Identification of Bacterial Key Genes and Therapeutic Targets in Hypertensive Patients with Type 2 Diabetes using Bioinformatics Analysis

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Tahalil Islam Rahat, Shermin Akter Sumi, Mifta Nurejannath, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7760709/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 36 You are reading this latest preprint version Abstract Hypertension (HTN) coexisting with type 2 diabetes (T2D) significantly elevate the risk of cardiovascular complications and growing evidence suggests that gut microbiota may contribute to the development of these conditions. Yet, microbial gene-level insights in hypertensive patients with T2D (HTNT2D) are still limited. To address this gap, this study aimed to identify bacterial key genes (bKGs) associated with HTNT2D and to explore therapeutic agents targeting these bKGs through integrated bioinformatics approaches. This study analyzed 124 gut microbiome samples downloaded from NCBI which includes 95 healthy controls and 29 HTNT2D cases. After quality control, 92% of raw 16S rRNA reads were retained which yielded 53,311 representative OTUs. Building on this dataset, diversity analysis showed significantly higher microbial richness in HTNT2D and revealed distinct clustering between groups which indicates an altered microbial structure. Differential abundance analysis further identified 19 bacterial genera across four dominant phyla. Functional prediction then explored 195 enriched metabolic pathways and 139 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 of these targets revealed three top-ranked drug candidates named Naringin-fusA, Neohesperidin-mdh, and Bromocriptine-gyrB and subsequent molecular dynamics simulations confirmed the stability of their complexes. 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 relevance. The findings highlight the promise of microbiome-based diagnostics and targeted drug strategies for managing patients with HTNT2D. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Microbiology Hypertension Type 2 diabetes gut microbiome bacterial key genes drug repurposing stability analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Hypertension (HTN) and type 2 diabetes (T2D) are major public health concerns that frequently coexist and substantially increase the global burden of cardiovascular diseases and premature mortality [ 1 , 2 ]. According to the International Diabetes Federation (IDF), the number of people living with diabetes is estimated to reach 589 million by 2025 and could rise to 700 million by 2045 [ 3 , 4 ]. T2D currently affects approximately 171 million individuals worldwide, with projections of 366 million by 2030 [ 5 , 6 ] and 642 million by 2040 [ 7 ]. Similarly, around 1.4 billion people had hypertension in 2024, and nearly 600 million adults remain unaware of their condition [ 8 ]. The coexistence of HTN and T2D is particularly prevalent in low- and middle-income countries, where access to early diagnosis and personalized treatment is limited [ 2 ]. These conditions are closely linked through shared pathophysiological mechanisms, including insulin resistance, chronic low-grade inflammation, oxidative stress and dysregulation of the renin-angiotensin-aldosterone system (RAAS) [ 9 , 10 ]. Insulin resistance contributes to endothelial dysfunction, vascular stiffness and impaired nitric oxide production, promoting sustained high blood pressure while elevated arterial pressure exacerbates glucose intolerance and insulin secretion defects [ 11 – 13 ]. The co-occurrence of these conditions accelerates the development of microvascular complications such as nephropathy, retinopathy and neuropathy as well as macrovascular events including coronary artery disease, stroke and heart failure [ 1 , 14 , 15 ]. This bidirectional interplay complicates disease management as glycemic control alone may not prevent cardiovascular risk and antihypertensive therapy may be insufficient to mitigate metabolic abnormalities. Consequently, understanding the molecular and microbial mechanisms underlying their coexistence is critical for developing targeted therapeutic strategies. Recent studies have highlighted the important role of gut microbiota in the pathogenesis of metabolic and cardiovascular disorders [ 16 – 19 ]. Alterations in gut microbial composition, including shifts in Bacteroidetes and Firmicutes ratios, have been associated with T2D [ 20 – 23 ]. T2D patients often harbor higher proportions of Gram-negative bacteria from Bacteroidetes and Proteobacteria, which may promote endotoxemia through increased circulating lipopolysaccharides [ 24 ]. Similarly, hypertension has been linked to gut dysbiosis, with decreased abundance of Faecalibacterium, Lachnospiraceae_UCG-004, and Coprobacter, and increased Tyzzerella, Lachnospiraceae_FCS020_group, Enterobacterales, and Enterobacteriaceae [ 25 – 27 ]. However, most studies focus on community-level microbial changes and analyze bacterial taxa separately, limiting understanding of the gene-level functional mechanisms contributing to disease progression and hindering the development of precise therapeutic strategies. Gene-level analysis of the gut microbiome offers a powerful approach to identify bacterial genes and pathways that directly influence host physiology, providing potential biomarkers and therapeutic targets [ 28 ]. Despite growing evidence, few studies have explored bacterial gene-level alterations specifically in HTNT2D [ 29 ]. The lack of integrative bioinformatics analyses limits the translation of microbiome research into therapeutic applications for this high-risk population. To address this gap, the present study aims to identify bKGs and potential therapeutic agents in HTNT2D. By integrating 16S rRNA sequencing, functional prediction, network analysis, molecular docking and pharmacokinetic assessment. This work provides novel insights into microbiome-driven mechanisms and offers microbiome-based strategies for improved management of patients with coexisting hypertension and T2D. The workflow of this study presented in Fig. 1 . Results Preprocessing of 16S rRNA Sequence Data A total of 124 fecal microbiome samples were analyzed including 29 HTNT2D patients and 95 healthy controls. Following the acquisition of raw 16S rRNA sequencing data, quality control and preprocessing were performed to ensure reliability for downstream analyses. Approximately 92% of the reads were retained as high-quality sequences. Forward and reverse reads were subsequently merged and dereplicated and this resulted in 1,006,176 unique sequence features. These sequences were clustered into operational taxonomic units (OTUs) at 97% similarity and this produced 53,311 representative OTUs. The resulting OTU table provided a robust foundation for subsequent analyses including bacterial diversity assessment, taxonomic profiling and functional prediction. Analysis of Bacterial Diversity Bacterial diversity was evaluated to compare microbial community composition between HTNT2D patients and healthy controls. Alpha diversity was measured using the Observed species, Chao1 and Simpson indices, revealed a significant reduction in microbial richness and evenness in HTNT2D patients compared to healthy individuals ( p < 0.01, Fig. 2 a–c). Beta diversity was assessed using the Bray–Curtis dissimilarity metric and results visualized through PCoA analysis. The first two PCoA axes accounted for approximately 25% of the total variance in microbial composition (see Fig. 2 d). PERMANOVA analysis confirmed statistically significant differences between groups ( F = 11.967, p = 0.001). A Venn diagram indicated only 15% overlap in taxa between HTNT2D patients and healthy individuals (see Fig. 2 e). Taxonomic Profiling and Identification of Differentially Abundant Bacteria Consistent with diversity analyses, taxonomic profiling revealed significant alterations in bacterial composition between HTNT2D patients and healthy controls. Taxonomic classification at the phylum and genus levels identified a total of 18 bacterial phyla and 179 genera across all samples, with the 15 most abundant phyla and genera shown in Fig. 3 a and 3 b. At the phylum level, the gut microbiota was predominantly composed of Bacteroidetes, Firmicutes, and Proteobacteria. Compared to healthy controls, HTNT2D patients exhibited a reduced relative abundance of Bacteroidetes (32.91% vs. 56.59%) and increased abundances of Firmicutes (45.76% vs. 34.45%) and Proteobacteria (15.60% vs. 7.09%). The remaining phyla displayed minimal changes between groups. At the genus level, dominant taxa included Pyropia, Raphanus, Bdellovibrio, Helicobacter, Campylobacter, Desulfovibrio, Bilophila, Paracoccus and Kaistobacter. Several genera including Pyropia, Raphanus, Bdellovibrio, Helicobacter, Desulfovibrio, Bilophila, Paracoccus and Kaistobacter were less abundant in HTNT2D patients whereas Campylobacter, Arcobacter, Rhizobium, Sphingomonas, Bradyrhizobium, Agrobacterium and Akkermansia were significantly enriched. Notably, Campylobacter emerged as the most dominant genus in HTNT2D patients which highlights its potential role in disease-associated microbial dysbiosis. Table 1 Common bacterial genera identified as differentially abundant in HTNT2D patients by both DESeq2 and metagenomeSeq analyses ( n = 19). Phylum Class Order Family Genus Species Firmicutes Clostridia Clostridiales Lachnospiraceae Ruminococcus gnavus Firmicutes Clostridia Clostridiales Lachnospiraceae Anaerostipes NA Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides NA Actinobacteria Actinobacteria Bifidobacteriales Bifidobacteriaceae Bifidobacterium longum Firmicutes Clostridia Clostridiales Lachnospiraceae Blautia NA Firmicutes Clostridia Clostridiales Ruminococcaceae Butyricicoccus pullicaecorum Firmicutes Clostridia Clostridiales Clostridiaceae Clostridium celatum Firmicutes Clostridia Clostridiales Lachnospiraceae Coprococcus NA Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus ruminis Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae Haemophilus parainfluenzae Firmicutes Clostridia Clostridiales Lachnospiraceae Lachnospira NA Firmicutes Clostridia Clostridiales Veillonellaceae Megamonas NA Bacteroidetes Bacteroidia Bacteroidales [Odoribacteraceae] Odoribacter NA Bacteroidetes Bacteroidia Bacteroidales Porphyromonadaceae Parabacteroides NA Firmicutes Clostridia Clostridiales Veillonellaceae Phascolarctobacterium NA Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella copri Firmicutes Clostridia Clostridiales Lachnospiraceae Roseburia NA Firmicutes Bacilli Lactobacillales Streptococcaceae Streptococcus NA Firmicutes Clostridia Clostridiales Veillonellaceae Veillonella dispar Although taxonomic profiling provided an overview of bacterial composition, it did not indicate which taxes were significantly associated with HTNT2D. To identify potential disease-associated bacteria, differential abundance analyses were performed using both DESeq2 and metagenomeSeq methods. DESeq2 identified 387 differentially abundant taxa while metagenomeSeq revealed 417 taxa. To enhance robustness, the intersection of both methods was taken that has resulted in 19 common unique genera which are likely the most relevant bacterial taxa contributing to HTNT2D pathogenesis (see Table 1 ). These genera spanned four major phyla named Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. Notable genera from the Firmicutes phylum included Ruminococcus, Anaerostipes, Blautia, Butyricicoccus, Clostridium, Coprococcus, Lactobacillus, Lachnospira, Megamonas, Phascolarctobacterium, Roseburia, Streptococcu , and Veillonella . From Bacteroidetes, the genera Bacteroides, Odoribacter, Parabacteroides and Prevotella were identified while Bifidobacterium represented Actinobacteria and Haemophilus