Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells

PharmGWAS: a GWAS-based knowledgebase for drug repurposing

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Apatinib Through Activating the RhoA/ROCK Signaling Pathway to Cause Dysfunction of Vascular Smooth Muscle Cells

Vascular smooth muscle cells (VSMCs) are associated with differentiated, organized, and contractile phenotype under the effect of various types of physiological conditions those are associated with migratory, proliferative, and synthetic phenotype under the effect of various types of stimuli, which dysfunction drives many cardiovascular diseases. Abnormal cell proliferation and invasion of VSMCs are among the primary causes of hypertension. Apatinib is a small-molecule tyrosine kinase inhibitor (TKI) that highly selectively binds to and strongly inhibits VEGFR-2. Previous studies have confirmed that the TKIs can raise blood pressure through RhoA/ROCK pathway. LARG is a key gene in the RhoA/ROCK pathway and plays a critical role in the continuous vasoconstriction function because it regulates part of signal transduction in VSMCs. In this study, an in vitro experiment was conducted to observe that apatinib caused dysfunction of MOVAS cells through the RhoA/ROCK signalling pathway and Y27632, a nonspecific ROCK inhibitor, and knockout of LARG gene can improve the proliferation, antiapoptosis, oxidative stress, and mitochondrial autophagy of apatinib-induced MOVAS cells. These findings suggest that activation of the RhoA/ROCK signalling pathway could be the underlying mechanism of apatinib-induced dysfunction of MOVAS cells, while ROCK inhibitor and knockout of LARG gene have potential therapeutic value.

PDE-Mediated Cyclic Nucleotide Compartmentation in Vascular Smooth Muscle Cells: From Basic to a Clinical Perspective

Cardiovascular diseases are important causes of mortality and morbidity worldwide. Vascular smooth muscle cells (SMCs) are major components of blood vessels and are involved in physiologic and pathophysiologic conditions. In healthy vessels, vascular SMCs contribute to vasotone and regulate blood flow by cyclic nucleotide intracellular pathways. However, vascular SMCs lose their contractile phenotype under pathological conditions and alter contractility or signalling mechanisms, including cyclic nucleotide compartmentation. In the present review, we focus on compartmentalized signaling of cyclic nucleotides in vascular smooth muscle. A deeper understanding of these mechanisms clarifies the most relevant axes for the regulation of vascular tone. Furthermore, this allows the detection of possible changes associated with pathological processes, which may be of help for the discovery of novel drugs.

Fetoplacental vasculature as a model to study human cardiovascular endocrine disruption

Increasing evidence has associated the exposure of endocrine-disrupting chemicals (EDCs) with the cardiovascular (CV) system. This exposure is particularly problematic in a sensitive window of development, pregnancy. Pregnancy exposome can affect the overall health of the pregnancy by dramatic changes in vascular physiology and endocrine activity, increasing maternal susceptibility. Moreover, fetoplacental vascular function is generally altered, increasing the risk of developing pregnancy complications (including cardiovascular diseases, CVD) and predisposing the foetus to adverse health risks later in life. Thus, our review summarizes the existing literature on exposures to EDCs during pregnancy and adverse maternal health outcomes, focusing on the human placenta, vein, and umbilical artery associated with pregnancy complications. The purpose of this review is to highlight the role of fetoplacental vasculature as a model for the study of human cardiovascular endocrine disruption. Therefore, we emphasize that the placenta, together with the umbilical arteries and veins, allows a better characterization of the pregnant woman's exposome. Consequently, it contributes to the protection of the mother and foetus against CV disorders in life.

Topological network measures for drug repositioning

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces

BACKGROUND

Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning.

RESULTS

In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values.

CONCLUSIONS

Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

MicroRNA-342-5p activates the Akt signaling pathway by downregulating PIK3R1 to modify the proliferation and differentiation of vascular smooth muscle cells

