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Altered microbial bile acid metabolism exacerbates T cell-driven inflammation during graft-versus-host disease

Nature Microbiology volume 9, pages 614–630 (2024)Cite this article

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Abstract

Microbial transformation of bile acids affects intestinal immune homoeostasis but its impact on inflammatory pathologies remains largely unknown. Using a mouse model of graft-versus-host disease (GVHD), we found that T cell-driven inflammation decreased the abundance of microbiome-encoded bile salt hydrolase (BSH) genes and reduced the levels of unconjugated and microbe-derived bile acids. Several microbe-derived bile acids attenuated farnesoid X receptor (FXR) activation, suggesting that loss of these metabolites during inflammation may increase FXR activity and exacerbate the course of disease. Indeed, mortality increased with pharmacological activation of FXR and decreased with its genetic ablation in donor T cells during mouse GVHD. Furthermore, patients with GVHD after allogeneic hematopoietic cell transplantation showed similar loss of BSH and the associated reduction in unconjugated and microbe-derived bile acids. In addition, the FXR antagonist ursodeoxycholic acid reduced the proliferation of human T cells and was associated with a lower risk of GVHD-related mortality in patients. We propose that dysbiosis and loss of microbe-derived bile acids during inflammation may be an important mechanism to amplify T cell-mediated diseases.

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Fig. 1: T cell-driven inflammation alters the intestinal BA pool.
Fig. 2: Alloreactive T cells alter host and microbial BA metabolism.
Fig. 3: FXR activity contributes to GVHD.
Fig. 4: Comparison of BA pools in patients with GVHD vs controls.
Fig. 5: BA biotransformation potential in transplant patients with GVHD vs controls.
Fig. 6: UDCA limits human effector T cell responses and is associated with improved GVHD-related mortality.

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Data availability

Metabolomics data including standards (Figs. 1, 4 and 5, and Extended Data Figs. 4–7 and Tables 4 and 5) are available at GNPS (https://gnps.ucsd.edu/) under MassIVE project ID #MSV000092300. The bulk RNA-seq data from murine experiments (Fig. 2 and Extended Data Fig. 2) are available at NCBI GEO under GEO accession GSE218343 and also in Supplementary tables. The 16S and shotgun sequencing data (Figs. 2 and 5, and Extended Data Fig. 7) are available at NCBI under accession numbers listed in the Supplementary tables. The processed scRNA-seq files are available under GEO accession GSE253360. For access to raw data, kindly request permission by contacting the contributing author at mvandenbrink@coh.org. Please anticipate a response within 2 weeks. Once legal agreements are approved, raw genomic data can be shared within an additional month. Source data for Figs. 1–3 and 6a–c, and for Extended Data Figs. 1–3 and 8 are available in the Supplementary tables. The datasets required to run the code were also made publicly available in GitHub and are also included in the Supplementary information. Our institutional data-sharing policies prevent us from publicly posting the patient-level information used to calculate clinical outcomes (Fig. 6t,u). However, interested parties may request access by contacting the contributing author at mvandenbrink@coh.org. Please anticipate a response within one month. Data sharing of patient-level information is contingent upon the establishment of a formal data transfer agreement between Memorial Sloan Kettering and the respective parties involved. Source data are provided with this paper.

Code availability

The code and the corresponding figures for Figs. 2d,e, 4, 5 and 6h–v, and for Extended Data Figs. 4–7 and 10 can be accessed in GitHub (https://github.com/orianamiltiadous/BAs_and_GVHD).

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Acknowledgements

We acknowledge R. Chaligne, the single-cell analytics innovation laboratory (SAIL), the Integrated Genomics Operation Core (IGO) and the molecular microbiology facility (MMF), which performed RNA sequencing (SAIL, IGO), as well as the 16S and metagenomic shotgun sequencing (MMF, IGO) for mouse and human studies. This research is funded by the National Cancer Institute (NCI) Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology NCI award numbers R01-CA228358, R01-CA228308, P30 CA008748 MSK Cancer Center Support Grant/Core Grant and P01-CA023766; National Heart, Lung and Blood Institute (NHLBI) award number R01-HL123340 and R01-HL147584; and the Tri Institutional Stem Cell Initiative. Additional funding was received from The Lymphoma Foundation, The Susan and Peter Solomon Family Fund, The Solomon Microbiome Nutrition and Cancer Program, Cycle for Survival, Parker Institute for Cancer Immunotherapy, Paula and Rodger Riney Multiple Myeloma Research Initiative, Starr Cancer Consortium, and Seres Therapeutics. S.L. was supported by the Deutsche Forschungsgemeinschaft (DFG, LI 3565/1-1) and DKMS. O.M. was supported by the American Society of Clinical Oncology Young Investigator Award, a Hyundai Hope on Wheels Young Investigator Award, a Cycle for Survival Equinox Innovation Award, a Collaborative Pediatric Cancer Research Program Award, a Michael Goldberg Fellowship and a Tow Center for Developmental Oncology Career Development Award. K.A.M. was supported by the DKMS and the ASH Scholar Award. J.U.P. reports funding from NHLBI NIH Award K08HL143189.

