使用跨物种的共同表达来预测微生物群中的代谢相互作用
Robert A Koetsier1, Zachary L Reitz1, Clara Belzer2
1Bioinformatics Group, Wageningen University, Wageningen, the Netherlands.
mSystems
|December 9, 2025
概括
跨物种基因共同表达分析预测微生物相互作用和代谢途径. 这种数据驱动的方法确定了资源竞争和专业功能,指导微生物组研究.
科学领域:
- 微生物生态学 微生物生态学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 代谢相互作用决定了微生物社区的结构和功能.
- 通过计算预测这些相互作用至关重要,但往往缺乏机械洞察力.
- 现有的工具往往错过了潜在的代谢途径,阻碍了实验验证.
研究的目的:
- 开发和验证一种使用跨物种共同表达来预测微生物相互作用的新方法.
- 确定参与竞争,交叉养和专业相互作用的特定代谢途径.
- 通过相互作用预测评估发现新基因功能的潜力.
主要方法:
- 应用跨物种共表达分析对微生物共培养RNA测序数据.
- 使用的菌素和基于饮食的最小微生物组 (MDb-MM) 和树球的徒步旅行者 (THOR) 数据集.
- 研究基因和通路的共同表达模式,以推断相互作用类型.
主要成果:
- 在MDb-MM数据集中成功预测了涉及资源竞争的途径.
- 在THOR数据集中确定了专业功能之间的联系,例如抗生素和多药物排放系统.
- 在特定的微生物联盟中提供了 siderophore 同表达驱动相互作用的证据.
结论:
- 跨物种共同表达是一种可行的数据驱动方法,用于预测微生物相互作用和潜在途径.
- 这种方法为复杂的模型构建提供了有价值的替代方案,减少了偏差.
- 该方法有助于发现新的基因功能,并为微生物组工程提供信息.
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