MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research

Abstract 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...

Full description

Bibliographic Details
Main Authors: Riccardo Scotti, Stuart Southern, Christine Boinett, Timothy P. Jenkins, Alba Cortés, Cinzia Cantacessi
Format: Article
Language:English
Published: BMC 2020-02-01
Series:Microbiome
Online Access:https://doi.org/10.1186/s40168-019-0782-7
id doaj-f067421d30c744c686c3c00af3e1d15f
record_format Article
spelling doaj-f067421d30c744c686c3c00af3e1d15f2021-02-07T12:48:12ZengBMCMicrobiome2049-26182020-02-018111510.1186/s40168-019-0782-7MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current researchRiccardo Scotti0Stuart Southern1Christine Boinett2Timothy P. Jenkins3Alba Cortés4Cinzia Cantacessi5Department of Veterinary Medicine, University of CambridgeDepartment of Veterinary Medicine, University of CambridgeDepartment of Veterinary Medicine, University of CambridgeDepartment of Veterinary Medicine, University of CambridgeDepartment of Veterinary Medicine, University of CambridgeDepartment of Veterinary Medicine, University of CambridgeAbstract 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.https://doi.org/10.1186/s40168-019-0782-7
collection DOAJ
language English
format Article
sources DOAJ
author Riccardo Scotti
Stuart Southern
Christine Boinett
Timothy P. Jenkins
Alba Cortés
Cinzia Cantacessi
spellingShingle Riccardo Scotti
Stuart Southern
Christine Boinett
Timothy P. Jenkins
Alba Cortés
Cinzia Cantacessi
MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
Microbiome
author_facet Riccardo Scotti
Stuart Southern
Christine Boinett
Timothy P. Jenkins
Alba Cortés
Cinzia Cantacessi
author_sort Riccardo Scotti
title MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
title_short MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
title_full MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
title_fullStr MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
title_full_unstemmed MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
title_sort michelindb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research
publisher BMC
series Microbiome
issn 2049-2618
publishDate 2020-02-01
description Abstract 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.
url https://doi.org/10.1186/s40168-019-0782-7
work_keys_str_mv AT riccardoscotti michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
AT stuartsouthern michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
AT christineboinett michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
AT timothypjenkins michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
AT albacortes michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
AT cinziacantacessi michelindbawebbasedtoolforminingofhelminthmicrobiotainteractiondatasetsandametaanalysisofcurrentresearch
_version_ 1724280811978489856