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Multi-omic analysis reveals transkingdom gut dysbiosis in metabolic dysfunction-associated steatotic liver disease

Nature Metabolism volume 7, pages 1476–1492 (2025)Cite this article

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Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common condition linked to obesity and the metabolic syndrome, yet its transkingdom connections have been under-investigated. We performed high-resolution multi-omic profiling—including stool metagenomes, metatranscriptomes and metabolomes—in 211 MASLD cases and 502 controls from a cohort of female nurses. Here we show that MASLD is associated with shifts in 66 gut bacterial species, including widespread enrichment of oral-typical microbes, and transkingdom dysbiosis involving not only bacterial but also viral taxa. Streptococcus spp. are more abundant in non-lean versus lean MASLD, the latter being a paradoxical subtype of a disease typically associated with increased adiposity. These microbial changes correspond with shifts in transcripts and metabolites, including increases in polyamines and acylcarnitines and reductions in secondary bile acids. We highlight gut viral perturbations in MASLD, showing that expansions of bacteriophage targeting oral-typical bacteria correspond to expansions of their bacterial hosts in the gut. We provide a comprehensive resource for understanding MASLD and highlight transkingdom multi-omic microbial shifts as potential contributors to its aetiopathogenesis.

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Fig. 1: Experimental design to link transkingdom gut multi-omics and MASLD.
Fig. 2: Oral-typical microbes drive differences in MASLD gut microbial ecology.
Fig. 3: Functional metagenomic and metatranscriptomic signatures of MASLD reveal disruptions in arginine and ornithine biosynthesis pathways in MASLD and perturbed cholesterol metabolism in lean versus non-lean cases.
Fig. 4: Widespread disruption of gut metabolites in MASLD.
Fig. 5: Comparatively understudied gut viral communities segregate individuals with MASLD and its subtypes.
Fig. 6: Combinatorial gut multi-omic analysis reveals transkingdom dysbiosis and accurately classifies MASLD status.

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Data availability

The raw metagenomic and paired metatranscriptomic sequences are available in the Sequence Read Archive under accession no. PRJNA1246224. Metagenomic sequencing data (under the data category ‘raw reads’), metabolomic profiles (under data category ‘mbx’) and clinical phenotype data have also been deposited at the BIOM-Mass under project ‘MLSC_BtB’ (https://biom-mass.org/projects). This work is also scheduled for inclusion in the next major release of the widely used curatedMetagenomicData package99. Further details on the NHS II parent cohort are available at http://www.nurseshealthstudy.org. Source data are provided with this paper.

Code availability

Analysis programs have been deposited on GitHub at https://github.com/biobakery/MASLD.

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Acknowledgements

This work was supported by the National Institutes of Health grants U01CA176726, T32CA009001 (to H.K.), R35CA253185 (to A.T.C.), R24DK110499 (to C.H.) and K23DK125838 (to L.H.N.), the American Gastroenterological Association Research Foundation’s Research Scholars Award (to L.H.N.) and Crohn’s and Colitis Foundation Career Development Award (to L.H.N.). A.T.C. is supported by the American Cancer Society Research Professorship. L.H.N. is the Massachusetts General Hospital/Chen Institute Transformative Scholar in Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health. Empress Therapeutics supported molecular data generation. This work was also supported by the Massachusetts Life Sciences Center. Computational work was conducted on the Harvard FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University. The authors also acknowledge their colleagues in the Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.

