Exploring Lipid Pathways and Phosphorylation Mechanisms: Insights from FatPlants and P3DB 4.0

Abstract:

Understanding the complex regulation of lipid metabolism and phosphorylation networks in plants is pivotal for advancing agricultural biotechnology and improving crop yields. This presentation highlights integrative studies on key bioinformatics platforms, including FatPlants and P3DB, which contribute to decoding the intricate relationships between metabolic pathways and regulatory networks in plant systems.

The FatPlants platform compiles comprehensive data on lipid-related genes, proteins, and pathways for model species such as Arabidopsis thaliana, Glycine max (soybean), and Camelina sativa. By integrating advanced computational tools, visualization modules, and machine learning, FatPlants serves as a one-stop resource for analyzing and hypothesizing about plant lipid metabolism. Complementing these resources, the research also delves into the evolutionary dynamics of heteromeric acetyl-CoA carboxylase (htACCase) regulatory subunits, unveiling adaptations that underscore metabolic efficiency under environmental stressors. Omics-based analyses in high-oil mutants have further revealed metabolic rearrangements that underscore the delicate balance between lipid accumulation and metabolic regulation.

Simultaneously, P3DB (Plant Protein Phosphorylation Database) has been pivotal in cataloging plant phosphorylation events, with recent expansions enhancing its utility. Updates have included broader data integration, enriched visualization tools, and improved coverage across phosphorylation types and species. P3DB’s capabilities now extend to supporting experimental data overlays, enhanced protein-protein interaction (PPI) mappings, and phosphorylation prediction using machine-learning. Extending these insights, P3DB envisions an AI-enhanced bioinformatics tool built on the P3DB framework. This tool will amalgamate KiC-assay data, high-resolution mass spectrometry, and metabolic pathway data to develop a cross-species phosphorylation network. The integration aims to identify conserved phosphorylation sites critical for plant stress responses, providing targets for crop improvement and resilience strategies.

Collectively, both projects is trying to make a robust approach to plant bioinformatics, bridging data integration and predictive analytics to propel innovations in crop science and sustainable agriculture.