Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Identification of bacterial key genes and therapeutic targets in hypertensive patients with type 2 diabetes through bioinformatics analysis
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 28 January 2026

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

  • Md. Tahalil Islam Rahat1,
  • Most. Shermin Akter Sumi1,
  • Mifta Nurejannath1,
  • Reaz Ahmmed2,3 &
  • …
  • Md. Kaderi Kibria1,2 

Scientific Reports , Article number:  (2026) Cite this article

  • 266 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Diseases
  • Microbiology

Abstract

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

Similar content being viewed by others

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Article 25 June 2024

Role of an unclassified Lachnospiraceae in the pathogenesis of type 2 diabetes: a longitudinal study of the urine microbiome and metabolites

Article Open access 05 August 2022

Identification of gut microbiome features associated with host metabolic health in a large population-based cohort

Article Open access 29 October 2024

Data availability

The raw 16 S 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.

References

  1. Agnieszka, P., Weronika, B. & Andrzej, P. Hypertension and type 2 Diabetes—The novel treatment possibilities. Int J. Mol. Sci 23, (2022).

  2. Hezam, A. A. M., Shaghdar, H. B. M. & Chen, L. The connection between hypertension and diabetes and their role in heart and kidney disease development.. J. Res. Med. Sci. https://doi.org/10.4103/jrms.jrms_470_23 (2024).

    Google Scholar 

  3. Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 157, (2019).

  4. International Diabetes Federation. IDF Diabetes Atlas 2025 _ IDF Diabetes Atlas. IDF official website. (2025).

  5. Carstensen, B., Rønn, P. F. & Jørgensen, M. E. Prevalence, incidence and mortality of type 1 and type 2 diabetes in Denmark 1996–2016.. BMJ Open Diabetes Res. Care https://doi.org/10.1136/bmjdrc-2019-001071 (2020).

    Google Scholar 

  6. Tian, S., Wu, J., Liu, J. S., Zou, B. S. & Kong, L. Q. Type 2 diabetes. Ann. Intern. Med. 172, 704–705 (2020).

    Google Scholar 

  7. Ogurtsova, K. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128, 40–50 (2017).

    Google Scholar 

  8. WHO & Hypertension (2025). https://www.who.int/news-room/fact-sheets/detail/hypertension

  9. D.I., P. et al. Hypertension in patients with type 2 diabetes mellitus: targets and management. Maturitas 112, 71–77 (2018).

  10. Massiéra, F. Adipose angiotensinogen is involved in adipose tissue growth and blood pressure regulation. FASEB J. 15, 2727–2729 (2001).

    Google Scholar 

  11. Muniyappa, R. & Quon, M. J. Insulin action and insulin resistance in vascular endothelium. Curr. Opin. Clin. Nutr. Metab. Care. 10, 523–530 (2007).

    Google Scholar 

  12. Cullen, M. & Taniguchi Brice Emanuelli & C. Ronald Kahn. Critical nodes in signalling pathways: insights into insulin action. Nat. Rev. Mol. cell. Biol. 7, 85–96 (2006).

    Google Scholar 

  13. Smulyan, H., Lieber, A., Safar, M. E. & Hypertension diabetes type II, and their association: role of arterial stiffness. Am. J. Hypertens. 29, 5–13 (2016).

    Google Scholar 

  14. Ninomiya, T. et al. Impact of metabolic syndrome on the development of cardiovascular disease in a general Japanese population: the Hisayama study. Stroke 38, 2063–2069 (2007).

    Google Scholar 

  15. Solini, A. et al. Diverging association of reduced glomerular filtration rate and albuminuria with coronary and noncoronary events in patients with type 2 diabetes: the renal insufficiency and cardiovascular events (RIACE) Italian multicenter study. Diabetes Care. 35, 143–149 (2012).

