Motivation: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. Results: In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA–disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.

Chronic use of antipsychotic medications entails a dilemma between the benefit of alleviating psychotic symptoms and the risk of troubling, sometimes life-shortening adverse effects. Antipsychotic-induced cardiotoxicity is one of the most life-threatening adverse effects that raises widespread concerns. These cardiotoxic effects range from arrhythmia to heart failure in the clinic, with myocarditis/cardiomyopathy, ischemic injuries, and unexplained cardiac lesions as the pathological bases. Multiple mechanisms have been proposed to underlie antipsychotic cardiotoxicity. This review aims to summarize the clinical signs and pathological changes of antipsychotic cardiotoxicity and introduce recent progress in understanding the underlying mechanisms at both the subcellular organelle level and the molecular level. We also provide an up-to-date perspective on future clinical monitoring and therapeutic strategies for antipsychotic cardiotoxicity. We propose that third-generation antipsychotics or drug adjuvant therapy, such as cannabinoid receptor modulators that confer dual benefits — i.e. , alleviating cardiotoxicity and improving metabolic disorders — deserve further clinical evaluation and marketing.

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Cannabinoid receptors typically include type 1 (CB1) and type 2 (CB2), and they have attracted extensive attention in the central nervous system (CNS) and immune system. Due to more in-depth studies in recent years, it has been found that the typical CB1 and CB2 receptors confer functional importance far beyond the CNS and immune system. In particular, many works have reported the critical involvement of the CB1 and CB2 receptors in myocardial injuries. Both pharmacological and genetic approaches have been used for studying CB1 and CB2 functions in these studies, revealing that the brother receptors have many basic differences and sometimes antagonistic functions in a variety of myocardial injuries, despite some sequence or location identity they share. Herein, we introduce the general differences of CB1 and CB2 cannabinoid receptors, and summarize the functional rivalries between the two brother receptors in the setting of myocardial injuries. We point out the importance of individual receptor-based modulation, instead of dual receptor modulators, when treating myocardial injuries.

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.

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BackgroundAcute coronary syndrome (ACS) consists of a range of acute myocardial ischemia-related manifestations. The adverse events of ACS are usually associated with ventricular dysfunction (VD), which could finally develop to heart failure. Currently, there is no satisfactory indicator that could specifically predict the development of ACS and its prognosis. Valosin-containing protein (VCP) has recently been proposed to protect against cardiac diseases. Hence, we aimed to assess whether VCP in serum can serve as a valuable biomarker for predicting ACS and its complication.MethodsHuman serum samples from 291 participants were collected and classified into four groups based on their clinical diagnosis, namely healthy control (n = 64), ACS (n = 40), chronic coronary syndrome (CCS, n = 99), and nonischemic heart disease (non-IHD, n = 88). Clinical characteristics of these participants were recorded and their serum VCP levels were detected by enzyme-linked immunosorbent assay (ELISA). Association of serum VCP with the development of ACS and its complication VD was statistically studied. Subsequently, GWAS and eQTL analyses were performed to explore the association between VCP polymorphism and monocyte count. A stability test was also performed to investigate whether VCP is a stable biomarker.ResultsSerum VCP levels were significantly higher in the ACS group compared with the rest groups. Besides, the VCP levels of patients with ACS with VD were significantly lower compared to those without VD. Multivariate logistic regression analysis revealed that VCP was associated with both the risk of ACS (P = 0.042, OR = 1.222) and the risk of developing VD in patients with ACS (P = 0.035, OR = 0.513) independently. The GWAS analysis also identified an association between VCP polymorphism (rs684562) and monocyte count, whereas the influence of rs684562 on VCP mRNA expression level was further verified by eQTL analysis. Moreover, a high stability of serum VCP content was observed under different preservation circumstances.ConclusionValosin-containing protein could act as a stable biomarker in predicting the development of ACS and its complication VD.

Long-term use of antipsychotics is a common cause of myocardial injury and even sudden cardiac deaths that often lead to drug withdrawn or discontinuation. Mechanisms underlying antipsychotics cardiotoxicity remain largely unknown. Herein we performed RNA sequencing and found that NLRP3 inflammasome-mediated pyroptosis contributed predominantly to multiple antipsychotics cardiotoxicity. Pyroptosis-based small-molecule compound screen identified cannabinoid receptor 1 (CB1R) as an upstream regulator of the NLRP3 inflammasome. Mechanistically, antipsychotics competitively bond to the CB1R and led to CB1R translocation to the cytoplasm, where CB1R directly interacted with NLRP3 inflammasome via amino acid residues 177–209, rendering stabilization of the inflammasome. Knockout of Cb1r significantly alleviated antipsychotic-induced cardiomyocyte pyroptosis and cardiotoxicity. Multi-organ-based investigation revealed no additional toxicity of newer CB1R antagonists. In authentic human cases, the expression of CB1R and NLRP3 inflammasome positively correlated with antipsychotics-induced cardiotoxicity. These results suggest that CB1R is a potent regulator of the NLRP3 inflammsome-mediated pyroptosis and small-molecule inhibitors targeting the CB1R/NLRP3 signaling represent attractive approaches to rescue cardiac side effects of antipsychotics.