MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research.
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Scotti, R., Southern, S., Boinett, C., Jenkins, T. P., Cortés, A., & Cantacessi, C. (2020). MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research.. Microbiome, 8 (1), 10. https://doi.org/10.1186/s40168-019-0782-7
Background The complex network of interactions occurring between gastrointestinal (GI) and extra-intestinal (EI) parasitic helminths of humans and animal hosts 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 bioinformatics and biostatistical approaches for analyses of metagenomics sequence datasets hinder the identification of bacterial targets for follow-up experimental investigations of helminth-microbiota crosstalk. Furthermore, comparative analyses of published datasets are made difficult by the unavailability of a unique repository for metagenomics 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 metagenomics sequence data linked to published studies on helminth-microbiota cross-talk in humans and veterinary species using a single bioinformatics 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.
Feces, Animals, Humans, Helminths, Bacteria, Helminthiasis, Helminthiasis, Animal, Intestinal Diseases, Parasitic, RNA, Ribosomal, 16S, Software, Metagenome, Data Mining, Microbiota, Datasets as Topic, Gastrointestinal Microbiome
Isaac Newton Trust (Minute 17.37(q))
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External DOI: https://doi.org/10.1186/s40168-019-0782-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/300509