在共同进化动态过程中产生的菌-细菌相互作用表型的菌株级突变驱动因素的推断
Adriana Lucia-Sanz1, Shengyun Peng2, Chung Yin Joey Leung3
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
bioRxiv : the preprint server for biology
|January 23, 2024
概括
由于遗传多样性,预测菌-细菌相互作用是具有挑战性的. 这项研究使用基因组数据的机器学习来准确预测感染结果,并确定影响菌体和大肠杆菌相互作用的关键突变.
科学领域:
- 微生物学和基因组学 微生物学和基因组学
- 计算生物学和机器学习
背景情况:
- 预测菌体 (菌体) 和细菌宿主相互作用是复杂的,因为巨大的遗传多样性和不具特征的感染机制 (吸附,注射,溶解).
- 了解这些相互作用对于菌体治疗和微生物生态学等领域至关重要.
研究的目的:
- 开发和评估用于预测菌-细菌相互作用和感染强度的机器学习框架.
- 在菌体和细菌中识别关键的基因突变,这些突变驱动相互作用结果.
主要方法:
- 在51个大肠杆菌菌株和45个菌体兰巴达菌株的基因组序列和表型相互作用数据上训练了一种机器学习模型.
- 在没有对驱动器突变的先验知识的情况下,利用多种推断策略来预测相互作用.
- 分析特征的重要性,以确定有影响力的突变.
主要成果:
- 最有效的模型准确预测了86%的菌体-细菌相互作用.
- 该模型将估计感染强度的相对误差降低了40%.
- 鉴定了菌体兰巴和大肠杆菌影响感染结果的已知和新型突变,包括未知功能的基因突变.
结论:
- 机器学习可以有效地利用基因组和表型数据预测菌菌感染结果和优势.
- 该方法成功地确定了这些相互作用的关键遗传驱动因素,有助于了解细菌耐药性和菌体感染性.
- 这个框架在复杂的微生物群落中赋予遗传驱动因素的潜在应用.
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