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Author notes

  1. These authors contributed equally: Sarah Lindner, Oriana Miltiadous.

Authors and Affiliations

  1. Department of Immunology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Sarah Lindner, Jenny Paredes, Anastasia I. Kousa, Anqi Dai, Nicholas R. Waters, Keimya Sadeghi, Gabriel K. Armijo, Romina Ghale, Kristen Victor, Brianna Gipson, Chi L. Nguyen, John Slingerland, Hana Andrlova, Marina Burgos da Silva & Marcel R. M. van den Brink

  2. Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Oriana Miltiadous

  3. Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ruben J. F. Ramos & Justin R. Cross

  4. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Teng Fei

  5. Transplantation and Cell Therapy Program, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA

    Emma Lauder & Pavan Reddy

  6. Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    John Frame & Ying Taur

  7. Center of Comparative Medicine and Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Sebastien Monette

  8. Gene Editing and Screening Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Marco Vincenzo Russo

  9. Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA

    Marco Vincenzo Russo

  10. Division of Medical Oncology, University of Washington, Seattle, WA, USA

    Kate A. Markey

  11. Fred Hutchinson Cancer Center, Seattle, WA, USA

    Kate A. Markey

  12. Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Sergio Giralt, Miguel-Angel Perales, Jonathan U. Peled & Marcel R. M. van den Brink

  13. Department of Medicine, Weill Cornell Medical College, New York, NY, USA

    Sergio Giralt, Miguel-Angel Perales, Jonathan U. Peled & Marcel R. M. van den Brink

  14. Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Melody Smith

  15. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Clarissa Campbell

  16. Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA

    Marcel R. M. van den Brink

  17. Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA, USA

    Marcel R. M. van den Brink

  18. Comprehensive Cancer Center, City of Hope, Los Angeles, CA, USA

    Marcel R. M. van den Brink

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  1. Sarah Lindner
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S.L., O.M., C.C. and M.R.M.v.d.B designed the study and wrote the manuscript. S.L. and C.C. performed harvest experiments and flow cytometric analyses. S.L. performed BM + T experiments and FXR reporter assays. O.M. selected the patient cohort and analysed human bile acid profiling and metagenomic data. R.J.F.R. and J.R.C. quantified and analysed the LC–MS/MS bile acid profiling data and advised on study design. A.I.K. analysed the mouse RNA and human scRNA-seq data. A.D. analysed the mouse metagenomic sequencing data. N.R.W. assisted with code resources and oversight. K.S. assisted with organizing the sequencing files and creating biorepositories. J.P. performed human and mice in vitro T cell assays and flow cytometric analysis. T.F. performed biostatistical analysis. E.L. and P.R. performed ΔFXR recipient BM + T. J.F. assisted with the analysis of metagenomic data. G.K.A., R.G., K.V. and B.G. assisted with BM + T experiments. S.M. carried out histopathological analyses of tissues. J.S. coordinated the faecal microbiome collection. M.V.R., C.L.N., Y.T., K.A.M., H.A., M.B.d.S., J.U.P. and M.S. contributed to analysis strategies. S.G. and M.-A.P. contributed to clinical data collection. C.C. and M.R.M.v.d.B. supervised the study and contributed equally. S.L. and O.M. contributed equally. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Clarissa Campbell or Marcel R. M. van den Brink.

