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Bacteria and bacteriophage consortia are associated with protective intestinal metabolites in patients receiving stem cell transplantation

Nature Cancer volume 5, pages 187–208 (2024)Cite this article

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Abstract

The microbiome is a predictor of clinical outcome in patients receiving allogeneic hematopoietic stem cell transplantation (allo-SCT). Microbiota-derived metabolites can modulate these outcomes. How bacteria, fungi and viruses contribute to the production of intestinal metabolites is still unclear. We combined amplicon sequencing, viral metagenomics and targeted metabolomics from stool samples of patients receiving allo-SCT (n = 78) and uncovered a microbiome signature of Lachnospiraceae and Oscillospiraceae and their associated bacteriophages, correlating with the production of immunomodulatory metabolites (IMMs). Moreover, we established the IMM risk index (IMM-RI), which was associated with improved survival and reduced relapse. A high abundance of short-chain fatty acid-biosynthesis pathways, specifically butyric acid via butyryl-coenzyme A (CoA):acetate CoA-transferase (BCoAT, which catalyzes EC 2.8.3.8) was detected in IMM-RI low-risk patients, and virome genome assembly identified two bacteriophages encoding BCoAT as an auxiliary metabolic gene. In conclusion, our study identifies a microbiome signature associated with protective IMMs and provides a rationale for considering metabolite-producing consortia and metabolite formulations as microbiome-based therapies.

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Fig. 1: Study design and sampling scheme.
Fig. 2: Longitudinal dynamics of the intestinal bacteriome, fungome, virome and metabolome in patients receiving allo-SCT.
Fig. 3: MOFA identifies bacterial and bacteriophage consortia associated with intestinal IMMs.
Fig. 4: MOFA factors are associated with outcome, and levels of MOFA-identified IMMs decline progressively after allo-SCT.
Fig. 5: An IMM-RI is associated with OS, relapse, TRM and GvHD.
Fig. 6: Microbial SCFA- and butyric acid-biosynthesis pathways are more abundant in IMM-RI low-risk patients.
Fig. 7: Detection of the BCoAT AMG encoded in VC-1 and VC-2 bacteriophages.
Fig. 8: Onset of GI-GvHD and initiation of antibiotics deplete IMMs.

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Data availability

Microbial sequencing (bacterial and fungal amplicon data and viral metagenomic sequencing data) that support the findings of this study have been deposited at the European Nucleotide Archive under accession number PRJEB53547 (https://www.ebi.ac.uk/ena/browser/view/PRJEB53547). MS data have been deposited at Zenodo under accession number 6603017 (https://zenodo.org/record/6603017). Both repositories are annotated with clinical metadata. All other data supporting the findings of this study are available from the corresponding author on reasonable request. The stool samples analyzed in this study comprised a unique biosample collection and have been expended for the analyses performed in this study. No additional material is available. Source data are provided with this paper.

Code availability

The scripts and packages used for whole shotgun metagenomic sequencing, viral metagenomic sequencing and MOFA have been deposited at GitHub (https://github.com/guardianre/MOFA-in-allo-SCT.git).