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Author notes

  1. These authors contributed equally: Curtis Huttenhower, Long H. Nguyen.

Authors and Affiliations

  1. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA

    Hanseul Kim, Etienne Nzabarushimana, Jiaxian Shen, Jordan Jensen, Amrisha Bhosle, Lauren McIver, Eric A. Franzosa, Kelsey N. Thompson, Curtis Huttenhower & Long H. Nguyen

  2. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hanseul Kim, Etienne Nzabarushimana, Jiaxian Shen, Nawon Lee, Tracey G. Simon, Andrew T. Chan & Long H. Nguyen

  3. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hanseul Kim, Etienne Nzabarushimana, Jiaxian Shen, Tracey G. Simon, Andrew T. Chan & Long H. Nguyen

  4. Empress Therapeutics, Cambridge, MA, USA

    Paul Nelson & Boris Hayete

  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Amrisha Bhosle, Brittany Berdy, Giana Frongillo, Jonathan Livny, Eric A. Franzosa, Kelsey N. Thompson, Curtis Huttenhower & Long H. Nguyen

  6. The Harvard Chan Microbiome in Public Health Center, Harvard TH Chan School of Public Health, Boston, MA, USA

    Chengchen Li, Christine Everett, Lauren McIver, Eric A. Franzosa, Kelsey N. Thompson, Curtis Huttenhower & Long H. Nguyen

  7. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Christine Everett & Frank B. Hu

  8. Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA

    Frank B. Hu

  9. Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA

    Curtis Huttenhower

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Contributions

C.H. and L.H.N. conceived of the study. H.K., P.N., E.N., J.S. and N.L. performed analyses. J.J., A.B., B.B., G.F., J.L., L.M. and E.A.F. provided technical support. C.L., C.E., F.B.H., T.G.S., A.T.C., B.H. and K.N.T. provided datasets and resources. H.K. wrote the initial draft of the paper. C.H. and L.H.N. co-supervised this work. All authors contributed to writing and revising the paper.

Corresponding authors

Correspondence to Curtis Huttenhower or Long H. Nguyen.

Ethics declarations

Competing interests

P.N. and B.H. were employees of Empress Therapeutics. C.H. is on the Scientific Advisory Board of Empress Therapeutics, Seres Therapeutics and ZOE Nutrition. All others declare no competing interests.

Peer review

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Nature Metabolism thanks Tae Gyu Oh, Sonja Lang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Bacterial alpha- and beta-diversity differ by MASLD case status and MASLD subtype.

a. There was a small but statistically significant difference in beta-diversity based on case/control status, largely attributable to characteristic and previously documented trade-offs between Bacteroidetes and Firmicutes. The plot shows a principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity. Multivariable PERMANOVA adjusting for age, body mass index, physical activity, diabetes mellitus, and diet quality was performed among prevalent species (after 10% prevalence filter) with two-sided p-value reported. b. In 211 individuals with MASLD (compared to 502 non-MASLD controls), alpha-diversity was reduced, which is a broad measure of overall community structure and indicates lower species richness and evenness. Boxplots are presented as median with the lower and upper hinges corresponding to the interquartile range. The lower and upper whiskers show the smallest and largest value within the 1.5 × interquartile range. Statistical comparisons were performed using the Wilcoxon rank-sum test (p-value = 2.1e-6). ***: Two-sided p-value ≤ 0.001 c. Alpha-diversity was similarly lower among 37 participants with lean MASLD and 174 participants with non-lean MASLD. Boxplots are presented as median with the lower and upper hinges corresponding to the 25% and 75%, respectively. The lower and upper whiskers show the smallest and largest value within the 1.5 × interquartile range. Statistical comparisons were performed using the Wilcoxon rank-sum test (p-value = 9.2e-5 comparing non-lean MASLD vs. controls; p-value = 4.1e-4 comparing lean MASLD vs. controls; p-value = 0.12 comparing non-lean MASLD vs. lean MASLD). ***: Two-sided p-value ≤ 0.001.

