Sequential activation of different pathway networks in ischemia-affected and non-affected myocardium, inducing intrinsic remote conditioning to prevent left ventricular remodeling

Abstract

We have analyzed the pathway networks of ischemia-affected and remote myocardial areas after repetitive ischemia/reperfusion (r-I/R) injury without ensuing myocardial infarction (MI) to elaborate a spatial- and chronologic model of cardioprotective gene networks to prevent left ventricular (LV) adverse remodeling. Domestic pigs underwent three cycles of 10/10 min r-I/R by percutaneous intracoronary balloon inflation/deflation in the mid left anterior descending artery, without consecutive MI. Sham interventions (n = 8) served as controls. Hearts were explanted at 5 h (n = 6) and 24 h (n = 6), and transcriptomic profiling of the distal (ischemia-affected) and proximal (non-affected) anterior myocardial regions were analyzed by next generation sequencing (NGS) and post-processing with signaling pathway impact and pathway network analyses. In ischemic region, r-I/R induced early activation of Ca-, adipocytokine and insulin signaling pathways with key regulator STAT3, which was also upregulated in the remote areas together with clusterin (CLU) and TNF-alpha. During the late phase of cardioprotection, antigen immunomodulatory pathways were activated with upregulation of STAT1 and CASP3 and downregulation of neprilysin in both zones, suggesting r-I/R induced intrinsic remote conditioning. The temporo-spatially differently activated pathways revealed a global myocardial response, and neprilysin and the STAT family as key regulators of intrinsic remote conditioning for prevention of adverse remodeling.

Practical guidelines for rigor and reproducibility in preclinical and clinical studies on cardioprotection

“…Currently, transcriptomics seems to be useful for unbiased mechanistic studies at the transcript level, including coding or non-coding RNAs, since current technologies allow full and accurate assessment of all transcripts in biological samples [333, 337, 432, 433]. Transcriptomics can be performed by different techniques [337].…”

Intrinsic remote conditioning of the myocardium as a comprehensive cardiac response to ischemia and reperfusion

“…As reported earlier, the NGS-based analysis of global gene expression patterns in ischemic, border, and remote zones [ 2 ] showed distinct changes of several pathways and a number of genes that were previously not linked to ischemic preconditioning. The gene expression patterns show a quick onset of regulatory response after r-I/R in the directly affected tissue with up-regulation of genes involved with stress response and apoptosis.…”

“…We collected a total of 52 tissue samples for each pig heart and compared the individual expression levels of seven genes of interest – HIF-1α, caspase-3, transcription factor GATA4, myocyte enhancer factor 2C (MEF2c), hexokinase 2 (HK2), clusterin (CLU) and excision repair cross-complementation group 4 (ERCC4) between control animals and pigs that underwent r-I/R (Figure 1 ). The gene selection was based on earlier NGS analyses of ischemic and remote tissue areas, and we primarily selected genes with currently incompletely elucidated functions in ischemic injury (except for HIF-1α) [ 2 ]. Two distinct time points were investigated; five hours after r-I/R, to examine quick transcriptional regulation, and 24 h after r-I/R, to gain information on transcriptional changes relevant for later, sustained cardioprotection, (SWOP).…”

“…Metabolism and gene expression patterns are changing intensively in the ischemic regions of an infarcted heart, but also remote heart regions respond to the injury quickly. We have recently shown that both acute and chronic ischemia alters the molecular signals of the ischemia non-affected, but adjacent regions, termed as intrinsic remote conditioning against adverse left ventricular (LV) remodeling [ 1 , 2 ].…”

Survival pathways in cardiac conditioning: individual data vs. meta-analyses. What do we learn?

“…Technical limitations in full proteomics from heart tissue (see, e.g., [2]) and metabolomics show that these technologies may not be ready for fishing approaches. Therefore, currently, transcriptomics seems to be useful for unbiased target identification at the transcript level (see for reviews [9,10] and a recent original paper in pigs [8]). Maybe, an easier way of unbiased omics approach is to detect miRNA fingerprints (miRNAs are of limited number and they regulate gene expression) of non-ischemic and ischemia/reperfused hearts with and without conditioning, and identify cardioprotective miRNAs termed "protectomiRs" by a systematic comparison of miRNA expression changes between groups as described [11].…”