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Acknowledgements

This study was supported by the Deutsche Forschungsgemeinschaft (Projektnummer 360372040—SFB 1335 (to S.H., H.P., K.S. and F.B.), Projektnummer 395357507—SFB 1371 (to L.D., H.P., J. Ruland, E.T.O., J.C.F., K.-P.J., M.Q., K.S., S.J., D.H.B. and E.H.), Projektnummer 324392634—TRR 221 (to H.P., W.H., D.Wolff, M.E., D.Weber, A.G. and E.H.), Projektnummer 464797012—SPP 2330 (to L.D.), DE 2360/6-1 (to L.D.), BA 2851/6-1 (to F.B.), DE 2360/1-1 (Emmy Noether Program, to L.D.)), German Cancer Aid (70114547 to H.P.), the Wilhelm Sander Foundation (2021.040.1 to H.P.), the Bavarian Cancer Research Center (BZKF to H.P. and F.B.), the European Hematology Association (to H.P.), the Else Kröner-Fresenius-Stiftung (funding line, Else-Kröner Forschungskolleg to E.T.O. and E.M.), the Bavarian State Ministry of Science and Art (to H.P.), the DKMS Foundation for Giving Life (to H.P.), the German José Carreras Leukemia Foundation (grant DJCLS 01 GvHD/2016 to E.H.), the European Research Commission (project BCM-UPS, grant no. 682473 to F.B. and EU ERC StG—GA no. 803077 to L.D.), the Deutsches Konsortium für Translationale Krebsforschung (fellowship to E.T.O.) and the Deutsche Gesellschaft für Innere Medizin (fellowship to E.T.O.). H.P. is supported by the EMBO Young Investigator Program. We thank the REG allo-SCT team, especially H. Bremm, M. Caioni, T. Schifferstein and Y. Schumann for their help in collecting and cryopreserving stool samples and S. Gleich for data management. We express gratitude to the MUC allo-SCT team, especially K. Braitsch, K. Koch, L. Oßwald, K. Nickel and the entire D2a nursing staff for their excellency in sample acquisition. We acknowledge A. Conrad and W. Johannes (K.-P.J. laboratory) for help with biobanking and A. Wahida for logistical support as well as the MUC ColoBAC team: R. Schmid, M. Middelhoff, J. Horstmann and L. Fricke. We thank the tissue bank of MRI and TUM (MTBIO) for excellent technical support. We acknowledge R.R. Jenq for critical review of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Erik Thiele Orberg, Elisabeth Meedt, Andreas Hiergeist, Jinling Xue, André Gessner, Li Deng, Ernst Holler, Hendrik Poeck.

Authors and Affiliations

  1. Department of Internal Medicine III, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany

    Erik Thiele Orberg, Melanie Tiefgraber, Sophia Göldel, Tina Eismann, Alix Schwarz, Simon Heidegger, Peter Herhaus, Mareike Verbeek & Florian Bassermann

  2. German Cancer Consortium (DKTK), partner-site Munich, a partnership between DKFZ and Klinikum rechts der Isar, Munich, Germany

    Erik Thiele Orberg, Katja Steiger, Jürgen Ruland & Florian Bassermann

  3. Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany

    Erik Thiele Orberg, Simon Heidegger, Jürgen Ruland & Florian Bassermann

  4. Department of Internal Medicine III, Hematology and Medical Oncology, University Medical Center, Regensburg, Germany

    Elisabeth Meedt, Paul Heinrich, Sakhila Ghimire, Sascha Göttert, Daniela Weber, Matthias Edinger, Daniel Wolff, Wolfgang Herr, Ernst Holler & Hendrik Poeck

  5. Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany

    Andreas Hiergeist & André Gessner

  6. Institute of Virology, Helmholtz Zentrum Munich, Munich, Germany

    Jinling Xue, Jinlong Ru & Li Deng

  7. Chair of Prevention for Microbial Infectious Disease, Central Institute of Disease Prevention and School of Life Sciences, Technical University of Munich, Munich, Germany

    Jinling Xue, Jinlong Ru & Li Deng

  8. Leibniz Institute for Immunotherapy, Regensburg, Germany

    Paul Heinrich, Matthias Edinger & Hendrik Poeck

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

    Oriana Miltiadous

  10. Department of Immunology, Sloan Kettering Institute, New York, NY, USA

    Sarah Lindner & Marcel R. M. van den Brink

  11. Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany

    Sebastian Jarosch & Dirk H. Busch

  12. Comparative Experimental Pathology, School of Medicine, Technical University of Munich, Munich, Germany

    Katja Steiger

  13. Institute of Pathology, School of Medicine, Technical University of Munich, Munich, Germany

    Katja Steiger

  14. Department of Internal Medicine II, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany

    Christian Schulz

  15. German Center for Infection Research (DZIF), partner site Munich, Munich, Germany

    Christian Schulz & Dirk H. Busch

  16. Bavarian Center for Biomolecular Mass Spectrometry, School of Life Sciences, Technical University of Munich, Freising, Germany

