Investigation of Genetic Variants Among Hypertension Patients of Khyber Pakhtunkhwa and Reprofiling of Antihypertensive drugs against complex trait

Abstract

Background: Hypertension is a growing global public health problem. Hypertension is a leading cause of cardiovascular disease, stroke, and premature mortality across the world. Because of its link to both mortality and disability, hypertension is among the world's leading causes of illness and death in humans. This illness is more common in underdeveloped regions than in developed ones. Drug research and development is a time-consuming and complex process with a high failure rate. In recent years, drug development has reached its lowest point in the pharmaceutical industry's history. This work aimed to uncover hypertension-related genes and target proteins genome-wide, revealing novel gene signatures and prospective therapeutic targets. In addition, drug reprofiling was performed for certain genes, which might prove to be an effective strategy in the search for novel therapeutic agents. Methodology: In this study initially the candidate hypertension genes (differential expressed genes (DEGs) identified through differential expression analysis and extensive data mapping were studied for their role in hypertension progression. Total 22 datasets of hypertension were analyzed using a systemically based approach. The gene enrichment analysis was used to determine the changes in gene expression at the cellular level. The transcription and motif analysis were used to identify the potential DEGs regulators and the connection between structure of DEGs. The mutation study was performed to find the genetic variation. The miRDB predictor miRNA targets for genes, involved in the hypertension development. The target protein (gene) interaction with other target proteins was studied that were thought to be involved in hypertension prognosis. The pathway interactome analysis was performed to understand the molecular mechanism participating hypertension. The drug gene network identifies the potential targets for these genes. The selected DEGs further validated among the Kpk population using quantitative real time polymerase chain reaction (qRT-PCR) to find the gene regulation. The most effective medication candidate for hypertension was identified utilizing the DEGs technique and an in-silico drug repurposing strategy. A molecular docking analysis of all drugs and hypertension medications licensed by the FDA was performed to help with the identification of potential leads. The stability of the interacting complexes was further analyzed using molecular docking (MD) modelling. Results: Based on the fold changes and p-value 18 genes were shortlisted from 50 DEGs. Further 7 genes (ANGPTL4, NFIL3, MSR1, CEBPD, USP8, ADM, and EDN1) were shortlisted through data curation of these gene that can be used as potential candidate for hypertension. Some potential DEGs were found after screening out the extracellular proteins. Cluster analysis and expression profiling revealed enriched GO keywords. Hypertension-related miRNA targets include hsa-miR 365a-3p, hsa-miR-2052, hsa-miR-3065-3p, hsa-miR-603, hsa-miR-7113-3p, hsa-miR-3923, and hsa-miR-524-5p. Functional interactions between source DEGs and EGFR, AGT, AVP, APOE, RHOA, SRC, APOB, STAT3, UBC, LPL, APOA1, AKT1 have been reported. DEGs participate in myometrial pathways, MAPK, and G-alpha signaling, which are all connected to hypertension. The mutations in the sequences of NFIL3, USP8, and ADM were projected to cause severe disorder in 71.2, 48.8, and 45.4 percent of the regions, respectively. Through Real-time PCR, elevated genes were identified. The expression profile of these DEGs (ADM, EDN1, ANGPTL4, and USP8) demonstrated higher fold change expression. Molecular docking was utilized to test FDA-approved all medications and hypertensive treatments against four overexpressed DEGs. Three drugs were chosen for each target protein. The docked complexes were verified using a 100- ns molecular dynamics (MD) simulation. This study demonstrates the value of systems genetics in identifying four prospective hypertension therapeutic targets. Conclusion: These network-based algorithms disclose genetic variation data, which helps us locate therapeutic targets rapidly and treat hypertension. The differential expression of DEGs in cases relative to controls was shown by qRT-PCR analysis and expression profiling, showing their pathogenic function in disease progression. The interaction and stability of FDA-approved ligands against overexpressed genes has been anticipated using molecular-docking and simulation analyses. This study identified some common DEGs expressed in local kpk population of Pakistan which explore their therapeutic potential as drug target in hypertension. This research will aid in the development of hypertension treatment techniques.