represented Proteobacteria. These findings indicate that these bacterial genera may play pivotal roles in dysbiosis associated with HTNT2D and provide candidate targets for functional and therapeutic investigations. Pathway-Based Gene Identification from Differentially Abundant Bacteria To investigate the functional role of HTNT2D-associated bacteria, PICRUSt2 analysis was performed to predict pathway-based bacterial genes. Functional profiling compared the predicted gene abundances against KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologs. Significant differences in mean pathway abundances were observed between HTNT2D patients and healthy controls across 20 MetaCyc signaling pathways out of 1107. Among these, 14 pathways including Ribosome, Oxidative Phosphorylation, Purine metabolism and Pyrimidine metabolism were significantly enriched in HTNT2D patients whereas 6 pathways such as Galactose metabolism, Other glycan degradation, Glycosaminoglycan degradation and Sphingolipid metabolism were significantly reduced in the HTNT2D group (see Fig. 4 a). Gene Set Enrichment Analysis (GSEA) further identified pathways exhibiting statistically significant and concordant differences between HTNT2D and healthy groups. A total of 13 pathways were significantly enriched (adjusted p < 0.05). Several pathways, including ko00330 (Arginine and proline metabolism), ko02010 (ABC transporters) and ko00720 (Other carbon fixation pathways) showed strong upregulation with large gene sets and low adjusted p -values. Conversely, ko03010 (Ribosome) displayed the most pronounced downregulation with a large gene set and minimally adjusted p -value. Additional pathways, such as ko00190 (Oxidative phosphorylation), ko00970 (Aminoacyl-tRNA biosynthesis) and ko00860 (Porphyrin metabolism) were downregulated but involved smaller gene sets. Seven other pathways (ko00520, ko00500, ko00910, ko00250, ko00670, ko03430, and ko00290) were statistically insignificant (see Fig. 4 b). Overall, 139 genes associated with 195 differentially abundant pathways (DAPs) were identified out of 1107 total pathways based on a zero-inflated Gaussian mixture model with thresholds of |LogFC| >2.0 and p-value < 0.05. PPI network analysis revealed the top 10 bKGs named gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, and purF which were selected as potential drug targets. These genes were functionally linked to both upregulated and downregulated pathways identified in the GSEA that highlights their central role in HTNT2D pathogenesis (see Fig. 4 c). Bacterial Key Genes-Guided Drug Repurposing and Binding Stability Analysis To identify potential therapeutic agents for HTNT2D, molecular docking analysis was conducted using the ten identified bKG-mediated proteins (acpP, dnaG, fusA, gltB, guaA, gyrB, lacZ, mdh, purF, and tktA) as receptors. Protein structures were retrieved from the Protein Data Bank (PDB) with corresponding IDs 7pdi, 4ehs, 2mzw, 6s6s, 3tqi, 4hyp, 4duw, 6bal, 6ott, and 8r3p, and subsequently stabilized through energy minimization to ensure structural reliability. Prepared ligands, representing 189 published drugs, were docked against the protein receptors using AutoDock Vina to evaluate binding interactions. Binding affinities were expressed in kcal/mol for each receptor-ligand pair. The resulting docking scores were organized in a matrix with receptors as rows and ligands as columns, ranked from strongest to weakest binding. A heatmap visualization of these interactions is presented in Fig. 5 . Based on docking results, the top three candidate compounds named Naringin, Neohesperidin and Bromocriptine exhibited the highest binding affinities with the bKG-mediated receptors that suggests their potential as repurposable therapeutic agents for managing HTNT2D. Following the identification of candidate drug targets, molecular interactions between the selected drugs (Naringin, Neohesperidin and Bromocriptine) and their respective protein targets (fusA, mdh and gyrB) were examined. Naringin exhibited a strong binding affinity to fusA with a binding energy of − 9.961 kcal/mol that indicats a robust and stable interaction. This suggests that Naringin may serve as an effective inhibitor of fusA. In comparison, Neohesperidin and Bromocriptine displayed slightly lower binding affinities toward mdh and gyrB with binding energies of − 9.515 kcal/mol and − 9.446 kcal/mol, respectively. These comparatively lower energies suggest relatively weaker interactions which is implying that Neohesperidin and Bromocriptine may exhibit reduced inhibitory potential against mdh and gyrB. Detailed interaction profiles including hydrogen bonding and hydrophobic contacts for each drug-target complex are illustrated in Fig. 6 . The results presented in Table S2 summarize the non-bonded interactions between receptor proteins and the selected ligands based on binding affinity. The Naringin–fusA complex demonstrated three conventional hydrogen bonds with TYR537, HIS572, and VAL574 at distances of 2.5–2.9 Å, along with two carbon–hydrogen bonds involving ASP573 at 3.8 and 3.5 Å, respectively. Hydrophobic interactions included a π–sigma interaction with PHE581 (3.64 Å), a π–π stacked interaction with PHE581 (4.52 Å), two alkyl interactions with VAL533 and PRO534 (4.6 and 5.4 Å), and two π–alkyl interactions with TYR537 and VAL541 (4.9 and 5.3 Å). Similarly, the Neohesperidin–mdh complex formed five conventional hydrogen bonds with THR211, GLY176, VAL146, GLY210, and MET227 at distances of 2.6–2.9 Å, in addition to one carbon–hydrogen bond with ASP301 at 2.5 Å. An electrostatic π–anion interaction was observed with ASP86 (4.12 Å). The Bromocriptine–gyrB complex displayed a carbon–hydrogen bond with THR34 (3.35 Å), an electrostatic π–cation interaction with HIS38 (4.46 Å), a π–sigma interaction with LEU119 (3.91 Å), three alkyl interactions with ILE190 and LEU119 (3.9–5.5 Å), and two π–alkyl interactions with PHE41 and LEU119 (5.1 Å). To assess the binding stability of the top three drug–target complexes molecular dynamics simulations were carried out over a 100 ns timescale and revealed distinct stability profiles (see Fig. 7 ). The RMSD analysis showed that Neohesperidin–mdh and Bromocriptine–gyrB attained stable conformations after ~ 20 ns with lower fluctuations whereas the Naringin–fusA complex exhibited higher deviations indicating reduced stability. MM-PBSA profiles indicated that Neohesperidin–mdh had more favorable and consistent binding free energy compared to the other complexes. The Rg analysis revealed that Bromocriptine–gyrB and Neohesperidin–mdh maintained compact structures whereas Naringin–fusA displayed greater expansion. SASA analysis further demonstrated lower solvent exposure for Bromocriptine–gyrB and Neohesperidin–mdh compared to Naringin–fusA. These findings indicates that Neohesperidin–mdh and Bromocriptine–gyrB form more stable and compact complexes and Neohesperidin–mdh shows the most favorable binding and stability. Evaluation of Drug-Likeness and ADMET Properties Table 3 summarizes the drug-likeness properties of Naringin, Neohesperidin and Bromocriptine based on Lipinski’s Rule of Five and related criteria. Both Naringin and Neohesperidin exhibit high molecular weights (> 500 Da), elevated numbers of hydrogen bond donors (HBD > 5) and acceptors (HBA > 10) and large topological polar surface areas (TPSA > 200 Ų) which indicates strong hydrophilicity and suggesting limited oral bioavailability. Although they comply with the thresholds for rotatable bonds (< 10) and Log P (< 5), their deviations from multiple Lipinski, Veber and Ghose criteria highlight potential challenges in absorption and permeability. In contrast, Bromocriptine, with a molecular weight of 654.606 Da, a moderate Log P (3.19) and fewer hydrogen bond donors/acceptors (HBD = 3; HBA = 6) that demonstrates more favorable lipophilic properties. While it meets several Lipinski parameters including acceptable Log P , HBD, HBA and rotatable bonds it still exceeds the molecular weight and TPSA thresholds, implying restricted compliance with drug-likeness rules. These findings suggest that all three candidates show partial adherence to established drug-likeness guidelines, with Bromocriptine exhibiting comparatively better alignment than Naringin and Neohesperidin though all may face limitations in oral bioavailability. Table 3 Drug likeness properties of the proposed candidate drug molecules. Properties Naringin Neohesperidin Bromocriptine Molecular Weight(5frMW) SB < 500 580.539 610.565 654.606 Log P (SB < 5) -1.1652 -1.1566 3.1928 HBA (SB < 10) 14 15 6 HBD (SB < 5) 8 8 3 TPSA (Å 2 ) (SB < 140) 233.028 244.507 259.451 Rotatable bond (ROTB) (SB < 10) 6 7 5 The pharmacokinetic and toxicity evaluation of Naringin, Neohesperidin and Bromocriptine revealed property-specific differences (see Table 4 ). In terms of absorption, Naringin exhibited low Caco-2 permeability (logPapp = − 0.658) whereas Neohesperidin (0.57) and Bromocriptine (0.45) showed higher permeability; all three were predicted to be absorbed in the intestine with comparable moderate skin permeability. Distribution analysis indicated moderate plasma protein binding for Naringin (15.9% unbound) and Neohesperidin (14.8%) while Bromocriptine displayed a higher unbound fraction (25%) which suggests greater systemic availability. None of the compounds inhibited major cytochrome P450 isoenzymes which implies a low risk of metabolic drug–drug interactions. Excretion profiles showed slow clearance for Naringin and Neohesperidin (0.222) and slightly higher clearance for Bromocriptine (0.327) with no OCT2 inhibition predicted. Toxicity assessment indicated no mutagenicity, cardiotoxicity, hepatotoxicity or skin sensitization; however, all compounds showed potential genotoxicity. Maximum tolerated doses highlighted moderate safety margins for Naringin (0.43) and Neohesperidin (0.38) whereas Bromocriptine (− 0.92) suggested a narrower therapeutic window and comparatively higher toxicity risk. Table 4 Pharmacokinetic and toxicity properties of the proposed drugs derived using Deep-PK server. Property Naringin Neohesperidin Bromocriptine Absorption Caco2 permeability -0.658 0.57 0.449 HIA (Human Intestinal Absorption) 25.796 20.652 71.357 Skin permeability -2.735 -2.735 -2.734 Distribution Volume of Distribution (Vd) 0.619 0.348 1.011 Fraction unbound (human) 0.159 0.148 0.25 Metabolism CYP 1A2 Inhibitor Non-Inhibitor Non-Inhibitor Non-Inhibitor CYP 2C19 Inhibitor Non-Inhibitor Non-Inhibitor Non-Inhibitor CYP 2C9 Inhibitor Non-Inhibitor Non-Inhibitor Non-Inhibitor CYP 2D6 Inhibitor Non-Inhibitor Non-Inhibitor Non-Inhibitor Excretion Clearance (CL) 0.222 0.222 0.327 Organic Cation Transporter 2 Non-Inhibitor Non-Inhibitor Non-Inhibitor Toxicity AMES toxicity Safe Safe Safe hERG I inhibitor Safe Safe Safe Max. tolerated dose (human) 0.43 0.389 -0.915 Hepatotoxicity Safe Safe Safe Skin Sensitisation Safe Safe Safe Discussion Hypertension with type 2 diabetes is increasingly recognized as a multifactorial disorder in which gut microbial dysbiosis may play a critical role in modulating host metabolic and cardiovascular dysfunction [ 66 , 67 ]. This study provides comprehensive insights into the gut microbiome alterations, bacterial gene-level changes, and potential therapeutic targets in patients with HTNT2D. The findings strongly suggest that gut microbiota composition and function play a pivotal role in HTNT2D pathogenesis, potentially influencing host metabolic and cardiovascular outcomes. Alpha diversity indices revealed a marked reduction in microbial richness and evenness in HTNT2D patients that indicates a loss of community complexity. Such decreases in diversity have consistently been reported in metabolic disorders and cardiometabolic comorbidities where dysbiosis often