    Michael Gigl & Karin Kleigrewe

  17. Department of Radiation Oncology, School of Medicine, Technical University of Munich (TUM), Klinikum rechts der Isar TUM, Munich, Germany

    Julius C. Fischer

  18. Department of Surgery, School of Medicine, Technical University of Munich (TUM), Klinikum rechts der Isar TUM, Munich, Germany

    Klaus-Peter Janssen

  19. Department of Internal Medicine II, University Medical Center, Freiburg, Germany

    Michael Quante

  20. Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany

    Jürgen Ruland

  21. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Marcel R. M. van den Brink

  22. Weill Cornell Medical College, New York, NY, USA

    Marcel R. M. van den Brink

  23. Bavarian Cancer Research Center (BZKF), Munich, Germany

    Florian Bassermann

  24. Bavarian Cancer Research Center (BZKF), Regensburg, Germany

    Hendrik Poeck

Authors
  1. Erik Thiele Orberg
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  2. Elisabeth Meedt
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Contributions

Conceptualization: E.T.O., L.D., E.H., H.P. Methodology: J.Ru, A.H., J.X., M.G., K.K., S.J., D.H.B., K.S., M.Q. Formal analysis: E.T.O., E.M., A.H., J.X., P.Heinrich. Investigation: E.T.O., E.M., S. Ghimire, T.E., S. Göldel, A.S. Resources: C.S., D.Weber, D.Wolff, M.E., D.H.B., W.H., P.Herhaus, M.V., F.B., M.Q., K.-P.J. Data curation: E.T.O., E.M., M.T. Writing (original draft): E.T.O., E.M. Writing (review and editing): E.T.O., E.M., P.Heinrich, O.M., J.X., F.B., S. Göttert, S.L., J.C.F., S.H., M.R.M.v.d.B., L.D., E.H., H.P. Visualization: E.T.O., E.M., A.H., J.X., J.R., T.E., S. Göldel, A.S., P.Heinrich. Supervision: E.T.O., D.H.B., A.G., L.D., E.H. and H.P. Project administration: E.T.O., E.M. Contributions are specified according to CRediT (Contributor Roles Taxonomy). All authors read, revised and approved the final draft.

Corresponding authors

Correspondence to Erik Thiele Orberg or Hendrik Poeck.

Ethics declarations

Competing interests

E.T.O.: honoraria (BeiGene), travel (BeiGene). M.R.M.v.d.B.: 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, GlaxoSmithKline, Da Volterra, Thymofox, Garuda, Novartis (spouse), Synthekine (spouse), BeiGene (spouse), 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). E.H.: scientific advisory board (MaaT Pharma, PharmaBiome (Novartis–Medac)), honoraria and research funding (Neovii, Novartis and Medac). H.P.: honoraria (Novartis, Gilead–Kite, AbbVie, Pfizer, MSD, Bristol Myers Squibb (BMS), Servier, Janssen-Cilag), travel (Janssen-Cilag, Novartis, AbbVie, Novartis, Jazz, Gilead–Kite, AMGEN), research (BMS). C.S.: honoraria (Lilly, Tillotts, Juvisé), research (Luvos). S.H. has been a consultant for BMS, Novartis, Merck, AbbVie and Roche; has received research funding from BMS and Novartis; and is an employee of and holds equity interest in Roche–Genentech. M.V.: honoraria from Novartis, Medac, AbbVie and Jazz Pharmaceuticals as well as travel grants from Medac, Gilead and Jazz Pharmaceuticals. A.S.: honoraria (BeiGene), travel (BeiGene). W.H.: honoraria (Amgen, Novartis), travel (Amgen, Janssen-Cilag). The remaining authors declare no competing interests.