    Google Scholar 

  16. Novakovic, M. et al. Role of gut microbiota in cardiovascular diseases. World J. Cardiol. 12, 110–122 (2020).

    Google Scholar 

  17. Drapkina, O. M. & Shirobokikh, O. E. Role of gut microbiota in the pathogenesis of cardiovascular diseases and metabolic syndrome | Роль кишечной микробиоты в патогенезе сердечно-сосудистых заболеваний и метаболического синдрома. Ration. Pharmacother. Cardiol. 14, 567–574 (2018).

    Google Scholar 

  18. Freeman, A., Harricharran, G. & Crasta, M. The intricate gut-Heart Connection; role of gut microbiota in the pathogenesis of cardiovascular disease. Arch Intern. Med. Res 7, (2024).

  19. Mohsenzadeh, A. et al. The gut microbiota and cardiovascular disease: Exploring the role of microbial dysbiosis and metabolites in pathogenesis and therapeutics.. Life Sci. 123981. https://doi.org/10.1016/j.lfs.2025.123981 (2025).

    Google Scholar 

  20. Bano, N. & Raza, K. Unravelling mutation patterns in Extended-Spectrum β-lactamases for precision drug design against AMR in Enterobacteriaceae. Mol. Genet. Genomics https://doi.org/10.1007/s00438-025-02300-3 (2025).

    Google Scholar 

  21. Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085 (2010).

    Google Scholar 

  22. Li, Q. et al. Implication of the gut microbiome composition of type 2 diabetic patients from Northern China. Sci Rep 10, (2020).

  23. Lê, K. A. et al. Alterations in fecal Lactobacillus and Bifidobacterium species in type 2 diabetic patients in Southern China population. Front. Physiol. 3 (2013).

  24. Y., W. et al. Gut microbiome analysis of type 2 diabetic patients from the Chinese minority ethnic groups the Uygurs and Kazaks. PLoS One https://doi.org/10.1371/journal.pone.0172774 (2017).

    Google Scholar 

  25. Razavi, S. et al. Gut microbiota composition and type 2 diabetes: Are these subjects linked Together? Heliyon 10, (2024).

  26. Chen, M. S. et al. Characterizing the gut microbiota composition in Taiwanese hypertensive patients using 16S rRNA sequencing analysis. Int. J. Med. Sci. 22, 2460–2469 (2025).

    Google Scholar 

  27. Dan, X. et al. Differential analysis of hypertension-associated intestinal microbiota. Int. J. Med. Sci. 18, 3748–3748 (2021).

    Google Scholar 

  28. Li, H. et al. Characteristics of gut microbiota in patients with hypertension and/or hyperlipidemia: A cross-sectional study on rural residents in Xinxiang county, Henan Province. Microorganisms 7, (2019).

  29. Nichols, R. G. & Davenport, E. R. The relationship between the gut microbiome and host gene expression: A review. Hum. Genet. 140, 747–760 (2021).

    Google Scholar 

  30. Ding, H. et al. Gut Microbiome profile of Chinese hypertension patients with and without type 2 diabetes mellitus. BMC Microbiol 23, (2023).

  31. Kumar, J. Microbial metabolites and cardiovascular dysfunction: A new era of diagnostics and therapy. Cells 14, (2025).

  32. Yu, Y., Ding, Y., Wang, S. & Jiang, L. Gut microbiota dysbiosis and its impact on type 2 diabetes: From pathogenesis to therapeutic strategies. Metabolites https://doi.org/10.3390/metabo15060397 (2025).

    Google Scholar 

  33. Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, (2017).

  34. Miao, C. et al. The causality between gut microbiota and hypertension and hypertension-related complications: A bidirectional two-sample Mendelian randomization analysis. Hellenic J. Cardiol. 83, 38–50 (2025).

    Google Scholar 

  35. Takagi, T. et al. Changes in the gut microbiota are associated with hypertension, hyperlipidemia, and type 2 diabetes mellitus in Japanese subjects. Nutrients 12, 1–13 (2020).

    Google Scholar 

  36. Wang, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    Google Scholar 

  37. Yang, T. et al. Gut microbiota dysbiosis is linked to hypertension. Hypertension 65, 1331 (2015).

    Google Scholar 

  38. Magne, F. The firmicutes/bacteroidetes ratio: A relevant marker of gut dysbiosis in obese patients?. Nutrients https://doi.org/10.3390/nu12051474 (2020).

