Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province

作者：Li Luo, Bangwei Chen, Shengyin Zeng, Yaxin Li, X. T. Chen, Jianguo Zhang, Xiangjie Guo, Shujin Li, Lei Ruan, Shida Zhu, Cairong Gao, Cuntai Zhang, Tao Li · 发表于：Frontiers in Microbiology · 年份：2025 · DOI：10.3389/fmicb.2025.1586195 · 被引用次数：2 · 研究领域：Gut microbiota and health、Clostridium difficile and Clostridium perfringens research、Epigenetics and DNA Methylation

Background The individual specificity and temporal stability of the human gut microbiota have revealed significant compositional differences across geographical provenances. However, the gut microbiota variations among people residing in different regions within a province remain enigmatic. Methods Shotgun metagenomics sequencing was performed to analyze the gut microbiota of 381 unrelated Chinese Han individuals living in two cities (Wuhan and Shiyan) of Hubei Province. To obtain the optimal model that can distinguish geographically close populations, three machine learning (ML) algorithms based on microbiota or functions were employed. Results Significant differences in microbial α diversity and β diversity were observed. Flavonifractor plautii and Bacteroides stercoris were region-specific markers that presented higher relative abundances in Wuhan individuals. By utilizing the genus-level index commonly used for 16 s RNA as the base model, the prediction accuracy was greatly improved when species and functional data were added. Among the three ML algorithms, the random forest algorithm achieved the best performance, with an AUC of 0.943. Conclusion The gut microbiota of individuals residing in the same province is significantly similar; however, pronounced differences in bacterial composition were noted between individuals. Integrating the gut microbiota and functions using machine learning algorithm can distinguish people from geographically close environments, offering a...