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Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
by
McAulay, Katherine
, Fitzgerald, Daniel W.
, Wipperman, Matthew F.
, Francois, Daphie
, Vorkas, Charles Kyriakos
, Bean, James
, Mathurin, Laurent
, Nathan, Carl
, Bucci, Vanni
, Bhattarai, Shakti K.
, Vilbrun, Stalz Charles
, Glickman, Michael S.
, Maringati, Venkata Suhas
, Walsh, Kathleen F.
, Taur, Ying
in
38
/ 38/23
/ 38/91
/ 631/114/2413
/ 631/250/255/1856
/ 631/326/107
/ 631/326/2565/2134
/ Abundance
/ Adult
/ Algorithms
/ Anti-Bacterial Agents - pharmacology
/ Anti-Bacterial Agents - therapeutic use
/ Antibiotics
/ Antitubercular Agents - pharmacology
/ Antitubercular Agents - therapeutic use
/ Bacilli
/ Bacterial Load - drug effects
/ Biodiversity
/ Case-Control Studies
/ Cohort Studies
/ Composition
/ Correlation analysis
/ Data analysis
/ Digestive system
/ Gastrointestinal Microbiome - drug effects
/ Gastrointestinal tract
/ Gene expression
/ Gene Expression Regulation - drug effects
/ Gut microbiota
/ Humanities and Social Sciences
/ Humans
/ Immune clearance
/ Immune response
/ Immunomodulation
/ Infectious diseases
/ Inflammation
/ Inflammation - complications
/ Inflammation - microbiology
/ Inflammation - pathology
/ Inflammatory response
/ Intestinal microflora
/ Learning algorithms
/ Longitudinal studies
/ Machine learning
/ Microbiomes
/ Microbiota
/ Models, Biological
/ multidisciplinary
/ Pathogenesis
/ Pathogens
/ Peripheral blood
/ Regression analysis
/ Reproducibility of Results
/ Science
/ Science (multidisciplinary)
/ Sputum
/ Sterilization
/ Therapy
/ Transcriptomes
/ Tuberculosis
/ Tuberculosis - complications
/ Tuberculosis - drug therapy
/ Tuberculosis - microbiology
/ Tuberculosis - pathology
2021
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Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
by
McAulay, Katherine
, Fitzgerald, Daniel W.
, Wipperman, Matthew F.
, Francois, Daphie
, Vorkas, Charles Kyriakos
, Bean, James
, Mathurin, Laurent
, Nathan, Carl
, Bucci, Vanni
, Bhattarai, Shakti K.
, Vilbrun, Stalz Charles
, Glickman, Michael S.
, Maringati, Venkata Suhas
, Walsh, Kathleen F.
, Taur, Ying
in
38
/ 38/23
/ 38/91
/ 631/114/2413
/ 631/250/255/1856
/ 631/326/107
/ 631/326/2565/2134
/ Abundance
/ Adult
/ Algorithms
/ Anti-Bacterial Agents - pharmacology
/ Anti-Bacterial Agents - therapeutic use
/ Antibiotics
/ Antitubercular Agents - pharmacology
/ Antitubercular Agents - therapeutic use
/ Bacilli
/ Bacterial Load - drug effects
/ Biodiversity
/ Case-Control Studies
/ Cohort Studies
/ Composition
/ Correlation analysis
/ Data analysis
/ Digestive system
/ Gastrointestinal Microbiome - drug effects
/ Gastrointestinal tract
/ Gene expression
/ Gene Expression Regulation - drug effects
/ Gut microbiota
/ Humanities and Social Sciences
/ Humans
/ Immune clearance
/ Immune response
/ Immunomodulation
/ Infectious diseases
/ Inflammation
/ Inflammation - complications
/ Inflammation - microbiology
/ Inflammation - pathology
/ Inflammatory response
/ Intestinal microflora
/ Learning algorithms
/ Longitudinal studies
/ Machine learning
/ Microbiomes
/ Microbiota
/ Models, Biological
/ multidisciplinary
/ Pathogenesis
/ Pathogens
/ Peripheral blood
/ Regression analysis
/ Reproducibility of Results
/ Science
/ Science (multidisciplinary)
/ Sputum
/ Sterilization
/ Therapy
/ Transcriptomes
/ Tuberculosis
/ Tuberculosis - complications
/ Tuberculosis - drug therapy
/ Tuberculosis - microbiology
/ Tuberculosis - pathology
2021
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Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
by
McAulay, Katherine
, Fitzgerald, Daniel W.
, Wipperman, Matthew F.
, Francois, Daphie
, Vorkas, Charles Kyriakos
, Bean, James
, Mathurin, Laurent
, Nathan, Carl
, Bucci, Vanni
, Bhattarai, Shakti K.
, Vilbrun, Stalz Charles
, Glickman, Michael S.
, Maringati, Venkata Suhas
, Walsh, Kathleen F.
, Taur, Ying
in
38
/ 38/23
/ 38/91
/ 631/114/2413
/ 631/250/255/1856
/ 631/326/107
/ 631/326/2565/2134
/ Abundance
/ Adult
/ Algorithms
/ Anti-Bacterial Agents - pharmacology
/ Anti-Bacterial Agents - therapeutic use
/ Antibiotics
/ Antitubercular Agents - pharmacology
/ Antitubercular Agents - therapeutic use
/ Bacilli
/ Bacterial Load - drug effects
/ Biodiversity
/ Case-Control Studies
/ Cohort Studies
/ Composition
/ Correlation analysis
/ Data analysis
/ Digestive system
/ Gastrointestinal Microbiome - drug effects
/ Gastrointestinal tract
/ Gene expression
/ Gene Expression Regulation - drug effects
/ Gut microbiota
/ Humanities and Social Sciences
/ Humans
/ Immune clearance
/ Immune response
/ Immunomodulation
/ Infectious diseases
/ Inflammation
/ Inflammation - complications
/ Inflammation - microbiology
/ Inflammation - pathology
/ Inflammatory response
/ Intestinal microflora
/ Learning algorithms
/ Longitudinal studies
/ Machine learning
/ Microbiomes
/ Microbiota
/ Models, Biological
/ multidisciplinary
/ Pathogenesis
/ Pathogens
/ Peripheral blood
/ Regression analysis
/ Reproducibility of Results
/ Science
/ Science (multidisciplinary)
/ Sputum
/ Sterilization
/ Therapy
/ Transcriptomes
/ Tuberculosis
/ Tuberculosis - complications
/ Tuberculosis - drug therapy
/ Tuberculosis - microbiology
/ Tuberculosis - pathology
2021
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Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
Journal Article
Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis
2021
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Overview
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.
Antibiotic therapy can lead to pathogen clearance, but also to alterations in the gut microbiota and systemic immune responses. Here, the authors analyze data from patients with tuberculosis and healthy subjects to show that pathogen clearance and gut microbiota alterations are independently associated with antibiotic-induced changes of the inflammatory response of active tuberculosis.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 38/23
/ 38/91
/ Adult
/ Anti-Bacterial Agents - pharmacology
/ Anti-Bacterial Agents - therapeutic use
/ Antitubercular Agents - pharmacology
/ Antitubercular Agents - therapeutic use
/ Bacilli
/ Bacterial Load - drug effects
/ Gastrointestinal Microbiome - drug effects
/ Gene Expression Regulation - drug effects
/ Humanities and Social Sciences
/ Humans
/ Inflammation - complications
/ Science
/ Sputum
/ Therapy
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