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蜱传病原体检测:有哪些新进展?

Tick-borne pathogen detection: what's new?

机构信息

UMR BIPAR, INRA, ANSES, Ecole Nationale Vétérinaire d'Alfort, Université Paris-Est, Maisons-Alfort, 94700, France; Faculty of Science, University of South Bohemia, 37005, České Budějovice, Czech Republic; Institute of Parasitology, Biology Center, Czech Academy of Sciences, 37005, České Budějovice, Czech Republic.

UMR BIPAR, INRA, ANSES, Ecole Nationale Vétérinaire d'Alfort, Université Paris-Est, Maisons-Alfort, 94700, France.

出版信息

Microbes Infect. 2018 Aug-Sep;20(7-8):441-444. doi: 10.1016/j.micinf.2017.12.015. Epub 2018 Jan 9.

Abstract

Ticks and the pathogens they transmit constitute a growing burden for human and animal health worldwide. Traditionally, tick-borne pathogen detection has been carried out using PCR-based methods that rely in known sequences for specific primers design. This approach matches with the view of a 'single-pathogen' epidemiology. Recent results, however, have stressed the importance of coinfections in pathogen ecology and evolution with impact in pathogen transmission and disease severity. New approaches, including high-throughput technologies, were then used to detect multiple pathogens, but they all need a priori information on the pathogens to search. Thus, those approaches are biased, limited and conceal the complexity of pathogen ecology. Currently, next generation sequencing (NGS) is applied to tick-borne pathogen detection as well as to study the interactions between pathogenic and non-pathogenic microorganisms associated to ticks, the pathobiome. The use of NGS technologies have surfaced two major points: (i) ticks are associated to complex microbial communities and (ii) the relation between pathogens and microbiota is bidirectional. Notably, a new challenge emerges from NGS experiments, data analysis. Discovering associations among a high number of microorganisms is not trivial and therefore most current NGS studies report lists of microorganisms without further insights. An alternative to this is the combination of NGS with analytical tools such as network analysis to unravel the structure of microbial communities associated to ticks in different ecosystems.

摘要

蜱虫及其传播的病原体给全球人类和动物健康带来了日益严重的负担。传统上,蜱传病原体的检测是通过基于 PCR 的方法进行的,该方法依赖于特定引物设计的已知序列。这种方法符合“单病原体”流行病学的观点。然而,最近的研究结果强调了合并感染在病原体生态学和进化中的重要性,这对病原体的传播和疾病严重程度有影响。然后,采用了包括高通量技术在内的新方法来检测多种病原体,但这些方法都需要病原体的先验信息来进行搜索。因此,这些方法具有偏见、局限性并且掩盖了病原体生态学的复杂性。目前,下一代测序 (NGS) 已应用于蜱传病原体的检测,以及研究与蜱虫相关的致病性和非致病性微生物(即pathobiome)之间的相互作用。NGS 技术的应用突显了两个主要问题:(i)蜱虫与复杂的微生物群落有关;(ii)病原体与微生物群之间的关系是双向的。值得注意的是,NGS 实验带来了一个新的挑战,即数据分析。发现大量微生物之间的关联并非易事,因此大多数当前的 NGS 研究仅报告了微生物列表,而没有进一步的深入分析。一种替代方法是将 NGS 与网络分析等分析工具结合使用,以揭示不同生态系统中与蜱虫相关的微生物群落的结构。

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