Identification of bacterial key genes and therapeutic targets in hypertensive patients with type 2 diabetes through bioinformatics analysis.

HTNT2D patients showed significantly higher microbial richness and distinct clustering compared to healthy controls, indicating altered microbial community structure.

• A total of 124 gut microbiome samples were analyzed, including 95 healthy controls (HC) and 29 HTNT2D cases.
• Diversity analysis revealed significantly higher microbial richness in HTNT2D compared to healthy controls.
• Distinct clustering was observed in HTNT2D samples, indicating a structurally different microbial community.
• The study focused on the combined microbial gene-level mechanisms of coexisting HTN and T2D, which had been previously overlooked.

Differential abundance analysis identified 19 bacterial genera across four dominant phyla in HTNT2D patients.

• Differential abundance analysis was performed to identify bacterial genera that differed between HTNT2D cases and healthy controls.
• 19 bacterial genera across four dominant phyla were identified as differentially abundant.
• Functional prediction uncovered 195 enriched metabolic pathways associated with these genera.
• A total of 257 genes were associated with the enriched metabolic pathways identified through functional prediction.

Protein-protein interaction analysis identified 10 hub genes as potential drivers of HTNT2D pathogenesis.

• The 10 hub genes identified were: acpP, dnaG, fusA, gltB, guaA, gyrB, lacZ, mdh, purF, and tktA.
• These hub genes were selected from the 257 genes identified through functional prediction using protein-protein interaction (PPI) network analysis.
• These genes were designated as bacterial key genes (bKGs) and considered potential drivers of HTNT2D pathogenesis.
• PPI analysis was used to refine findings from the larger set of 257 associated genes down to the 10 most central hub genes.

Molecular docking analysis of the 10 hub genes against drug candidates revealed binding affinities ranging from -4.109 to -9.961 kcal/mol.

• The overall binding affinity range across all docking analyses was -4.109 to -9.961 kcal/mol.
• The top-ranked drug-gene pair was Naringin-fusA with a binding affinity of -9.961 kcal/mol.
• The second-ranked pair was Neohesperidin-mdh with a binding affinity of -9.818 kcal/mol.
• The third-ranked pair was Bromocriptine-gyrB with a binding affinity of -9.446 kcal/mol.
• These three complexes were selected as potential drug candidates based on their binding affinities.

Molecular dynamics simulations performed for 100 ns confirmed the stability of the three top-ranked drug-gene complexes.

• Simulations were conducted for all three top-ranked complexes: Naringin-fusA, Neohesperidin-mdh, and Bromocriptine-gyrB.
• Each simulation was run for 100 nanoseconds (ns).
• The stability of the complexes was confirmed by the simulations, supporting their biological relevance.
• Stable complex formation across 100 ns was interpreted as supporting the viability of these compounds as therapeutic candidates.

Drug-likeness and ADMET evaluations identified Bromocriptine as the most suitable compound among the three top-ranked candidates.

• ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties were evaluated for all top candidates.
• Drug-likeness assessments were also performed as part of the compound evaluation.
• Bromocriptine targeting gyrB was identified as the most suitable compound based on combined drug-likeness and ADMET criteria.
• The authors noted that further safety validation will be necessary for Bromocriptine despite its favorable computational profile.
• Naringin and Neohesperidin were the other two candidates evaluated but ranked below Bromocriptine in overall suitability.