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Title: A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection
Authors: Singhania, Akul
Verma, Raman
Graham, Christine M.
Lee, Jo
Trang, Trang
Richardson, Matthew
Lecine, Patrick
Leissner, Philippe
Berry, Matthew P. R.
Wilkinson, Robert J.
Kaiser, Karine
Rodrigue, Marc
Woltmann, Gerrit
Haldar, Pranabashis
O'Garra, Anne
First Published: 19-Jun-2018
Publisher: Nature Publishing Group
Citation: Nature Communications, 2018, 9, 2308
Abstract: 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 the information from the entire transcriptome, which includes overabundance of type I interferon-inducible genes and underabundance 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 that methods targeting gene selection across multiple discriminant modules can improve the development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach, we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.
DOI Link: 10.1038/s41467-018-04579-w
eISSN: 2041-1723
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: Sequence data that support the findings of this study has been deposited in NCBI GEO database with the primary accession code GSE107995 and in BioProject with the primary accession code PRJNA422124. TB datasets referenced in this study as comparators are available in GEO with the primary accession codes GSE37250 and GSE79362, in BioProject with the primary accession code PRJNA315611 and in SRA with the primary accession codes SRP071965, GSE20346, GSE68310, GSE42026, GSE60244 and GSE42834.
Appears in Collections:Published Articles, Dept. of Infection, Immunity and Inflammation

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