correlates with impaired host metabolic functions [ 68 ]. Beta diversity analysis confirmed distinct clustering between groups highlighting structural reorganization of the microbial community. These findings parallel previous research that reported distinct microbial community shifts in diabetes and hypertensive cohorts [ 69 , 70 ]. At the taxonomic level, Firmicutes, Bacteroidetes and Proteobacteria were predominant and aligned with the core gut microbiota structure reported in earlier studies [ 71 , 72 ]. However, the relative enrichment of Firmicutes and Proteobacteria and depletion of Bacteroidetes in HTNT2D patients agrees with evidence that Firmicutes expansion and Proteobacterial overgrowth are markers of metabolic endotoxemia and low-grade inflammation [ 73 ]. However, the relative enrichment of Firmicutes and Proteobacteria and depletion of Bacteroidetes in HTNT2D patients agrees with evidence that Firmicutes expansion and Proteobacterial overgrowth are markers of metabolic endotoxemia and low-grade inflammation. Conversely, enrichment of genera such as Prevotella, Veillonella and Streptococcus mirrors findings in other cardiometabolic cohorts where these taxa have been associated with enhanced pro-inflammatory potential and increased gut permeability [ 27 , 74 – 76 ]. Together, these results strengthen the evidence that HTNT2D is characterized by a dual disruption loss of protective taxa and expansion of pro-inflammatory genera that likely synergize to aggravate metabolic and cardiovascular risk. Functional predictions revealed significant alterations in microbial pathways. The enrichment of ribosome, oxidative phosphorylation and nucleotide metabolism pathways in HTNT2D suggests an upregulation of bacterial biosynthetic and energy-demanding processes which may increase microbial metabolic activity and competition with the host for nutrients [ 77 , 78 ]. Conversely, depletion of glycan and sphingolipid metabolism pathways indicates impaired microbial degradation of dietary polysaccharides and host–microbiome lipid signaling both of which are crucial for maintaining intestinal barrier integrity and metabolic regulation [ 79 , 80 ]. These functional shifts are consistent with previous reports where patients with metabolic disorders showed enhanced microbial energy metabolism and reduced saccharolytic capacity [ 77 – 80 ]. Moreover, GSEA analysis highlighted concordant enrichment of arginine and proline metabolism, ABC transporters and carbon fixation pathways, pathways previously implicated in microbial stress adaptation and host–microbe interaction during metabolic disease [ 81 ]. The consistent downregulation of ribosomal pathways, however, contrasts with some earlier studies which suggest that microbial gene expression changes in HTNT2D may involve context-specific metabolic reprogramming that warrants deeper validation. At the gene level, 139 bacterial genes corresponding to 195 differentially abundant pathways were identified, of which ten (gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, purF) emerged as central bKGs within the protein–protein interaction network [ 82 ]. Many of these genes, such as gyrB (DNA gyrase subunit B) and fusA (elongation factor G), have been well-studied as essential genes in bacteria and are known targets for antimicrobial drug development [ 83 , 84 ]. Their identification in HTNT2D suggests that microbial adaptation in this comorbidity may rely on conserved survival mechanisms, making them attractive candidates for therapeutic targeting. Furthermore, genes such as mdh (malate dehydrogenase) and tktA (transketolase) are linked to central carbon metabolism which may directly impact host glucose homeostasis, highlighting their relevance to diabetes-related metabolic disturbances [ 85 ]. Drug repurposing analysis identified Naringin, Neohesperidin and Bromocriptine as top-ranking molecules targeting these bKGs [ 86 – 88 ]. Naringin and Neohesperidin flavonoids with strong antioxidants and anti-inflammatory properties which have previously been shown to improve insulin sensitivity, lipid metabolism and vascular function in diabetic and hypertensive models [ 89 ]. Their strong binding affinities to fusA and mdh in this study further support their therapeutic relevance although their high molecular weights and poor predicted oral bioavailability pose translational challenges. Bromocriptine, a dopamine agonist already approved for T2D management, was also identified as a promising candidate that consistent with clinical evidence showing its benefits on glycemic control and cardiovascular outcomes [ 90 ]. However, our ADMET analysis indicated that Bromocriptine has a narrower safety margin and potential toxicity concerns, underscoring the need for cautious evaluation. Collectively, these findings not only validate the use of insilico repurposing in identifying microbiome-targeted therapies but also emphasize the need for structural optimization or formulation strategies to enhance the pharmacokinetic performance of the candidate compounds. Despite these promising results, this study has certain limitations. The use of 16S rRNA sequencing and predictive functional analyses provides only inferred functional potential rather than direct metagenomic or transcriptomic evidence. The relatively small sample size and single-population design further limit generalizability. Future studies employing shotgun metagenomics, metabolomics and experimental in vivo validation will be essential to confirm the identified bacterial key genes and evaluate the efficacy and safety of the proposed therapeutic agents. Methods Data Source and Description This study utilized publicly available gut microbiome data to investigate bKGs in HTNT2D. Raw 16S rRNA sequencing data from human stool samples, along with corresponding metadata, were obtained from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). A total of 124 fecal microbiome samples were included, comprising 29 HTNT2D patients and 95 healthy controls corresponding to BioProject numbers PRJNA885601 and PRJNA670300. All samples were derived from adult participants representative of the population in China. For the purpose of drug repurposing analysis, a comprehensive dataset of 189 drug molecules was curated through a systematic review of 26 published studies related to T2D and hypertension (see Table S1 ). Of these, 128 drugs were primarily indicated for T2D while 61 were associated with hypertension management. These drug candidates were subsequently utilized for molecular docking against the identified bKGs. Preprocessing of 16S rRNA Sequencing Data The downloaded 16S rRNA sequences of HTNT2D and control samples from the NCBI database were preprocessed for comprehensive microbiome analysis. Quality assessment of the raw reads was performed using FASTQC to identify low-quality bases, adapter contamination and overrepresented sequences. Poor-quality reads and adapter sequences were removed with Trimmomatic(v0.39) using default parameters [ 30 ] retaining only high-quality reads for downstream analysis. Paired-end reads were subsequently merged using NGmerge v0.3 with a minimum overlap of 5 base pairs and a maximum of 10% mismatches [ 31 ]. The merged sequences were imported into QIIME2 (v2024.10) [ 32 ] which is a widely used platform for microbiome analysis. Within QIIME2, sequences were dereplicated using VSEARCH [ 33 ] which collapsed redundant sequences into unique sequence variants. An open-reference clustering approach was then applied to group sequences into operational taxonomic units (OTUs) at 97% similarity against the Greengenes reference database. The resulting OTU table containing the abundance of each taxon across all samples and is used for further downstream analyses. Analysis of Bacterial Diversity Bacterial diversity was assessed using QIIME2 (v2024.10) and R software (v4.4.4). Within-sample diversity (alpha diversity) was quantified using Observed species, Chao1, and ACE indices [ 34 ] which implemented through the phyloseq R package. Visualization of alpha diversity was performed using the ggplot2 package [ 35 , 36 ] and group differences were statistically tested using the Wilcoxon rank-sum test [ 37 ]. On the other hand, between sample diversity (beta diversity) was assessed using Bray-Curtis dissimilarity [ 38 ] and calculated using “ microbiotaProcess ” R package. To visualize compositional differences among samples, Principal Coordinate Analysis (PCoA) plots were generated [ 39 ]. Clustering patterns in microbial community structures were further tested using permutational multivariate analysis of variance (PERMANOVA) with 999 permutations, conducted via the vegan R package [ 40 ]. Taxonomy Profiling and Differential Abundance Analysis Taxonomic classification of representative sequences was performed using the Naïve Bayesian algorithm implemented in the RDP Classifier (v2.2). Relative abundances of bacterial taxa were determined at multiple taxonomic ranks including phylum, class, order, family, genus and species, using QIIME2 (v2024.10). To identify differentially abundant bacterial taxa (DABT) between HTNT2D patients and healthy controls, a Zero-Inflated Gaussian Mixture Model (ZIGMM) was applied to mean group abundance data. Taxa were considered statistically significant based on a threshold of log fold change (logFC) and p ≤ 0.05 [ 41 ]. Pathway-Based Gene Identification from Differentially Abundant Bacteria Functional profiling of bacterial communities was conducted using PICRUSt2 to predict potential metabolic pathways associated with the gut microbiome. Differentially abundant metabolic pathways (DAMPs) between HTNT2D patients and control groups were identified using Welch’s t -test. Genes associated with these DAMPs were subsequently considered HTNT2D-causing bacterial genes. To explore bKGs, Protein-protein interaction (PPI) networks were built using the STRING database and interaction visualized using Cytoscape. Bacterial Key Genes Guided Drug Repurposing and Binding Stability Analysis Drug repurposing offers an efficient strategy to reduce both cost and time compared with de novo drug discovery [ 42 – 45 ]. To identify potential repurposable drugs targeting bKGs, molecular docking analysis was performed using AutoDock Vina [ 46 ]. Protein structures were obtained from the Protein Data Bank (PDB) and pre-processed to remove heteroatoms, water molecules and non-essential ligands using BIOVIA Discovery Studio [ 47 ] and PyMOL [ 48 ]. Energy minimization of proteins was carried out with Swiss-Pdb Viewer [ 49 ]. Ligands were minimized using Avogadro software with the MMFF94 force field [ 50 ] by applying a conjugate gradient algorithm (200 steps, state updates every 1 step, energy difference threshold = 0.1) [ 51 ] and then converted to PDBQT format. Docking analysis was performed in AutoDock Vina with the exhaustiveness parameter set to 10. Non-bonded interactions including hydrogen bonds, hydrophobic interactions and other non-covalent forces were examined using BIOVIA Discovery Studio (v3.0) and PyMOL (v3.1.6.1). To evaluate the stability of top protein–ligand complexes, molecular dynamics (MD) simulations were conducted in YASARA [ 52 ] with the AMBER14 force field [ 53 ] under physiological conditions (298 K, pH 7.4, 0.9% NaCl). Simulation included neutralization and energy minimization steps and each simulation ran for 100ns. Binding free energies were estimated using the MM-Poisson–Boltzmann Surface Area (MM-PBSA) method [ 54 , 55 ] implemented in the YASARA macro [ 56 ] where more negative values indicated stronger binding affinities. Additionally, simulation trajectories were analyzed for root-mean-square deviation (RMSD), radius of gyration (Rg) and solvent-accessible surface area (SASA) to assess the structural stability of protein–ligand