Ethics declarations

Competing interests

M.-A.P. reports honoraria from Adicet, Allovir, Caribou Biosciences, Celgene, Bristol-Myers Squibb, Equilium, Exevir, Incyte, Karyopharm, Kite/Gilead, Merck, Miltenyi Biotec, MorphoSys, Nektar Therapeutics, Novartis, Omeros, OrcaBio, Syncopation, VectivBio AG and Vor Biopharma; he serves on DSMBs for Cidara Therapeutics, Medigene and Sellas Life Sciences, and the scientific advisory board of NexImmune; he has ownership interests in NexImmune and Omeros; and he has received institutional research support for clinical trials from Incyte, Kite/Gilead, Miltenyi Biotec, Nektar Therapeutics and Novartis. K.A.M. holds equity and is on the advisory board of Postbiotics Plus, and has consulted for Incyte. J.U.P. reports research funding, intellectual property fees and travel reimbursement from Seres Therapeutics, and consulting fees from Da Volterra, CSL Behring and MaaT Pharma; he serves on the Advisory board of and holds equity in Postbiotics Plus Research; and he has filed intellectual property applications related to the microbiome (reference numbers 62/843,849, 62/977,908 and 15/756,845). M.R.M.v.d.B. has received research support and stock options from Seres Therapeutics, and stock options from Notch Therapeutics and Pluto Therapeutics; he has received royalties from Wolters Kluwer; he has consulted, received honorarium from or participated in advisory boards for Seres Therapeutics, Vor Biopharma, Rheos Medicines, Frazier Healthcare Partners, Nektar Therapeutics, Notch Therapeutics, Ceramedix, Lygenesis, Pluto Therapeutics, GlaskoSmithKline, Da Volterra, Thymofox, Garuda, Novartis (Spouse), Synthekine (Spouse), Beigene (Spouse) and Kite (Spouse); he has IP Licensing with Seres Therapeutics and Juno Therapeutics; and he holds a fiduciary role on the Foundation Board of DKMS (a nonprofit organization). Memorial Sloan Kettering has institutional financial interests relative to Seres Therapeutics. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Impact of alloreactive T cells on BA ratios and clinical markers.

Lethally irradiated 6-8 week old female BALBc mice were transplanted with 10 ×106 B6 BM cells alone (BM) or together with 1 ×106 T cells (BM + T). BAs were quantified on day 7 post-transplant by LC-MS in the cecal contents and plasma: (a) ratio of microbe- to host-derived BAs, (b) ratio of unconjugated to glycine- and taurine-conjugated BA and (c) estimated cecal levels of the T cell modulatory BAs 3-oxoLCA and isoLCA (below the linear range of quantification. Weight loss (d), clinical GVHD scores (e) and cumulative food intake per mouse (f), plasma levels of AST (g), ALT (h), albumin (i), cholesterol (j), and triglycerides (k) at day 7 post-transplant of these mice. (a, b, d-k) Data combined from two independent experiments (n = 10). (c) Data is representative of two independent experiments (n = 5). (a-k) Data shown as mean ± S.D and statistical significance determined by two-tailed Mann-Whitney test.

Extended Data Fig. 2 FXR Signaling in T cells modulates GVHD.

(a) Principle of the FXR luciferase reporter assay used in Fig. 3a and b and Extended Data Fig. 2b. (b) Stably transfected HepG2 cells expressing luciferase under the control of an FXR-responsive element were treated with the indicated doses of CDCA. Luciferase units (luminescence) were normalized to cell viability assessed by Hoechst 33342 staining (fluorescence). Data representative of two independent experiments and presented as technical triplicates with means connected. Weight loss (c) and clinical GVHD score (d) of survival experiment shown in Fig. 3d. Data combined from three independent experiments (BM group n = 20, BM + T groups n = 30 per group) and means ± S.D connected. (e) Survival of cohoused WT or nr1h4-/-l (Δ FXR) B6 mice receiving BALBc BM + T. Data combined from three independent experiments (n = 13 per group). Statistical significance was determined using log-rank test. (f-h) BALBc recipient mice transplanted with 10 ×106 B6 BM cells alone or together with 1 ×106 T cells from either Nr1h4fl/fl (BM + TWT) or Cd4Cre Nr1h4fl/fl (BM + TΔFXR) mice on a C57Bl/6 N background. (f) Organ-specific and compound histopathological scores at day 28 post-transplant of transplanted mice with representative histology images shown in (g). Data from one experiment (n = 10 per group) and statistical significance was determined by two-tailed Mann-Whitney test. (h) Production of IFNγ by CD4+ and CD8+ T cells in the smallI and large intestine lamina propria 14 days after transplant. Data combined from two (n = 10 per group) and presented as mean ± S.D. Statistical significance was determined by by two-tailed Mann-Whitney test.

Extended Data Fig. 3 Consort diagram.

Criteria for the cohort selection used for data shown in Figs. 4, 5 and Extended Data Fig. 5-8. PBSC: peripheral blood stem cell graft.

Extended Data Fig. 4 Effects of UDCA exposure on the intestinal BA pool.