    Google Scholar 

  39. Verhaar, B. J. H., Prodan, A., Nieuwdorp, M. & Muller, M. Gut microbiota in hypertension and atherosclerosis: A review. Nutr 12, 298212–2982 (2020).

    Google Scholar 

  40. Yan, D. et al. Regulatory effect of gut microbes on blood pressure. Anim. Models Exp. Med. 5, 513 (2022).

    Google Scholar 

  41. Chi, J. et al. Precision probiotics regulate blood glucose, cholesterol, body fat percentage, and weight under eight-week high-fat diet. Metabolites 15, 642 (2025).

    Google Scholar 

  42. Yang, M., Luo, J., Zheng, Y. & Chen, Q. Identification of shared pathways and molecules between type 2 diabetes and lung adenocarcinoma and the impact of high glucose environment on lung adenocarcinoma. Int. J. Endocrinol. https://doi.org/10.1155/ije/7734237 (2025).

    Google Scholar 

  43. Zhang, X., Fu, Z., Wang, H. & Sheng, L. Metabolic pathways, genomic alterations, and post-translational modifications in pulmonary hypertension and cancer as therapeutic targets and biomarkers. Front. Pharmacol. https://doi.org/10.3389/fphar.2024.1490892 (2024).

    Google Scholar 

  44. Jyoti & Dey, P. Mechanisms and implications of the gut microbial modulation of intestinal metabolic processes. NPJ Metab. Heal Dis. 3, (2025).

  45. Basson, A., Trotter, A., Rodriguez-Palacios, A. & Cominelli, F. Mucosal interactions between genetics, diet, and Microbiome in inflammatory bowel disease. Front. Immunol. 7, (2016).

  46. Guo, J. et al. Altitude adaptation: The unseen work of gut microbiota. hLife 3, 5–20 (2025).

    Google Scholar 

  47. Tan, Y. S., Zhang, R. K., Liu, Z. H., Li, B. Z. & Yuan, Y. J. Microbial adaptation to enhance stress tolerance. Front. Microbiol. https://doi.org/10.3389/fmicb.2022.888746 (2022).

    Google Scholar 

  48. Bano, N., Mohammed, S. A. & Raza, K. Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: A systematic review. J. Drug Target. 33, 384–396 (2025).

    Google Scholar 

  49. Maxwell, A. DNA gyrase as a drug target. Trends Microbiol. 5, 102–109 (1997).

    Google Scholar 

  50. Heddle, J. & Maxwell, A. Quinolone-binding pocket of DNA gyrase: Role of GyrB. Antimicrob. Agents Chemother. 46, 1805–1815 (2002).

    Google Scholar 

  51. Stieger, M., Angehen, P., Wohlgensinger, B. & Gmünder, H. GyrB mutations in Staphylococcus aureus strains resistant to cyclothialidine, coumermycin, and novobiocin. Antimicrob. Agents Chemother. 40, 1060–1062 (1996).

    Google Scholar 

  52. Gilbert, E. J. & Maxwell, A. The 24 kDa N-terminal sub‐domain of the DNA gyrase B protein binds coumarin drugs. Mol. Microbiol. 12, 365–373 (1994).

    Google Scholar 

  53. Tsai, F. T. F. et al. The high-resolution crystal structure of a 24‐kDa gyrase B fragment from E. coli complexed with one of the most potent coumarin inhibitors, clorobiocin. Proteins 28, 41–52 (1997).

    Google Scholar 

  54. Nakada, N., Gmünder, H., Hirata, T. & Arisawa, M. Characterization of the binding site for cyclothialidine on the B subunit of DNA gyrase. J. Biol. Chem. 270, 14286–14291 (1995).

    Google Scholar 

  55. Gurram, S. R. & Azam, M. A. GyrB inhibitors as potential antibacterial agents: A review. Monatsh. Chem. 152, 725–744 (2021).