interactions [ 57 – 61 ]. Evaluation of Drug-Likeness and ADMET Properties The drug-likeness properties of the candidates compounds were assessed using pkCSM [ 62 ] and SwissADME tools [ 63 ]. pkCSM predicted pharmacokinetic properties including molecular weight, lipophilicity, hydrogen bond donors and acceptors, topological polar surface area and rotatable bonds. It also evaluated compliance with drug-likeness rules such as Lipinski’s Rule of Five, Veber’s Rule and Ghose’s Rule. SwissADME further examined drug-likeness, solubility and oral bioavailability which provides complementary pharmacokinetic insights. To predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), the Deep-PK web-tools was used [ 64 ]. This platform estimated parameters such as human intestinal absorption, Caco-2 cell permeability, blood–brain barrier penetration, plasma protein binding, cytochrome P450 inhibition and clearance rates. In addition, toxicity endpoints including Ames mutagenicity, hepatotoxicity, cardiotoxicity and skin/eye irritation were evaluated to determine the overall safety profile of the compounds [ 65 ]. Conclusions This study systematically analyzed 16S rRNA sequencing profiles to identify bKGs associated with HTNT2D and explored their potential inhibitory drug molecules. Differential abundance analysis revealed 19 bacterial genera significantly altered in HTNT2D patients compared to healthy controls. Functional enrichment of these genera identified 20 MetaCyc signaling pathways. Among them 13 pathways were significantly enriched and collectively encompassed 139 genes implicated in HTNT2D pathogenesis. PPI network analysis further prioritized 10 bKGs (gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, and purF) as potential therapeutic targets. Molecular docking identified three top-ranked candidate drugs Naringin, Neohesperidin and Bromocriptine capable of effectively binding these bKG-mediated proteins. Subsequent molecular dynamics simulations confirmed the structural stability and strong interactions of Naringin-fusA, Neohesperidin-mdh and Bromocriptine-gyrB complexes. Drug-likeness and ADMET analyses indicated Bromocriptine as the most favorable candidate, with optimal pharmacokinetic properties and a comparatively safer profile. These findings provide a comprehensive framework for microbiome-informed drug repurposing and highlight Bromocriptine and other candidate compounds as promising therapeutic strategies against HTNT2D. Experimental validation in vitro and in vivo is warranted to confirm these bioinformatics predictions and to translate these insights into clinically relevant interventions. Declarations Acknowledgements The authors have nothing to acknowledge. Author’s Contributions Statement MTIR and MKK conceptualized the study. MTIR and MSAS performed statistical analyses of 16S rRNA sequence data and carried out upstream and downstream bioinformatics analyses and also contributing to the draft manuscript writing. MTIR and MJ conducted drug screening via molecular docking and jointly drafted the docking section. MSAS and RA compiled hub genes and relevant published drug molecules through comprehensive literature review. Finally, MKK critically reviewed the manuscript and prepared it for submission. Data Availability Statement The raw 16S rRNA sequence profile dataset analyzed in this study is publicly available. It can be freely downloaded from the online NCBI database with bioproject number PRJNA885601 and PRJNA670300 . Funding The authors declare that no funding was obtained for this study. Competing Interests The authors declare no competing interests. References Agnieszka, P., Weronika, B. & Andrzej, P. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7760709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532509130,"identity":"dadf3fbf-0411-4959-b939-2b191fd429d6","order_by":0,"name":"Md. 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23:23:53","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":215837,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/b84f29f715862dc0a6a1f031.html"},{"id":94048494,"identity":"4db4ac77-462d-4d6f-a6b9-440001155513","added_by":"auto","created_at":"2025-10-21 23:23:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187939,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated bioinformatics workflow for the identification of bacterial key genes and candidate therapeutic agents in HTNT2D patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/79f7057f62a56ad496e6871d.png"},{"id":94048492,"identity":"2943b1f0-b34f-4925-9df9-337e52697990","added_by":"auto","created_at":"2025-10-21 23:23:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120334,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in gut bacterial profiles between HTNT2D patients and healthy controls. (a–c) Alpha diversity of the gut microbiota measured by Observed species, Chao1 and Simpson indices in HTNT2D and healthy samples, respectively. (d) Principal Coordinate Analysis (PCoA) showing clustering of samples by group with the percentage of variation explained by the first two coordinates indicated. (e) Venn diagram illustrates the shared and unique taxa between HTNT2D patients and healthy controls.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/469eb8715ecf9968a556c616.png"},{"id":94049332,"identity":"40788016-29ec-44c0-9d75-c85bed2b3490","added_by":"auto","created_at":"2025-10-21 23:31:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75879,"visible":true,"origin":"","legend":"\u003cp\u003eGut bacterial composition at the phylum and genus levels in HTNT2D patients and healthy controls. (a) Relative abundances of the most prevalent bacterial phyla and (b) genera across HTNT2D and healthy samples.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/f7cfbd4c27854ade42e2a521.png"},{"id":94049335,"identity":"8426e52f-93b6-41d3-a919-63e04a7f62dc","added_by":"auto","created_at":"2025-10-21 23:31:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273841,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key metabolic pathways and bacterial key genes (bKGs) associated with HTNT2D. (a) Differentially abundant metabolic pathways between HTNT2D patients and healthy controls. Blue and red bars represent mean pathway proportions in HTNT2D and healthy individuals, respectively. (b) GSEA of HTNT2D-associated pathways. The \u003cem\u003eX\u003c/em\u003e-axis shows the Normalized Enrichment Score (NES) and the \u003cem\u003eY\u003c/em\u003e-axis represents pathway names. Dot size reflects the number of genes in each pathway, and color indicates statistical significance based on the \u003cem\u003ep\u003c/em\u003e-value. (c) Protein-protein interaction network of HTNT2D-associated pathway genes, highlighting identified bKGs labeled in white.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/1970d7840d5e9e499fadbad3.png"},{"id":94048500,"identity":"14e98211-fac7-4472-868c-2f78571093bd","added_by":"auto","created_at":"2025-10-21 23:23:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":109658,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of protein-ligand binding affinities between bKG-mediated proteins and top-ranked drug molecules. The \u003cem\u003eX\u003c/em\u003e-axis represents the 30 highest-ranked drug candidates and the \u003cem\u003eY\u003c/em\u003e-axis indicates the ten bKG-mediated proteins. Colors reflect the binding affinity scores and illustrats the strength of interactions between each protein-ligand pair.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/3b40edc04206d6ddc2691db4.png"},{"id":94048502,"identity":"77b26c2d-4fa8-4ab9-931a-d668a325cc0f","added_by":"auto","created_at":"2025-10-21 23:23:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":386508,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular interactions between selected drugs and their respective protein targets: (a) fusA–Naringin, (b) mdh–Neohesperidin and (c) gyrB–Bromocriptine. Each panel illustrates the surface representation of the protein–ligand complex, the docking pose of the interaction and detailed views of hydrogen bonds and amino acid contacts within the binding sites.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/09e51be8cee31c704369a06d.png"},{"id":94049337,"identity":"7a5e401f-1379-4c92-b197-f632608f6228","added_by":"auto","created_at":"2025-10-21 23:31:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":182497,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation profiles of the top-ranked drug–target complexes over a 100 ns timescale: Naringin–fusA (blue), Neohesperidin–mdh (red), and Bromocriptine–gyrB (green).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/9a873ecbd47fee4e722fd74c.png"},{"id":101690539,"identity":"3164fdd1-5b6c-42bb-a18f-267c1013e264","added_by":"auto","created_at":"2026-02-02 16:05:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2417185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/c2973d4d-5e14-4739-b661-4f49934bd16f.pdf"},{"id":94048493,"identity":"338d24ca-9c76-4d39-9bdb-dda45b1bd87c","added_by":"auto","created_at":"2025-10-21 23:23:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":49735,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7760709/v1/971648112ba337eba0a93b10.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Bacterial Key Genes and Therapeutic Targets in Hypertensive Patients with Type 2 Diabetes using Bioinformatics Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension (HTN) and type 2 diabetes (T2D) are major public health concerns that frequently coexist and substantially increase the global burden of cardiovascular diseases and premature mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the International Diabetes Federation (IDF), the number of people living with diabetes is estimated to reach 589\u0026nbsp;million by 2025 and could rise to 700\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. T2D currently affects approximately 171\u0026nbsp;million individuals worldwide, with projections of 366\u0026nbsp;million by 2030 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and 642\u0026nbsp;million by 2040 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, around 1.4\u0026nbsp;billion people had hypertension in 2024, and nearly 600\u0026nbsp;million adults remain unaware of their condition [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The coexistence of HTN and T2D is particularly prevalent in low- and middle-income countries, where access to early diagnosis and personalized treatment is limited [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These conditions are closely linked through shared pathophysiological mechanisms, including insulin resistance, chronic low-grade inflammation, oxidative stress and dysregulation of the renin-angiotensin-aldosterone system (RAAS) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Insulin resistance contributes to endothelial dysfunction, vascular stiffness and impaired nitric oxide production, promoting sustained high blood pressure while elevated arterial pressure exacerbates glucose intolerance and insulin secretion defects [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The co-occurrence of these conditions accelerates the development of microvascular complications such as nephropathy, retinopathy and neuropathy as well as macrovascular events including coronary artery disease, stroke and heart failure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This bidirectional interplay complicates disease management as glycemic control alone may not prevent cardiovascular risk and antihypertensive therapy may be insufficient to mitigate metabolic abnormalities. Consequently, understanding the molecular and microbial mechanisms underlying their coexistence is critical for developing targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003eRecent