Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana

Plants can rapidly mitigate the effects of suboptimal growth environments by phenotypic plasticity of fitness-traits. While genetic variation for phenotypic plasticity offers the means for breeding climate-resilient crop lines, accurate genomic prediction models for plasticity of fitness-related traits are still lacking.Here, we employed condition- and accession-specific metabolic models for 67 Arabidopsis thaliana accessions to dissect and predict plasticity of rosette growth to changes in nitrogen availability.We showed that specific reactions in photorespiration, linking carbon and nitrogen metabolism, as well as key pathways of central carbon metabolism exhibited substantial genetic variation for flux plasticity. We also demonstrated that, in comparison with a genomic prediction model for fresh weight (FW), genomic prediction of growth plasticity improves the predictability of FW under low nitrogen by 58.9% and by additional 15.4% when further integrating data on plasticity of metabolic fluxes.Therefore, the combination of metabolic and statistical modeling provides a stepping stone in understanding the molecular mechanisms and improving the predictability of plasticity for fitness-related traits.

Automated summary

Plants can adapt to suboptimal growth environments and mitigate the effects on their fitness-related traits through phenotypic plasticity. However, accurate genomic prediction models for plasticity of fitness-related traits, especially for rosette growth under nitrogen availability changes, are still lacking. In this study, metabolic and statistical modeling were combined to explore and predict the genetic variation and predictability of flux plasticity in Arabidopsis thaliana. The results showed significant genetic variation in key metabolic pathways and photorespiration reactions, and the genomic prediction of growth plasticity improved the predictability of fresh weight under low nitrogen conditions.