Exploring Targets for Heart Failure Using Mendelian Randomization, Colocalization, Summary Data-based Mendelian Randomization, and Drug Prediction

Summary

This study identified eight potential drug targets for heart failure through genetic analysis and explored candidate therapeutic compounds associated with these identified targets.

Abstract

Heart failure (HF) is a prevalent cardiovascular disease that significantly impairs quality of life in its advanced stages. Despite a variety of current treatment strategies, the disease burden of HF remains significant. Therefore, exploring novel therapeutic targets is urgently needed. This study utilized Mendelian randomization (MR) to evaluate the causal relationships between druggable genes and HF. Colocalization analysis and summary data-based Mendelian randomization (SMR) were conducted to further validate the relationship between them. Finally, 8 potential therapeutic targets for HF were identified, including 4 risk factors (CYP11A1, GALT, KCNH2, and METRN) and 4 protective factors (APOM, CHD4, IL11RA, and LPAR5). The GO enrichment, KEGG enrichment, and protein-protein interaction (PPI) network analysis indicated that these genes were primarily involved in metabolic regulation and cardiac electrophysiological processes. Moreover, potential drugs targeting these targets were identified using drug prediction and molecular docking, including mitotane, Adehl, Benzofurans, 1,3-Di-o-tolylguanidine, 96-69-5, and bromoenol lactone. Furthermore, PCR results also demonstrated that mitotane might be associated with CYP11A1 and KCNH2. Drugs designed based on these potential therapeutic targets may offer higher success rates and improve clinical outcomes.

Introduction

Heart failure (HF) is a prevalent cardiovascular syndrome defined by impaired ventricular filling or myocardial contractility, resulting in inadequate cardiac output to meet metabolic demands^1,2,3. Clinically, HF is characterized by pulmonary and systemic congestion, tissue hypoperfusion, and symptoms such as dyspnea, fatigue, and peripheral edema^1,4,5. Despite therapeutic advances, HF remains a leading cause of morbidity and mortality worldwide, affecting over 64 million individuals according to the Global Burden of Disease (GBD) study^6,7,8. The substantial disease burden underscores the urgent need for novel therapeutic strategies.

Current pharmacologic and device-based interventions for HF are limited by suboptimal efficacy and adverse effects, contributing to persistently high rehospitalization rates and impaired quality of life^9,10,11,12. Identifying new molecular targets with causal links to HF pathogenesis is, therefore, critical for improving clinical outcomes. Integrating genomics into drug discovery represents a highly effective strategy for improving efficiency, as genetically validated therapies demonstrate a significantly higher success rate in clinical trials^13,14,15. Furthermore, proteins encoded by these actionable genes serve as prime therapeutic targets for both small molecules and monoclonal antibodies^16,17.

Recent advances in human genetics have facilitated the prioritization of drug targets through integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data. Mendelian randomization (MR) leverages genetic variants as instrumental variables to estimate causal effects, minimizing confounding and reverse causation inherent in observational studies^13,14. When combined with colocalization and summary-based MR (SMR), this approach enables robust prioritization of genes whose modulation may influence disease risk, offering a genetically anchored strategy for target discovery^18,19,20. Compared with conventional association studies, integrating MR, SMR, and colocalization overcomes confounding and reverse causality to establish robust causal inferences. Furthermore, this framework filters out false-positive signals from linkage disequilibrium by ensuring that gene expression and heart failure risk share the same underlying causal variant, optimizing target prioritization. It is worth noting that while cardiovascular tissues exhibit high specificity, this study primarily utilizes large-scale blood cis-eQTL data to maximize statistical power. Although this approach may not fully capture tissue-specific mechanisms inherent to HF, blood-derived variants serve as accessible biomarkers and provide robust insights into systemic genetic drivers of the disease.

In this study, large-scale GWAS and eQTL datasets were used to evaluate the causal effects of druggable genes on HF risk. Significant associations were further validated through colocalization and SMR analyses. To elucidate the biological functions of prioritized targets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, along with protein–protein interaction (PPI) network analysis, were performed. Therapeutic potential was subsequently assessed through drug prediction and molecular docking. Finally, the associations between selected druggable genes and candidate drugs were validated through in vitro experiments.

In conclusion, this study offers important insights for the discovery of new therapeutic targets for HF. By employing MR, SMR, colocalization analysis, gene enrichment analysis, PPI network construction, drug prediction, molecular docking, and in vitro experiments, these findings may contribute to the development of more effective therapeutic strategies for HF.

Protocol

All datasets used in this study were obtained from previously published studies with ethical approval. The reagents and the equipment used are listed in the Table of Materials.

