Meta-Analysis of Extracellular Matrix Dynamics after Myocardial Infarction Using RNA-Sequencing Transcriptomic Database.
María OrtegaCésar Ríos-NavarroJose GavaraElena de DiosNerea Perez-SoléVictor Marcos-GarcesAntonio Ferrández-IzquierdoVicente BodíAmparo Ruiz-SauríPublished in: International journal of molecular sciences (2022)
Extracellular matrix (ECM) changes after myocardial infarction (MI) need precise regulation, and next-generation sequencing technologies provide omics data that can be used in this context. We performed a meta-analysis using RNA-sequencing transcriptomic datasets to identify genes involved in post-MI ECM turnover. Eight studies available in Gene Expression Omnibus were selected following the inclusion criteria. We compare RNA-sequencing data from 92 mice submitted to permanent coronary ligation or sham, identifying differentially expressed genes (p-value < 0.05 and Log2FoldChange ≥ 2). Functional enrichment analysis was performed based on Gene Ontology biological processes (BPs). BPs implicated in response to extracellular stimulus, regulation of ECM organization, and ECM disassembly were detected soon after ischemia onset. ECM disassembly occurred between days one to seven post-MI, compared with ECM assembly from day seven onwards. We identified altered mRNA expression of 19 matrix metalloproteinases and four tissue inhibitors of metalloproteinases at post-infarcted ECM remodeling and altered transcriptomic expression of 42 genes encoding 26 collagen subunits at the fibrotic stage. To our knowledge, this is the first meta-analysis using RNA-sequencing datasets to evaluate post-infarcted cardiac interstitium healing, revealing previously unknown mechanisms and molecules actively implicated in ECM remodeling post-MI, which warrant further validation.
Keyphrases
- extracellular matrix
- single cell
- rna seq
- gene expression
- systematic review
- genome wide
- dna methylation
- healthcare
- electronic health record
- copy number
- coronary artery disease
- type diabetes
- poor prognosis
- left ventricular
- long non coding rna
- adipose tissue
- skeletal muscle
- insulin resistance
- machine learning
- artificial intelligence
- metabolic syndrome
- body composition
- postmenopausal women