Collaborative Research: Assessing the connections between genetic interactions, environments, and phenotypes in Arabidopsis thaliana

Organismal complexity is due in large part to genes working not in isolation but with each other. Knowledge of such interactions will facilitate improving plant productivity and resilience to increasingly extreme conditions. However, studying the impacts of gene interactions on plant traits is challenging for two reasons.

First, there can be millions of possible interactions to sieve through. Second, both nature (i.e., genes and gene interactions) and nurture (i.e., the environment) are important. Even when a gene interaction is identified as being important, its relevance is frequently known only for one environment.

This project will address these challenges by investigating the question of how nature and nurture jointly impact plant traits. Specifically, interactions between hundreds of pairs of genes in the model plant Arabidopsis will be examined by measuring survival traits under different temperatures. Artificial intelligence-based approaches will be used to measure traits and to build models that predict gene interactions under different environments.

These prediction models will also incorporate existing knowledge of interactions among similar genes from non-plant species. The predictions will be tested experimentally and will provide insight into how nature and nurture jointly influence plant survival and fitness. Such insight will facilitate better predictions of gene functions in both model and crop plants and provide candidate genes for engineering productive and resilient plants.

Findings from this project will serve as examples illustrating to the scientific community and the public the benefits of integrating experimental and computational approaches.

Advances in genetics and genomics have led to an unprecedented understanding of how genotypes connect with phenotypes and the roles of genetic interactions and the environment in controlling phenotype. However, the environmental dependency of gene × gene interactions is frequently not considered, particularly in multicellular species. The goal of this project is to better understand the connection between genotypes and phenotype by assessing the impact of environmental perturbation on genetic interactions and by identifying the genetic components underlying this plasticity in the model plant Arabidopsis thaliana using protein kinase genes as examples.

This will be accomplished through phenotyping experiments coupled with computational modeling. First, models predicting genetic interactions specific to an environmental context will be generated through multi-omics data integration and the use of existing genetic interaction data from Arabidopsis and other model species (e.g., yeast and worm) and new experimental data generated from 150–200 pairs of single and double kinase mutants grown in 3–5 different environmental contexts (i.e., temperature regimes), yielding multiple trait values, which will be used to calculate quantitative measures of genetic interactions between gene pairs and the environment.

Next, model predictions will be validated using the experimental data, and the results will be used to further refine the models. The refined models will be dissected using model interpretation methods to reveal the molecular features important for specifying context-specific genetic interactions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.