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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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.
References
Hamilton, J. P. et al. Human cecal bile acids: concentration and spectrum. Am. J. Physiol. Gastrointest. Liver Physiol. 293, G256–G263 (2007).
Chen, M. L., Takeda, K. & Sundrud, M. S. Emerging roles of bile acids in mucosal immunity and inflammation. Mucosal Immunol. 12, 851–861 (2019).
Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell 157, 121–141 (2014).
Russell, D. W. The enzymes, regulation, and genetics of bile acid synthesis. Annu. Rev. Biochem. 72, 137–174 (2003).
Ridlon, J. M., Kang, D. J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2006).
Begley, M., Hill, C. & Gahan, C. G. Bile salt hydrolase activity in probiotics. Appl. Environ. Microbiol. 72, 1729–1738 (2006).
Wells, J. E. & Hylemon, P. B. Identification and characterization of a bile acid 7alpha-dehydroxylation operon in Clostridium sp. strain TO-931, a highly active 7alpha-dehydroxylating strain isolated from human feces. Appl. Environ. Microbiol. 66, 1107–1113 (2000).
Devlin, A. S. & Fischbach, M. A. A biosynthetic pathway for a prominent class of microbiota-derived bile acids. Nat. Chem. Biol. 11, 685–690 (2015).
Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).
Foley, M. H. et al. Bile salt hydrolases shape the bile acid landscape and restrict Clostridioides difficile growth in the murine gut. Nat. Microbiol. 8, 611–628 (2023).
Patterson, A. et al. Bile acids are substrates for amine n-acyl transferase activity by bile salt hydrolase. Preprint at Res. Square https://doi.org/10.21203/rs.3.rs-2050120/v1 (2022).
Shin, D. J. & Wang, L. Bile acid-activated receptors: a review on FXR and other nuclear receptors. Handb. Exp. Pharmacol. 256, 51–72 (2019).
Hang, S. et al. Bile acid metabolites control T(H)17 and T(reg) cell differentiation. Nature 576, 143–148 (2019).
Paik, D. et al. Human gut bacteria produce Τ(Η)17-modulating bile acid metabolites. Nature 603, 907–912 (2022).
Campbell, C. et al. Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells. Nature 581, 475–479 (2020).
Gratwohl, A. et al. Hematopoietic stem cell transplantation: a global perspective. JAMA 303, 1617–1624 (2010).
Gooley, T. A. et al. Reduced mortality after allogeneic hematopoietic-cell transplantation. N. Engl. J. Med. 363, 2091–2101 (2010).
Aljurf, M. et al. Worldwide Network for Blood & Marrow Transplantation (WBMT) special article, challenges facing emerging alternate donor registries. Bone Marrow Transpl. 54, 1179–1188 (2019).
Peled, J. U. et al. Microbiota as predictor of mortality in allogeneic hematopoietic-cell transplantation. N. Engl. J. Med. 382, 822–834 (2020).
Taur, Y. et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood 124, 1174–1182 (2014).
Jenq, R. R. et al. Regulation of intestinal inflammation by microbiota following allogeneic bone marrow transplantation. J. Exp. Med. 209, 903–911 (2012).
Holler, E. et al. Metagenomic analysis of the stool microbiome in patients receiving allogeneic stem cell transplantation: loss of diversity is associated with use of systemic antibiotics and more pronounced in gastrointestinal graft-versus-host disease. Biol. Blood Marrow Transpl. 20, 640–645 (2014).
Burgos da Silva, M. et al. Preservation of fecal microbiome is associated with reduced severity of graft-versus-host disease. Blood 140, 2385–2397 (2022).
Taur, Y. et al. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin. Infect. Dis. 55, 905–914 (2012).
Ara, T. & Hashimoto, D. Novel insights into the mechanism of GVHD-induced tissue damage. Front. Immunol. 12, 713631 (2021).
Zeiser, R. & Blazar, B. R. Acute graft-versus-host disease – biologic process, prevention, and therapy. N. Engl. J. Med. 377, 2167–2179 (2017).
Malard, F., Holler, E., Sandmaier, B. M., Huang, H. & Mohty, M. Acute graft-versus-host disease. Nat. Rev. Dis. Prim. 9, 27 (2023).
Reddy, P., Negrin, R. & Hill, G. R. Mouse models of bone marrow transplantation. Biol. Blood Marrow Transpl. 14, 129–135 (2008).
Palmer, R. H. The formation of bile acid sulfates: a new pathway of bile acid metabolism in humans. Proc. Natl Acad. Sci. USA 58, 1047–1050 (1967).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Gadaleta, R. M. et al. Farnesoid X receptor activation inhibits inflammation and preserves the intestinal barrier in inflammatory bowel disease. Gut 60, 463–472 (2011).
