Collaborating Organizations:

Centre for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, USA.

Division of Computational Biomedicine, Boston University School of Medicine and Bioinformatics Program, Boston University, Boston, MA, USA.

Jawaharlal Institute of Postgraduate Medical Education and Research, Pondicherry, India.

Boston Medical Centre, Boston, MA, USA; Boston University, School of Public Health, Boston, MA, USA.

Boston Medical Centre, Boston, MA, USA.

Boston University, School of Public Health, Boston, MA, USA.

Centre for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, USA. 

 

Citation: Leong, S, Yue Zhao, Joseph NM,  Hochberg NS, Sarkar S, Pleskunas J, Hom D, Lakshminarayanan S,  Horsburgh Jr, CR, Roy G, Ellner JJ, Johnson WE, Salgame, P:Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from South IndiaTuberculosis (.Edinb). 2018 Mar;109:41-51. doi: 10.1016/j.tube.2018.01.002. Epub 2018 Jan 31.

Abstract

Several studies have identified blood transcriptomic signatures that can distinguish active from latent Tuberculosis (TB). The purpose of this study was to assess how well these existing gene profiles classify TB disease in a South Indian population. RNA sequencing was performed on whole blood PAXgene samples collected from 28 TB patients and 16 latently TB infected (LTBI) subjects enrolled as part of an ongoing household contact study. Differential gene expression and clustering analyses were performed and compared with explicit predictive testing of TB and LTBI individuals based on established gene signatures. We observed strong predictive performance of TB disease states based on expression of known gene sets (ROC AUC 0.9007-0.9879). Together, our findings indicate that previously reported classifiers generated from different ethnic populations can accurately discriminate active TB from LTBI in South Indian patients. Future work should focus on converting existing gene signatures into a universal TB gene signature for diagnosis, monitoring TB treatment, and evaluating new drug regimens.

Author(s):
Leong, S, Yue Zhao, Joseph NM, Hochberg NS, Sarkar S, Pleskunas J, Hom D, Lakshminarayanan S, Horsburgh Jr, CR, Roy G, Ellner JJ, Johnson WE, Salgame, P
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