使用机器学习方法预测家禽相关微生物组中的食品传播病原体和益生菌类
Moses B Ayoola1,2, Nisha Pillai3, Bindu Nanduri2
1Geosystems Research Institute, Mississippi State University, Starkville, MS, 39762, USA.
Animal microbiome
|November 16, 2023
概括
这项研究使用机器学习分析肠道微生物组,发现特定的农场管理实践可以增加有益的益生菌,以减少坎比洛巴克特病原体的流行. 这些发现为改善从农场到餐桌的食品安全提供了见解.
科学领域:
- 微生物组研究的研究.
- 食品安全 食品安全
- 禽畜科学是一门学科.
背景情况:
- 肠道微生物组是健康的关键指标,受到饮食和污染物的影响.
- 微生物组研究通常评估多样性 (α和β).
- 这项研究应用机器学习来预测病原体的存在和益生菌的关联.
研究的目的:
- 使用微生物组数据预测食物传播的病原体 (沙门氏菌,坎比洛巴克,李斯特菌).
- 将病原体的流行与益生菌种群,农场管理和物理化学性质联系起来.
- 通过微生物组调制来确定减少病原体患病率的战略.
主要方法:
- 禽类便和土壤样本的16S rRNA基因测序.
- 机器学习应用于操作分类学单位 (OTU) 的相对丰度数据.
- 对不同家禽生长阶段的时间样本进行分析.
主要成果:
- 在中期生长阶段观察到益生菌 (Bacillus,Clostridium) 和Campylobacter之间的负相关性.
- 潜在的益生菌在早期和终端阶段与坎皮洛巴克特有负相关性.
- 农场管理因素 (住房,饮食,动物存在) 可以影响益生菌的丰富性并减少Campylobacter.
结论:
- 机器学习整合了微生物组数据和农场实践,以减少Campylobacter.
- 改变管理实践可以促进益生菌,减少病原体的流行.
- 确定了细菌属 (Caloramator, DA101, Parabacteroides, Faecalibacterium) 作为潜在的益生菌.
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