深度数据挖掘揭示了微生物繁殖操纵者在各种宿主物种内和它们之间的多样性和分布
Paloma Medina1, Shelbi L Russell1, Russell Corbett-Detig1
1Genomics Institute, Department of Biomolecular Engineering UC Santa Cruz, Santa Cruz, CA, United States of America.
PloS one
|July 11, 2023
概括
一种新的数据挖掘方法揭示了无脊椎动物中的细菌共生体感染频率和标位. 沃尔巴基亚细菌感染了大约44%的节肢动物物种,影响了宿主进化和生物控制策略.
科学领域:
- 无脊椎动物的生态和进化
- 微生物生态学 微生物生态学
- 基因组学就是基因组学.
背景情况:
- 操纵宿主繁殖的细菌共生体是无脊椎动物进化的关键.
- 估计感染流行率和共生体密度 (titer) 对生物控制至关重要,但目前的方法有限.
- 需要高通量方法来评估共生体感染动态.
研究的目的:
- 开发一种数据挖掘方法,用于估计宿主组织中的共生体感染频率和标位.
- 选公开可用的序列数据以检测常见共生宿主种群中的感染.
- 为了研究影响共生体标位的宿主遗传因素.
主要方法:
- 开发了一种数据挖掘方法来分析约32,000个公共序列样本.
- 从常见的节肢动物和线虫种群中选样本,以检测细菌共生体感染.
- 利用Drosophila melanogaster的种群基因组数据来识别与共生体标位相关的基因.
主要成果:
- 发现了2,083个受感染的关节动物和119个受感染的线虫样本.
- 估计Wolbachia感染的流行率为关节动物的44%,线虫的34%.
- 确定了宿主物种和共生菌株作为解释36%Wolbachia标位变化的重要因素.
结论:
- 数据挖掘是一个强大的,可访问的工具,用于检测细菌感染和量化标位.
- 这项研究提供了对无脊椎动物生殖操纵者患病率的大规模估计.
- 这些发现为研究宿主共生体进化和生物控制开辟了新的途径.
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