Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis

Matthew F. Wipperman, Shakti K. Bhattarai, Charles Kyriakos Vorkas, Venkata Suhas Maringati, Ying Taur, Laurent Mathurin, Katherine McAulay, Stalz Charles Vilbrun, Daphie Francois, James Bean, Kathleen F. Walsh, Carl Nathan, Daniel W. Fitzgerald, Michael S. Glickman () and Vanni Bucci ()
Additional contact information
Matthew F. Wipperman: Memorial Sloan Kettering Cancer Center
Shakti K. Bhattarai: University of Massachusetts Medical School
Charles Kyriakos Vorkas: Memorial Sloan Kettering Cancer Center
Venkata Suhas Maringati: University of Massachusetts Medical School
Ying Taur: Memorial Sloan Kettering Cancer Center
Laurent Mathurin: Haitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO)
Katherine McAulay: Weill Cornell Medicine
Stalz Charles Vilbrun: Haitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO)
Daphie Francois: Haitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO)
James Bean: Memorial Sloan Kettering Cancer Center
Kathleen F. Walsh: Weill Cornell Medicine
Carl Nathan: Weill Cornell Graduate School
Daniel W. Fitzgerald: Weill Cornell Medicine
Michael S. Glickman: Memorial Sloan Kettering Cancer Center
Vanni Bucci: University of Massachusetts Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: Abstract 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-21475-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21475-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-21475-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().