Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.

BackgroundRice is a critical global food source, but it faces challenges due to nutritional deficiencies and the pressures of a growing population. Understanding the molecular mechanisms and protein functions in rice seed development is essential to improve yield and grain quality. However, there is still a significant knowledge gap regarding the key proteins and their interactions that govern rice seed development. Protein-protein interaction (PPI) analysis is a powerful tool for studying developmental processes like seed development, though its potential in rice research is yet to be fully realized. With the aim of unravelling the protein interaction landscape associated with rice seed development, this systems biology study conducted a PPI network-based analysis. Using a list of known seed development proteins from the Gene Ontology (GO) knowledgebase and literature, novel candidate proteins for seed development were predicted using an ensemble of network-based algorithms, including majority voting (MV), Hishigaki, Functional flow, and Random Walk with Restart (RWR). The predictions were validated using enrichment analysis, and the rice seed development subnetwork was further analyzed for community and hub detection. ResultsThe study predicted 196 new proteins linked to rice seed development and identified 14 sub-modules within the network, each representing different developmental pathways such as endosperm development and seed growth regulation. Of these, 17 proteins were identified as intra-modular hubs and 6 as inter-modular hubs. Notably, the protein SDH1 emerged as a dual hub, acting as both an intra-modular and inter-modular hub, highlighting its crucial role in coordinating interactions during rice seed development. ConclusionsThese findings, including the identified hub proteins and sub-modules, provide a better understanding of the PPI interaction landscape governing seed development in rice. This information is useful for improving rice cultivars for better yield and quality through genetic engineering and breeding. This study implements an ensemble of algorithms for the analysis and showcases how systems biology techniques can be applied in developmental biology.