studies have highlighted the important role of gut microbiota in the pathogenesis of metabolic and cardiovascular disorders [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Alterations in gut microbial composition, including shifts in Bacteroidetes and Firmicutes ratios, have been associated with T2D [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. T2D patients often harbor higher proportions of Gram-negative bacteria from Bacteroidetes and Proteobacteria, which may promote endotoxemia through increased circulating lipopolysaccharides [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, hypertension has been linked to gut dysbiosis, with decreased abundance of Faecalibacterium, Lachnospiraceae_UCG-004, and Coprobacter, and increased Tyzzerella, Lachnospiraceae_FCS020_group, Enterobacterales, and Enterobacteriaceae [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, most studies focus on community-level microbial changes and analyze bacterial taxa separately, limiting understanding of the gene-level functional mechanisms contributing to disease progression and hindering the development of precise therapeutic strategies. Gene-level analysis of the gut microbiome offers a powerful approach to identify bacterial genes and pathways that directly influence host physiology, providing potential biomarkers and therapeutic targets [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite growing evidence, few studies have explored bacterial gene-level alterations specifically in HTNT2D [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The lack of integrative bioinformatics analyses limits the translation of microbiome research into therapeutic applications for this high-risk population. To address this gap, the present study aims to identify bKGs and potential therapeutic agents in HTNT2D. By integrating 16S rRNA sequencing, functional prediction, network analysis, molecular docking and pharmacokinetic assessment. This work provides novel insights into microbiome-driven mechanisms and offers microbiome-based strategies for improved management of patients with coexisting hypertension and T2D. The workflow of this study presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePreprocessing of 16S rRNA Sequence Data\u003c/h2\u003e\u003cp\u003eA total of 124 fecal microbiome samples were analyzed including 29 HTNT2D patients and 95 healthy controls. Following the acquisition of raw 16S rRNA sequencing data, quality control and preprocessing were performed to ensure reliability for downstream analyses. Approximately 92% of the reads were retained as high-quality sequences. Forward and reverse reads were subsequently merged and dereplicated and this resulted in 1,006,176 unique sequence features. These sequences were clustered into operational taxonomic units (OTUs) at 97% similarity and this produced 53,311 representative OTUs. The resulting OTU table provided a robust foundation for subsequent analyses including bacterial diversity assessment, taxonomic profiling and functional prediction.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis of Bacterial Diversity\u003c/h3\u003e\n\u003cp\u003eBacterial diversity was evaluated to compare microbial community composition between HTNT2D patients and healthy controls. Alpha diversity was measured using the Observed species, Chao1 and Simpson indices, revealed a significant reduction in microbial richness and evenness in HTNT2D patients compared to healthy individuals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c). Beta diversity was assessed using the Bray\u0026ndash;Curtis dissimilarity metric and results visualized through PCoA analysis. The first two PCoA axes accounted for approximately 25% of the total variance in microbial composition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). PERMANOVA analysis confirmed statistically significant differences between groups (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.967, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). A Venn diagram indicated only 15% overlap in taxa between HTNT2D patients and healthy individuals (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTaxonomic Profiling and Identification of Differentially Abundant Bacteria\u003c/h3\u003e\n\u003cp\u003eConsistent with diversity analyses, taxonomic profiling revealed significant alterations in bacterial composition between HTNT2D patients and healthy controls. Taxonomic classification at the phylum and genus levels identified a total of 18 bacterial phyla and 179 genera across all samples, with the 15 most abundant phyla and genera shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. At the phylum level, the gut microbiota was predominantly composed of Bacteroidetes, Firmicutes, and Proteobacteria. Compared to healthy controls, HTNT2D patients exhibited a reduced relative abundance of Bacteroidetes (32.91% vs. 56.59%) and increased abundances of Firmicutes (45.76% vs. 34.45%) and Proteobacteria (15.60% vs. 7.09%). The remaining phyla displayed minimal changes between groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the genus level, dominant taxa included Pyropia, Raphanus, Bdellovibrio, Helicobacter, Campylobacter, Desulfovibrio, Bilophila, Paracoccus and Kaistobacter. Several genera including Pyropia, Raphanus, Bdellovibrio, Helicobacter, Desulfovibrio, Bilophila, Paracoccus and Kaistobacter were less abundant in HTNT2D patients whereas Campylobacter, Arcobacter, Rhizobium, Sphingomonas, Bradyrhizobium, Agrobacterium and Akkermansia were significantly enriched. Notably, Campylobacter emerged as the most dominant genus in HTNT2D patients which highlights its potential role in disease-associated microbial dysbiosis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCommon bacterial genera identified as differentially abundant in HTNT2D patients by both DESeq2 and metagenomeSeq analyses (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhylum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOrder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRuminococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003egnavus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eAnaerostipes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteroidetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacteroidia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacteroidales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBacteroidaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActinobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eActinobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBifidobacteriales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBifidobacteriaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003elongum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRuminococcaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eButyricicoccus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003epullicaecorum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClostridiaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eClostridium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ecelatum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCoprococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacilli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLactobacillales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLactobacillaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eruminis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProteobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGammaproteobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePasteurellales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePasteurellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHaemophilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eparainfluenzae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLachnospira\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVeillonellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMegamonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteroidetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacteroidia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacteroidales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[Odoribacteraceae]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eOdoribacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteroidetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacteroidia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacteroidales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePorphyromonadaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eParabacteroides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVeillonellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePhascolarctobacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteroidetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacteroidia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacteroidales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevotellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ecopri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLachnospiraceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRoseburia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacilli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLactobacillales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStreptococcaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClostridia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClostridiales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVeillonellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eVeillonella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003edispar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough taxonomic profiling provided an overview of bacterial composition, it did not indicate which taxes were significantly associated with HTNT2D. To identify potential disease-associated bacteria, differential abundance analyses were performed using both DESeq2 and metagenomeSeq methods. DESeq2 identified 387 differentially abundant taxa while metagenomeSeq revealed 417 taxa. To enhance robustness, the intersection of both methods was taken that has resulted in 19 common unique genera which are likely the most relevant bacterial taxa contributing to HTNT2D pathogenesis (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These genera spanned four major phyla named Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. Notable genera from the Firmicutes phylum included \u003cem\u003eRuminococcus, Anaerostipes, Blautia, Butyricicoccus, Clostridium, Coprococcus, Lactobacillus, Lachnospira, Megamonas, Phascolarctobacterium, Roseburia, Streptococcu\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e. From Bacteroidetes, the \u003cem\u003egenera Bacteroides, Odoribacter, Parabacteroides\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e were identified while \u003cem\u003eBifidobacterium\u003c/em\u003e represented Actinobacteria and \u003cem\u003eHaemophilus\u003c/em\u003e represented Proteobacteria. These findings indicate that these bacterial genera may play pivotal roles in dysbiosis associated with HTNT2D and provide candidate targets for functional and therapeutic investigations.