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Use of a Contained Mycobacterium tuberculosis Mouse Infection Model to Predict Active Disease and Containment in Humans
Abstract Previous studies have identified whole-blood transcriptional risk and disease signatures for tuberculosis; however, several lines of evidence suggest that these signatures primarily reflect bacterial burden, which increases before symptomatic disease. We found that the peripheral blood tran...
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Published in: | The Journal of infectious diseases 2022-05, Vol.225 (10), p.1832-1840 |
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Main Authors: | , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Abstract
Previous studies have identified whole-blood transcriptional risk and disease signatures for tuberculosis; however, several lines of evidence suggest that these signatures primarily reflect bacterial burden, which increases before symptomatic disease. We found that the peripheral blood transcriptome of mice with contained Mycobacterium tuberculosis infection (CMTI) has striking similarities to that of humans with active tuberculosis and that a signature derived from these mice predicts human disease with accuracy comparable to that of signatures derived directly from humans. A set of genes associated with immune defense are up-regulated in mice with CMTI but not in humans with active tuberculosis, suggesting that their up-regulation is associated with bacterial containment. A signature comprising these genes predicts both protection from tuberculosis disease and successful treatment at early time points where current signatures are not predictive. These results suggest that detailed study of the CMTI model may enable identification of biomarkers for human tuberculosis.
We previously described the mouse contained tuberculosis model, which protects mice against aerosol challenge. Here we show that blood RNA signatures derived from this model correlate with disease and tuberculosis containment in multiple human cohorts. |
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ISSN: | 0022-1899 1537-6613 |
DOI: | 10.1093/infdis/jiab130 |