Identification of reduced host transcriptomic signatures for tuberculosis disease and digital PCR-based validation and quantification

Identification of reduced host transcriptomic signatures for tuberculosis disease and digital PCR-based validation and quantification. Gliddon, H. D, Kaforou, M., Alikian, M., Habgood-Coote, D., Zhou, C., Oni, T., Anderson, S. T, Brent, A. J, Crampin, A. C., Eley, B., Heyderman, R., Kern, F., Langford, P. R, Ottenhoff, T. H M, Hibberd, M. L, French, N., Wright, V. J, Dockrell, H. M, Coin, L. J, Wilkinson, R. J, & Levin, M. Frontiers in Immunology, 12:637164, Frontiers, mar, 2021.

Paper doi abstract bibtex

Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate diagnostic test development. To identify minimal transcript signatures, we applied a transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were further investigated using reverse transcriptase (RT)—digital PCR (dPCR). A four-transcript signature ( GBP6, TMCC1, PRDM1 , and ARG1 ) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8% (CI 95% 82.2–100%). A three-transcript signature ( FCGR1A, ZNF296, and C1QB ) differentiated TB from LTBI (AUC 97.3%, CI 95% : 93.3–100%), regardless of HIV. These signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.

@article{Gliddon2021,
abstract = {Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate diagnostic test development. To identify minimal transcript signatures, we applied a transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were further investigated using reverse transcriptase (RT)—digital PCR (dPCR). A four-transcript signature ( GBP6, TMCC1, PRDM1 , and ARG1 ) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8{\%} (CI 95{\%} 82.2–100{\%}). A three-transcript signature ( FCGR1A, ZNF296, and C1QB ) differentiated TB from LTBI (AUC 97.3{\%}, CI 95{\%} : 93.3–100{\%}), regardless of HIV. These signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.},
author = {Gliddon, Harriet D and Kaforou, Myrsini and Alikian, Mary and Habgood-Coote, Dominic and Zhou, Chenxi and Oni, Tolu and Anderson, Suzanne T and Brent, Andrew J and Crampin, Amelia C. and Eley, Brian and Heyderman, Robert and Kern, Florian and Langford, Paul R and Ottenhoff, Tom H M and Hibberd, Martin L and French, Neil and Wright, Victoria J and Dockrell, Hazel M and Coin, Lachlan J and Wilkinson, Robert J and Levin, Michael},
doi = {10.3389/fimmu.2021.637164},
file = {:C$\backslash$:/Users/01462563/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gliddon et al. - 2021 - Identification of reduced host transcriptomic signatures for tuberculosis disease and digital PCR-based validati.pdf:pdf},
issn = {1664-3224},
journal = {Frontiers in Immunology},
keywords = {OA,biomarkers,dPCR,fund{\_}ack,gene expression,original,signatures,transcriptomics,tuberculosis},
mendeley-tags = {OA,fund{\_}ack,original},
month = {mar},
pages = {637164},
pmid = {33763081},
publisher = {Frontiers},
title = {{Identification of reduced host transcriptomic signatures for tuberculosis disease and digital PCR-based validation and quantification}},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2021.637164/full},
volume = {12},
year = {2021}
}

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