One of the most important challenges for humanity in the 21st century will be to produce enough food. Global pressure on agriculture is set to increase due to a number of reasons, including population growth and climate change. Climate change induced increases in both abiotic and biotic crop stressors (heat, drought, flooding, and pathogen spread) have already resulted in regional crop failures. Prevention and mitigation can be accomplished by improving the resilience of crops, requiring a holistic and deep understanding of the plant response to various stressors. The plant response to stress triggers a complex signalling network, involving a multitude of transcriptional regulators, massive gene activity changes and phytohormone synthesis and signalling.
Basic research in model species has led to improved molecular understanding of signalling and regulation mechanisms in plants. This knowledge has been compiled into a single large plant stress signalling network, available to researchers as an online resource. However, a similar level of insight is missing in most crop species. This can be improved by inferring the function of crop genes from extensively studied and well annotated model species. Such inference is most often done by identifying orthologous genes through sequence similarity. Unfortunately, orthologous genes between model and crop species do not always perform exactly the same function, especially in response to stress. For actionable translations, we need to instead identify “functional orthologs” that may have only moderate sequence similarity, but shared molecular function. The first objective of this proposal is to generate a high quality, integrative approach, involving multiple algorithms with different assumptions, to translate genes involved in plant stress signalling from model to crop species. This will both improve our current knowledge in stress signalling by integrating knowledge from multiple species to fill knowledge gaps, and translate current available knowledge to crops to better understand the stress response. On the other hand, even with quality model-to-crop orthology translation, we can expect differences between species in the dynamic responses of genes to stress. Therefore, the second objective of this proposal is to quantify and investigate the dynamical responses of the translated stress networks in an important set of example plants: the model species Arabidopsis, a well studied crop tomato, and an important, but less studied, crop potato. This will be done using Boolean networks and semi-quantitative modelling, integrated with time series transcriptomic data. This will provide understanding of how responses differ between species under multiple stress conditions, and of special interest, the differences in responses between weeds and crops. Exploiting these models, we will also be able to perturb the system to identify important components of plant stress resilience, and thereby hypothesise targets for crop resilience improvements.
The project received funding as a post-doctoral project by the Slovenian Research and Innovation Agency (ARIS).
Duration: 1.10.2023 – 30.9.2025.