Abnormal cell proliferation and invasion of vascular smooth muscle cells are among the primary causes of cardiovascular disease. Studies have shown that microRNA(miR)-342-5p participates in the development of cardiovascular diseases. The current study aimed to explore the role of miR-342-5p in the proliferation and differentiation of mouse aortic vascular smooth muscle (MOVAS) cells. MOVAS cells were transfected with miR-342-5p mimics, miR-342-5p inhibitor or their respective negative controls, and co-transfected with small interfering (si)RNA targeting phosphatidylinositol 3-kinase regulatory subunit α (PIK3R1) and miR-342-5p inhibitor. The cell proliferation of MOVAS cells was detected using the Cell Counting Kit-8, while cell migration and cell invasion were investigated using a wound healing and Transwell assays, respectively. Target genes for miR-342-5p were confirmed using reverse transcription-quantitative PCR (RT-qPCR) and dual luciferase reporter assay. The relative mRNA and protein expression levels of miR-342-5p were measured using RT-qPCR and western blot analysis. MOVAS cells were treated with a PI3K inhibitor (LY294002) to explore the role of miR-342-5p on the Akt pathway. The results revealed that miR-342-5p mimics promoted cell viability, migration and invasion, and increased the expression of vimentin and phosphorylated-Akt but reduced a-smooth muscle actin (α-SMA) and PIK3R1 expression. However, miR-342-5p inhibitor produced the opposite effects. PIK3R1 was the target gene for miR-342-5p and the effect of siPIK3R1 on MOVAS cells was similar to that of miR-342-5p mimics, while siPIK3R1 partially reversed the effect of miR-342-5p inhibitor on MOVAS cells. The Akt signaling pathway was activated by miR-342-5p mimics or siPIK3R1. Moreover, miR-342-5p mimics partially activated the Akt signaling pathway inhibited by LY294002. MiR-342-5p could promote the proliferation and differentiation of MOVAS and phenotypic transformation. The mechanism behind these processes may be associated with the activation of the Akt signaling pathway induced by PIK3R1 inhibition.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Systems biology identifies cytosolic PLA2 as a target in vascular calcification treatment

Although cardiovascular disease (CVD) is the leading cause of morbimortality worldwide, promising new drug candidates are lacking. We compared the arterial high-resolution proteome of patients with advanced versus early-stage CVD to predict, from a library of small bioactive molecules, drug candidates able to reverse this disease signature. Of the approximately 4000 identified proteins, 100 proteins were upregulated and 52 were downregulated in advanced-stage CVD. Arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor was predicted as the top drug able to reverse the advanced-stage CVD signature. Vascular cPLA2 expression was increased in patients with advanced-stage CVD. Treatment with AACOCF3 significantly reduced vascular calcification in a cholecalciferol-overload mouse model and inhibited osteoinductive signaling in vivo and in vitro in human aortic smooth muscle cells. In conclusion, using a systems biology approach, we have identified a potentially new compound that prevented typical vascular calcification in CVD in vivo. Apart from the clear effect of this approach in CVD, such strategy should also be able to generate novel drug candidates in other complex diseases.

MiR-665 regulates VSMCs proliferation via targeting FGF9 and MEF2D and modulating activities of Wnt/β-catenin signaling

Abnormal proliferation of vascular smooth muscle cells (VSMCs) contributes to the development of cardiovascular diseases. Studies have showed the great impact of microRNAs (miRNAs) on the cell proliferation in VSMCs. This study examined the in vitro functional roles of miR-665 in the VSMCs and explored the underlying molecular mechanisms. The mRNA and protein expression levels were determined by qRT-PCR and western blot assays, respectively. CCK-8, transwell invasion and wound healing assays were performed to measure VSMCs proliferation, invasion and migration, respectively. The miR-665 targeted-3'UTR of fibroblast growth factor 9 (FGF9) and myocyte enhancer factor 2D (MEF2D) was confirmed by luciferase reporter assay. Platelet-derived growth factor-bb (PDGF-bb) and 20% serum promoted cell proliferation and suppressed the expression of miR-665 in VSMCs. In vitro functional assays demonstrated that miR-665 inhibited VSMCs proliferation, invasion and migration. Bioinformatics analysis showed that FGF9 and MEF2D were found to be downstream targets of miR-665. Luciferase report assay confirmed that FGF9 and MEF2D 3'UTRs are direct targets of miR-665, and miR-665 overexpression suppressed both the mRNA and protein expression levels of FGF9 and MEF2D. Furthermore, rescue experiments showed that enforced expression of FGF9 or MEF2D attenuated the inhibitory effects of miR-665 on VSMCs proliferation. More importantly, overexpression of miR-665 also suppressed the mRNA and protein expression levels of β-catenin, c-myc and cyclin D1. In summary, miR-665 suppressed the VSMCs proliferation, invasion and migration via targeting FGF9 and MEF2D, and the in vitro effects of miR-665 on VSMCs may be associated with modulation of Wnt/β-catenin signaling activities.