    Google Scholar 

  56. Fernandes, P. Fusidic acid: A bacterial elongation factor inhibitor for the oral treatment of acute and chronic staphylococcal infections. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a025437 (2016).

    Google Scholar 

  57. Bibens, L., Becker, J. P., Dassonville-Klimpt, A. & Sonnet, P. A review of fatty acid biosynthesis enzyme inhibitors as promising antimicrobial drugs. Pharmaceuticals https://doi.org/10.3390/ph16030425 (2023).

    Google Scholar 

  58. Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Google Scholar 

  59. Kuron, A. et al. Evaluation of DNA primase DnaG as a potential target for antibiotics. Antimicrob. Agents Chemother 58, 1699–1706 (2014).

    Google Scholar 

  60. Mulhbacher, J. et al. Novel riboswitch ligand analogs as selective inhibitors of guanine-related metabolic pathways. PLoS Pathog. 6, 1–11 (2010).

    Google Scholar 

  61. Wen, X. GltB encoding glutamate synthase is involved in persister and biofilm formation and virulence in Staphylococcus aureus. Microbiol. Spectr. https://doi.org/10.1128/spectrum.00511-25 (2025).

    Google Scholar 

  62. Brabcova, J., Carrasco-Lopez, C., Bavaro, T., Hermoso, J. A. & Palomo, J. M. Escherichia coli LacZ β-galactosidase inhibition by monohydroxy acetylated glycopyranosides: Role of the acetyl groups. J. Mol. Catal. B Enzym. 107, 31–38 (2014).

    Google Scholar 

  63. Juers, D. H., Matthews, B. W. & Huber, R. E. LacZ β-galactosidase: Structure and function of an enzyme of historical and molecular biological importance. Protein Sci. 21, 1792–1807 (2012).

    Google Scholar 

  64. Pahel, G., Zelenetz, A. D. & Tyler, B. M. gltB Gene and regulation of nitrogen metabolism by glutamine synthetase in Escherichia coli. J. Bacteriol. 133, 139–148 (1978).

    Google Scholar 

  65. Ning, Z. et al. How perturbated metabolites in diabetes mellitus affect the pathogenesis of hypertension? Front Physiol 12, (2021).

  66. Lee, H. J. et al. Increased glutamate in type 2 diabetes in the Korean population is associated with increased plasminogen levels. J. Diabetes 15, 777–786 (2023).

    Google Scholar 

  67. Kasahara, K. et al. Gut bacterial metabolism contributes to host global purine homeostasis. Cell Host Microbe 31, 1038-1053e.e10 (2023).

    Google Scholar 

  68. Mendizábal, Y., Llorens, S. & Nava, E. Hypertension in metabolic syndrome: Vascular pathophysiology. Int. J. Hypertens. https://doi.org/10.1155/2013/230868 (2013).

    Google Scholar 

  69. Kanagala, A. Metabolic dysregulation and its multifaceted impact on cardiovascular autonomic control in type 2 diabetes mellitus: Insights from comprehensive assessment. Cureus https://doi.org/10.7759/cureus.59776 (2024).

    Google Scholar 

  70. Garcia-Gutierrez, E., O’Mahony, A. K., Dos Santos, R. S., Marroquí, L. & Cotter, P. D. Gut microbial metabolic signatures in diabetes mellitus and potential preventive and therapeutic applications. Gut Microbes https://doi.org/10.1080/19490976.2024.2401654 (2024).

    Google Scholar 

  71. Chopra, S. et al. Evaluation of gyrase B as a drug target in Mycobacterium tuberculosis. J. Antimicrob. Chemother. 67, 415–421 (2012).

    Google Scholar 

  72. Gil-Gil, T., Corona, F., Martínez, J. L. & Bernardini, A. The inactivation of enzymes belonging to the central carbon metabolism Is a novel mechanism of developing antibiotic resistance. mSystems 5, (2020).

  73. Brown, E. M., Clardy, J. & Xavier, R. J. Gut microbiome lipid metabolism and its impact on host physiology. Cell Host Microbe 31, 173–186 (2023).

