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对早产敏感性的生物标志物识别:使用系统生物学和机器学习方法进行阴道微生物组元分析

Sudeepti Kulshrestha¹, Priyanka Narad², Brojen Singh³

¹Systems Biology and Data Analytics Research Lab, Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India.

American journal of reproductive immunology (New York, N.Y. : 1989)

|July 21, 2024

概括

阴道的微生物组

科学领域:

微生物组研究的研究.
基因组学就是基因组学.
传染病传染病传染病传染病

背景情况:

阴道微生物组在妊娠结果中发挥着关键作用,包括早产 (PTB).
现有的微生物组分析生物信息学方法在识别PTB相关因素方面存在局限性.
了解这些因素对于减少全球新生儿死亡率至关重要.

研究的目的:

确定导致早产 (PTB) 的关键微生物,遗传和代谢因素.
应用综合系统生物学和机器学习方法进行综合分析.
发现预防PTB的潜在目标.

主要方法:

从公开数据集中分析了3757个阴道微生物组16SrRNA样本.
对样本进行分类,分为早产 (PTB) 和满期出生组.
利用并行META 3进行分类学和功能性分析,其次是系统生物学和机器学习用于特征识别.

主要成果:

确定了与PTB相关的九个显著特征:Shuttleworthia,Megasphaera,Sneathia,近接管二碳酸盐回收通路,全身性红斑狼通路,转录机械通路,lepA,pepX和rpoD.
在三个月内观察到这些特征的丰富度的变化.
突出了与PTB相关的特定微生物和代谢变化.

结论:

涉及Shuttleworthia,Megasphaera和Sneathia的细菌性阴道炎可能会增加PTB易感性.
改变的代谢途径 (例如,脂多糖,叶酸,视网膜生物合成) 与PTB风险有关.
鉴定的微生物和分子特征为PTB预防提供了潜在的治疗点.

关键词:

生物信息学是一种生物信息学.机器学习是机器学习.怀孕的结果.过早分娩过早分娩是什么生殖健康生殖健康系统生物学系统生物学阴道的微生物组

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