Diabetic cardiomyopathy is a common cardiac condition in patients with diabetes mellitus, which can result in cardiac hypertrophy and subsequent heart failure, associated with pyroptosis, the pro-inflammatory programmed cell death. MicroRNAs (miRNAs), small endogenous non-coding RNAs, have been shown to be involved in diabetic cardiomyopathy. However, whether miRNAs regulate pyroptosis in diabetic cardiomyopathy remains unknown. Our study revealed that mir-30d expression was substantially increased in streptozotocin (STZ)-induced diabetic rats and in high-glucose-treated cardiomyocytes as well. Upregulation of mir-30d promoted cardiomyocyte pyroptosis in diabetic cardiomyopathy; conversely, knockdown of mir-30d attenuated it. In an effort to understand the signaling mechanisms underlying the pro-pyroptotic property of mir-30d, we found that forced expression of mir-30d upregulated caspase-1 and pro-inflammatory cytokines IL-1β and IL-18. Moreover, mir-30d directly repressed foxo3a expression and its downstream protein, apoptosis repressor with caspase recruitment domain (ARC). Furthermore, silencing ARC by siRNA mimicked the action of mir-30d: upregulating caspase-1 and inducing pyroptosis. These findings promoted us to propose a new signaling pathway leading to cardiomyocyte pyroptosis under hyperglycemic conditions: mir-30d↑→foxo3a↓→ ARC↓→caspase-1↑→IL-1β, IL-18↑→pyroptosis↑. Therefore, mir-30d may be a promising therapeutic target for the management of diabetic cardiomyopathy.

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These findings suggest that 6 mRNAs (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL), 3 miRNAs (hsa-miR-125a-3p, hsa-miR-200a, hsa-miR-142-3p) and 3 lncRNAs (MALAT1, TUG1, FGD5-AS1) might be involved in the lncRNA-associated ceRNA network of periodontitis. This study sought to illuminate further the genetic and epigenetic mechanisms of periodontitis through constructing an lncRNA-associated ceRNA network.

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Calcineurin signalling plays a critical role in the pathogenesis of many cardiovascular diseases. Calcineurin has been proven to affect a series of signalling pathways and to exert a proapoptotic effect in cardiomyocytes. However, whether it is able to regulate autophagy remains largely unknown. Here, we report that prolonged oxidative stress-induced activation of calcineurin contributes to the attenuation of adaptive AMP-activated protein kinase (AMPK) signalling and inhibits autophagy in cardiomyocytes. Primary cardiomyocytes exhibited rapid formation of autophagosomes, microtubule-associated protein 1 light chain 3 (LC3) expression and phosphorylation of AMPK in response to hydrogen peroxide (H2O2) treatment. However, prolonged (12 h) H2O2 treatment attenuated these effects and was accompanied by a significant increase in calcineurin activity and apoptosis. Inhibition of calcineurin by FK506 restored AMPK function and LC3 expression, and decreased the extent of apoptosis caused by prolonged oxidative stress. In contrast, overexpression of the constitutively active form of calcineurin markedly attenuated the increase in LC3 induced by short-term (3 h) H2O2 treatment and sensitised cells to apoptosis. In addition, FK506 failed to induce autophagy and alleviate apoptosis in cardiomyocytes expressing a kinase-dead K45R AMPK mutant. Furthermore, inhibition of autophagy by 3-methylanine (3-MA) or by knockdown of the essential autophagy-related gene ATG7 abrogated the protective effect of FK506. These findings suggest a novel role of calcineurin in suppressing adaptive autophagy during oxidative stress by downregulating the AMPK signalling pathway. The results also provide insight into how altered calcineurin and autophagic signalling is integrated to control cell survival during oxidative stress and may guide strategies to prevent cardiac oxidative damage.

Drug repositioning, also known as drug repurposing or reprofiling, is the process of finding new indications for established drugs. Because drug repositioning can reduce costs and enhance the efficiency of drug development, it is of paramount importance in medical research. Here, we present a systematic computational method to identify potential novel indications for a given drug. This method utilizes some prior knowledge such as 3D drug chemical structure information, drug-target interactions and gene semantic similarity information. Its prediction is based on another form of 'expression profile', which contains scores ranging from -1 to 1, reflecting the consensus response scores (CRSs) between each drug of 965 and 1560 proteins. The CRS integrates chemical structure similarity and gene semantic similarity information. We define the degree of similarity between two drugs as the absolute value of their correlation coefficients. Finally, we establish a drug similarity network (DSN) and obtain 33 modules of drugs with similar modes of action, determining their common indications. Using these modules, we predict new indications for 143 drugs and identify previously unknown indications for 42 drugs without ATC codes. This method overcomes the instability of gene expression profiling derived from experiments due to experimental conditions, and predicts indications for a new compound feasibly, requiring only the 3D structure of the compound. In addition, the high literature validation rate of 71.8% also suggests that our method has the potential to discover novel drug indications for existing drugs.

Objective. To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer’s disease (AD). Background. Periodontitis, a common oral infectious disease, is associated with Alzheimer’s disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported. Methods. Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes. Results. Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD. Conclusions. Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD.

Objective. To identify the shared genetic and epigenetic mechanisms between the osteogenic differentiation of dental pulp stem cells (DPSC) and bone marrow stem cells (BMSC). Materials and Methods. The profiling datasets of miRNA expression in the osteogenic differentiation of mesenchymal stem cells from the dental pulp (DPSC) and bone marrow (BMSC) were searched in the Gene Expression Omnibus (GEO) database. The differential expression analysis was performed to identify differentially expressed miRNAs (DEmiRNAs) dysregulated in DPSC and BMSC osteodifferentiation. The target genes of the DEmiRNAs that were dysregulated in DPSC and BMSC osteodifferentiation were identified, followed by the identification of the signaling pathways and biological processes (BPs) of these target genes. Accordingly, the DEmiRNA-transcription factor (TFs) network and the DEmiRNAs-small molecular drug network involved in the DPSC and BMSC osteodifferentiation were constructed. Results. 16 dysregulated DEmiRNAs were found to be overlapped in the DPSC and BMSC osteodifferentiation, including 8 DEmiRNAs with a common expression pattern (8 upregulated DEmiRNAs (miR-101-3p, miR-143-3p, miR-145-3p/5p, miR-19a-3p, miR-34c-5p, miR-3607-3p, miR-378e, miR-671-3p, and miR-671-5p) and 1 downregulated DEmiRNA (miR-671-3p/5p)), as well as 8 DEmiRNAs with a different expression pattern (i.e., miR-1273g-3p, miR-146a-5p, miR-146b-5p, miR-337-3p, miR-382-3p, miR-4508, miR-4516, and miR-6087). Several signaling pathways (TNF, mTOR, Hippo, neutrophin, and pathways regulating pluripotency of stem cells), transcription factors (RUNX1, FOXA1, HIF1A, and MYC), and small molecule drugs (curcumin, docosahexaenoic acid (DHA), vitamin D3, arsenic trioxide, 5-fluorouracil (5-FU), and naringin) were identified as common regulators of both the DPSC and BMSC osteodifferentiation. Conclusion. Common genetic and epigenetic mechanisms are involved in the osteodifferentiation of DPSCs and BMSCs.