    Google Scholar 

  74. Yan, X. et al. From microbial homeostasis to systemic pathogenesis: A narrative review on gut flora’s role in neuropsychiatric, metabolic, and cancer disorders. J. Inflamm. Res. 18, 8851–8873 (2025).

    Google Scholar 

  75. Yang, J. et al. The role of the intestinal Microbiome in the pathogenesis and treatment of hyperuricemia: A review. Food Sci. Nutr. 13, (2025).

  76. Gurung, M. et al. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 51, (2020).

  77. Młynarska, E. Gut microbiota and gut–brain axis in hypertension: Implications for kidney and cardiovascular health—a narrative review. Nutrients https://doi.org/10.3390/nu16234079 (2024).

    Google Scholar 

  78. Feng, W., Liu, J., Ao, H., Yue, S. & Peng, C. Targeting gut microbiota for precision medicine: Focusing on the efficacy and toxicity of drugs. Theranostics 10, 11278–11301 (2020).

    Google Scholar 

  79. Ghosh, S., Sarkar, S., Mistry, J. & Biswas, M. Antidiabetic and antihypertensive properties of naringin: A review. Int. J. Zool. Investig. 9, 171–177 (2023).

    Google Scholar 

  80. Gallo, G. & Savoia, C. New insights into endothelial dysfunction in cardiometabolic diseases: Potential mechanisms and clinical implications. Int. J. Mol. Sci. https://doi.org/10.3390/ijms25052973 (2024).

    Google Scholar 

  81. Gaziano, J. M. et al. Randomized clinical trial of quick-release bromocriptine among patients with type 2 diabetes on overall safety and cardiovascular outcomes. Diabetes Care 33, 1503–1508 (2010).

    Google Scholar 

  82. Malakul, W., Pengnet, S., Kumchoom, C. & Tunsophon, S. Naringin ameliorates endothelial dysfunction in fructose-fed rats. Exp. Ther. Med. 15, 3140–3146 (2018).

    Google Scholar 

  83. Pijl, H. et al. Bromocriptine: A novel approach to the treatment of type 2 diabetes. Diabetes Care 23, 1154–1161 (2000).

    Google Scholar 

  84. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Google Scholar 

  85. Gaspar, J. M. NGmerge: Merging paired-end reads via novel empirically-derived models of sequencing errors. BMC Bioinformatics https://doi.org/10.1186/s12859-018-2579-2 (2018).

    Google Scholar 

  86. Importing data — QIIME 2 2024.10.1 documentation.

  87. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ e2584 (2016). (2016).

  88. Ning, Y. et al. Characteristics of the urinary microbiome from patients with gout: A prospective study. Front. Endocrinol. (Lausanne) 11, (2020).

  89. Kibria, M. K. et al. Discovery of bacterial key genes from 16S rRNA-seq profiles that are associated with the complications of SARS-CoV-2 infections and provide therapeutic indications. Pharmaceuticals https://doi.org/10.3390/ph17040432 (2024).

    Google Scholar 

  90. McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).

    Google Scholar 

  91. Cox, K. D. et al. Community assessment techniques and the implications for rarefaction and extrapolation with Hill numbers. Ecol. Evol. 7, 11213–11226 (2017).

    Google Scholar 

  92. Legendre, P., Borcard, D. & Peres-Neto, P. R. Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol. Monogr. 75, 435–450 (2005).

    Google Scholar 

  93. Lü, F. et al. Metaproteomics of cellulose methanisation under thermophilic conditions reveals a surprisingly high proteolytic activity. ISME J. 8, 88–102 (2014).

    Google Scholar 

  94. Chen, J. et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28, 2106–2113 (2012).

    Google Scholar 

  95. Lan, Y., Wang, Q., Cole, J. R. & Rosen, G. L. Using the RDP classifier to predict taxonomic novelty and reduce the search space for finding novel organisms. PLoS One https://doi.org/10.1371/journal.pone.0032491 (2012).

