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基于微生物群的机器学习方法用于预测热应激和识别相关微生物
Himani Joshi1, Michael Caprio2, Lindsey Reon1
1Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS, 39762, USA.
Microbial ecology
|November 25, 2025
概括
热压对奶牛产生负面影响,改变它们的小肠微生物. 分析乳头细菌和古生物可以准确预测热应激,为缓解策略提供目标.
科学领域:
- 动物科学动物科学
- 微生物学 微生物学
- 环境科学 环境科学
背景情况:
- 热应激显著挑战可持续畜牧业生产,影响动物福利和生产力.
- 热应激动物中胃肠道微生物生态系统的变化会影响营养消化和宿主生产.
- 以往研究中的不一致性凸显了对标准化微生物群分析的需要,以确定可靠的热应激相关微生物.
研究的目的:
- 在哺乳期的荷尔斯坦牛中确定受热应激影响的一致的小肠微生物种群.
- 评估 rumen microbiota 个人资料在预测热应激方面的潜力.
- 发现潜在的微生物生物标志物用于减轻热应激.
主要方法:
- 从八项研究中收集和分析了公开可用的16S rRNA基因扩增序列测序数据.
- 使用一致的生物信息管道进行微生物组成分析.
- 开发和比较机器学习模型 (包括随机森林) 用于使用肠微生物群和动物因素预测热应激.
主要成果:
- 在热应激的哺乳期霍尔斯坦牛中,鉴定出一种明显的小肠微生物群特征.
- 特定的微生物种群,包括乳酸菌类,鲁米诺科卡家族UCG-001和甲基微生物,被选为潜在的生物标志物.
- 与没有微生物群数据 (AUC:0.440) 的模型相比,机器学习模型包含肠微生物群的热应激预测准确度显著提高 (AUC:0.851).
结论:
- 乳液微生物组成是乳牛热应激的可靠指标.
- 已识别的微生物种群可以作为热应激的生物标志物.
- 针对特定的小肠微生物是减轻乳牛热应激的有希望的策略.
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