Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

TB disease classification

ABSTRACT: Validation of gene expression levels to assess classification of TB patients and healthy controls qPCR gene expression profiling. Whole blood gene expression from TB patients (positive in GenXpert assay) and healthy controls; both tuberculin skin test positive (TSTpos) and -negative (TSTneg).

ORGANISM(S): Homo sapiens

SUBMITTER: Jeroen Maertzdorf

PROVIDER: E-GEOD-74092 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Concise gene signature for point-of-care classification of tuberculosis.

Maertzdorf Jeroen J McEwen Gayle G Weiner January J Tian Song S Lader Eric E Schriek Ulrich U Mayanja-Kizza Harriet H Ota Martin M Kenneth John J Kaufmann Stefan He SH

EMBO molecular medicine 20160201 2

There is an urgent need for new tools to combat the ongoing tuberculosis (TB) pandemic. Gene expression profiles based on blood signatures have proved useful in identifying genes that enable classification of TB patients, but have thus far been complex. Using real-time PCR analysis, we evaluated the expression profiles from a large panel of genes in TB patients and healthy individuals in an Indian cohort. Classification models were built and validated for their capacity to discriminate samples f ...[more]

PMID: 26682570

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Project description:Human infection with Mycobacterium tuberculosis results in a continuum of ill-defined, clinical manifestations with stable latent M. tuberculosis infection (LTBI) and severe active disease at the ends. Identifying different states of infection is of importance to tuberculosis (TB) control since risk of developing active disease varies among different asymptomatic states while infectiousness varies among patients with different bacterial burden. We investigated changes in proteome-scale antibody responses during disease progression in a non-human primate model of tuberculosis. We probed M. tuberculosis proteome microarrays with serial sera collected from three infection-outcome groups (active, reactivation, and latent). We found that each infection outcome is associated with characteristic changes in the antibody levels and number of antigenic targets, which suggested an association between antibody responses and bacillary burden. Additional proteome-scale serological profiling of > 400 human TB suspects established that antibody responses are positively associated with bacterial load. Thus tuberculosis-specific antibody levels and number of antigenic targets increases with disease progression. Serum samples collected from adult patients with suspected tuberculosis during a multi-site study was used to probe whole proteome microarrays. Subject recruitment was conducted under uniform protocols approved by the institutional ethics committee at each site. Final diagnosis of active TB was based on positive M. tuberculosis culture results. The active TB patients were further subdivided into smear-positive and negative disease based on results of Ziehl-Neelsen staining of sputum smears for acid fast bacilli. Active TB was excluded as a diagnosis (Non-TB Disease [NTBD] patients) based on having negative M. tuberculosis culture and smear results and on having an alternate diagnosis. All subjects were presumably negative for HIV infection given the very low incidence of HIV infection in the study sites. Sera from 169 TB and 242 NTBD patients were selected for microarray probing. The control sera (n = 14) which was used to generate negative control distribution for each protein were negative to latent M. tuberculosis infection, as indicated by negative results to tuberculin skin test.

Dataset Information

TB disease classification

Publications

Concise gene signature for point-of-care classification of tuberculosis.

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