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Nature Cancer thanks Stewart J. Anderson and Gerard Socie for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Longitudinal dynamics of bacterial, fungal and viral community compositions in patients receiving allo-SCT.

a) Quantification of the bacterial and fungal load by 16S and 28S rDNA copy numbers per gram of stool at time-points relative to all-SCT (Day 0). Solid lines represent the smoothed conditional means for the entire cohort (black), Munich (MUC, orange) or Regensburg (REG, red) calculated by locally weighted regression using the locally estimated scatterplot smoothing (LOESS) method. The gray shading indicates the 95% confidence interval for entire cohort. Each individual patient stool sample is plotted as a gray dot superimposed on the graph. The number of samples is indicated. b) Beta diversity analysis illustrating changes in bacteriome, fungome and virome by time-points relative to allo-SCT (Day 0). For bacteriome and fungome, beta diversity was calculated by weighted UniFrac. For virome, beta diversity was calculated by Bray-Curtis dissimilarity. Distances were projected in Principal coordinate analysis (PCoA). Bacteriome: Comparisons between Day +7 (p = 0.001), Day + 14 (p = 0.001), Day +21 (p = 0.001), Day +28 (p = 0.001) vs baseline (Day −7) are significant (pairwise Adonis test adjusted for multiple comparisons). Virome: Comparisons between Day +7 (p = 0.013) and Day +28 (p = 0.001) vs baseline are significant (PERMANOVA test). The number of samples is indicated. c) Beta diversity analysis illustrating changes in bacteriome, fungome and virome according to study center (MUC or REG) and whether patients received antifungal therapy (’No Antifungals’ or ‘Antifungals’). For bacteriome and virome, beta diversity was calculated by weighted UniFrac. For virome, beta diversity was calculated by Bray-Curtis dissimilarity. Distances were projected in Principal coordinate analysis (PCoA). The number of samples is indicated.

Source data

Extended Data Fig. 2 Longitudinal dynamics of SCFAs, BCFAs, IIMs, PBAs and SBAs in patients receiving in allo-SCT.

a) Heatmap of normalized Panel 1 metabolite levels in stool samples of allo-SCT patients averaged by time-points relative to allo-SCT. Normalized concentrations are indicated in the adjacent color legend. Clustering based on metabolite expression patterns using the Ward algorithm. Distance measure is Euclidian. The number of samples is indicated. b) Heatmap of normalized Panel 2 metabolite levels in stool samples of allo-SCT patients averaged by time-points relative to allo-SCT. Normalized concentrations are indicated in the adjacent color legend. Clustering based on metabolite expression patterns using the Ward algorithm. Distance measure is Euclidian. The number of samples is indicated. c) Principal component analysis (PCA) of Panel 1 metabolite profiles by time-points relative to allo-SCT. Comparisons at Day 0 (p = 0.004), Day +7 (p = 0.001), Day +14 (p = 0.001), Day +21 (p = 0.001) and Day +28 (p = 0.001) vs Day -7 are significant (pairwise Adonis test of Euclidean distances adjusted for multiple comparisons). d) PCA of Panel 2 metabolite profiles by time-points relative to allo-SCT. Comparisons at Day +7 (p = 0.001), Day +14 (p = 0.001), Day +21 (p = 0.001) and Day +28 (p = 0.001) vs Day −7 are significant (pairwise Adonis test of Euclidean distances adjusted for multiple comparisons).

Extended Data Fig. 3 MOFA (multi-omics factor analysis) and MEFISTO (a method for the functional integration of spatial omics data) in allo-SCT patients.

a) Correlation between MOFA-identified Factors and normalized intestinal metabolite concentrations. Associations between Factor values and metabolites were analyzed via Pearson’s correlation. The correlation coefficient is indicated in the adjacent color legend. The p-values associated with the correlations were corrected for multiple testing with the FDR approach. b) Top 15 Features in bacteriome, virome and metabolites in Factor 4 in descending order according to Feature weight. Larger weights indicate a higher correlation with that Factor, while the positive or negative sign indicates the directionality of that variation, that is, ‘+’ indicates a positive association, ‘−’ a negative association. c) Bar plot of time scale parameters assigned to Factors 1 through 10 identified by MEFISTO. MEFISTO assigns a time scale value between 0 and 1 to each Factor, which reflects the degree to which that Factor is dependent on time. A value of 0 implies no time-dependency, a value of 1 strong time-dependency. Of note, results pertaining to the identified Factors, their weight/covariance structure, variance explained across omics entities and Factor values obtained by MEFISTO modelling were almost identical and thus comparable to the output of our original MOFA model. d) Heatmap of normalized abundance of viral contigs assigned to eukaryotic and prokaryotic viruses at time-points relative to allo-SCT. The number of samples is indicated.