Campbell, C. et al. FXR mediates T cell-intrinsic responses to reduced feeding during infection. Proc. Natl Acad. Sci. USA 117, 33446–33454 (2020).
Sayin, S. I. et al. Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metab. 17, 225–235 (2013).
Shono, Y. et al. Increased GVHD-related mortality with broad-spectrum antibiotic use after allogeneic hematopoietic stem cell transplantation in human patients and mice. Sci. Transl. Med. 8, 339ra371 (2016).
Ruutu, T. et al. Ursodeoxycholic acid for the prevention of hepatic complications in allogeneic stem cell transplantation. Blood 100, 1977–1983 (2002).
Ruutu, T. et al. Improved survival with ursodeoxycholic acid prophylaxis in allogeneic stem cell transplantation: long-term follow-up of a randomized study. Biol. Blood Marrow Transpl. 20, 135–138 (2014).
Guzior, D. V. & Quinn, R. A. Review: microbial transformations of human bile acids. Microbiome 9, 140 (2021).
Claudel, T., Staels, B. & Kuipers, F. The Farnesoid X receptor: a molecular link between bile acid and lipid and glucose metabolism. Arterioscler Thromb. Vasc. Biol. 25, 2020–2030 (2005).
Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. B 34, 187–202 (1972).
Fine, J. P. & Gray, R. J. A proportional hazards model for the subdistribution of a competing risk. Theory Method 94, 496–509 (1997).
Haring, E. et al. Bile acids regulate intestinal antigen presentation and reduce graft-versus-host disease without impairing the graft-versus-leukemia effect. Haematologica 106, 2131–2146 (2021).
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
Peled, J. U. et al. Intestinal microbiota and relapse after hematopoietic-cell transplantation. J. Clin. Oncol. 35, 1650–1659 (2017).
McCarville, J. L., Chen, G. Y., Cuevas, V. D., Troha, K. & Ayres, J. S. Microbiota metabolites in health and disease. Annu. Rev. Immunol. 38, 147–170 (2020).
Yao, L. et al. A selective gut bacterial bile salt hydrolase alters host metabolism. Elife 7, e37182 (2018).
Zhang, Y. et al. Ursodeoxycholic acid accelerates bile acid enterohepatic circulation. Br. J. Pharmacol. 176, 2848–2863 (2019).
Winston, J. A., Rivera, A., Cai, J., Patterson, A. D. & Theriot, C. M. Secondary bile acid ursodeoxycholic acid alters weight, the gut microbiota, and the bile acid pool in conventional mice. PLoS ONE 16, e0246161 (2021).
Song, X. et al. Microbial bile acid metabolites modulate gut RORγ(+) regulatory T cell homeostasis. Nature 577, 410–415 (2020).
Li, W. et al. A bacterial bile acid metabolite modulates T(reg) activity through the nuclear hormone receptor NR4A1. Cell Host Microbe 29, 1366–1377.e9 (2021).
Lee, J. W. J. et al. Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Cell Host Microbe 29, 1294–1304.e4 (2021).
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
Smith, M. et al. Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat. Med. 28, 713–723 (2022).
Stein-Thoeringer, C. K. et al. Lactose drives Enterococcus expansion to promote graft-versus-host disease. Science 366, 1143–1149 (2019).
Mathewson, N. D. et al. Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat. Immunol. 17, 505–513 (2016).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Andrews, S. FastQC v.0.11.9 (Babraham Bioinformatics, 2015).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Peled, J. U., Gomes, A. L. C. & van den Brink, M. R. M. Microbiota and allogeneic hematopoietic-cell transplantation. Reply. N. Engl. J. Med. 382, 2378–2379 (2020).
Miltiadous, O. et al. Early intestinal microbial features are associated with CD4 T-cell recovery after allogeneic hematopoietic transplant. Blood 139, 2758–2769 (2022).
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Dubin, K. A. et al. Diversification and evolution of vancomycin-resistant Enterococcus faecium during intestinal domination. Infect. Immun. 87, e00102–e00119 (2019).
Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 10, e65088 (2021).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).
Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
Kousa, A. I. & Lemarquis, A. The shunPykeR’s guide to single cell analysis (v.1.0.0). GitHub https://github.com/kousaa/shunPykeR?tab=readme-ov-file (2023).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Ntranos, V., Yi, L., Melsted, P. & Pachter, L. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat. Methods 16, 163–166 (2019).
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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Authors and Affiliations
Contributions
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
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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. 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.
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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DOI: https://doi.org/10.1038/s41564-024-01617-w