A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Singhania, A., Verma, R., Graham, C. M, Lee, J., Tran, T., Richardson, M., Lecine, P., Leissner, P., Berry, M. P R, Wilkinson, R. J, Kaiser, K., Rodrigue, M., Woltmann, G., Haldar, P., & O'Garra, A. Nature Communications, 9:2308, Nature Publishing Group, dec, 2018. ![A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{Singhania2018,
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.},
author = {Singhania, Akul and Verma, Raman and Graham, Christine M and Lee, Jo and Tran, Trang and Richardson, Matthew and Lecine, Patrick and Leissner, Philippe and Berry, Matthew P R and Wilkinson, Robert J and Kaiser, Karine and Rodrigue, Marc and Woltmann, Gerrit and Haldar, Pranabashis and O'Garra, Anne},
doi = {10.1038/s41467-018-04579-w},
file = {:C$\backslash$:/Users/01462563/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Singhania et al. - 2018 - A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection.pdf:pdf},
journal = {Nature Communications},
keywords = {OA,fund{\_}ack,original},
mendeley-tags = {OA,fund{\_}ack,original},
month = {dec},
pages = {2308},
pmid = {29921861},
publisher = {Nature Publishing Group},
title = {{A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection}},
url = {http://www.nature.com/articles/s41467-018-04579-w},
volume = {9},
year = {2018}
}
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