Extended Data Fig. 4 Correlation between top 15 bacterial and metabolite as well as bacterial and viral high-weight Features in Factors 1, 3 and 4.

a) Heatmaps of pairwise Pearson’s correlations of top Features across different omics modalities. The Feature values of the top 15 high-weight Features of a given omics modality were correlated with that of another omics modality. (Left) bacterial taxa at genus level and metabolites, (right) bacterial taxa at genus level and bacteriophages at species level. The correlation coefficient is indicated in the adjacent color legend. The p-values associated with Pearson correlation have been corrected for multiple testing by applying the FDR approach to each set of correlations of two omics modalities of a given Factor. b) As in a) for Factor 3. c) As in a) for Factor 4.

Source data

Extended Data Fig. 5 Co-abundance of MOFA-identified bacterial and viral Features is associated with high-level IMM expression, which declines progressively after allo-SCT.

a) Correlation scatter plots of high-weight Features within Factor 4, comparing normalized abundance of bacterial taxa at genus level (x-axis) with that of bacteriophages at species level (y-axis) together with the metabolite propionic acid. Dots represent samples from individual patients at different time points (91 samples from 45 patients), all of which had both 16S and viral metagenomic sequencing data. Dots are colored by intestinal levels of propionic acid, or in grey if no propionic acid data was available. Normalized concentrations of metabolites are indicated in the adjacent heatmap. Associations between bacterial genera and viral species were analyzed via Pearson correlation and linear regression. The R- and p-values are indicated in each plot. The regression line is drawn in blue and the 95 % confidence interval of the regression line is shaded in grey. b) As in a) for Features in Factor 3 and the metabolite isovaleric acid. c) As in a) for Features in Factor 3 and the metabolite DAT. d) As in a) for Features in Factor 3 and the metabolite ICA. e) Levels of intestinal microbiota-derived metabolites at time-points relative to allo-SCT (Day 0) in µmol per gram of dried stool measured by targeted mass spectrometry. Number of patients per time-point is indicated in Fig. 4d. Significance by two-sided Kruskal-Wallis test corrected for multiple testing via Dunn’s test of all time-points against baseline (Day -7). In the box plots, the box ranges from the 25th to 75th percentiles. The line in the middle of the box is plotted at the median. The whiskers are drawn down to the 10th and up to the 90th percentile. Points below and above the whiskers are drawn as individual points. Points indicate individual patient stool samples sampled at the specified time-points. SBA…secondary bile acids; UDCA…ursodeoxycholic acid; TUDCA…tauroursodeoxycholic acid.

Extended Data Fig. 6 Intestinal bacterial but not fungal nor viral diversity predicts outcome after allo-SCT.

a) 2-year OS after Day 21 stratified according to higher and lower fungal (left) and viral (right) alpha diversity. The mean alpha diversity of patient samples at Days +7–21 was calculated and patients were stratified into higher (blue curve) and lower (red curve) diversity groups, defined as above or below the center-specific median Inverse Simpson’s diversity index. For fungome, there were 12 deaths among 32 patients in the lower-diversity group (estimated mean survival time 521 (95% CI 428-614) days) and 10 deaths among 30 patients in the higher-diversity group (estimated mean survival time 597 (95% CI 522-672) days). For virome, there were 4 deaths among 16 patients in the lower-diversity group (estimated mean survival time 587 (95% CI 479-696) days) and 4 deaths among 15 patients in the higher-diversity group (estimated mean survival time 576 (95% CI 450-701) days). Analysis via Kaplan–Meier estimator, significance according to the log-rank test. b) 2-year cumulative incidence of relapse and transplantation-related mortality (TRM) in a competing risks analysis stratified according to higher and lower bacterial (top), fungal (middle) and viral (bottom) alpha diversity, calculated as in a). For bacteriome, there were 12 cases of TRM and 6 relapses among 35 patients in the lower-diversity group and one case of TRM and 8 relapses among 33 patients in the higher-diversity group. For fungome, there were 7 cases of TRM and 6 relapses among 32 patients in the lower-diversity group and 5 cases of TRM and 7 relapses among 30 patients in the lower-diversity. For virome, there was one case of TRM and 3 relapses among 16 patients in the lower-diversity group and 3 cases of TRM and 2 relapses among 15 patients in the higher-diversity. Significance according to Gray’s test. c) 2-year cumulative incidence of GvHD and its competing risk Death in a competing risks analysis stratified by alpha diversity as in a). For bacteriome, there were 12 cases of GvHD and 6 deaths among 35 patients in the lower-diversity group and 1 case of GvHD and 8 deaths among 33 patients in the higher-diversity group. For fungome, there were 7 cases of GvHD and 6 deaths among 32 patients in the lower-diversity group and 5 cases of GvHD and 7 deaths among 30 patients in the lower-diversity group. For virome, there was one case of GvHD and 3 deaths among 16 patients in the lower-diversity group and 3 cases of GvHD and 2 deaths among 15 patients in the higher-diversity group. Statistics as in b).

