Frontiers in Immunology (Jul 2020)
An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile
- Olivia Estévez,
- Olivia Estévez,
- Luis Anibarro,
- Luis Anibarro,
- Luis Anibarro,
- Elina Garet,
- Elina Garet,
- Ángeles Pallares,
- Laura Barcia,
- Laura Calviño,
- Cremildo Maueia,
- Tufária Mussá,
- Tufária Mussá,
- Florentino Fdez-Riverola,
- Florentino Fdez-Riverola,
- Florentino Fdez-Riverola,
- Daniel Glez-Peña,
- Daniel Glez-Peña,
- Daniel Glez-Peña,
- Miguel Reboiro-Jato,
- Miguel Reboiro-Jato,
- Miguel Reboiro-Jato,
- Hugo López-Fernández,
- Hugo López-Fernández,
- Hugo López-Fernández,
- Nuno A. Fonseca,
- Nuno A. Fonseca,
- Rajko Reljic,
- África González-Fernández,
- África González-Fernández
Affiliations
- Olivia Estévez
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Olivia Estévez
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Luis Anibarro
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Luis Anibarro
- Tuberculosis Unit, Department of Infectious Diseases and Internal Medicine, University Hospital Complex of Pontevedra, Pontevedra, Spain
- Luis Anibarro
- Grupo de Estudio de Infecciones por Micobacterias (GEIM), Spanish Society of Infectious Diseases (SEIMC), Madrid, Spain
- Elina Garet
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Elina Garet
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Ángeles Pallares
- Department of Microbiology, University Hospital Complex of Pontevedra, Pontevedra, Spain
- Laura Barcia
- Tuberculosis Unit, Department of Infectious Diseases and Internal Medicine, University Hospital Complex of Pontevedra, Pontevedra, Spain
- Laura Calviño
- Tuberculosis Unit, Department of Infectious Diseases and Internal Medicine, University Hospital Complex of Pontevedra, Pontevedra, Spain
- Cremildo Maueia
- Departamento de Plataformas Tecnológicas, Instituto Nacional de Saúde, Ministério da Saúde, Maputo, Mozambique
- Tufária Mussá
- Departamento de Plataformas Tecnológicas, Instituto Nacional de Saúde, Ministério da Saúde, Maputo, Mozambique
- Tufária Mussá
- Department of Microbiology, Faculty of Medicine, Eduardo Mondlane University, Maputo, Mozambique
- Florentino Fdez-Riverola
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Florentino Fdez-Riverola
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Florentino Fdez-Riverola
- ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Universitario As Lagoas s/n, Universidad de Vigo, Ourense, Spain
- Daniel Glez-Peña
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Daniel Glez-Peña
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Daniel Glez-Peña
- ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Universitario As Lagoas s/n, Universidad de Vigo, Ourense, Spain
- Miguel Reboiro-Jato
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Miguel Reboiro-Jato
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Miguel Reboiro-Jato
- ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Universitario As Lagoas s/n, Universidad de Vigo, Ourense, Spain
- Hugo López-Fernández
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- Hugo López-Fernández
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Hugo López-Fernández
- ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Universitario As Lagoas s/n, Universidad de Vigo, Ourense, Spain
- Nuno A. Fonseca
- European Bioinformatics Institute, Cambridge, United Kingdom
- Nuno A. Fonseca
- 0CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources, Universidade do Porto, Vairão, Portugal
- Rajko Reljic
- 1St. George's, University of London, London, United Kingdom
- África González-Fernández
- CINBIO, Universidade de Vigo, Immunology Group, Campus Universitario Lagoas-Marcosende, Vigo, Spain
- África González-Fernández
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- DOI
- https://doi.org/10.3389/fimmu.2020.01470
- Journal volume & issue
-
Vol. 11
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.
Keywords