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Extended Data Fig. 2 Differences in overall metabolomic profiles in MASLD and correlations between bacteria and acylcarnitines by chain length in controls.

a. The community-level metabolomic abundance in MASLD and controls is depicted by principal coordinates analysis (PCoA) using Bray–Curtis dissimilarity. Multivariable PERMANOVA results (with two-sided p-value) demonstrate distinct metabolomic landscapes between the groups. b. Similar to samples from MASLD, among controls, there were clear clustering patterns between bacteria and acylcarnitines based on chain length and dietary intake. Alternative Healthy Eating Index (AHEI) and fibre represent long-term dietary intake using the cumulative average prior to stool collection (Methods). Cells are coloured by Spearman correlation coefficient. *: PFDR < 0.20, adjusted for multiple comparisons between metabolites.

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Extended Data Fig. 3 Distinct correlation patterns between oral-typical bacteria and MASLD-associated metabolites in non-lean vs. lean MASLD.

a. The correlations between oral-typical bacteria and metabolites vary between non-lean and lean MASLD cases. The analysis includes all oral-typical bacteria and MASLD-associated metabolites. Dot size represents the magnitude of the absolute difference in correlations between non-lean and lean MASLD cases (that is, |⍴(bacteria and metabolites) for non-lean - ⍴(bacteria and metabolites) for lean|). Dot colour reflects the directionality of the correlations in each group: for example, green dots signify correlations that are negative for non-lean and positive for lean MASLD, whereas blue dots indicate correlations that are positive for non-lean and negative for lean MASLD, respectively. This visualization highlights the nuanced interplay between oral-typical microbes and metabolites across MASLD phenotypes. b. Selected microbe-metabolite pairs show different interaction patterns between non-lean vs. lean MASLD (Supplementary Table 9). Spearman correlation test was used to fit the line with the corresponding two-sided p-value shown. Bacteria are arcsine square root transformed and metabolites are log2 transformed.

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Extended Data Fig. 4 Distinct correlation patterns between bacterial taxa and long-chain acylcarnitines in non-lean vs. lean MASLD.

The analysis includes bacterial taxa with at least four instances of absolute correlation differences greater than 0.3 and acylcarnitines. Dot size represents the magnitude of the absolute difference in correlations between non-lean and lean MASLD cases (that is, |⍴(bacteria and acylcarnitines) for non-lean - ⍴(bacteria and acylcarnitines) for lean|). Dot colour reflects the directionality of the correlations in each group. This visualization highlights the interplay between microbes and acylcarnitines (based on chain length) across MASLD phenotypes.

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Extended Data Fig. 5 Alpha-diversity and gut viral taxa.

a. Compared to non-MASLD controls (502 individuals), both non-lean MASLD (174 individuals) and lean MASLD (37 individuals) had reduced viral alpha-diversity. Boxplots are presented as median with the lower and upper hinges corresponding to the 25% and 75%, respectively. The lower and upper whiskers show the smallest and largest value within the 1.5 × interquartile range. Statistical comparisons were performed using the Wilcoxon rank-sum test (p-value = 9.1e-4 comparing non-lean MASLD vs. controls; p-value = 7.5e-3 comparing lean MASLD vs. controls; p-value = 0.24 comparing non-lean MASLD vs. lean MASLD). ***: Two-sided p-value ≤ 0.001; **: p-value ≤ 0.01 b. Similar to metagenomic analysis, metatranscriptomic analysis also demonstrated that lean MASLD had a different proportion of classified/unclassified viruses compared to non-lean MASLD. ꞵ coefficients of non-lean MASLD (vs. controls) are plotted against ꞵ coefficients of lean MASLD (vs. controls) from multivariable linear models. Black dots indicate classified or known RNA viral species, while grey dots indicate unclassified RNA viral species. c. As with bacteria/archaea, in MASLD, there were distinct clustering patterns between MASLD-associated viruses (largely unclassified) and acylcarnitines based on chain length. *: PFDR < 0.20, adjusted for multiple comparisons between metabolites. The heatmap includes viral taxa with at least ten significant correlations (PFDR < 0.20) with acylcarnitines.

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Extended Data Fig. 6 Co-occurrence and co-exclusion of oral-typical bacteria and MASLD-associated viruses in lean MASLD.

