Identification of drug repurposing candidates based on a miRNA-mediated drug and pathway network for cardiac hypertrophy and acute myocardial infarction
Cardiac hypertrophy and acute myocardial infarction (AMI) are two common heart diseases worldwide. However, research is needed into the exact pathogenesis and effective treatment strategies for these diseases. Recently, microRNAs (miRNAs) have been suggested to regulate the pathological pathways of heart disease, indicating a potential role in novel treatments.
In our study, we constructed a miRNA-gene-drug network and analyzed its topological features. We also identified some significantly dysregulated miRNA-gene-drug triplets (MGDTs) in cardiac hypertrophy and AMI using a computational method. Then, we characterized the activity score profile features for MGDTs in cardiac hypertrophy and AMI. The functional analyses suggested that the genes in the network held special functions. We extracted an insulin-like growth factor 1 receptor-related subnetwork in cardiac hypertrophy and a vascular endothelial growth factor A-related subnetwork in AMI. Finally, we considered insulin-like growth factor 1 receptor and vascular endothelial growth factor A as two candidate drug targets by utilizing the cardiac hypertrophy and AMI pathways.
These results provide novel insights into the mechanisms and treatment of cardiac hypertrophy and AMI.
KeywordsDrug repurposing miRNAs Pathway Cardiac hypertrophy Acute myocardial infarction
Acute myocardial infarction
Gene Expression Omnibus
Kyoto Encyclopedia of Genes and Genomes
Pearson correlation coefficients
Cardiovascular disease, especially coronary heart disease and stroke, remains the leading cause of death and disability-adjusted life years for all regions worldwide . Cardiac hypertrophy and acute myocardial infarction (AMI) are two types of common cardiovascular diseases. Cardiac hypertrophy is the heart’s response to a variety of extrinsic and intrinsic stimuli that impose increased biomechanical stress . AMI is a common disease with serious consequences in mortality, morbidity, and cost to society . Over the past several years, numerous studies have enhanced our understanding of the mechanism and treatment of cardiovascular health. However, divergent results have created confusion among patients. Drug treatment options and reduction of side effects remain urgent problems for studies of heart disease.
Recent studies have revealed that microRNAs (miRNAs) play essential roles in cardiovascular diseases, including cardiac hypertrophy and AMI. For example, miR-378 suppresses myocardial fibrosis through a paracrine mechanism at the early stage of cardiac hypertrophy following mechanical stress . miR-133 is downregulated in thyroid hormone-mediated cardiac hypertrophy partially via the type 1 angiotensin II receptor . Wei et al. identified miR-101 as an important regulator of cardiac hypertrophy and suggested its potential application in therapy for cardiac hypertrophy . Additionally, many studies have suggested important roles in AMI. For instance, circulating miR-1 and miR-499 have potential as novel biomarkers for AMI [7, 8]. miR-214 inhibits left ventricular remodeling in AMI by suppressing cellular apoptosis via the phosphatase and tensin homolog . In addition, high-throughput microarray and sequencing experiments have identified numerous novel microRNAs in cardiovascular diseases, such as cardiac hypertrophy and AMI [10, 11]. However, many valuable and significant data available for cardiac hypertrophy and AMI had not been abundantly utilized.
Integration of mRNA and miRNA double expression profiles can provide novel insights into research of the mechanisms underlying cardiac hypertrophy and AMI. For example, Yang et al. combined miRNA and mRNA sequencing to identify the protective transcriptome signature of enhanced PI3K signaling in the context of pathological hypertrophy; the results demonstrated that regulation of TGF/miR-21 contributed to the protection of enhanced PI3K signaling against cardiac hypertrophy . Santana et al. compared mRNA and miRNA expression profiles to identify transcriptional and post-transcriptional changes in AMI . Moreover, a network-based method is an effective approach to globally identify biomarkers of disease. For example, Song et al. constructed and analyzed a cardiac hypertrophy-associated long non-coding RNA-mRNA network based on competitive endogenous RNA and revealed functional lncRNAs involved in cardiac hypertrophy. However, these networks have almost exclusively integrated mRNA, miRNA, or lncRNA expression and have lacked drug information.
