Kodate Shun, Sato Mitsuharu, Hishinuma Eiji, Kojima Kaname, Motoike Ikuko N, Koshiba Seizo, Yamamoto Masayuki, Yamada Kazunori D, Kinoshita Kengo
Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
Unprecedented-Scale Data Analytics Center, Tohoku University, Sendai, Miyagi, 980-0845, Japan.
Sci Rep. 2025 May 16;15(1):17035. doi: 10.1038/s41598-025-01634-7.
Advancements in large-scale analysis of metabolites in human peripheral blood samples revealed the links between metabolite concentrations and genetic variations. This field is known as metabolome-genome-wide association study (MGWAS). Although MGWAS is a powerful tool, it has some limitations, particularly in terms of the number of metabolites that can be measured. Whether the observed associations are directly due to genetic variation or indirectly due to changes in unmeasured metabolites is unclear. To address this, we used simulations of metabolic pathway models to investigate the influence of genetic variants on metabolite concentrations and enhance the interpretation of MGWAS results. By systematically adjusting the enzyme reaction rates to simulate genetic variants, we observed changes in the metabolite levels. Our simulations accurately represented most of the variant-metabolite pairs identified by MGWAS with significant p-values, thereby demonstrating the potential of our approach. Furthermore, our simulations revealed additional marked fluctuations in metabolite levels that the MGWAS did not detect, suggesting that some variant-metabolite pairs might become more significant with larger sample sizes. We also categorized the enzymes into three types based on their impact on metabolite concentrations, highlighting enzymes with minimal impact. This indicated that genetic variations in these enzymes may have limited biological significance. Our study not only validates key MGWAS findings, but also provides a systematic framework for understanding enzyme-metabolite relationships. This approach offers valuable insights for future experimental studies and potential therapeutic interventions.
人类外周血样本代谢物大规模分析的进展揭示了代谢物浓度与基因变异之间的联系。这一领域被称为代谢组-全基因组关联研究（MGWAS）。尽管MGWAS是一个强大的工具，但它有一些局限性，特别是在可测量的代谢物数量方面。观察到的关联是直接由于基因变异还是间接由于未测量代谢物的变化尚不清楚。为了解决这个问题，我们使用代谢途径模型模拟来研究基因变异对代谢物浓度的影响，并增强对MGWAS结果的解释。通过系统地调整酶反应速率以模拟基因变异，我们观察到了代谢物水平的变化。我们的模拟准确地再现了MGWAS鉴定的大多数具有显著p值的变异-代谢物对，从而证明了我们方法的潜力。此外，我们的模拟揭示了MGWAS未检测到的代谢物水平的其他显著波动，这表明随着样本量的增加，一些变异-代谢物对可能会变得更显著。我们还根据酶对代谢物浓度的影响将酶分为三种类型，突出了影响最小的酶。这表明这些酶的基因变异可能具有有限的生物学意义。我们的研究不仅验证了MGWAS的关键发现，还为理解酶-代谢物关系提供了一个系统框架。这种方法为未来的实验研究和潜在的治疗干预提供了有价值的见解。
