# Predicting bacterial-mediated entomopathogenicity through comparative genomics and statistical modeling

## TL;DR

This study combines genomics and statistical models to predict which bacterial genes kill insects, offering a faster way to find natural pest control solutions.

## Contribution

A predictive framework integrating comparative genomics, statistical modeling, and experimental validation to identify insecticidal genes in new hosts.

## Key findings

- Natural variation in insecticidal gene presence and activity was observed across Pseudomonas strains.
- Eight operons in P. aeruginosa PAO1 were identified as necessary for killing D. melanogaster.
- Genetic predictors of virulence vary across Pseudomonas phylogenetic groups, suggesting targeted biocontrol strategies.

## Abstract

Bacterial genomes encode vast functional diversity and have both beneficial and detrimental effects on insect hosts. While genotype-to-phenotype relationships are known for specific insecticidal genes on individual insect hosts, whether these mechanisms will be effective on a phylogenetically distinct insect host is not always known. To determine if known virulence genes are effective on a new host, we developed a method to merge existing mechanistic knowledge with in vivo tests on a small number of bacterial isolates to predict bacterial genes associated with entomopathogenesis. We used a model consisting of Drosophila melanogaster interactions with pathogenic and commensal genome-sequenced strains of Pseudomonas bacteria. We compiled a database of previously described insecticidal and biocontrol genes within the Pseudomonas genus and used comparative genomics to probe the distribution of these genes across Pseudomonas strains. We found natural variation in the presence of known insecticidal genes across the genus. We tested the insect-killing capacity of 13 Pseudomonas spp. strains against D. melanogaster and found natural variation in insecticidal activity. To identify bacterial genes associated with fly mortality, we employed two statistical models to correlate bacterial virulence with the presence of previously described insecticidal activity. To validate our predictions, we used a P. aeruginosa PAO1 transposon mutant library and identified eight operons that are necessary for killing D. melanogaster. We show that by combining existing literature with phenotyping a small number of strains, we identified both known and novel genes associated with insecticidal activity in D. melanogaster, using a rapid, scalable screening framework. More broadly, these findings illustrate a discovery pipeline for bacterial virulence mechanisms, accelerating the discovery of insect pest biocontrol mechanisms.

Bacteria with insecticidal properties offer a promising alternative to chemical pesticides, but identifying effective strains and their underlying mechanisms remains a challenge. Here, we used Pseudomonas-D. melanogaster as a model to develop a predictive framework for determining which known bacterial genes with insecticidal activity are effective in a new host. By integrating comparative genomics, statistical modeling, and experimental validation, we identified insecticidal genes that are effective in D. melanogaster and highlighted new candidates for future study, demonstrating the utility of our integrative modeling approach. Our findings show that genetic predictors of virulence vary across Pseudomonas phylogenetic groups, highlighting the potential for targeted biocontrol strategies. We also demonstrate that disrupting specific pathways significantly reduces insecticidal activity, confirming their role in bacterial virulence. As Pseudomonas strains are found in diverse environments, this approach may be broadly applicable for predicting insecticidal efficacy in other bacterial genera. By improving our ability to identify and engineer microbial biocontrol agents, this work advances sustainable pest management strategies and provides new tools for reducing reliance on conventional pesticides.