bacLIFE: a user-friendly computational workflow for genome analysis and prediction of lifestyle-associated genes in bacteria

Guillermo Guerrero-Egido, Adrian Pintado, Kevin M. Bretscher, Luisa-Maria Arias-Giraldo, Joseph N. Paulson, Herman P. Spaink, Dennis Claessen, Cayo Ramos, Francisco M. Cazorla, Marnix H. Medema, Jos M. Raaijmakers, Víctor J. Carrión* (Co-auteur)

*Bijbehorende auteur voor dit werk

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review

Samenvatting

Bacteria have an extensive adaptive ability to live in close association with eukaryotic hosts, exhibiting detrimental, neutral or beneficial effects on host growth and health. However, the genes involved in niche adaptation are mostly unknown and their functions poorly characterized. Here, we present bacLIFE (https://github.com/Carrion-lab/bacLIFE) a streamlined computational workflow for genome annotation, large-scale comparative genomics, and prediction of lifestyle-associated genes (LAGs). As a proof of concept, we analyzed 16,846 genomes from the Burkholderia/Paraburkholderia and Pseudomonas genera, which led to the identification of hundreds of genes potentially associated with a plant pathogenic lifestyle. Site-directed mutagenesis of 14 of these predicted LAGs of unknown function, followed by plant bioassays, showed that 6 predicted LAGs are indeed involved in the phytopathogenic lifestyle of Burkholderia plantarii and Pseudomonas syringae pv. phaseolicola. These 6 LAGs encompassed a glycosyltransferase, extracellular binding proteins, homoserine dehydrogenases and hypothetical proteins. Collectively, our results highlight bacLIFE as an effective computational tool for prediction of LAGs and the generation of hypotheses for a better understanding of bacteria-host interactions.
Originele taal-2Engels
Artikelnummer2072
TijdschriftNature Communications
Volume15
DOI's
StatusGepubliceerd - 07 mrt. 2024

Vingerafdruk

Duik in de onderzoeksthema's van 'bacLIFE: a user-friendly computational workflow for genome analysis and prediction of lifestyle-associated genes in bacteria'. Samen vormen ze een unieke vingerafdruk.

Citeer dit