posted on 2018-08-09, 13:21authored byAkul Singhania, Raman Verma, Christine M. Graham, Jo Lee, Trang Trang, Matthew Richardson, Patrick Lecine, Philippe Leissner, Matthew P. R. Berry, Robert J. Wilkinson, Karine Kaiser, Marc Rodrigue, Gerrit Woltmann, Pranabashis Haldar, Anne O'Garra
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.
Funding
We acknowledge the Francis Crick Advanced Sequencing Facility, and Bioinformatics and Biostatistics Science Technology Platforms for their contribution to our sequencing processing. We acknowledge the NIHR Leicester Biomedical Research Centre for their support of the study at Leicester. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We thank the patients for their participation. We thank Asmaà Fritah-Lafont for help in co-ordinating the meetings regarding the study. We thank Dr. Lúcia Moreira-Teixeira for reviewing the manuscript and for the valuable discussion. A.O.G., C.M.G., and A.S. were funded by The Francis Crick Institute (Crick 10126; Crick 10468), which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust; and the sequencing project by the BIOASTER Microbiology Technology Institute, Lyon, France; Medical Diagnostic Discovery Department, bioMérieux SA, Marcy l’Etoile, France; and funded in part by Illumina Inc., San Diego, CA, USA. R.V. and J.L., University of Leicester, were funded by BIOASTER Microbiology Technology Institute, Lyon, France. This work has received, through BIOASTER investment, the funding from the French Government through the Investissement d’Avenir program (Grant No. ANR-10-AIRT-03). R.J.W. was supported by The Francis Crick Institute (Crick 10128), which receives its core funding from Cancer Research UK, the UK Medical Research Council and Wellcome; by Wellcome (104803; 203135); MRC South Africa under strategic health innovation partnerships; and NIH 019 AI 111276.
History
Citation
Nature Communications, 2018, 9, 2308
Author affiliation
/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Infection, Immunity and Inflammation
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.