预测性多原子生物标志物用于城市动物病溢出检测:一个综合性审查
Iliana C Martínez-Ortiz1, Igor Garcia-Atutxa2, Javier I Sanchez-Villamil1
1Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA) Unidad Morelos del Instituto Politécnico Nacional (IPN), Xochitepec, Morelos, Mexico.
Frontiers in public health
|January 30, 2026
概括
城市野生动物监测是检测动物传播疾病的关键. 整合多个omics数据 (宿主microRNAs,宿主病原体遗传标记,微生物组,病毒组,宿主转录组) 创建了一个预测框架,用于早期溢出风险评估.
科学领域:
- * 综合生物学 综合生物学
- * 动物传播疾病监测监测
- * 一个健康倡议.
背景情况:
- * 城市野生动物种群是动物传染病传播的关键接口.
- *现有的监控方法往往缺乏检测新出现威胁的灵敏度.
- *生物多样性热点与人与动物的密切接触放大了溢出风险.
研究的目的:
- * 综合多omics数据用于预测框架,用于早期发现动物传染病溢出.
- * 识别不同种类和病原体之间感染的趋同分子标志.
- * 为加强动物传染病监测提出工作流程.
主要方法:
- *对五个奥米克层进行审查和合成:宿主微RNA (miRNA),宿主-病原体遗传学,细菌微生物组,病毒组和宿主转录组.
- * 开发一个交叉验证的多omics预测架构.
- * 从非侵入性采样到预测建模的工作流程的概要.
主要成果:
- * 识别感染的融合分子特征,包括对病原体有反应的miRNA和特定的细菌基因.
- *检测与病原体耐受性相关的新型病毒序列和转录基因特征.
- * 通过多omics集成来证明用于检测已知和神秘病原体的增强灵敏度.
结论:
- * 综合的多学科方法为预期性动物病毒风险概况提供了一个变革性的工具.
- *解决现场保护和测试标准化等障碍对于实际实施至关重要.
- * 这一框架支持主动的"一个健康"战略,以预测和预防动物传染病流行.
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