Abstract
Hypertension (HTN) coexisting with type 2 diabetes (T2D) significantly increases cardiovascular risk, yet most microbiome studies have focused on these diseases separately and have overlooked their combined microbial gene-level mechanisms. The coexistence of HTN and T2D may create a distinct gut microbial environment where metabolic and vascular pathways intersect but the specific bacterial genes and molecular interactions underlying this dual phenotype remain largely unknown. To address this gap, this study aimed to identify bacterial key genes (bKGs) associated with hypertension coexisting with type-2 diabetes (HTNT2D) and to explore therapeutic agents targeting these bKGs through integrated bioinformatics approaches. A total of 124 gut microbiome samples, including 95 healthy controls (HC) and 29 HTNT2D cases were analyzed. Diversity analysis revealed significantly higher microbial richness and distinct clustering in HTNT2D, indicating altered microbial community structure. Differential abundance analysis identified 19 bacterial genera across four dominant phyla, while functional prediction uncovered 195 enriched metabolic pathways and 257 associated genes. To refine these finding, protein-protein interaction analysis highlighted 10 hub genes (acpP, dnaG, fusA, gltB, guaA, gyrB, lacZ, mdh, purF and tktA) as potential drivers of HTNT2D pathogenesis. Molecular docking analysis of these bKGs revealed binding affinities ranging from - 4.109 to -9.961 kcal/mol and three top-ranked drug candidates named Naringin-fusA (-9.961 kcal/mol), Neohesperidin-mdh (-9.818 kcal/mol), and Bromocriptine-gyrB (-9.446 kcal/mol) were selected as potential drugs based on their binding affinities. Subsequent molecular dynamics simulations performed for 100 ns confirmed the stability of their complexes, supporting their biological relevance. Drug-likeness and ADMET evaluations pointed to Bromocriptine as the most suitable compound though further safety validation will be necessary. Overall, this study provides novel insights into the gut microbiome signatures of HTNT2D and identifies bKGs with therapeutic potential. These computationally identified candidates can be prioritized for experimental validation to advance microbiome-based diagnostics and targeted therapies for HTNT2D management.