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| Página principal > Artículos > Artículos publicados > Deep Learning Analyses to Delineate the Molecular Remodeling Process after Myocardial Infarction |
(Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Anaxomics Biotech S.L.) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)| Fecha: | 2021 |
| Resumen: | Specific proteins and processes have been identified in post-myocardial infarction (MI) pathological remodeling, but a comprehensive understanding of the complete molecular evolution is lacking. We generated microarray data from swine heart biopsies at baseline and 6, 30, and 45 days after infarction to feed machine-learning algorithms. We cross-validated the results using available clinical and experimental information. MI progression was accompanied by the regulation of adipogenesis, fatty acid metabolism, and epithelial-mesenchymal transition. The infarct core region was enriched in processes related to muscle contraction and membrane depolarization. Angiogenesis was among the first morphogenic responses detected as being sustained over time, but other processes suggesting post-ischemic recapitulation of embryogenic processes were also observed. Finally, protein-triggering analysis established the key genes mediating each process at each time point, as well as the complete adverse remodeling response. We modeled the behaviors of these genes, generating a description of the integrative mechanism of action for MI progression. This mechanistic analysis overlapped at different time points; the common pathways between the source proteins and cardiac remodeling involved IGF1R, RAF1, KPCA, JUN, and PTN11 as modulators. Thus, our data delineate a structured and comprehensive picture of the molecular remodeling process, identify new potential biomarkers or therapeutic targets, and establish therapeutic windows during disease progression. |
| Ayudas: | Agencia Estatal de Investigación SAF2017-84324-C2-1-R Agencia Estatal de Investigación PID2019-110137RB-I00 Instituto de Salud Carlos III PI17/01487 Instituto de Salud Carlos III PIC18/00014 Instituto de Salud Carlos III ICI19/00039 Instituto de Salud Carlos III PI18/00256 Instituto de Salud Carlos III PI18/01227 Instituto de Salud Carlos III ICI20/00135 Instituto de Salud Carlos III PI21/01703 Ministerio de Economía y Competitividad RD16/0011/0006 Ministerio de Economía y Competitividad CB16/11/00403 |
| Derechos: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.  |
| Lengua: | Anglès |
| Documento: | Article ; recerca ; Versió publicada |
| Materia: | Deep learning ; Gene regulation ; Myocardial infarction ; Transcriptomics |
| Publicado en: | Cells, Vol. 10 (november 2021) , ISSN 2073-4409 |
