PAN: Development of knowledge graphs to decipher the gene regulatory network in plants upon interaction with biotic agents

Plants live in a constantly changing environment that is often unfavorable or even hostile. As sessile organisms, plants cannot actively escape multiple aggressive encounters. Instead, they developed high phenotypic plasticity that includes rapid responses to aggressive environmental factors and adaptations to changing environments (Leisner et al. 2022). Changes in gene expression underlie this phenotypic plasticity. Several actors control gene expression, including epigenetic marks and small non-coding RNA. The epigenetic variation could be a key player in plant responses to stress factors and environmental adaptation (Ashapkin et al. 2020). The most thoroughly studied type of epigenetic phenomena in plants is DNA methylation. Besides DNA methylation, plants and other eukaryotic organisms have another set of epigenetic marks–covalent modifications of various amino acid residues of histone proteins. On the other side, small non-coding microRNAs may act as the master regulators of this reprogramming of gene expression (Jaubert-Possamai et al., 2019). Although several studies have been performed to investigate very specific interactions, often involving one or very few proteins, a global overview of the impacted biological processes is still missing (McCormack et al., 2016; Gupta et al., 2022). Despite being very informative, taken individually these interactions give only a partial and incomplete picture of the system. In this project, we propose to bridge this gap by reconstructing a global network of interactions and developing multi-omics integration strategies coupled with network inference modeling. The study of gene regulatory networks can shed light on this complex network of interactions. Taking advantage of previous studies that have characterized and validated specific interactions, especially in model plants, we will first set up a novel knowledge-driven model and reconstruct a global network of interactions in plants. This global network of interactions will constitute a reference map describing molecular interactions in plants. Multi-omics data will be used to study the perturbations of this network in response to biotic agent attacks. The novel-acquired insights will be used to study the reconstructed network of interactions in less characterized and studied plants from a transfer learning perspective. The application of this framework will permit the characterization of the molecular signatures defining plant cell fate during biotic interactions with multiple parasites and to identify the complex network of interactions involved in plant cellular reprogramming upon infection. We will characterize whether plants activate similar or specific mechanisms to respond to pathogens. This will enable the connection of the master regulator genes to important pathways that are imbalanced during the infection and that are pathogen-specific or shared mechanisms.

Contact

• Maxime Multari
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