Extended Data Fig. 7 Characterization of differentially abundant microbial pathways and the species which encode them in IMM-RI low- vs high-risk patients via whole shotgun metagenomic sequencing.

a) Species-level association with MetaCyc pathways differentially abundant between IMM-RI low and high-risk patients (as shown in Fig. 6). The relative abundance and species-level identity of taxa encoding the indicated MetaCyc pathways are shown. b) Box plot of relative abundance of indicated MetaCyc pathways in IMM-RI low- vs high-risk patients. In the box plots, the box ranges from the 25th to 75th percentiles. The line in the middle of the box is plotted at the mean. The whiskers are drawn down to the minimum and up to the maximum. Samples outside the 1.5-fold IQR were regarded as outliers. Patient samples are plotted as a point superimposed on the graph (high IMM-RI: n = 10 patients; low IMM-RI: n = 7 patients). Significance by 1-sided Wilcoxon Rank Sum Test corrected for multiple comparisons via the Benjamini & Hochberg correction for multiple testing. c) Box plot of relative abundance of acetic acid and propionic acid superclasses in IMM-RI low- vs high-risk patients. Plots, numbers and statistics as in b).

Source data

Extended Data Fig. 8 BCoAT-coding VC-1 and VC-2 bacteriophages are associated with MOFA Factors 1 and 3 and the IMM-RI.

a) Gene alignment plot of VC-2. The identity overlap (in percent) is indicated in the adjacent color legend. The BCoAT AMG is highlighted in red. b) Gene alignment plot of VC-1 as in a). c) Box plots of Factor values for Factors 1 and 3 (averaged across time-points Days +7–21) according to whether VC-1 was detected by viral metagenomic sequencing (‘Yes’) or not (‘No‘). The center line corresponds to the median, the box ranges from the 25th to the 75th percentile. Whisker length corresponds to the largest/lowest data point that does not exceed the 75th/25th percentile +/− 1.5-fold IQR. Blue: detected (n = 16 patients); red: not detected (n = 13 patients). Significance by two-tailed Mann-Whitney-U-test corrected for multiple testing via FDR. d) As in c) for VC-2. Blue: detected (n = 13 patients); red: not detected (n = 16 patients). e) Detection of the BCoAT-coding VC-1 in patient samples stratified according to IMM-RI. Bar plots show percentage, exact values are provided. The numbers of samples screened vs those in which VC-1 was detected is indicated below. f) As in e) for VC-2.