Research into and development of novel drugs for cardiac hypertrophy and AMI are time-consuming and labor-intensive processes. Recently, drug repurposing has become an essential part of drug discovery that can uncover novel indications for existing drugs . Donner et al. used deep embeddings of gene expression profiles for drug repurposing . Drug repurposing has been successfully used in Parkinson’s disease and cancer [16, 17]. However, few studies have focused on drug repurposing for cardiac hypertrophy and AMI.
In this study, we used heart disease-related genes to construct a miRNA-gene-drug network and analyzed its topological features. We also developed a computational approach to identify candidate risk miRNA-gene-drug triplets (MGDTs) based on mRNA and miRNA expression data. The candidate risk MGDTs hold specific and strong activity score profiles in cardiac hypertrophy and AMI. We also extracted an insulin-like growth factor 1 receptor-related subnetwork in cardiac hypertrophy and a vascular endothelial growth factor A-related subnetwork in AMI. Finally, we utilized pathways to explore the mechanisms underlying cardiac hypertrophy and AMI and to identify candidate drugs for these diseases. These results provide novel insights into the underlying mechanisms and treatment of cardiac hypertrophy and AMI.
Global properties of the MGDT network in heart disease
Some MGDTs are specific for cardiac hypertrophy and AMI
Activity profiles of MGDTs for cardiac hypertrophy and AMI
Functional analyses and strong MGDTs in cardiac hypertrophy and AMI
Identification of novel drug repurposing candidates for cardiac hypertrophy and AMI based on the miRNA-regulated drug pathway
Cardiac hypertrophy and AMI are two common heart diseases worldwide. However, cardiac hypertrophy often carries a poor prognosis due to an increased risk of arrhythmia  and the development of congestive heart failure. Research into the underlying mechanisms and the development of novel drugs for cardiac hypertrophy and AMI are essential. Drug repurposing is an effective, time-saving, and cost-saving method to study novel drugs for heart disease. Here, we developed a comprehensive and computational approach to perform drug repurposing for cardiac hypertrophy and AMI by integrating heart disease-associated genes, experimentally verified miRNA-gene interactions, drug-gene interactions, mRNA and miRNA expression profiles, and pathway annotation data. A global MGDT network was constructed and characterized, and cardiac hypertrophy- and AMI-specific MGDTs were identified. These specific MGDTs were important candidates for drug repurposing analyses. This comprehensive computational approach identified some significantly dysregulated MGDTs, and a KEGG pathway enrichment was performed by the target genes of miRNAs. Some candidate drugs were considered had common and unknown mechanisms of action by shared common targets of miRNA and pathways. Thus, the comprehensive computational approach could realize drug repurposing.
In our analysis, we discovered that ATL1101 was an effective drug candidate for cardiac hypertrophy. ATL1101 is an antisense compound targeting insulin-like growth factor 1 receptor, or IGF1R. Karina Huynh et al. suggested that overexpression of insulin-like growth factor 1 receptor prevented diabetes-induced cardiac fibrosis and diastolic dysfunction. Knezevic et al. demonstrated the existence of a negative feedback loop between miR-378, IGF1R, and IGF1R that was associated with postnatal cardiac remodeling and regulation of cardiomyocyte survival during stress . Here, we suggested that IGF1R influenced cardiac hypertrophy through the FOXO signaling pathway by interacting with a series of miRNAs. In addition, other candidate drugs, such as Insulin Human, Insulin Lispro, and Insulin Pork which were all investigational or approved insulin-related drugs, were identified in our analysis as drug repurposing candidates for cardiac hypertrophy. The computational approach for drug repurposing provided novel insights into mechanistic and drug candidate research in cardiac hypertrophy and AMI.
miRNAs regulate gene expression through translational repression and degradation of mRNAs and significantly contribute to post-transcriptional regulation of drugs . Downregulation of miR-455-3p could be linked to proliferation and drug resistance of pancreatic cancer cells via targeting TAZ . Lopez-Riera et al. indicated that steatotic drugs induced a common set of hepatic miRNAs that could be used in drug screening during preclinical development for fatty liver disease . In our analyses, we also considered the essential role of miRNAs in drug repurposing. We constructed miRNA-mediated drug and pathway regulatory relationship networks for cardiac hypertrophy and AMI and identified enriched pathways based on miRNA target genes. Our results suggested that integrating miRNA interactions and expression was essential for drug repurposing. Well-designed experiments or clinical trials should be conducted in future works to determine whether these repurposing candidate drugs were applicable for the treatment of cardiac hypertrophy and AMI.
