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<p>Polycystic ovary syndrome is a prevalent, heterogeneous endocrine and metabolic disorder affecting approximately 10% of women of reproductive age, often characterized by hyperandrogenism, anovulation, and insulin resistance. Metformin, a first-line treatment for type 2 diabetes, is also widely prescribed for PCOS to reduce insulin resistance and induce ovulation. However, therapeutic response varies significantly across individuals, partly due to genetic variations in solute carrier (SLC) transporters. The organic cation transporter family, encoded by <i>SLC22A1</i> (O15245), <i>SLC22A2</i> (O15244), and <i>SLC22A3</i> (O75751), facilitates metformin uptake into hepatocytes and renal excretion. Using an in silico pipeline, Align GVGD, PolyPhen-2, PROVEAN, PANTHER, PhD-SNP, and the ensemble predictor Meta-SNP, we screened 14 deleterious nonsynonymous SNPs. Solvent accessibility values were derived using NetSurfP, indicating that L42R (RSA: 0.54, ASA: 96.2 Å<sup>2</sup>) and F422S (RSA: 0.49, ASA: 85.7 Å<sup>2</sup>) are surface-exposed and likely to influence protein interaction. In contrast, R175L (RSA: 0.07, ASA: 18.4 Å<sup>2</sup>) and T275M (RSA: 0.06, ASA: 21.1 Å<sup>2</sup>) were buried and may impact local folding. ΔΔG predictions using MuPro ranged from −0.01745 to −1.99395, consistently indicating decreased protein stability. Structural models were built in SWISS-MODEL and validated via MolProbity scores (&lt;1.5), ERRAT (&gt;85%), and PROSA Z-scores (−6.8 to −8.2). Functional enrichment of the PPI network (STRING-DB) highlighted significant roles in drug transmembrane transport (GO:0006857, <i>p</i> = 2·3e-06), organic cation transport (GO:0015695, <i>p</i> = 4·7e-05), and xenobiotic metabolism pathways. These mutations may impair metformin pharmacokinetics and contribute to inter-individual therapeutic variability. While this study is limited by its computational design, the findings identify high-priority SNPs for future experimental validation, offering a foundation for precision pharmacogenomics in PCOS management.</p>

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Identification of high-risk SNPs in SLC22A transporter genes: their potential role in PCOS and metformin uptake

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • R. Harini,
  • B. Usha,
  • R. Satish,
  • Pai Akshatha,
  • T. M. Vijaya kumar

摘要

Polycystic ovary syndrome is a prevalent, heterogeneous endocrine and metabolic disorder affecting approximately 10% of women of reproductive age, often characterized by hyperandrogenism, anovulation, and insulin resistance. Metformin, a first-line treatment for type 2 diabetes, is also widely prescribed for PCOS to reduce insulin resistance and induce ovulation. However, therapeutic response varies significantly across individuals, partly due to genetic variations in solute carrier (SLC) transporters. The organic cation transporter family, encoded by SLC22A1 (O15245), SLC22A2 (O15244), and SLC22A3 (O75751), facilitates metformin uptake into hepatocytes and renal excretion. Using an in silico pipeline, Align GVGD, PolyPhen-2, PROVEAN, PANTHER, PhD-SNP, and the ensemble predictor Meta-SNP, we screened 14 deleterious nonsynonymous SNPs. Solvent accessibility values were derived using NetSurfP, indicating that L42R (RSA: 0.54, ASA: 96.2 Å2) and F422S (RSA: 0.49, ASA: 85.7 Å2) are surface-exposed and likely to influence protein interaction. In contrast, R175L (RSA: 0.07, ASA: 18.4 Å2) and T275M (RSA: 0.06, ASA: 21.1 Å2) were buried and may impact local folding. ΔΔG predictions using MuPro ranged from −0.01745 to −1.99395, consistently indicating decreased protein stability. Structural models were built in SWISS-MODEL and validated via MolProbity scores (<1.5), ERRAT (>85%), and PROSA Z-scores (−6.8 to −8.2). Functional enrichment of the PPI network (STRING-DB) highlighted significant roles in drug transmembrane transport (GO:0006857, p = 2·3e-06), organic cation transport (GO:0015695, p = 4·7e-05), and xenobiotic metabolism pathways. These mutations may impair metformin pharmacokinetics and contribute to inter-individual therapeutic variability. While this study is limited by its computational design, the findings identify high-priority SNPs for future experimental validation, offering a foundation for precision pharmacogenomics in PCOS management.