Extended Data Fig. 9 Onset of acute GI-GvHD shifts intestinal bacterial and viral communities and impacts IMM expression profiles.

a) Quantification of the bacterial and fungal load by 16S and 28S rDNA copy numbers per gram of stool stratified by patients diagnosed with GI-GvHD (GI-GvHD, red) vs control allo-SCT patients (No GI-GvHD, blue). The box ranges from the 25th to 75th percentiles. The line in the middle of the box is plotted at the median. The whiskers are drawn down to the minimum and maximum. Samples outside the 1.5-fold IQR were regarded as outliers. Significance by two-tailed Wilcoxon rank sum test adjusted for multiple comparisons via the Benjamini & Hochberg procedure. Each individual patient is plotted as a point superimposed on the graph. Number of patients: for 16S & 28S n = 22 vs n = 37, corresponding to ‘No GI-GvHD‘ vs ‘GvHD‘, respectively. b) Intestinal fungal alpha diversity (Richness, Inverse Simpson’s diversity index) stratified by patients diagnosed with GI-GvHD (GI-GvHD) vs control allo-SCT patients (No GI-GvHD). Plots, numbers and statistics as in a). c) Beta diversity analysis illustrating changes in bacteriome, fungome and virome in patients with GI-GvHD vs control allo-SCT patients. For bacteriome and fungome, beta diversity was calculated by weighted UniFrac. For virome, beta diversity was calculated by Bray-Curtis dissimilarity. Distances were projected in PCoA. Bacteriome: Comparison between patients with GI-GvHD vs No GI-GvHD is significant (p = 0.026 by one-side pairwise Adonis test). Virome: Comparison between patients with GI-GvHD vs No GI-GvHD is significant (p = 0.03 by PERMANOVA test). d) Levels of indicated microbiota-derived metabolites stratified by patients diagnosed with GI-GvHD vs control allo-SCT patients. Significance by two-tailed Mann-Whitney test. In the scatter-dot plots, the box is plotted at the mean. Error bars indicate standard deviation. Each individual patient is plotted as a point superimposed on the graph. The number of patients per group are indicated in the legend.

Extended Data Fig. 10 Impact of antibiotics on bacterial abundance, bacterial and viral community composition and IMM expression profiles.

a) Quantification of the bacterial and fungal load by 16S and 28S rDNA copy numbers per gram of stool stratified by antibiotic exposure: No Antibiotics (‘No ABX’, blue) (blue) vs Antibiotics (‘ABX’, red). Once a patient was treated with antibiotics, the current and all subsequent samples were classified as ‘ABX’. The box ranges from the 25th to 75th percentiles. The line in the middle of the box is plotted at the median. The whiskers are drawn down to the minimum and maximum. Samples outside the 1.5-fold IQR were regarded as outliers. Significance by two-tailed Wilcoxon rank sum test adjusted for multiple comparisons via the Benjamini & Hochberg procedure. Each individual patient is plotted as a point superimposed on the graph. Number of patients: for 16S n = 59 vs n = 70, for 28S n = 56 vs n = 70, corresponding to ‘No ABX’ vs ‘ABX’, respectively. b) Intestinal fungal alpha diversity (Richness, Inverse Simpson’s diversity index) in paired patient samples according to antibiotic status as in a). Plots, numbers and statistics as in a). c) Beta diversity analysis illustrating the impact of antibiotics on the intestinal bacterial, fungal and viral communities. Each point represents individual patient samples annotated with metadata regarding concomitant antibiotic therapy. For bacteriome and virome, beta diversity was calculated by weighted UniFrac. For virome, beta diversity was calculated by Bray-Curtis dissimilarity. Distances were projected in PCoA. Bacteriome: Comparison between patients with ‘No ABX’ vs ‘ABX’ is significant (p = 0.001 by one-side pairwise Adonis test). Virome: Comparison between patients with ‘No ABX’ vs ‘ABX’ is significant (p = 0.003 by PERMANOVA test). d) Levels of indicated microbiota-derived metabolites in paired patient samples before and after exposure to ABX. Significance by two-tailed Wilcoxon matched-pairs signed rank test. In the scatter-dot plots, the box is plotted at the mean. Error bars indicate standard deviation. Each individual patient is plotted as a point superimposed on the graph. The number of patients per group are indicated in the legend.

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Thiele Orberg, E., Meedt, E., Hiergeist, A. et al. Bacteria and bacteriophage consortia are associated with protective intestinal metabolites in patients receiving stem cell transplantation. Nat Cancer 5, 187–208 (2024). https://doi.org/10.1038/s43018-023-00669-x

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