Evidence map›Paper›PMID 41193681›Full record

ArticlePlant cell reports2025

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

Hao-Yu Liu, Pei-Hsiu Kao, Supaporn Baiya, Chung-Feng Kao

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Article in Plant cell reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Hao-Yu LiuPlant Sciences Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID http://orcid.org/0009-0009-5667-8906
Pei-Hsiu KaoFaculty of Science, University of Melbourne, Parkville, Australia.ORCID http://orcid.org/0009-0005-0733-8068
Supaporn BaiyaDepartment of Resource and Environment, Faculty of Science at Sriracha, Kasetsart University at Sriracha Campus, Chonburi, Thailand. supaporn.bai@ku.th.ORCID http://orcid.org/0000-0002-2641-8101
Chung-Feng KaoDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan. kaoc@nchu.edu.tw.ORCID http://orcid.org/0000-0002-4779-6566

Funding

Kasetsart University (No.00042023)National Chung-Hsing University Chung Cheng Agriculture ScienceNational Chung-Hsing University Social Welfare Foundation
6 · The paper itself

Abstract

key messageSNFE framework identifies 10 key CTgenes and reveals novel cold-tolerance mechanisms in soybean. Cold stress poses a significant threat to soybean (Glycine max (L.) Merr) productivity, during early developmental stages. Traditional approaches for identifying cold-responsive genes have been limited by 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), to uncover key cold-tolerant genes (CTgenes) by leveraging both panomics 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 demonstrating 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; we also discuss the potential of multiplex "full gene packages" as a downstream engineering avenue to enhance cold resilience.

Indexed as

Glycine maxPlant Growth RegulatorsSystems BiologyAbscisic AcidCold-Shock ResponseCold TemperatureGene Expression ProfilingGene Expression Regulation, PlantGene Regulatory NetworksPlant ProteinsTranscription FactorsTranscriptomeAbscisic AcidPlant Growth RegulatorsPlant ProteinsTranscription FactorsDual-phase regulatorsFull gene packagesHormone–membrane crosstalkMulti-layered systems biologyPanomicsSNFE framework

Identifiers

PMID41193681
PMCPMC12589308

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.