count trajectories of 24 FMT trial patients. Thin lines: raw data (blue: post-FMT); thick black: mean per day, thick blue: mean+post-FMT coefficient. Means and confidence intervals (shaded region) without (black) and after FMT (blue), as well as the coefficient estimate for FMT treatment and its P value from a linear mixed effects model relating cell counts over time to the FMT treatment (Methods).
Extended Data Fig. 3 Results of the feature selection stage 1 regression.
a–c, Stage 1 regression on neutrophil, lymphocyte, and monocyte dynamics, respectively, on patients without microbiome data. Coefficients from tenfold cross-validated elastic net regression daily changes in neutrophils. gr: intercept; TCD: T cell depleted graft (ex-vivo) by CD34+ selection; PBSC: peripheral blood stem cells; BM: bone marrow; cord: umbilical cord blood; NONABL: Nonmyeloablative; REDUCE: reduced-intensity conditioning regimen; F: female; N: patients, n: samples (daily changes in neutrophils).
Extended Data Fig. 4 Additional coefficients, posterior convergence evaluation and validation.
a–c, Additional posterior coefficient estimates of medications, additional genera and HCT metadata from the Bayesian stage 2 regression, see also Fig. 3. REDUCE: reduced-intensity conditioning regimen; NONABL: non-myeloablative conditioning regimen. F: female. d–f, posterior sampling convergence. Histograms of the ranked posterior draws from the model of neutrophil, lymphocyte and monocyte dynamics, respectively, in PBSC patients (ranked over all chains), plotted separately for each chain show no substantial differences between chains. g–i, Predictors of WBC dynamics using data from patients treated at Duke. Heatmaps indicate the slope coefficients from individual univariate regressions of microbiome and clinical predictors with changes in neutrophils, lymphocytes and monocyte, and for comparison the corresponding coefficients signs from the Bayesian multiple linear regressions in stage 2 of the analysis of WBC dynamics in MSK patients (Fig. 3). Pvalues were adjusted for multiple hypothesis testing using Bonferroni correction: ***P < 0.001, **P < 0.01, *P < 0.05; P > 0.05: n.s. Sign of coefficients from MSK PBSC patients for comparison. j, Equivalent validation analysis from patients treated at Duke using partial least squares regression of microbiome and clinical predictors identified in stage 2 of our analysis on daily changes in neutrophils, lymphocytes and monocyte.
Extended Data Fig. 5 Validation using absolute instead of relative abundance bacterial genus data.
a–d, Validation analysis of the main model using absolute bacterial abundances as predictors instead of relative abundances in Fig. 3. Results show inferred coefficients and P values from multiple linear regressions. One regression per analysed WBC type dynamics, that is, neutrophil, lymphocyte and monocyte daily log-changes, was conducted, and coefficients for medications (a), WBC feedbacks (b) metadata (c) and total genus abundances (d) are shown. This was only possible for only a subset of the data used in the main analysis for which we obtained absolute bacterial abundance estimates (Methods), n: samples, N: patients.
Extended Data Fig. 6 Jointly inferred association network between WBC and bacterial genus dynamics.
Strong regularization yields few non-zero coefficients and antibiotics dominate the dynamics.
Extended Data Fig. 7 Jointly inferred association network between WBC and bacterial genus dynamics with reduced regularization.
Reducing regularization strength (Methods) indicates potential bidirectional feedbacks, for example, between lymphocytes and [Ruminococcus] gnavus group (highlighter green boxes, and cartoon).
Extended Data Fig. 8 Functional analysis of microbiota samples.
To distinguish samples predicted to increase rates of WBCs, a microbiota potency score was calculated from posterior coefficients (Fig. 3, Methods) and the relative abundance of taxa in samples. Bars show linear discriminant analysis (LDA) scores of MetaCyc pathway profiles from 124 shotgun sequenced samples that distinguished positive and negative potency samples the most (LDA-score magnitude in the 95th percentile). Highlighted pathways are discussed in the main text. For each pathway, we tested whether pathway presence was enriched (depleted) in positive (negative) potency samples using one-sided Fisher’s exact test; ***P < 0.001, **P < 0.01, *P < 0.05.
Extended Data Fig. 9 Abundance profiles of bacterial genera across analysed samples.
a, The relative non-zero abundance of Staphylococcus is inversely related to microbiome alpha diversity, bold line: regression line from a linear model of the mean of the log10 Staphylococcus relative abundance, shaded: 95% confidence intervals (n = 1,381 samples with non-zero Staphylococcus abundances). b, Abundance profiles of the two genera, Faecalibacterium and Ruminococcus 2, most strongly associated with WBC increase; number of times detected (left) and log10 abundance distribution when above detection (right).
Extended Data Fig. 10 Survival analysis and confirmation of model results with different priors.
a, Kaplan–Meier plot of patient 3-year survival with sufficient available blood data (Supplementary Information, Extended Data Fig. 1). b, posterior association coefficients do not depend on the choice of prior for σ in the main Bayesian model. Plotted are the posterior means from our main analysis against the equivalent inference with an inverse Gamma prior (alpha = 1, beta = 1).
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Schluter, J., Peled, J.U., Taylor, B.P. et al. The gut microbiota is associated with immune cell dynamics in humans. Nature 588, 303–307 (2020). https://doi.org/10.1038/s41586-020-2971-8
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DOI: https://doi.org/10.1038/s41586-020-2971-8
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