\u003c/p\u003e\n\u003ch3\u003ePathway-Based Gene Identification from Differentially Abundant Bacteria\u003c/h3\u003e\n\u003cp\u003eTo investigate the functional role of HTNT2D-associated bacteria, PICRUSt2 analysis was performed to predict pathway-based bacterial genes. Functional profiling compared the predicted gene abundances against KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologs. Significant differences in mean pathway abundances were observed between HTNT2D patients and healthy controls across 20 MetaCyc signaling pathways out of 1107. Among these, 14 pathways including Ribosome, Oxidative Phosphorylation, Purine metabolism and Pyrimidine metabolism were significantly enriched in HTNT2D patients whereas 6 pathways such as Galactose metabolism, Other glycan degradation, Glycosaminoglycan degradation and Sphingolipid metabolism were significantly reduced in the HTNT2D group (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) further identified pathways exhibiting statistically significant and concordant differences between HTNT2D and healthy groups. A total of 13 pathways were significantly enriched (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Several pathways, including ko00330 (Arginine and proline metabolism), ko02010 (ABC transporters) and ko00720 (Other carbon fixation pathways) showed strong upregulation with large gene sets and low adjusted \u003cem\u003ep\u003c/em\u003e-values. Conversely, ko03010 (Ribosome) displayed the most pronounced downregulation with a large gene set and minimally adjusted \u003cem\u003ep\u003c/em\u003e-value. Additional pathways, such as ko00190 (Oxidative phosphorylation), ko00970 (Aminoacyl-tRNA biosynthesis) and ko00860 (Porphyrin metabolism) were downregulated but involved smaller gene sets. Seven other pathways (ko00520, ko00500, ko00910, ko00250, ko00670, ko03430, and ko00290) were statistically insignificant (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Overall, 139 genes associated with 195 differentially abundant pathways (DAPs) were identified out of 1107 total pathways based on a zero-inflated Gaussian mixture model with thresholds of |LogFC| \u0026gt;2.0 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. PPI network analysis revealed the top 10 bKGs named gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, and purF which were selected as potential drug targets. These genes were functionally linked to both upregulated and downregulated pathways identified in the GSEA that highlights their central role in HTNT2D pathogenesis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003ch3\u003eBacterial Key Genes-Guided Drug Repurposing and Binding Stability Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify potential therapeutic agents for HTNT2D, molecular docking analysis was conducted using the ten identified bKG-mediated proteins (acpP, dnaG, fusA, gltB, guaA, gyrB, lacZ, mdh, purF, and tktA) as receptors. Protein structures were retrieved from the Protein Data Bank (PDB) with corresponding IDs 7pdi, 4ehs, 2mzw, 6s6s, 3tqi, 4hyp, 4duw, 6bal, 6ott, and 8r3p, and subsequently stabilized through energy minimization to ensure structural reliability. Prepared ligands, representing 189 published drugs, were docked against the protein receptors using AutoDock Vina to evaluate binding interactions. Binding affinities were expressed in kcal/mol for each receptor-ligand pair. The resulting docking scores were organized in a matrix with receptors as rows and ligands as columns, ranked from strongest to weakest binding. A heatmap visualization of these interactions is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Based on docking results, the top three candidate compounds named Naringin, Neohesperidin and Bromocriptine exhibited the highest binding affinities with the bKG-mediated receptors that suggests their potential as repurposable therapeutic agents for managing HTNT2D.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing the identification of candidate drug targets, molecular interactions between the selected drugs (Naringin, Neohesperidin and Bromocriptine) and their respective protein targets (fusA, mdh and gyrB) were examined. Naringin exhibited a strong binding affinity to fusA with a binding energy of \u0026minus;\u0026thinsp;9.961 kcal/mol that indicats a robust and stable interaction. This suggests that Naringin may serve as an effective inhibitor of fusA. In comparison, Neohesperidin and Bromocriptine displayed slightly lower binding affinities toward mdh and gyrB with binding energies of \u0026minus;\u0026thinsp;9.515 kcal/mol and \u0026minus;\u0026thinsp;9.446 kcal/mol, respectively. These comparatively lower energies suggest relatively weaker interactions which is implying that Neohesperidin and Bromocriptine may exhibit reduced inhibitory potential against mdh and gyrB. Detailed interaction profiles including hydrogen bonding and hydrophobic contacts for each drug-target complex are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results presented in \u003cb\u003eTable S2\u003c/b\u003e summarize the non-bonded interactions between receptor proteins and the selected ligands based on binding affinity. The Naringin\u0026ndash;fusA complex demonstrated three conventional hydrogen bonds with TYR537, HIS572, and VAL574 at distances of 2.5\u0026ndash;2.9 \u0026Aring;, along with two carbon\u0026ndash;hydrogen bonds involving ASP573 at 3.8 and 3.5 \u0026Aring;, respectively. Hydrophobic interactions included a π\u0026ndash;sigma interaction with PHE581 (3.64 \u0026Aring;), a π\u0026ndash;π stacked interaction with PHE581 (4.52 \u0026Aring;), two alkyl interactions with VAL533 and PRO534 (4.6 and 5.4 \u0026Aring;), and two π\u0026ndash;alkyl interactions with TYR537 and VAL541 (4.9 and 5.3 \u0026Aring;). Similarly, the Neohesperidin\u0026ndash;mdh complex formed five conventional hydrogen bonds with THR211, GLY176, VAL146, GLY210, and MET227 at distances of 2.6\u0026ndash;2.9 \u0026Aring;, in addition to one carbon\u0026ndash;hydrogen bond with ASP301 at 2.5 \u0026Aring;. An electrostatic π\u0026ndash;anion interaction was observed with ASP86 (4.12 \u0026Aring;). The Bromocriptine\u0026ndash;gyrB complex displayed a carbon\u0026ndash;hydrogen bond with THR34 (3.35 \u0026Aring;), an electrostatic π\u0026ndash;cation interaction with HIS38 (4.46 \u0026Aring;), a π\u0026ndash;sigma interaction with LEU119 (3.91 \u0026Aring;), three alkyl interactions with ILE190 and LEU119 (3.9\u0026ndash;5.5 \u0026Aring;), and two π\u0026ndash;alkyl interactions with PHE41 and LEU119 (5.1 \u0026Aring;).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the binding stability of the top three drug\u0026ndash;target complexes molecular dynamics simulations were carried out over a 100 ns timescale and revealed distinct stability profiles (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The RMSD analysis showed that Neohesperidin\u0026ndash;mdh and Bromocriptine\u0026ndash;gyrB attained stable conformations after ~\u0026thinsp;20 ns with lower fluctuations whereas the Naringin\u0026ndash;fusA complex exhibited higher deviations indicating reduced stability. MM-PBSA profiles indicated that Neohesperidin\u0026ndash;mdh had more favorable and consistent binding free energy compared to the other complexes. The Rg analysis revealed that Bromocriptine\u0026ndash;gyrB and Neohesperidin\u0026ndash;mdh maintained compact structures whereas Naringin\u0026ndash;fusA displayed greater expansion. SASA analysis further demonstrated lower solvent exposure for Bromocriptine\u0026ndash;gyrB and Neohesperidin\u0026ndash;mdh compared to Naringin\u0026ndash;fusA. These findings indicates that Neohesperidin\u0026ndash;mdh and Bromocriptine\u0026ndash;gyrB form more stable and compact complexes and Neohesperidin\u0026ndash;mdh shows the most favorable binding and stability.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of Drug-Likeness and ADMET Properties\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the drug-likeness properties of Naringin, Neohesperidin and Bromocriptine based on Lipinski\u0026rsquo;s Rule of Five and related criteria. Both Naringin and Neohesperidin exhibit high molecular weights (\u0026gt;\u0026thinsp;500 Da), elevated numbers of hydrogen bond donors (HBD\u0026thinsp;\u0026gt;\u0026thinsp;5) and acceptors (HBA\u0026thinsp;\u0026gt;\u0026thinsp;10) and large topological polar surface areas (TPSA\u0026thinsp;\u0026gt;\u0026thinsp;200 \u0026Aring;\u0026sup2;) which indicates strong hydrophilicity and suggesting limited oral bioavailability. Although they comply with the thresholds for rotatable bonds (\u0026lt;\u0026thinsp;10) and Log\u003cem\u003eP\u003c/em\u003e (\u0026lt;\u0026thinsp;5), their deviations from multiple Lipinski, Veber and Ghose criteria highlight potential challenges in absorption and permeability. In contrast, Bromocriptine, with a molecular weight of 654.606 Da, a moderate Log\u003cem\u003eP\u003c/em\u003e (3.19) and fewer hydrogen bond donors/acceptors (HBD\u0026thinsp;=\u0026thinsp;3; HBA\u0026thinsp;=\u0026thinsp;6) that demonstrates more favorable lipophilic properties. While it meets several Lipinski parameters including acceptable Log\u003cem\u003eP\u003c/em\u003e, HBD, HBA and rotatable bonds it still exceeds the molecular weight and TPSA thresholds, implying restricted compliance with drug-likeness rules. These findings suggest that all three candidates show partial adherence to established drug-likeness guidelines, with Bromocriptine exhibiting comparatively better alignment than Naringin and Neohesperidin though all may face limitations in oral bioavailability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDrug likeness properties of the proposed candidate drug molecules.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProperties\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaringin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeohesperidin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBromocriptine\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Weight(5frMW) SB\u0026thinsp;\u0026lt;\u0026thinsp;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e580.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e610.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e654.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog \u003cem\u003eP\u003c/em\u003e (SB\u0026thinsp;\u0026lt;\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.1652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.1566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBA (SB\u0026thinsp;\u0026lt;\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBD (SB\u0026thinsp;\u0026lt;\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPSA (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e) (SB\u0026thinsp;\u0026lt;\u0026thinsp;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e233.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e244.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e259.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRotatable bond (ROTB) (SB\u0026thinsp;\u0026lt;\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe pharmacokinetic and toxicity evaluation of Naringin, Neohesperidin and Bromocriptine revealed property-specific differences (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of absorption, Naringin exhibited low Caco-2 permeability (logPapp\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.658) whereas Neohesperidin (0.57) and Bromocriptine (0.45) showed higher permeability; all three were predicted to be absorbed in the intestine with comparable moderate skin permeability. Distribution analysis indicated moderate plasma protein binding for Naringin (15.9% unbound) and Neohesperidin (14.8%) while Bromocriptine displayed a higher unbound fraction (25%) which suggests greater systemic availability. None of the compounds inhibited major cytochrome P450 isoenzymes which implies a low risk of metabolic drug\u0026ndash;drug interactions. Excretion profiles showed slow clearance for Naringin and Neohesperidin (0.222) and slightly higher clearance for Bromocriptine (0.327) with no OCT2 inhibition predicted. Toxicity assessment indicated no mutagenicity, cardiotoxicity, hepatotoxicity or skin sensitization; however, all compounds showed potential genotoxicity. Maximum tolerated doses highlighted moderate safety margins for Naringin (0.43) and Neohesperidin (0.38) whereas Bromocriptine (\u0026minus;\u0026thinsp;0.92) suggested a narrower therapeutic window and comparatively higher toxicity risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePharmacokinetic and toxicity properties of the proposed drugs derived using Deep-PK server.