1. Study design

The workflow of this study is illustrated in Figure 1. First, exposure data were obtained from the cis-eQTLs of druggable genes. Second, MR analysis was conducted to investigate the causal relationship between druggable genes and HF. Candidate therapeutic targets for HF were subsequently identified by colocalization and SMR analyses. Gene enrichment analysis and PPI network construction were then performed. Finally, drug prediction and molecular docking were conducted to explore the affinity of drugs for genes.

2. Data source

A total of 6,888 druggable genes were identified from two sources. One dataset included 4,463 genes from a previously published study that integrated multiple data sources¹⁷. The other dataset contained 5,012 genes from the Drug-Gene Interaction Database (DGIdb)²¹. Detailed information was illustrated on the website (https://www.dgidb.org/). After merging and removing duplicates, 6,888 unique druggable genes were retained for analysis.

Cis-eQTL data were obtained from the eQTLGen Consortium (https://eqtlgen.org/cis-eqtls.html), which includes expression profiles from 31,684 blood samples and covers 16,987 genes²².

HF summary statistics were obtained from the R12 release of the FinnGen database²³, which included 37,653 cases and 462,695 controls. The diagnosis of HF was based on the International Classification of Diseases (ICD), including ICD-10—I11.0, I13.0, I13.2, I50, ICD-9—4029B|428, and ICD-8—42700|42710|428|7824. All participants were of European ancestry. Detailed information is available on the FinnGen website and the original article (https://www.finngen.fi/en/access_results).

3. Mendelian randomization analysis

Instrumental variables (IVs) were selected based on three core MR assumptions²⁴. Firstly, IVs are directly associated with exposure factors. Secondly, IVs are not associated with confounding factors. Finally, IVs affect outcome through exposure only. Given that proximal eQTLs exert a more immediate and robust regulatory control over target genes, MR exclusively utilized SNPs located within 100 kb regions of the genes to maximize biological proximity. Strict criteria were employed for SNP selection. All selected SNPs were located within 100 kb upstream of the transcription start site and 100 kb downstream of the transcription end site of each druggable gene. Only SNPs with genome-wide significance (p < 5×10^-8) were included²⁵. To minimize linkage disequilibrium (LD), LD clumping was performed using an r² threshold of 0.001 and a clumping distance of 10,000 kb²⁶. SNPs with F-statistics >10 were retained, calculated using the formula: F = [R²(N−k−1)]/[k(1−R²)]²⁷. SNP information is provided in Supplementary Table 1.

The primary analytical methods were inverse-variance weighted (IVW) and Wald ratio. A p-value less than 0.05 was considered statistically significant^28,29. False discovery rate (FDR) was applied to adjust the p-value to minimize false positive results³⁰. When only one SNP was available, the Wald ratio method was used; otherwise, IVW was applied. Analyses included MR-Egger, weighted median, weighted mode, and simple mode when enough SNPs were present.

The Cochran's Q test and MR-Egger intercept analysis were employed to test heterogeneity and pleiotropy. A Q-p-value greater than 0.05 indicated the absence of heterogeneity³¹. A p-value for the MR-Egger intercept less than 0.05 indicated the presence of directional pleiotropy¹⁸. Finally, the leave-one-out test was conducted to test if the results could be influenced by a single SNP. MR analyses were performed using the TwoSampleMR package (version 0.6.11) in R (version 4.4.2).

4. Colocalization analysis

Colocalization analysis was conducted using the coloc R package (version 6.0.1) to identify whether shared genetic variants influenced both gene expression and HF risk³². Prior probabilities for an SNP being associated with either trait and both traits were set to 1 × 10^-4 and 1 × 10^-5, respectively. The posterior probabilities of the five hypotheses can be calculated: (1) PPH0: SNPs are not linked with two traits. (2) PPH1/PPH2: SNPs are associated with gene expression or HF. (3) PPH3: SNPs are linked with HF and gene expression, but they are driven by different SNPs. (4) PPH4: SNPs are linked with HF and gene expression, and they are driven by the common SNPs. A PPH4 > 0.8 was considered evidence for colocalization.

5. SMR analysis

The SMR analysis was carried out to explore the relationship between the gene and HF using the SMR software (version 1.3.1)³³. The heterogeneity in dependent instruments (HEIDI) test was carried out to determine whether observed associations were due to linkage. A significant SMR p-value (< 0.05) combined with a HEIDI p-value > 0.05 was interpreted as evidence supporting a true causal relationship rather than a linkage scenario-caused association. The detailed operational protocols and computational settings can be accessed on the official website.

6. Enrichment analysis

GO and KEGG enrichment analysis was employed to characterize the biological functions and pathways of the screened potential therapeutic target genes³⁴. GO enrichment included biological processes (BP), molecular functions (MF), and cellular components (CC). The R package clusterProfiler (version 4.14.6) and Pathview (version 1.46.0) were utilized to conduct the enrichment analysis^35,36.

