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Phage-Host Prediction Using a Computational Tool Coupled with 16S rRNA Gene Amplicon Sequencing.
Andrianjakarivony, Harilanto Felana; Bettarel, Yvan; Armougom, Fabrice; Desnues, Christelle.
Afiliação
  • Andrianjakarivony HF; Microbes, Evolution, Phylogeny, and Infection (MEΦI), IHU-Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France.
  • Bettarel Y; Microbiologie Environnementale Biotechnologie (MEB), Mediterranean Institute of Oceanography (MIO), 163 Avenue de Luminy, 13009 Marseille, France.
  • Armougom F; MARBEC, Marine Biodiversity, Exploitation & Conservation, Université de Montpellier, CNRS, Ifremer, IRD, 093 Place Eugène Bataillon, 34090 Montpellier, France.
  • Desnues C; Microbiologie Environnementale Biotechnologie (MEB), Mediterranean Institute of Oceanography (MIO), 163 Avenue de Luminy, 13009 Marseille, France.
Viruses ; 15(1)2022 12 27.
Article em En | MEDLINE | ID: mdl-36680116
Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Bacteriófagos Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Africa Idioma: En Revista: Viruses Ano de publicação: 2022 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Bacteriófagos Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Africa Idioma: En Revista: Viruses Ano de publicação: 2022 Tipo de documento: Article País de afiliação: França