PT  - JOURNAL ARTICLE
AU  - Akul Singhania
AU  - Raman Verma
AU  - Christine M. Graham
AU  - Jo Lee
AU  - Tran Trang
AU  - Matthew Richardson
AU  - Patrick Lecine
AU  - Philippe Leissner
AU  - Matthew P.R. Berry
AU  - Robert J. Wilkinson
AU  - Karine Kaiser
AU  - Marc Rodrigue
AU  - Gerrit Woltmann
AU  - Pranabashis Haldar
AU  - Anne O’Garra
TI  - A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection
AID  - 10.1101/216879
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 216879
4099  - http://biorxiv.org/content/early/2018/06/05/216879.short
4100  - http://biorxiv.org/content/early/2018/06/05/216879.full
AB  - Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise information from the entire transcriptome, which includes over-abundance of type I interferon-inducible genes and under-abundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals, or those with acute viral and bacterial infections. We suggest methods targeting gene selection across multiple discriminant modules can improve development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.