Data from n = 280 samples from either peri-engraftment or peri-GVHD onset timepoints. Fecal concentrations of UDCA (a) and microbe-derived BAs (b). UDCA exposure status is shown in the x-axis (w = weeks; m = months since last exposure). Statistical significance determined by the 2-sided Wilcoxon Rank-sum test. The boxplot center line corresponds to the median, box limits correspond to the 25th and 75th percentile, and whiskers correspond to 1.5x interquartile range. Correlation of fecal UDCA concentrations with the levels of conjugated UDCA (conj-UDCA, c), total BAs (d), microbe-derived BAs (e), host-derived BAs (f), nonUDCA total BAs (g) nonUDCA microbe-derived BAs (h), and microbe- to host- derived (M/H) ratio excluding UDCA (nonUDCA, i). The solid line represents a linear regression model fitted to the data. The shaded region surrounding the line indicates a 95% confidence interval for the regression line. Total BAs nonUDCA (g) are measured in pmol/mg. (j,k) Correlation matrix of the BA species covarying with UDCA. (k) Showing BA species with a Pearson correlation coefficient (R > 0.4).

Extended Data Fig. 5 Fecal BA profiles at the peri-GVHD onset time point.

Showing the levels of total microbe-derived BAs (a), microbe- to host-derived (M/H) BA ratio (b), M*/H (that is, nonUDCA M/H) BA ratio (c), and the ratio of unconjugated to amidated BAs (d). Data representative of 57 control and 58 GVHD patients. Statistical significance determined with the 2-sided Wilcoxon Rank-sum test. The boxplot center line corresponds to the median, box limits correspond to the 25th and 75th percentile, and whiskers correspond to 1.5x interquartile range. (e-f) Differential abundance of BAs between GVHD and controls in peri-GVHD onset samples after multivariate adjustment. (e) Grid plot showing significance status, q-values, and log fold changes of BAs relative to indicated clinical variables. (f) Volcano plot showing log-transformed adjusted p-values vs log fold changes of BAs between GVHD and control patients. Statistical comparison was made using the two-sided empirical Bayes moderated t-test and p-values were adjusted using the Benjamini-Hochberg method.

Extended Data Fig. 6 Fecal BA profiles at the peri-engraftment time point.

Total BAs (a), host-derived (b), microbe-derived (c), microbe*-derived (d), microbe-derived to host-derived (M/H) BA ratio (e), M*/H (nonUDCA M/H) BA ratio in patients that develop GVHD vs controls (f). (g) Pie chart showing the averaged relative contributions of host-derived and microbe*-derived to the calculated total BA pool. Glycine- and taurine-conjugated (h), unconjugated (i), and sulfated (g) BAs. Pie chart showing the averaged percentages of glycine- and taurine-conjugated, unconjugated and sulfated BAs in patients with GVHD vs controls in peri-GVHD onset samples (k). Microbe*-derived BAs: Microbe-derived BAs excluding UDCA. Data representative of 90 control and 86 GVHD patients. The boxplot center line corresponds to the median, box limits correspond to the 25th and 75th percentile, and whiskers correspond to 1.5x interquartile range. Statistical significance determined with the 2-sided Wilcoxon Rank-sum test. (l,m) Differential abundance of BAs between GVHD and control patients in peri-engraftment samples after multivariate adjustment (l) Grid plot showing significance status, q-values, and log fold changes of BAs relative to indicated clinical variables. (m) Volcano plot showing log-transformed adjusted p-values vs log fold changes of BAs between GVHD and control patients. Statistical comparison was made using the two-sided empirical Bayes moderated t-test and p-values were adjusted using the Benjamini-Hochberg method.

Extended Data Fig. 7 Microbiome features in peri-GVHD onset samples.

Relative abundance of (a) Eggerthella lenta and (b) Ruminococcus gnavus. Data representative of 49 control and 46 GVHD patients (c) Correlation of the sum of bai operon gene and α-diveristy as measured by the Simpson reciprocal index. The solid line represents a linear regression model fitted to the data. The shaded region surrounding the line indicates a 95% confidence interval for the regression line. Data representative of 82 patients with peri-GVHD onset samples. (d) α-diversity, (e) sum of bai operon genes identified by shotgun metagenomic analysis (measured in counts per million), and (f) levels of microbe-derived BAs* (pmol/mg) in patients with or without intestinal pathogen domination. Data representative of 41 patients with and 74 patients without pathogen domination. Microbe-derived BAs*= microbe-derived BAs excluding UDCA. Statistical significance determined with the univariate 2-sided Wilcoxon Rank-sum test. The boxplot center line corresponds to the median, box limits correspond to the 25th and 75th percentile, and whiskers correspond to 1.5x interquartile range. R correspond to Pearson’s correlation coefficient.

Extended Data Fig. 8 In vitro human T cell proliferation in response to FXR activation or inhibition with drugs or BAs.

