Data availabilityMetagenomic data from IBD cohorts was downloaded from the Sequence Read Archive (SRA; PRJNA237362, PRJNA385949 and PRJNA400072). Paired LC–MS metabolite profiles are available at the Metabolomics Workbench (project ID PR000639)3. Data from the INTEGRATE and MLVS cohorts were obtained from the European Nucleotide Archive (ENA; PRJEB62473) and the SRA (PRJNA354235), respectively. Sequencing data from our colitis model, oxidative stress assay and IBD metatranscriptomic data have been deposited in the ENA under PRJEB79363, PRJEB114850 and PRJEB79362. Source data are provided with this paper.Code availabilityWorkflows are available via GitLab at https://gitlab.com/mpust/antisense-microbiome. Metastrand is released as an open-source tool available via GitLab at https://gitlab.com/mpust/metastrand.ReferencesQin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. 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Genome Biol. 15, 550 (2014).Article PubMed PubMed Central Google Scholar Download referencesAcknowledgementsWe are grateful to all patients who participated in the studies; their contributions and consent made this work possible. Metatranscriptomic libraries were generated at the Broad Institute by the Microbial Omics Core (MOC) and Genomics Platform with support from B. Berdy and J. Livny. We thank E. Winter for valuable discussions and advice. We acknowledge the Sequencing Facility at the University of Wisconsin Biotechnology Center for generating and sequencing the nanopore and NovaSeq libraries from HMW DNA (RRID: SCR_017759). Whole-genome sequencing of E. coli isolates was performed by PlasmidSaurus. We thank S. Aldrich and T. Reimels for editorial assistance.FundingThis work was supported by the German Research Foundation (DFG, grant no. 530694780) in the form of a Walter-Benjamin fellowship granted to M.-M.P.; by grants from the National Institutes of Health (NIH) (grant nos. P30 DK043351, R01 DK127171 and R01 AI172147) to R.J.X. E.C.-O. and A.C.D. are affiliated with the National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections at University of Liverpool, in partnership with the UK Health Security Agency (UKHSA), in collaboration with University of Warwick. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or the UK Health Security Agency.Author informationAuthors and AffiliationsBroad Institute of MIT and Harvard, Cambridge, MA, USAMarie-Madlen Pust, Ahmed M. T. Mohamed, Martin Stražar, Aranzazu Arias-Rojas, Eric M. Brown, Amanda Bumber, Gleb Pishchany, Chenhao Li, Hera Vlamakis, Damian R. Plichta & Ramnik J. XavierCenter for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAMarie-Madlen Pust, Ahmed M. T. Mohamed, Aranzazu Arias-Rojas, Eric M. Brown, Chenhao Li & Ramnik J. XavierDepartment of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAMarie-Madlen Pust, Chenhao Li & Ramnik J. XavierDepartment of Infection Biology and Microbiomes, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UKEdward Cunningham-Oakes & Alistair C. DarbyNIHR Health Protection Research Unit in Gastrointestinal Infections, Liverpool, UKEdward Cunningham-Oakes & Alistair C. DarbyDepartment of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UKEdward Cunningham-OakesDivision of Gastroenterology, Massachusetts General Hospital, Boston, MA, USAAshwin N. AnanthakrishnanCenter for Genomic Research, Institute of Integrative Biology, University of Liverpool, Liverpool, UKAlistair C. DarbyCenter for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USAHera Vlamakis & Ramnik J. XavierAuthorsMarie-Madlen PustView author publicationsSearch author on:PubMed Google ScholarAhmed M. T. MohamedView author publicationsSearch author on:PubMed Google ScholarMartin StražarView author publicationsSearch author on:PubMed Google ScholarAranzazu Arias-RojasView author publicationsSearch author on:PubMed Google ScholarEdward Cunningham-OakesView author publicationsSearch author on:PubMed Google ScholarEric M. BrownView author publicationsSearch author on:PubMed Google ScholarAmanda BumberView author publicationsSearch author on:PubMed Google ScholarGleb PishchanyView author publicationsSearch author on:PubMed Google ScholarChenhao LiView author publicationsSearch author on:PubMed Google ScholarAshwin N. AnanthakrishnanView author publicationsSearch author on:PubMed Google ScholarAlistair C. DarbyView author publicationsSearch author on:PubMed Google ScholarHera VlamakisView author publicationsSearch author on:PubMed Google ScholarDamian R. PlichtaView author publicationsSearch author on:PubMed Google ScholarRamnik J. XavierView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: M.