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...
Main Authors: | , , , , , |
---|---|
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 |