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Deep Learning Analyses to Delineate the Molecular Remodeling Process after Myocardial Infarction

Authors :
Oriol Iborra-Egea
Carolina Gálvez-Montón
Cristina Prat-Vidal
Santiago Roura
Carolina Soler-Botija
Elena Revuelta-López
Gemma Ferrer-Curriu
Cristina Segú-Vergés
Araceli Mellado-Bergillos
Pol Gomez-Puchades
Paloma Gastelurrutia
Antoni Bayes-Genis
Source :
Cells, Vol 10, Iss 12, p 3268 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20734409
Volume :
10
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Cells
Publication Type :
Academic Journal
Accession number :
edsdoj.53d773785f54a7da75c6b9a1c5e0001
Document Type :
article
Full Text :
https://doi.org/10.3390/cells10123268