A multi-layered systems biology framework reveals dual-phased regulators and hormonal crosstalk underlying soybean cold tolerance

Cold stress poses a significant threat to soybean ( Glycine max (L.) Merr) productivity, particularly during early developmental stages. Traditional approaches for identifying cold-responsive genes have been limited by gene size bias, pathway redundancy, and lack of integrative validation. To address these challenges, we developed a multi-layered systems biology framework, termed SNFE (systems and network-based feature engineering), designed to uncover key cold-tolerant genes (CTgenes) by leveraging both omics and non-omics data in a network-informed context. The SNFE framework integrates five analytical layers: functional pathway enrichment, pathway crosstalk, co-functional network construction, network topology analysis, and experimental validation. From an initial pool of cold-responsive genes, SNFE identified 10 key CTgenes that demonstrated high connectivity, regulatory importance, and consistent differential expression in short- and mid-term cold conditions. These genes were validated via independent transcriptomic datasets, Quantitative real-time PCR analysis, and hormone profiling. Notably, SNFE revealed novel regulatory mechanisms, including dual-timed transcription factors, ABA–JA hormone synergy in membrane stabilization, and convergence of abiotic and biotic stress signaling. A Sankey diagram and volcano plot further confirmed that most CTgenes reside at key regulatory nodes, linking upstream functions to downstream cold-tolerance pathways. SNFE is a reliable, efficient, and interpretable tool that not only improves prediction accuracy but also enables the discovery of novel biological insights. Its scalability and analytical depth make it a powerful platform for dissecting complex stress responses in crops. This framework provides a strategic foundation for molecular breeding programs aiming to enhance climate resilience in soybean and other crops.