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProperty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaringin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeohesperidin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBromocriptine\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbsorption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaco2 permeability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIA (Human Intestinal Absorption)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin permeability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolume of Distribution (Vd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFraction unbound (human)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP 1A2 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP 2C19 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP 2C9 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP 2D6 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExcretion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClearance (CL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic Cation Transporter 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Inhibitor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eToxicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMES toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehERG I inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax. tolerated dose (human)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin Sensitisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHypertension with type 2 diabetes is increasingly recognized as a multifactorial disorder in which gut microbial dysbiosis may play a critical role in modulating host metabolic and cardiovascular dysfunction [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This study provides comprehensive insights into the gut microbiome alterations, bacterial gene-level changes, and potential therapeutic targets in patients with HTNT2D. The findings strongly suggest that gut microbiota composition and function play a pivotal role in HTNT2D pathogenesis, potentially influencing host metabolic and cardiovascular outcomes. Alpha diversity indices revealed a marked reduction in microbial richness and evenness in HTNT2D patients that indicates a loss of community complexity. Such decreases in diversity have consistently been reported in metabolic disorders and cardiometabolic comorbidities where dysbiosis often correlates with impaired host metabolic functions [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Beta diversity analysis confirmed distinct clustering between groups highlighting structural reorganization of the microbial community. These findings parallel previous research that reported distinct microbial community shifts in diabetes and hypertensive cohorts [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the taxonomic level, Firmicutes, Bacteroidetes and Proteobacteria were predominant and aligned with the core gut microbiota structure reported in earlier studies [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. However, the relative enrichment of Firmicutes and Proteobacteria and depletion of Bacteroidetes in HTNT2D patients agrees with evidence that Firmicutes expansion and Proteobacterial overgrowth are markers of metabolic endotoxemia and low-grade inflammation [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, the relative enrichment of Firmicutes and Proteobacteria and depletion of Bacteroidetes in HTNT2D patients agrees with evidence that Firmicutes expansion and Proteobacterial overgrowth are markers of metabolic endotoxemia and low-grade inflammation. Conversely, enrichment of genera such as \u003cem\u003ePrevotella, Veillonella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e mirrors findings in other cardiometabolic cohorts where these taxa have been associated with enhanced pro-inflammatory potential and increased gut permeability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Together, these results strengthen the evidence that HTNT2D is characterized by a dual disruption loss of protective taxa and expansion of pro-inflammatory genera that likely synergize to aggravate metabolic and cardiovascular risk. Functional predictions revealed significant alterations in microbial pathways. The enrichment of ribosome, oxidative phosphorylation and nucleotide metabolism pathways in HTNT2D suggests an upregulation of bacterial biosynthetic and energy-demanding processes which may increase microbial metabolic activity and competition with the host for nutrients [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Conversely, depletion of glycan and sphingolipid metabolism pathways indicates impaired microbial degradation of dietary polysaccharides and host\u0026ndash;microbiome lipid signaling both of which are crucial for maintaining intestinal barrier integrity and metabolic regulation [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. These functional shifts are consistent with previous reports where patients with metabolic disorders showed enhanced microbial energy metabolism and reduced saccharolytic capacity [\u003cspan additionalcitationids=\"CR78 CR79\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Moreover, GSEA analysis highlighted concordant enrichment of arginine and proline metabolism, ABC transporters and carbon fixation pathways, pathways previously implicated in microbial stress adaptation and host\u0026ndash;microbe interaction during metabolic disease [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The consistent downregulation of ribosomal pathways, however, contrasts with some earlier studies which suggest that microbial gene expression changes in HTNT2D may involve context-specific metabolic reprogramming that warrants deeper validation. At the gene level, 139 bacterial genes corresponding to 195 differentially abundant pathways were identified, of which ten (gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, purF) emerged as central bKGs within the protein\u0026ndash;protein interaction network [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Many of these genes, such as gyrB (DNA gyrase subunit B) and fusA (elongation factor G), have been well-studied as essential genes in bacteria and are known targets for antimicrobial drug development [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Their identification in HTNT2D suggests that microbial adaptation in this comorbidity may rely on conserved survival mechanisms, making them attractive candidates for therapeutic targeting. Furthermore, genes such as mdh (malate dehydrogenase) and tktA (transketolase) are linked to central carbon metabolism which may directly impact host glucose homeostasis, highlighting their relevance to diabetes-related metabolic disturbances [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Drug repurposing analysis identified Naringin, Neohesperidin and Bromocriptine as top-ranking molecules targeting these bKGs [\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Naringin and Neohesperidin flavonoids with strong antioxidants and anti-inflammatory properties which have previously been shown to improve insulin sensitivity, lipid metabolism and vascular function in diabetic and hypertensive models [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Their strong binding affinities to fusA and mdh in this study further support their therapeutic relevance although their high molecular weights and poor predicted oral bioavailability pose translational challenges. Bromocriptine, a dopamine agonist already approved for T2D management, was also identified as a promising candidate that consistent with clinical evidence showing its benefits on glycemic control and cardiovascular outcomes [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. However, our ADMET analysis indicated that Bromocriptine has a narrower safety margin and potential toxicity concerns, underscoring the need for cautious evaluation. Collectively, these findings not only validate the use of insilico repurposing in identifying microbiome-targeted therapies but also emphasize the need for structural optimization or formulation strategies to enhance the pharmacokinetic performance of the candidate compounds. Despite these promising results, this study has certain limitations. The use of 16S rRNA sequencing and predictive functional analyses provides only inferred functional potential rather than direct metagenomic or transcriptomic evidence. The relatively small sample size and single-population design further limit generalizability. Future studies employing shotgun metagenomics, metabolomics and experimental in vivo validation will be essential to confirm the identified bacterial key genes and evaluate the efficacy and safety of the proposed therapeutic agents.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData Source and Description\u003c/h2\u003e\u003cp\u003eThis study utilized publicly available gut microbiome data to investigate bKGs in HTNT2D. Raw 16S rRNA sequencing data from human stool samples, along with corresponding metadata, were obtained from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). A total of 124 fecal microbiome samples were included, comprising 29 HTNT2D patients and 95 healthy controls corresponding to BioProject numbers PRJNA885601 and PRJNA670300. All samples were derived from adult participants representative of the population in China. For the purpose of drug repurposing analysis, a comprehensive dataset of 189 drug molecules was curated through a systematic review of 26 published studies related to T2D and hypertension (see \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Of these, 128 drugs were primarily indicated for T2D while 61 were associated with hypertension management. These drug candidates were subsequently utilized for molecular docking against the identified bKGs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePreprocessing of 16S rRNA Sequencing Data\u003c/h2\u003e\u003cp\u003eThe downloaded 16S rRNA sequences of HTNT2D and control samples from the NCBI database were preprocessed for comprehensive microbiome analysis. Quality assessment of the raw reads was performed using FASTQC to identify low-quality bases, adapter contamination and overrepresented sequences. Poor-quality reads and adapter sequences were removed with Trimmomatic(v0.39) using default parameters [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] retaining only high-quality reads for downstream analysis. Paired-end reads were subsequently merged using NGmerge v0.3 with a minimum overlap of 5 base pairs and a maximum of 10% mismatches [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The merged sequences were imported into QIIME2 (v2024.10) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] which is a widely used platform for microbiome analysis. Within QIIME2, sequences were dereplicated using VSEARCH [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] which collapsed redundant sequences into unique sequence variants. An open-reference clustering approach was then applied to group sequences into operational taxonomic units (OTUs) at 97% similarity against the Greengenes reference database. The resulting OTU table containing the abundance of each taxon across all samples and is used for further downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of Bacterial Diversity\u003c/h2\u003e\u003cp\u003eBacterial