Predicting bacterial-mediated entomopathogenicity through comparative genomics and statistical modeling
Notice bibliographique
Résumé
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. IMPORTANCE 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».