7. Candidate drug prediction

To identify potential therapeutic compounds, drug prediction analysis was performed using the Drug Signatures Database (DSigDB) (http://dsigdb.tanlab.org/DSigD Bv1.0/)³⁷. 22,527 gene sets are included in the DSigDB, including 17,389 unique compounds covering 19,531 genes. Associations between candidate genes and compounds were analyzed, followed by enrichment analysis to identify potentially relevant drugs targeting HF-associated genes.

8. Protein interaction network construction

Protein-protein interaction (PPI) networks were constructed using GeneMANIA (https://genem ania.org/)³⁸. GeneMANIA could help to generate gene function hypotheses, analyze gene lists, and prioritize genes for functional testing.

9. Molecular docking

Molecular docking was performed to validate the druggability of the genes and their relationship to the drug candidates. Docking simulations help assess binding affinity and interaction patterns between compounds and target proteins, thereby informing candidate prioritization and optimization. Protein structures were retrieved from the Protein Data Bank (PDB) (http://www.rcsb.org/), and compound structures were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/)³⁹. Docking was performed using CB-Dock2 (https://cadd.labshare.cn/cb-dock2/index.php), which uses cavity detection to guide blind docking^40,41. Detailed operation procedures and calculation settings information could be obtained on the official website.

10. Cell culture

Human immortalized cardiomyocyte cells (AC16) were cultured in AC16 cell-specific culture medium under sterile conditions. Cells were maintained at 37 °C in a humidified incubator containing 5% CO2. The culture medium was replaced every 2–3 days, and cells were passaged at approximately 70%–80% confluence to maintain optimal growth conditions. Cell morphology and confluence were monitored routinely using light microscopy before each experiment. All cell culture procedures were performed in a biosafety cabinet to minimize contamination risk. For treatment experiments, AC16 cells were seeded into 6-well plates and allowed to adhere overnight before treatment administration.

11. Quantitative reverse transcription PCR (RT-PCR)

AC16 cells were treated with 200 µM palmitic acid (PA) or 20 µM mitotane for 24 h after reaching approximately 60%–70% confluence. PA was dissolved in bovine serum albumin (BSA), whereas mitotane was dissolved in dimethyl sulfoxide (DMSO). All potentially hazardous reagents were handled in accordance with standard laboratory safety procedures using appropriate personal protective equipment, including gloves and laboratory coats.

Following treatment, total RNA was extracted using an RNA extraction kit according to the manufacturer’s instructions under RNase-free conditions. RNA extraction procedures were performed on ice whenever possible to minimize RNA degradation. RNA concentration and purity were assessed before downstream experiments, and only samples with an A260/A280 ratio between 1.8 and 2.0 were used for subsequent analyses.

Complementary DNA (cDNA) was synthesized using a reverse transcription kit according to the manufacturer’s protocol. Quantitative RT-PCR was subsequently performed using SYBR Green Master Mix on a real-time PCR detection system. Each reaction was prepared in a final volume of 10 µL containing 5 µL SYBR Green Master Mix, 0.4 µL forward primer, 0.4 µL reverse primer, 1 µL cDNA template, and 3.2 µL nuclease-free water. Amplification was performed under the following cycling conditions: initial denaturation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s and annealing/extension at 60 °C for 30 s. Each experiment was performed with three biological replicates, and all reactions were conducted in triplicate.

GAPDH was used as the internal reference gene, and the relative mRNA expression levels of CYP11A1 and KCNH2 were calculated using the 2^−ΔΔCt method. A single peak in melt curve analysis was considered indicative of specific amplification. The primer sequences used for RT-PCR are listed in Supplementary Table 2.

Results

Mendelian randomization
The MR results evaluating the causal effects between druggable gene expression and HF were presented in Supplementary Table 3. An FDR-adjusted P value less than 0.05 was considered statistically significant. A total of 11 genes with significant causal associations with HF were identified (Figure 2). Among them, six genes were associated with an increased risk of HF, including CDKN1A (P_FDR: 0.03, OR: 1.293), CYP11A1 <...

Discussion

This research systematically evaluated the causal effects of druggable genes on HF risk by using MR, identifying eight potential therapeutic targets. These included four protective factors (APOM, CHD4, IL11RA, and LPAR5) and four risk factors (CYP11A1, GALT, KCNH2, and METRN). These associations were further supported by colocalization analysis and SMR. Functional annotation through GO, KEGG enrichment, and PPI network analyses suggested that these genes may influence HF primarily through pathways related to metabolism a...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
2× Universal SYBR Green Fast qPCR Mix	Abclonal	RK21203
AC16	HyCyte	TCH-C119
AC16 cell-specific culture medium	HyCyte	TCH-G119
cDNA reverse kit	Abclonal	RK20400
CFX96™ Real-Time system	Bio-Rad Laboratories
Mitotane	TargetMol	T1199
Palmitic acid	TargetMol	T2908
RNA extraction kit	Abclonal	RK30120