diversity was assessed using QIIME2 (v2024.10) and R software (v4.4.4). Within-sample diversity (alpha diversity) was quantified using Observed species, Chao1, and ACE indices [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] which implemented through the \u003cem\u003ephyloseq\u003c/em\u003e R package. Visualization of alpha diversity was performed using the ggplot2 package [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and group differences were statistically tested using the Wilcoxon rank-sum test [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. On the other hand, between sample diversity (beta diversity) was assessed using Bray-Curtis dissimilarity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and calculated using \u0026ldquo;\u003cem\u003emicrobiotaProcess\u003c/em\u003e\u0026rdquo; R package. To visualize compositional differences among samples, Principal Coordinate Analysis (PCoA) plots were generated [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Clustering patterns in microbial community structures were further tested using permutational multivariate analysis of variance (PERMANOVA) with 999 permutations, conducted via the vegan R package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eTaxonomy Profiling and Differential Abundance Analysis\u003c/h2\u003e\u003cp\u003eTaxonomic classification of representative sequences was performed using the Na\u0026iuml;ve Bayesian algorithm implemented in the RDP Classifier (v2.2). Relative abundances of bacterial taxa were determined at multiple taxonomic ranks including phylum, class, order, family, genus and species, using QIIME2 (v2024.10). To identify differentially abundant bacterial taxa (DABT) between HTNT2D patients and healthy controls, a Zero-Inflated Gaussian Mixture Model (ZIGMM) was applied to mean group abundance data. Taxa were considered statistically significant based on a threshold of log fold change (logFC) and p\u0026thinsp;\u0026le;\u0026thinsp;0.05 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePathway-Based Gene Identification from Differentially Abundant Bacteria\u003c/h2\u003e\u003cp\u003eFunctional profiling of bacterial communities was conducted using PICRUSt2 to predict potential metabolic pathways associated with the gut microbiome. Differentially abundant metabolic pathways (DAMPs) between HTNT2D patients and control groups were identified using Welch\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. Genes associated with these DAMPs were subsequently considered HTNT2D-causing bacterial genes. To explore bKGs, Protein-protein interaction (PPI) networks were built using the STRING database and interaction visualized using Cytoscape.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eBacterial Key Genes Guided Drug Repurposing and Binding Stability Analysis\u003c/h2\u003e\u003cp\u003eDrug repurposing offers an efficient strategy to reduce both cost and time compared with de novo drug discovery [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To identify potential repurposable drugs targeting bKGs, molecular docking analysis was performed using AutoDock Vina [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Protein structures were obtained from the Protein Data Bank (PDB) and pre-processed to remove heteroatoms, water molecules and non-essential ligands using BIOVIA Discovery Studio [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and PyMOL [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Energy minimization of proteins was carried out with Swiss-Pdb Viewer [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Ligands were minimized using Avogadro software with the MMFF94 force field [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] by applying a conjugate gradient algorithm (200 steps, state updates every 1 step, energy difference threshold\u0026thinsp;=\u0026thinsp;0.1) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and then converted to PDBQT format. Docking analysis was performed in AutoDock Vina with the exhaustiveness parameter set to 10. Non-bonded interactions including hydrogen bonds, hydrophobic interactions and other non-covalent forces were examined using BIOVIA Discovery Studio (v3.0) and PyMOL (v3.1.6.1). To evaluate the stability of top protein\u0026ndash;ligand complexes, molecular dynamics (MD) simulations were conducted in YASARA [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] with the AMBER14 force field [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] under physiological conditions (298 K, pH 7.4, 0.9% NaCl). Simulation included neutralization and energy minimization steps and each simulation ran for 100ns. Binding free energies were estimated using the MM-Poisson\u0026ndash;Boltzmann Surface Area (MM-PBSA) method [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] implemented in the YASARA macro [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] where more negative values indicated stronger binding affinities. Additionally, simulation trajectories were analyzed for root-mean-square deviation (RMSD), radius of gyration (Rg) and solvent-accessible surface area (SASA) to assess the structural stability of protein\u0026ndash;ligand interactions [\u003cspan additionalcitationids=\"CR58 CR59 CR60\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of Drug-Likeness and ADMET Properties\u003c/h2\u003e\u003cp\u003eThe drug-likeness properties of the candidates compounds were assessed using pkCSM [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and SwissADME tools [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. pkCSM predicted pharmacokinetic properties including molecular weight, lipophilicity, hydrogen bond donors and acceptors, topological polar surface area and rotatable bonds. It also evaluated compliance with drug-likeness rules such as Lipinski\u0026rsquo;s Rule of Five, Veber\u0026rsquo;s Rule and Ghose\u0026rsquo;s Rule. SwissADME further examined drug-likeness, solubility and oral bioavailability which provides complementary pharmacokinetic insights. To predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), the Deep-PK web-tools was used [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This platform estimated parameters such as human intestinal absorption, Caco-2 cell permeability, blood\u0026ndash;brain barrier penetration, plasma protein binding, cytochrome P450 inhibition and clearance rates. In addition, toxicity endpoints including Ames mutagenicity, hepatotoxicity, cardiotoxicity and skin/eye irritation were evaluated to determine the overall safety profile of the compounds [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study systematically analyzed 16S rRNA sequencing profiles to identify bKGs associated with HTNT2D and explored their potential inhibitory drug molecules. Differential abundance analysis revealed 19 bacterial genera significantly altered in HTNT2D patients compared to healthy controls. Functional enrichment of these genera identified 20 MetaCyc signaling pathways. Among them 13 pathways were significantly enriched and collectively encompassed 139 genes implicated in HTNT2D pathogenesis. PPI network analysis further prioritized 10 bKGs (gltB, guaA, dnaG, fusA, lacZ, gyrB, acpP, mdh, tktA, and purF) as potential therapeutic targets. Molecular docking identified three top-ranked candidate drugs Naringin, Neohesperidin and Bromocriptine capable of effectively binding these bKG-mediated proteins. Subsequent molecular dynamics simulations confirmed the structural stability and strong interactions of Naringin-fusA, Neohesperidin-mdh and Bromocriptine-gyrB complexes. Drug-likeness and ADMET analyses indicated Bromocriptine as the most favorable candidate, with optimal pharmacokinetic properties and a comparatively safer profile. These findings provide a comprehensive framework for microbiome-informed drug repurposing and highlight Bromocriptine and other candidate compounds as promising therapeutic strategies against HTNT2D. Experimental validation in vitro and in vivo is warranted to confirm these bioinformatics predictions and to translate these insights into clinically relevant interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to acknowledge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMTIR and MKK conceptualized the study. MTIR and MSAS performed statistical analyses of 16S rRNA sequence data and carried out upstream and downstream bioinformatics analyses and also contributing to the draft manuscript writing. MTIR and MJ conducted drug screening via molecular docking and jointly drafted the docking section. MSAS and RA compiled hub genes and relevant published drug molecules through comprehensive literature review. Finally, MKK critically reviewed the manuscript and prepared it for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw 16S rRNA sequence profile dataset analyzed in this study is publicly available. It can be freely downloaded from the online NCBI database with bioproject number\u0026nbsp;PRJNA885601 and PRJNA670300\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding was obtained for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgnieszka, P., Weronika, B. \u0026amp; Andrzej, P. 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Bromocriptine: A Novel Approach to the Treatment of Type 2 Diabetes. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 1154\u0026ndash;1161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/DIACARE.23.8.1154\u003c/span\u003e\u003cspan address=\"10.2337/DIACARE.23.8.1154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Type 2 diabetes, gut microbiome, bacterial key genes, drug repurposing, stability analysis","lastPublishedDoi":"10.21203/rs.3.rs-7760709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7760709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypertension (HTN) coexisting with type 2 diabetes (T2D) significantly elevate the risk of cardiovascular complications and growing evidence suggests that gut microbiota may contribute to the development of these conditions. Yet, microbial gene-level insights in hypertensive patients with T2D (HTNT2D) are still limited. To address this gap, this study aimed to identify bacterial key genes (bKGs) associated with HTNT2D and to explore therapeutic agents targeting these bKGs through integrated bioinformatics approaches. This study analyzed 124 gut microbiome samples downloaded from NCBI which includes 95 healthy controls and 29 HTNT2D cases. After quality control, 92% of raw 16S rRNA reads were retained which yielded 53,311 representative OTUs. Building on this dataset, diversity analysis showed significantly higher microbial richness in HTNT2D and revealed distinct clustering between groups which indicates an altered microbial structure. Differential abundance analysis further identified 19 bacterial genera across four dominant phyla. Functional prediction then explored 195 enriched metabolic pathways and 139 associated genes. To refine these finding, protein\u0026ndash;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 of these targets revealed three top-ranked drug candidates named Naringin-fusA, Neohesperidin-mdh, and Bromocriptine-gyrB and subsequent molecular dynamics simulations confirmed the stability of their complexes. 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 relevance. The findings highlight the promise of microbiome-based diagnostics and targeted drug strategies for managing patients with HTNT2D.\u003c/p\u003e","manuscriptTitle":"Identification of Bacterial Key Genes and Therapeutic Targets in Hypertensive Patients with Type 2 Diabetes using Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:23:47","doi":"10.21203/rs.3.rs-7760709/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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