Deep learning-assisted proteomic dissection reveals sex biased and shared proteomic patterns in <i>Populus deltoides</i> under waterlogging stress and subsequent recovery

Sexual dimorphism in dioecious tree species and proteomic responses under stress represents an underappreciated axis of stress resilience. Here, we investigated sex-biased proteomic patterns in Populus deltoides exposed to long-term waterlogging and subsequent recovery. By integrating iTRAQ- based quantitative proteomics with machine learning approaches, including autoencoders, graph neural networks (GNNs), and SHAP-based model explainability, we explored latent structure within high-dimensional protein abundance data. Unsupervised autoencoders captured dominant stress-related variation but showed limited resolution of sex-associated differences. In contrast, GNN and GraphSAGE embeddings disentangled sex-biased proteins embedded within broader waterlogging and recovery responses, highlighting a structured and topologically coherent proteomic differentiation. SHAP analyses applied to Random Forest, and XGBoost models trained on latent features confirmed that waterlogging is the most decisive contributor to sex-biased compared to recovery. Further, in males, selective retention of photosynthetic capacity was evident through the increased protein abundance of Photosystem II components (PsbH, PsbR) and light-harvesting proteins (Lhcb1, Lhcb2), coupled with targeted decreased protein abundance of cytochrome b6f and Photosystem I subunits during stress and a partial rebound post-waterlogging. In contrast, females displayed widespread suppression across the photosynthetic apparatus, suggesting a reduced capacity for functional recovery. Stress-specific modulation of phenylpropanoid and proline pathways further revealed divergent metabolic strategies. Together, our findings revealed sex-biasedproteomic plasticity as a critical determinant of recovery potential in P. deltoides and established a graph-based machine-learning framework for decoding the hidden layers of functional dimorphism under abiotic stress.