-M.P. and R.J.X.; methodology: M.-M.P., A.M.T.M., M.S., E.C.-O., E.M.B., A.B., G.P., C.L., A.C.D., H.V., D.R.P. and R.J.X.; validation: M.-M.P., A.M.T.M., E.M.B., A.A.-R., M.S. and A.B.; formal analysis: M.-M.P.; investigation: M.-M.P., A.M.T.M., E.M.B., A.B. and R.J.X.; resources: M.-M.P., E.C.-O., H.V., A.N.A., A.C.D. and R.J.X.; data curation: M.-M.P. and M.S.; writing (original draft preparation): M.-M.P.; writing (review and editing): M.-M.P., A.M.T.M., M.S., A.A.-R., E.M.B., A.B., G.P., C.L., E.C.-O., A.N.A., A.C.D., H.V., D.R.P. and R.J.X.; visualization: M.-M.P.; supervision: M.-M.P., D.R.P. and R.J.X.; project administration and funding acquisition: M.-M.P. and R.J.X.Corresponding authorCorrespondence to Ramnik J. Xavier.Ethics declarationsCompeting interestsR.J.X. is a co-founder of Convergence Bio, scientific advisory board member at Nestlé and Magnet BioMedicine and board director at MoonLake Immunotherapeutics; these organizations had no role in this study. D.R.P. is employed at Novonesis A/S. All other authors declare no competing interests.Peer reviewPeer review informationNature Microbiology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Overview of study cohorts.Dataset 1a. Paired dataset from 295 stool samples of 156 participants across IBD cohorts, comprising 295 metatranscriptomic and 295 metagenomic data. Stool samples were classified as active IBD (Samples = 125), IBD in remission (Samples = 80), or healthy controls (HC, Samples = 90). Dataset 1b. Cross-sectional metabolomic data from 64 patients in Dataset 1a. Untargeted LC–MS profiles (Samples/Datasets = 64) were integrated with matched metatranscriptomic and metagenomic data. Dataset 2a. We re-analyzed paired metatranscriptomic (Datasets = 970) and metagenomic (Datasets = 970) data from stool samples of UK patients with pathogen-confirmed gastroenteritis (INTEGRATE cohort). Dataset 2b. An external cohort of healthy controls, including metatranscriptomics (datasets = 342) and metagenomics (datasets = 342), from 96 men (MLVS cohort). Dataset 3a. We gathered 509 genomes from Bifidobacteria, Eggerthella, and Escherichia species, either obtained through whole-genome sequencing of stool isolates or recovered from publicly available resources. Dataset 3b. DSS colitis model. Longitudinal stool samples were collected from five untreated SPF mice and five SPF mice treated with 2.5% DSS in drinking water for seven days. For each animal, paired metatranscriptomic and short- and long-read metagenomic data were generated. Baseline samples (days –2, –1, and 0) and post-treatment samples (days 13, 14, 15), were pooled per replicate to obtain sufficient input material for Illumina metagenomics (n = 10) and metatranscriptomics (n = 10), and nanopore metagenomics (n = 10). During DSS treatment, stool was collected on day 2, producing 10 metatranscriptomic and 10 short-read metagenomic datasets. Dataset 3c. E. coli K12 oxidative stress perturbation assay. Five independent cultures were exposed to H2O2 stress. DNA sequencing (Oxford Nanopore) was performed at baseline (0 min.), 10 min., 20 min., 30 min., 60 min., and 24 h. In parallel, strand-specific RNA was sequenced at the same time points, with additional sampling at 4 h and 8 h to capture intermediate transcriptional dynamics. For three untreated cultures, DNA was sequenced (Oxford Nanopore) at baseline and 24 h. Figure created in BioRender; Pust, M. https://biorender.com/sk0qhsg (2026).Source dataExtended Data Fig. 2 Simulation-based evaluation of factors influencing DEG recovery in sMTX and unMTX modes.(a) Relative contribution of asMTX DEGs. Relative contribution ratios across a range of adjusted p-values (0.001 - 0.25) were obtained with MTXcon= asMTXcon/ sMTXcon, where asMTXcon is the percentage of asMTX DEGs divided by the percentage of mapped asMTX reads. sMTXcon is the percentage of sMTX DEGs divided by the percentage of mapped sMTX reads. Thus, MTXcon > 1 indicates a greater relative effect of asMTX compared with sMTX on DEGs after normalizing for the number of mapped reads. (b) Bar plots of t-values from linear mixed-effects models showing associations between predictor variables and F1 score. For sMTX (left), F1 was modestly influenced by FDR threshold (t = –31, p