In lean MASLD, hierarchical all-against-all association testing demonstrated broad co-occurrence and co-exclusion of oral-typical bacteria and MASLD-associated viruses. *: differentially abundant bacteria in MASLD.

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Extended Data Fig. 7 Co-occurrence and co-exclusion of oral-typical bacteria and MASLD-associated viruses in non-lean MASLD.

In non-lean MASLD, hierarchical all-against-all association testing demonstrated broad co-occurrence and co-exclusion of oral-typical bacteria and MASLD-associated viruses. *: differentially abundant bacteria in MASLD.

Source data

Extended Data Fig. 8 Comparison of machine learning models.

Random forest, kernel support vector machine, linear support vector machine, elastic net, LASSO, and ridge regression models using bacterial/archaeal, metabolomic, viral, and metatranscriptomic features along with clinical metadata, with random forest model showing a comparatively high area under the receiver operating curve (AUC = 0.691) and the highest area under the precision-recall curve (0.599).

Source data

Extended Data Fig. 9 Top multi-omic features distinguishing non-lean vs. lean MASLD and comparative classification performance across MASLD subtypes.

a. Feature importance for the comprehensive random forest model (that is, with all multi-omic data types with clinical metadata) differentiating non-lean MASLD cases vs. lean MASLD cases. Z-scores for the top selected features with high median Gini importance (Supplementary Table 12) are displayed as a heatmap. b. The classifications of non-lean vs. lean MASLD cases, non-lean MASLD cases vs. controls, lean MASLD cases vs. controls, non-lean MASLD cases vs. non-lean cases, and lean MASLD cases vs. lean controls were performed using bacteria/archaea, metabolites (MBX), unstratified metatranscriptomic (MTX) pathways, and viral features.

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Extended Data Fig. 10 Strong correlation between cases with and without defined cardiometabolic diagnostic criteria.

a. Confirmed MASLD cases and potential non-MASLD cases demonstrated reasonable correlation given the small sample size of steatotic liver disease without a confirmed cardiometabolic comorbidity (N = 11). ꞵ coefficients for bacteria/archaea among potential non-MASLD cases (vs. controls) are plotted against ꞵ coefficients of confirmed MASLD cases (vs. controls) from multivariable linear models adjusted for age, body mass index, physical activity, diabetes mellitus, and diet quality. Spearman correlation test was used to fit the line, with the corresponding two-sided p-value shown. b. Confirmed MASLD cases and all potential MASLD cases demonstrated high correlation. ꞵ coefficients for bacteria/archaea among all potential MASLD cases (vs. controls) are plotted against ꞵ coefficients of confirmed MASLD cases (vs. controls) from multivariable linear models. Spearman correlation test was used to fit the line, with the corresponding two-sided p-value shown. c. Effect estimates in microbial differences from multivariable linear modelling between MASLD vs. controls with and without MASLD-defining comorbidities were generally concordant. Comparing the effect estimates for cases vs. controls without comorbid conditions (body mass index ≥ 25 kg/m2, have type 2 diabetes, high blood pressure, high cholesterol, or reported using medications for hypertension, diabetes, or high cholesterol) demonstrated high correlation with those for cases vs. all controls. Multivariable ꞵ coefficients for confirmed MASLD cases vs. controls without cardiometabolic comorbidities are on the y-axis vs. ꞵ coefficients for confirmed MASLD cases vs. all controls are on the x-axis. Spearman correlation test was used to fit the line with the corresponding two-sided p-value shown.

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Supplementary information

Supplementary Information

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Reporting Summary

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Kim, H., Nelson, P., Nzabarushimana, E. et al. Multi-omic analysis reveals transkingdom gut dysbiosis in metabolic dysfunction-associated steatotic liver disease. Nat Metab 7, 1476–1492 (2025). https://doi.org/10.1038/s42255-025-01318-6

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