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

## TL;DR

The paper introduces bacLIFE, a computational tool for predicting genes in bacteria that are associated with their lifestyle, particularly in relation to host interactions.

## Contribution

The novel contribution is the development of bacLIFE, a workflow that identifies lifestyle-associated genes (LAGs) in bacteria through genome analysis and comparative genomics.

## Key findings

- Analysis of 16,846 bacterial genomes identified hundreds of genes potentially linked to a plant pathogenic lifestyle.
- Experimental validation confirmed six predicted LAGs involved in the phytopathogenic lifestyle of Burkholderia and Pseudomonas.
- The confirmed LAGs included a glycosyltransferase, extracellular binding proteins, homoserine dehydrogenases, and hypothetical proteins.

## Abstract

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.

Many bacteria live in close association with eukaryotic hosts, exhibiting detrimental, neutral or beneficial effects on host growth and health. Here, the authors present a streamlined computational workflow for bacterial genome annotation, large-scale comparative genomics, and prediction of genes potentially involved in niche adaptation.