In summary, we constructed a miRNA-gene-drug network and analyzed its topological features based on heart disease-associated genes and experimentally verified gene-miRNA and gene-drug interactions. We also presented a computational and integrated approach to identify candidate risk MGDTs, followed by mRNA and miRNA expression profiles, in cardiac hypertrophy and AMI. The candidate risk MGDTs had specific and strong activity score profiles in the cardiac hypertrophy and AMI networks. The IGFR1-related subnetwork in cardiac hypertrophy and the VEGFA-related subnetwork in AMI were extracted for analysis. Finally, we explored the underlying mechanisms and identified candidate drugs for cardiac hypertrophy and AMI based on common pathways of target genes for all miRNAs in the subnetworks. Collectively, our results contributed to a better understanding of the relationships among genes, miRNAs, pathways, and drugs and identified promising drug repurposing candidates for cardiac hypertrophy and AMI.
Collection of high-throughput miRNA and gene data
We downloaded expression profiles for cardiac hypertrophy and AMI from the Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo). The two relevant studies included both miRNA and gene expression profiles from the same sample. A total of three disease samples and three control samples for cardiac hypertrophy were obtained from GSE60291 , and four disease samples and one control sample for AMI were obtained from GSE24591 (unpublished data).
Cardiovascular disease-associated genes
We downloaded cardiovascular disease-associated genes from DisGeNET, which integrates disease-associated genes and variants from expert-curated repositories, GWAS catalogs, animal models, and the scientific literature . We extracted keywords for diseases named “heart” or “cardiac”. Finally, we found 2293 cardiovascular disease-associated genes and their corresponding risk scores for subsequent analysis.
Experimentally validated miRNA-gene and gene-drug interactions
We downloaded the gene-miRNA associations from the public database miRTarBase 7.0, which accumulated more than three hundred and sixty thousand miRNA-gene interactions . We only extracted experimentally validated miRNA-gene interactions. Then, we obtained gene-drug interaction data from DrugBank, which is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions, and their targets . We constructed a MGDT network based on these experimentally validated interactions used Cytoscape 3.0 (http://www.cytoscape.org/).
Dissecting topological characteristics of the MGDT network
We used the degree distributions and topological coefficients to assess the entire MGDT network for all nodes. All analyses were performed using Cytoscape 3.0 (http://www.cytoscape.org/).
Identification of cardiac hypertrophy- and AMI-specific MGDTs based on expression data
Srisk = risk − score
SP = PmRNA∗PmiRNA
Spcc = ∣ Dpcc − Cpcc∣
In the three equations, Srisk represents the verified level between the disease and genes. PmRNA and PmiRNA represent the P values of mRNAs and miRNAs, respectively, for each MGDT derived from the above t test. SP is the difference between the expression level of the MGDT between the samples with disease and the corresponding controls. Dpcc and Cpcc refer to the PCCs of the mRNA-miRNA interaction pairs for the disease and control samples, respectively. Spcc is the absolute distinction of the PCC score between the disease and control samples in each MGDT. We used an equally weighted approach to rank all MGDTs following the above three comprehensive scores . We obtained three ranked lists, and the ranking positions in the three lists were used to calculate the last ranking score list for each MGDT in the cardiac hypertrophy and AMI networks. Higher ranking scores referred to stronger dysregulated MGDTs between the disease and control samples. The permutation-based final ranking scores were generated by randomly disturbing all sample labels in the expression profiles 1000 times. We compared each final ranking score of a MGDT with the permutation-based final ranking score to obtain significant P values (P < 0.05). All analyses were performed using the R software (version 3.2.3; https://www.r-project.org/).
Pathway enrichment analysis
We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to annotate enriched pathways for the genes . We extracted enriched pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) with a P value cutoff of 0.05. Then, we used the target gene sets of miRNAs to identify miRNA-associated pathways. Finally, we constructed relationships, including drugs, pathways, genes, and miRNAs.
Heilongjiang Provincial Department of Health (2011–032).
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
SJT and LN conceived and designed the experiments. YJM and CJ analyzed the data. DX and SJT wrote the manuscript. All authors read and approved the final manuscript.
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Consent for publication
The authors declare that they have no competing interests.
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