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dc.contributor.authorWipperman, Matthew F.
dc.contributor.authorBhattarai, Shakti K.
dc.contributor.authorVorkas, Charles Kyriakos
dc.contributor.authorMaringati, Venkata Suhas
dc.contributor.authorTaur, Ying
dc.contributor.authorMathurin, Laurent
dc.contributor.authorMcAulay, Katherine
dc.contributor.authorVilbrun, Stalz Charles
dc.contributor.authorFrancois, Daphie
dc.contributor.authorBean, James
dc.contributor.authorWalsh, Kathleen F.
dc.contributor.authorNathan, Carl
dc.contributor.authorFitzgerald, Daniel W.
dc.contributor.authorGlickman, Michael S.
dc.contributor.authorBucci, Vanni
dc.date2022-08-11T08:09:59.000
dc.date.accessioned2022-08-23T16:51:13Z
dc.date.available2022-08-23T16:51:13Z
dc.date.issued2021-02-18
dc.date.submitted2021-05-28
dc.identifier.citation<p>Wipperman MF, Bhattarai SK, Vorkas CK, Maringati VS, Taur Y, Mathurin L, McAulay K, Vilbrun SC, Francois D, Bean J, Walsh KF, Nathan C, Fitzgerald DW, Glickman MS, Bucci V. Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis. Nat Commun. 2021 Feb 18;12(1):1141. doi: 10.1038/s41467-021-21475-y. PMID: 33602926; PMCID: PMC7892575. <a href="https://doi.org/10.1038/s41467-021-21475-y">Link to article on publisher's site</a></p>
dc.identifier.issn2041-1723 (Linking)
dc.identifier.doi10.1038/s41467-021-21475-y
dc.identifier.pmid33602926
dc.identifier.urihttp://hdl.handle.net/20.500.14038/41814
dc.description.abstractThe composition of the gastrointestinal microbiota influences systemic immune responses, but how this affects infectious disease pathogenesis and antibiotic therapy outcome is poorly understood. This question is rarely examined in humans due to the difficulty in dissociating the immunologic effects of antibiotic-induced pathogen clearance and microbiome alteration. Here, we analyze data from two longitudinal studies of tuberculosis (TB) therapy (35 and 20 individuals) and a cross sectional study from 55 healthy controls, in which we collected fecal samples (for microbiome analysis), sputum (for determination of Mycobacterium tuberculosis (Mtb) bacterial load), and peripheral blood (for transcriptomic analysis). We decouple microbiome effects from pathogen sterilization by comparing standard TB therapy with an experimental TB treatment that did not reduce Mtb bacterial load. Random forest regression to the microbiome-transcriptome-sputum data from the two longitudinal datasets reveals that renormalization of the TB inflammatory state is associated with Mtb pathogen clearance, increased abundance of Clusters IV and XIVa Clostridia, and decreased abundance of Bacilli and Proteobacteria. We find similar associations when applying machine learning to peripheral gene expression and microbiota profiling in the independent cohort of healthy individuals. Our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and the response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=33602926&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rightsCopyright © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPredictive medicine
dc.subjectTuberculosis
dc.subjectClinical microbiology
dc.subjectMicrobiome
dc.subjectBacterial Infections and Mycoses
dc.subjectMicrobiology
dc.subjectSystems Biology
dc.titleGastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
dc.typeJournal Article
dc.source.journaltitleNature communications
dc.source.volume12
dc.source.issue1
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=5646&amp;context=oapubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/4615
dc.identifier.contextkey23121434
refterms.dateFOA2022-08-23T16:51:13Z
html.description.abstract<p>The composition of the gastrointestinal microbiota influences systemic immune responses, but how this affects infectious disease pathogenesis and antibiotic therapy outcome is poorly understood. This question is rarely examined in humans due to the difficulty in dissociating the immunologic effects of antibiotic-induced pathogen clearance and microbiome alteration. Here, we analyze data from two longitudinal studies of tuberculosis (TB) therapy (35 and 20 individuals) and a cross sectional study from 55 healthy controls, in which we collected fecal samples (for microbiome analysis), sputum (for determination of Mycobacterium tuberculosis (Mtb) bacterial load), and peripheral blood (for transcriptomic analysis). We decouple microbiome effects from pathogen sterilization by comparing standard TB therapy with an experimental TB treatment that did not reduce Mtb bacterial load. Random forest regression to the microbiome-transcriptome-sputum data from the two longitudinal datasets reveals that renormalization of the TB inflammatory state is associated with Mtb pathogen clearance, increased abundance of Clusters IV and XIVa Clostridia, and decreased abundance of Bacilli and Proteobacteria. We find similar associations when applying machine learning to peripheral gene expression and microbiota profiling in the independent cohort of healthy individuals. Our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and the response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation.</p>
dc.identifier.submissionpathoapubs/4615
dc.contributor.departmentProgram in Systems Biology
dc.contributor.departmentCenter for Microbiome Research
dc.contributor.departmentDepartment of Microbiology and Physiological Systems
dc.source.pages1141


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Copyright © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.