Increased Risk of Aortic Dissection with Perlecan Deficiency

Perlecan (HSPG2) promotes structural, contractile, and metabolic development of human cardiomyocytes

Perlecan (HSPG2), a heparan sulfate proteoglycan similar to agrin, is key for extracellular matrix (ECM) maturation and stabilization. Although crucial for cardiac development, its role remains elusive. We show that perlecan expression increases as cardiomyocytes mature in vivo and during human pluripotent stem cell differentiation to cardiomyocytes (hPSC-CMs). Perlecan-haploinsuffient hPSCs (HSPG2+/-) differentiate efficiently, but late-stage CMs have structural, contractile, metabolic, and ECM gene dysregulation. In keeping with this, late-stage HSPG2+/- hPSC-CMs have immature features, including reduced ⍺-actinin expression and increased glycolytic metabolism and proliferation. Moreover, perlecan-haploinsuffient engineered heart tissues have reduced tissue thickness and force generation. Conversely, hPSC-CMs grown on a perlecan-peptide substrate are enlarged and display increased nucleation, typical of hypertrophic growth. Together, perlecan appears to play the opposite role of agrin, promoting cellular maturation rather than hyperplasia and proliferation. Perlecan signaling is likely mediated via its binding to the dystroglycan complex. Targeting perlecan-dependent signaling may help reverse the phenotypic switch common to heart failure.

Machine learning and bioinformatics to identify 8 autophagy-related biomarkers and construct gene regulatory networks in dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a condition of impaired ventricular remodeling and systolic diastole that is often complicated by arrhythmias and heart failure with a poor prognosis. This study attempted to identify autophagy-related genes (ARGs) with diagnostic biomarkers of DCM using machine learning and bioinformatics approaches. Differential analysis of whole gene microarray data of DCM from the Gene Expression Omnibus (GEO) database was performed using the NetworkAnalyst 3.0 platform. Differentially expressed genes (DEGs) matching (|log2FoldChange ≥ 0.8, p value < 0.05|) were obtained in the GSE4172 dataset by merging ARGs from the autophagy gene libraries, HADb and HAMdb, to obtain autophagy-related differentially expressed genes (AR-DEGs) in DCM. The correlation analysis of AR-DEGs and their visualization were performed using R language. Gene Ontology (GO) enrichment analysis and combined multi-database pathway analysis were served by the Enrichr online enrichment analysis platform. We used machine learning to screen the diagnostic biomarkers of DCM. The transcription factors gene regulatory network was constructed by the JASPAR database of the NetworkAnalyst 3.0 platform. We also used the drug Signatures database (DSigDB) drug database of the Enrichr platform to screen the gene target drugs for DCM. Finally, we used the DisGeNET database to analyze the comorbidities associated with DCM. In the present study, we identified 23 AR-DEGs of DCM. Eight (PLEKHF1, HSPG2, HSF1, TRIM65, DICER1, VDAC1, BAD, TFEB) molecular markers of DCM were obtained by two machine learning algorithms. Transcription factors gene regulatory network was established. Finally, 10 gene-targeted drugs and complications for DCM were identified.

Is There Enough Evidence to Support the Role of Glycosaminoglycans and Proteoglycans in Thoracic Aortic Aneurysm and Dissection?—A Systematic Review

Altered proteoglycan (PG) and glycosaminoglycan (GAG) distribution within the aortic wall has been implicated in thoracic aortic aneurysm and dissection (TAAD). This review was conducted to identify literature reporting the presence, distribution and role of PGs and GAGs in the normal aorta and differences associated with sporadic TAAD to address the question; is there enough evidence to establish the role of GAGs/PGs in TAAD? 75 studies were included, divided into normal aorta (n = 51) and TAAD (n = 24). There is contradictory data regarding changes in GAGs upon ageing; most studies reported an increase in GAG sub-types, often followed by a decrease upon further ageing. Fourteen studies reported changes in PG/GAG or associated degradation enzyme levels in TAAD, with most increased in disease tissue or serum. We conclude that despite being present at relatively low abundance in the aortic wall, PGs and GAGs play an important role in extracellular matrix maintenance, with differences observed upon ageing and in association with TAAD. However, there is currently insufficient information to establish a cause-effect relationship with an underlying mechanistic understanding of these changes requiring further investigation. Increased PG presence in serum associated with aortic disease highlights the future potential of these biomolecules as diagnostic or prognostic biomarkers.

Impact of the Heparan Sulfate Proteoglycan Perlecan on Human Disease and Health

Perlecan, a basement membrane-type heparan sulfate proteoglycan, is an important molecule in the functional diversity of organisms because of the diversity of its glycan chains and the multifunctionality of its core proteins. Human diseases associated with perlecan have been identified using gene-deficient mice. Two human diseases related to perlecan have been reported. One is Silverman-Handmaker type Dyssegmental Dysplasia, resulting from complete loss of function of the HSPG2 gene which encods perlecan core protein which maps to chromosome 1p36. The other is Schwartz-Jampel syndrome from partial loss of function of the HSPG2 gene. Subsequent in vivo and in vitrostudies have revealed the organ-specific functions of perlecan, suggesting its involvement in the pathogenesis of various human diseases. In this review, we discuss the role of perlecan in human diseases and summarize our knowledge about perlecan as a future therapeutic target to treat the related diseases and for healthy longevity.