A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Akul Singhania, Raman Verma, Christine M. Graham, Jo Lee, Trang Tran, Matthew Richardson, Patrick Lecine, Philippe Leissner, Matthew P. R. Berry, Robert J. Wilkinson, Karine Kaiser, Marc Rodrigue, Gerrit Woltmann, Pranabashis Haldar and Anne O’Garra ()
Additional contact information
Akul Singhania: The Francis Crick Institute
Raman Verma: University of Leicester
Christine M. Graham: The Francis Crick Institute
Jo Lee: University of Leicester
Trang Tran: BIOASTER Microbiology Technology Institute
Matthew Richardson: University of Leicester
Patrick Lecine: BIOASTER Microbiology Technology Institute
Philippe Leissner: BIOASTER Microbiology Technology Institute
Matthew P. R. Berry: Imperial College Healthcare NHS Trust, St Mary’s Hospital
Robert J. Wilkinson: University of Cape Town
Karine Kaiser: bioMérieux SA
Marc Rodrigue: bioMérieux SA
Gerrit Woltmann: University of Leicester
Pranabashis Haldar: University of Leicester
Anne O’Garra: The Francis Crick Institute

Nature Communications, 2018, vol. 9, issue 1, 1-17

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-04579-w

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