RT Journal Article
T1 An RNA-seq based machine learning approach identifies latent tuberculosis patients with an active tuberculosis profile
A1 Estévez Martínez, Olivia
A1 Anibarro García, Luis
A1 Garet, Elina
A1 Pallares, Ángeles
A1 Barcia, Laura
A1 Calviño, Laura
A1 Maueia, Cremildo
A1 Mussá, Tufária
A1 Fernández Riverola, Florentino
A1 González Peña, Daniel
A1 Reboiro Jato, Miguel
A1 López Fernández, Hugo
A1 Fonseca, Nuno A.
A1 Reljic, Rajko
A1 González Fernández, Maria Africa
K1 3205.08 Enfermedades Pulmonares
K1 3205.05 Enfermedades Infecciosas
K1 2412 Inmunología
AB A better understanding of the response against Tuberculosis (TB) infection is requiredto 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 profileof active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts,recruited in Spain and Mozambique, to build a class-prediction model that identifiesindividuals with a TB infection profile. Following this approach, we have identifiedseveral genes and metabolic pathways that provide important information of the immunemechanisms triggered against TB infection. As a novelty of our work, a combinationof this class-prediction model and the direct measurement of different immunologicalparameters, was used to identify a subset of LTBI contacts (called TB-like) whosetranscriptional and immunological profiles are suggestive of infection with a higherprobability of developing active TB. Validation of this novel approach to identifying LTBIindividuals with the highest risk of active TB disease merits further longitudinal studieson larger cohorts in TB endemic areas.
PB Frontiers in Immunology
SN 16643224
YR 2020
FD 2020-07-14
LK http://hdl.handle.net/11093/2331
UL http://hdl.handle.net/11093/2331
LA eng
NO Frontiers in Immunology, 11, 01470 (2020)
NO Xunta de Galicia | Ref. ED431C 2016/041
DS Investigo
RD 30-sep-2023