    Google Scholar 

  96. Zhang, X., Guo, B. & Yi, N. Zero-inflated gaussian mixed models for analyzing longitudinal microbiome data. PLoS One https://doi.org/10.1371/journal.pone.0242073 (2020).

    Google Scholar 

  97. Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 13, (2022).

  98. Paulson, J. N., Colin Stine, O., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10(12), 1200–1202 (2013).

    Google Scholar 

  99. Monnoyer, R. Functional profiling reveals altered metabolic activity in divers’ oral microbiota during commercial heliox saturation diving. Front. Physiol. https://doi.org/10.3389/fphys.2021.702634 (2021).

    Google Scholar 

  100. Szklarczyk, D. et al. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).

    Google Scholar 

  101. Adeyemo, O. M. et al. Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer. Cancer Rep 7, (2024).

  102. Ahmmed, R. Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications. Sci. Rep. https://doi.org/10.1038/s41598-024-69302-w (2024).

    Google Scholar 

  103. Mosharaf, M. P., Alam, K., Gow, J., Mahumud, R. A. & Mollah, M. N. H. Common molecular and pathophysiological underpinnings of delirium and Alzheimer’s disease: Molecular signatures and therapeutic indications. BMC Geriatr. https://doi.org/10.1186/s12877-024-05289-3 (2024).

    Google Scholar 

  104. Ali, Z. & Bhandari, U. Exploring the therapeutic potential of natural plants in modulating molecular and cellular pathways involved in diabetic neuropathy: Mechanism and biochemical evaluation. Curr. Pharm. Des. 31, 1087–1099 (2025).

    Google Scholar 

  105. Ansari, H. R., Kordrostami, Z. & Mirzaei, A. In-vehicle wireless driver breath alcohol detection system using a microheater integrated gas sensor based on Sn-doped CuO nanostructures. Sci. Rep. 13, 1–13 (2023).

    Google Scholar 

  106. Bano, N., Ahmad, S., Gupta, D. & Raza, K. FDA-approved Levophed as an alternative multitargeted therapeutic against cervical cancer transferase, cell cycle, and regulatory proteins. Comput. Biol. Med. https://doi.org/10.1016/j.compbiomed.2025.110163 (2025).

    Google Scholar 

  107. Di Muzio, E., Toti, D. & Polticelli, F. DockingApp: A user friendly interface for facilitated docking simulations with AutoDock Vina. J. Comput. Aided. Mol. Des. https://doi.org/10.1007/s10822-016-0006-1 (2017).

    Google Scholar 

  108. BIOVIA Discovery. Studio Visualizer 4.5 – Molecular Visualization – My Biosoftware – Bioinformatics Softwares Blog.

  109. Thread [PyMOL] PyMOL 2.3 released | PyMOL Molecular Graphics System.

  110. Kaplan, W. & Littlejohn, T. G. Swiss-PDB viewer (Deep View). Brief. Bioinform. https://doi.org/10.1093/bib/2.2.195 (2001).

    Google Scholar 

  111. Hanwell, M. D. et al. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 4, (2012).

  112. Aviat, F., Lagardère, L. & Piquemal, J.-P. The truncated conjugate gradient (TCG), a non-iterative/fixed-cost strategy for computing polarization in molecular dynamics: Fast evaluation of analytical forces. J. Chem. Phys. https://doi.org/10.1063/1.4985911 (2017).

    Google Scholar 

  113. Land, H. & Humble, M. S. YASARA: A tool to obtain structural guidance in biocatalytic investigations. Methods Mol. Biol. 1685, 43–67 (2018).

    Google Scholar 

  114. Case, D. A. et al. The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668–1688 (2005).

    Google Scholar 

  115. Dash, R. et al. Structural and dynamic characterizations highlight the deleterious role of SULT1A1 R213H polymorphism in substrate binding. Int J. Mol. Sci 20, (2019).

  116. Mehmood, A., Kaushik, A. C., Wang, Q., Li, C. D. & Wei, D. Q. Bringing structural implications and deep learning-based drug identification for KRAS mutants. J. Chem. Inf. Model. 61, 571–586 (2021).

