BACKGROUND The complex network of interactions occurring between gastrointestinal (GI) and extra-intestinal (EI) parasitic helminths of humans and animals and the resident gut microbial flora is attracting increasing attention from biomedical researchers, because of the likely implications for the pathophysiology of helminth infection and disease. Nevertheless, the vast heterogeneity of study designs and microbial community profiling strategies, and of bioinformatic and biostatistical approaches for analyses of metagenomic sequence datasets hinder the identification of bacterial targets for follow-up experimental investigations of helminth-microbiota cross-talk. Furthermore, comparative analyses of published datasets are made difficult by the unavailability of a unique repository for metagenomic sequence data and associated metadata linked to studies aimed to explore potential changes in the composition of the vertebrate gut microbiota in response to GI and/or EI helminth infections. RESULTS Here, we undertake a meta-analysis of available metagenomic sequence data linked to published studies on helminth-microbiota cross-talk in humans and veterinary species using a single bioinformatic pipeline, and introduce the 'MICrobiome HELminth INteractions database' (MICHELINdb), an online resource for mining of published sequence datasets, and corresponding metadata, generated in these investigations. CONCLUSIONS By increasing data accessibility, we aim to provide the scientific community with a platform to identify gut microbial populations with potential roles in the pathophysiology of helminth disease and parasite-mediated suppression of host inflammatory responses, and facilitate the design of experiments aimed to disentangle the cause(s) and effect(s) of helminth-microbiota relationships. Video abstract.

中文翻译：

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MICHELINdb：一种基于网络的工具，用于挖掘蠕虫-微生物群相互作用数据集，并对当前研究进行荟萃分析。

背景人类和动物胃肠道（GI）和肠外（EI）寄生蠕虫与常驻肠道微生物菌群之间发生的复杂相互作用网络正吸引着生物医学研究人员越来越多的关注，因为这可能对蠕虫感染的病理生理学产生影响。和疾病。然而，研究设计和微生物群落分析策略以及宏基因组序列数据集分析的生物信息学和生物统计学方法的巨大异质性阻碍了蠕虫-微生物群串扰后续实验研究的细菌靶标的识别。此外，由于缺乏宏基因组序列数据和相关元数据的独特存储库，因此对已发表的数据集进行比较分析变得困难，这些数据与旨在探索脊椎动物肠道微生物群组成对胃肠道和/或肠蠕虫感染的反应的潜在变化的研究相关。 。结果在这里，我们使用单一生物信息学管道对与已发表的人类和兽医物种蠕虫微生物群串扰研究相关的可用宏基因组序列数据进行荟萃分析，并引入“MICrobiome HELminth INteractions 数据库”(MICHELINdb)，这是一个用于挖掘这些调查中生成的已发布序列数据集和相应元数据的在线资源。结论通过增加数据的可访问性，我们的目标是为科学界提供一个平台，以识别肠道微生物种群在蠕虫疾病的病理生理学和寄生虫介导的宿主炎症反应抑制中的潜在作用，并促进旨在解开这些问题的实验设计。蠕虫与微生物群关系的原因和影响。视频摘要。