在自然微生物群落中普遍的基因水平双模式.
Juken Hong1, Wenzhi Xue1, Teng Wang1
1State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Cell reports
|February 19, 2026
概括
微生物组基因分布通常表现为双模式,特定的基因与利基适应和疾病有关. 这一发现为微生物组功能类型和疾病生物标志物发现提供了新的基因中心方法.
科学领域:
- 微生物组研究的研究.
- 生态基因组学是生态基因组学.
- 系统生物学 系统生物学
背景情况:
- 双模性,一种具有两个峰值的特征分布,在自然界中很常见,并且在微生物群物种丰富中观察到.
- 微生物组基因丰度分布中的双模式的流行及其功能影响在很大程度上仍未被探索.
研究的目的:
- 调查在多种微生物组中广泛发生的基因水平双模式.
- 探索双模基因在生态适应中的功能性作用.
- 开发一种新的基因中心框架,用于微生物组的功能类型,并识别潜在的疾病生物标志物.
主要方法:
- 在各种微生物组 (例如,人类肠道,海洋) 中对个体基因丰度分布的系统分析.
- 识别双模基因,并对相关途径进行丰富分析.
- 开发和应用基因为中心的微生物组功能类型框架.
- 机器学习模型构建使用双模基因用于疾病预测.
主要成果:
- 在各种微生物组中发现了广泛的基因水平双模式.
- 双模基因在利基特定的途径中得到丰富,这表明它们在社区适应中的作用.
- 建立了一个强大的基因中心微生物组功能类型框架.
- 在人类肠道微生物组中发现了11个双模基因,这些基因与肝硬化等疾病有关.
- 使用这些基因的机器学习模型证明了对疾病的预测能力.
结论:
- 基因丰富的双模式是微生物组中普遍存在的特征,为功能架构提供了洞察力.
- 已识别的双模基因作为潜在的生物标志物,用于基于微生物组的诊断和疾病预测.
- 以基因为中心的方法为传统基于分类的微生物组分析提供了有价值的替代方案.
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