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An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

Estévez, O; Anibarro, L; Garet, E; Pallares, Á; Barcia, L; Calviño, L; Maueia, C; Mussá, T; Fdez-Riverola, F; Glez-Peña, D; et al. Estévez, O; Anibarro, L; Garet, E; Pallares, Á; Barcia, L; Calviño, L; Maueia, C; Mussá, T; Fdez-Riverola, F; Glez-Peña, D; Reboiro-Jato, M; López-Fernández, H; Fonseca, NA; Reljic, R; González-Fernández, Á (2020) An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile. Front Immunol, 11. p. 1470. ISSN 1664-3224 https://doi.org/10.3389/fimmu.2020.01470
SGUL Authors: Reljic, Rajko

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Abstract

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.

Item Type: Article
Additional Information: Copyright © 2020 Estévez, Anibarro, Garet, Pallares, Barcia, Calviño, Maueia, Mussá, Fdez-Riverola, Glez-Peña, Reboiro-Jato, López-Fernández, Fonseca, Reljic and González-Fernández. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: RNA-seq, TB progression, latent tuberculosis, machine-learning, tuberculosis
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: Front Immunol
ISSN: 1664-3224
Language: eng
Dates:
DateEvent
14 July 2020Published
5 June 2020Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
643558Horizon 2020UNSPECIFIED
ED431C 2016/041Xunta de GaliciaUNSPECIFIED
FPU13/03026Spanish Ministry of EducationUNSPECIFIED
PubMed ID: 32760401
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/112280
Publisher's version: https://doi.org/10.3389/fimmu.2020.01470

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