    Google Scholar 

  117. Srinivasan, E. & Rajasekaran, R. Computational investigation of curcumin, a natural polyphenol that inhibits the destabilization and the aggregation of human SOD1 mutant (Ala4Val). RSC Adv. 6, 102744–102753 (2016).

    Google Scholar 

  118. Swargiary, A., Mahmud, S. & Saleh, M. A. Screening of phytochemicals as potent inhibitor of 3-chymotrypsin and papain-like proteases of SARS-CoV2: An in silico approach to combat COVID-19. J. Biomol. Struct. Dyn. 40, 2067–2081 (2022).

    Google Scholar 

  119. Mahmud, S. et al. Exploring the potent inhibitors and binding modes of phospholipase A2 through in silico investigation. J. Biomol. Struct. Dyn. 38, 4221–4231 (2020).

    Google Scholar 

  120. Elkarhat, Z., Charoute, H., Elkhattabi, L., Barakat, A. & Rouba, H. Potential inhibitors of SARS-cov-2 RNA dependent RNA polymerase protein: Molecular docking, molecular dynamics simulations and MM-PBSA analyses. J. Biomol. Struct. Dyn. 40, 361–374 (2022).

    Google Scholar 

  121. Chowdhury, K. H. et al. Drug repurposing approach against novel coronavirus disease (COVID-19) through virtual screening targeting SARS-CoV-2 main protease. Biology (Basel) 10, (2021).

  122. Khan, M. A. et al. Comparative molecular investigation of the potential inhibitors against SARS-CoV-2 main protease: A molecular docking study. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2020.1796813 (2020).

    Google Scholar 

  123. Pires, D. E. V., Blundell, T. L. & Ascher, D. B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 58, 4066–4072 (2015).

    Google Scholar 

  124. Daina, A., Michielin, O., Zoete, V. & SwissADME A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7, (2017).

  125. Myung, Y., De Sá, A. G. C. & Ascher, D. B. Deep-PK: Deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Res. 52, W469–W475 (2024).

    Google Scholar 

  126. Mishra, S. K. Computational analysis of lupenone derivatives as potential inhibitor of human papillomavirus oncoprotein E6 associated cervical cancer. Sci. Rep. https://doi.org/10.1038/s41598-025-96667-3 (2025).

    Google Scholar 

  127. Roman, D. L., Isvoran, A., Filip, M., Ostafe, V. & Zinn, M. In silico assessment of pharmacological profile of low molecular weight oligo-hydroxyalkanoates. Front. Bioeng. Biotechnol. https://doi.org/10.3389/fbioe.2020.584010 (2020).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh

    Md. Tahalil Islam Rahat, Most. Shermin Akter Sumi, Mifta Nurejannath & Md. Kaderi Kibria

  2. Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Reaz Ahmmed & Md. Kaderi Kibria

  3. Department of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Reaz Ahmmed

Authors
  1. Md. Tahalil Islam Rahat
    View author publications

    Search author on:PubMed Google Scholar

  2. Most. Shermin Akter Sumi
    View author publications

    Search author on:PubMed Google Scholar

  3. Mifta Nurejannath
    View author publications

    Search author on:PubMed Google Scholar

  4. Reaz Ahmmed
    View author publications

    Search author on:PubMed Google Scholar

  5. Md. Kaderi Kibria
    View author publications

    Search author on:PubMed Google Scholar

Contributions

MTIR and MKK conceptualized the study. MTIR and MSAS performed statistical analyses of 16 S 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.

Corresponding author

Correspondence to Md. Kaderi Kibria.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahat, M.T.I., Sumi, M.S.A., Nurejannath, M. et al. Identification of bacterial key genes and therapeutic targets in hypertensive patients with type 2 diabetes through bioinformatics analysis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36467-5

Download citation

  • Received: 01 October 2025

  • Accepted: 13 January 2026

  • Published: 28 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-36467-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Hypertension
  • Type 2 diabetes
  • Gut microbiome
  • Bacterial key genes
  • Drug repurposing
  • Stability analysis
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology