DigiAra Computationally Designs Plant Mutants for Resistance to Microbial Infection in Arabidopsis

Abstract

Plant breeding is a resource-intensive process that requires repeated cultivation and selection across multiple generations to develop varieties with desirable traits, yet computational tools capable of supporting this process remain limited. Here, we present DigiAra, an AI-based framework for designing Arabidopsis thaliana mutants with targeted traits, particularly enhanced microbial resistance. DigiAra implements an S3 pipeline--simulation, scoring, and screening: it simulates the transcriptional effects of genetic perturbations and microbial infections, scores the predicted responses in terms of relevant traits through biological pathway analysis, and screens candidate perturbations at multiple levels. In doing so, DigiAra enables the computational exploration of the genome-wide effects of genetic perturbations and diverse microbial infections in Arabidopsis. To develop DigiAra, we address two fundamental challenges. Methodologically, we introduce a hybrid architecture that integrates local gene-level interaction modeling with global transcriptional-state modeling to predict perturbation-induced changes in the Arabidopsis transcriptional state. From a data perspective, we establish a standardized pipeline for curating, harmonizing, and processing an integrated Arabidopsis-microbe transcriptional dataset comprising 495 samples from 26 projects. As a result, DigiAra accurately predicts gene-expression changes induced by unobserved genetic perturbations and microbial infections, achieving a Pearson correlation of 0.49. Moreover, it recapitulates the general non-self response (GNSR), a 24-gene program reflecting broad transcriptional reprogramming across bacterial perturbations. In an independent study, the predicted pattern-triggered immunity pathway scores further correlate with bacterial load, with a Pearson correlation of 0.57. Lastly, we deploy DigiAra to identify 27 gene knockouts through genome-wide screening that are predicted to enhance resistance to Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) while limiting growth compromise, 9 of which are supported by published studies. Together, these results establish DigiAra as an effective framework for the computational design of Arabidopsis mutants. We have made our implementation openly available at https://github.com/youlab2025/DigiAra.