Integrated Microarray for Identifying the Hub mRNAs and Constructed MiRNA-mRNA Network in Coronary In-stent Restenosis

A systematic review and bioinformatic study on clinical, paraclinical, and genetic factors predisposing to stent restenosis following percutaneous coronary intervention

BACKGROUND

Stent restenosis is a relatively common phenomenon among patients with coronary heart disease undergoing percutaneous coronary intervention (PCI). It seems that a set of clinical, laboratory, and even genetic factors make people susceptible to such a phenomenon and in fact, this is multi-factorial. We aimed to first determine the underlying clinical and laboratory risk factors for the occurrence of stent re-stenosis after PCI based on a systematic review study, and after that, through a bioinformatics study, to evaluate the related genes and microRNAs with the occurrence of stent re-stenosis.

MAIN TEXT

In the first step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords to introduce clinical and laboratory determinants of stent re-stenosis. In the bioinformatic phase, and following a review of the literature to identify genes and microRNAs involved in restenosis, the interaction of each gene with other genes associated with stent re-stenosis was determined by GeneMANIA network analysis and Cytoscape software. Overall, 67 articles (including 40,789 patients) on clinical and biochemical predictors for stent restenosis and 25 articles on genetic determinants of this event were eligible for the final analysis. The predictors for this event were categorized into four subgroups patient-based parameters including traditional cardiovascular risk profiles, stent-based parameters including type and diametric characteristics of the stents used, coronary lesion-based parameters including several two target lesions and coronary involvement severity and laboratory-based parameters particularly related to activation of inflammatory processes. In the bioinformatic phase, we uncovered 42 genes that have been described to be involved in such a phenomenon considering a special position for genes encoding inflammatory cytokines. Also, 12 microRNAs have been pointed to be involved in targeting genes involved in stent re-stenosis.

CONCLUSIONS

The incidence of stent re-stenosis will be the result of a complex interaction of clinical risk factors, laboratory factors mostly related to the activation of inflammatory processes, and a complex network of gene-to-gene interactions.

Pycard deficiency inhibits microRNA maturation and prevents neointima formation by promoting chaperone-mediated autophagic degradation of AGO2/argonaute 2 in adipose tissue

PYCARD (PYD and CARD domain containing), a pivotal adaptor protein in inflammasome assembly and activation, contributes to innate immunity, and plays an essential role in the pathogenesis of atherosclerosis and restenosis. However, its roles in microRNA biogenesis remain unknown. Therefore, this study aimed to investigate the roles of PYCARD in miRNA biogenesis and neointima formation using pycard knockout (pycard-/-) mice. Deficiency of Pycard reduced circulating miRNA profile and inhibited Mir17 seed family maturation. The systemic pycard knockout also selectively reduced the expression of AGO2 (argonaute RISC catalytic subunit 2), an important enzyme in regulating miRNA biogenesis, by promoting chaperone-mediated autophagy (CMA)-mediated degradation of AGO2, specifically in adipose tissue. Mechanistically, pycard knockout increased PRMT8 (protein arginine N-methyltransferase 8) expression in adipose tissue, which enhanced AGO2 methylation, and subsequently promoted its binding to HSPA8 (heat shock protein family A (Hsp70) member 8) that targeted AGO2 for lysosome degradation through chaperone-mediated autophagy. Finally, the reduction of AGO2 and Mir17 family expression prevented vascular injury-induced neointima formation in Pycard-deficient conditions. Overexpression of AGO2 or administration of mimic of Mir106b (a major member of the Mir17 family) prevented Pycard deficiency-mediated inhibition of neointima formation in response to vascular injury. These data demonstrate that PYCARD inhibits CMA-mediated degradation of AGO2, which promotes microRNA maturation, thereby playing a critical role in regulating neointima formation in response to vascular injury independently of inflammasome activity and suggest that modulating PYCARD expression and function may represent a powerful therapeutic strategy for neointima formation.Abbreviations: 6-AN: 6-aminonicotinamide; ACTB: actin, beta; aDMA: asymmetric dimethylarginine; AGO2: argonaute RISC catalytic subunit 2; CAL: carotid artery ligation; CALCOCO2: calcium binding and coiled-coil domain 2; CMA: chaperone-mediated autophagy; CTSB: cathepsin B; CTSD: cathepsin D; DGCR8: DGCR8 microprocessor complex subunit; DOCK2: dedicator of cyto-kinesis 2; EpiAdi: epididymal adipose tissue; HSPA8: heat shock protein family A (Hsp70) member 8; IHC: immunohistochemical; ISR: in-stent restenosis; KO: knockout; LAMP2: lysosomal-associated membrane protein 2; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; miRNA: microRNA; NLRP3: NLR family pyrin domain containing 3; N/L: ammonium chloride combined with leupeptin; PRMT: protein arginine methyltransferase; PVAT: peri-vascular adipose tissues; PYCARD: PYD and CARD domain containing; sDMA: symmetric dimethylarginine; ULK1: unc-51 like kinase 1; VSMCs: vascular smooth muscle cells; WT: wild-type.

Integrative bioinformatics analysis identifies APOB as a critical biomarker in coronary in-stent restenosis

Aim: Coronary artery disease (CAD) is a major contributor to the worldwide prevalence of cardiovascular disease. In-stent restenosis (ISR) is a common complication which can lead to stent implantation failure, necessitating repeated intervention and presenting a significant obstacle for CAD management. Methods: To accurately assess and determine the hub genes associated with ISR, CAD databases from the Gene Expression Omnibus were utilized and weighted gene coexpression network analysis was employed to identify key genes in blood samples. Results: APOB was identified as a risk gene for ISR occurrence. Subsequent correlation analysis of APOB demonstrated a positive association with ISR. Clinical validation further confirmed the predictive value of APOB in ISR detection. Conclusion: We have identified APOB as a critical predictive biomarker for ISR in CAD patients.