(a) Experimental design. Purified human T cells were activated with anti-CD3 and anti-CD28 antibodies in the presence of recombinant IL-2 for 2 days and further cultured either in the presence of anti-CD3/anti-CD28 antibodies and IL-2 (continuous activation control) or in their absence (vehicle control) with or without the indicated compounds for 96 hours. Showing T cell confluence in response to CDCA and UDCA (b) or GW4064 and DY268 (c) at the indicated concentrations. Cell viability (d, e, f) and representative histograms (g, h) showing CD25 levels determined by flow cytometric analysis. (i-l) CD25 expression in CD4+ and CD8+ T cells after 96 hours of treatment with CDCA and UDCA (i-j), or GW4064 and DY268 (k-l). Showing the geometric mean fluorescence intensity (MFI) of CD25 in CD25+ T cells. Values were normalized to the MFI of the vehicle-treated group. (m, n) Frequency of CD25 positive cells on day 4 post-activation. (o) CD25 expression from T cells of FXRWT or FXRWT mice treated with anti-CD3 and anti-CD28 antibodies in the presence of IL-2 for 2 days before incubation with CDCA (100 nM), or UDCA (100 nM), anti-CD3, anti-CD28 and IL-2 (continuous activation), or vehicle for 2 more days. CD25 expression was measured as geometric mean fluorescence intensity (MFI) of CD25 in CD25+ T cells normalized to the MFI of the vehicle-treated group. Statistical analysis was performed by two-way (b,c) or one-way ANOVA followed by multiple t-test with Bonferoni correction (d-f, i-o). Each data point in (i-n) shows the average of technical duplicates for a single donor. Bars denote the standard error of the mean. Data representative of 4 independent experiments with a total of 4 PBMC donors. Each data point in (g) shows the average of technical triplicates from two mice. Bars denote the standard error of the mean. Data representative of 3 independent experiments with a total of 6 mice.

Extended Data Fig. 9 Quality control of single cell RNA-sequencing profiling of in vitro activated T cells treated with FXR ligands, DMSO or activating signals for 24 h.

Visualization of 60,767 cells using a uniform manifold approximation and projection (UMAP) of (a) cells from the two donors and (b) per hashtag before eliminating any cells. (c) Total counts (log10 scale) (d) total genes, (e) ribosomal fraction, (f) mitochondrial fraction per cell, (g) predicted doublet and (h) doublet score. (i-k) Cells expressing markers for B and Natural Killer cells were defined as contaminants. (l-m) UMAP and stacked plot showing the fraction of retained (33,634) and removed cells (27,133).

Extended Data Fig. 10 Single cell RNA-sequencing profiling of in vitro activated T cells treated with CDCA (100 nM), UDCA (100 nM), GW4064 (1uM) and DY268 (1uM) for 24 h.

(a) Gene markers used to identify cell populations. Visualization of annotated cells using a uniform manifold approximation and projection (UMAP) of (b) subtypes after batch correction and (c) per treatment arm. (d) Gene Set Enrichment Analysis of pathways differentially regulated in the different conditions (CDCA 100 nM, UDCA 100 nM, GW4064 1uM, DY268 1uM) relative to the vehicle control in CD4+, CD8+ and regulatory T cell populations. Displaying significant pathways.

Supplementary information

Supplementary Information

Dot plot for metabolomics in mice, gating strategy and Supplementary Tables 1–9.

Reporting Summary

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Supplementary Table

Bulk RNA-sequencing of liver, small and large intestine; source data for extended data figures; ASV sequences; antibody dilutions.

Source data

Source Data Fig. 1

BA levels in mice with GVHD vs controls

Source Data Fig. 2

Abundance of BA-related genes and results of bulk RNA-sequencing of liver tissue and epithelial fractions of the small and large intestines in mice with GVHD vs controls.

Source Data Fig. 3

Transcriptional activity of FXR in response to treatment with individual bile acids; survival outcomes associated with enhanced FXR activity and FXR knockout (KO) conditions.

Source Data Fig. 4

Information on the cohort (GVHD vs controls), the concentrations/AUC of BAs, BA family.

Source Data Fig. 5

Abundance of BA-related genes, α-diversity, ASV counts used for the composition plot.

Source Data Fig. 6

T cell confluence in response to different treatment arms; CD25 MFI in response to different treatment arms; source data for fold-change plots in CD4 T cells.

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Lindner, S., Miltiadous, O., Ramos, R.J.F. et al. Altered microbial bile acid metabolism exacerbates T cell-driven inflammation during graft-versus-host disease. Nat Microbiol 9, 614–630 (2024). https://doi.org/10.1038/s41564-024-01617-w

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