Stage-Specific Regulatory Network Inference for Heat-Induced Silique Branching in Brassica napus
Keywords:
Brassica napus, Silique Branching, Gene Regulatory Networks, Heat StressAbstract
Climate change and the associated increase in global average temperatures pose a severe threat to agricultural productivity worldwide. Brassica napus, commonly known as rapeseed or canola, is a globally significant oilseed crop that exhibits high sensitivity to heat stress, particularly during its reproductive stages. Elevated temperatures during flowering and early silique development frequently lead to structural anomalies, including heat-induced silique branching, which ultimately affects seed yield and oil quality. Understanding the molecular mechanisms underpinning this morphological adaptation requires a comprehensive temporal analysis of gene expression. This study aims to construct stage-specific gene regulatory networks to elucidate the complex, time-dependent transcriptional reprogramming driving heat-induced silique branching in Brassica napus. By utilizing high-resolution time-course transcriptomic data collected across early, intermediate, and late stages of heat stress, we employ advanced computational inference methodologies to identify dynamic regulatory modules and crucial hub genes. Our analysis reveals stage-specific activation of heat shock factors, accompanied by intricate cross-talk with phytohormone signaling pathways, specifically auxin and cytokinin, which dictate architectural plasticity. The identification of stage-specific regulatory hubs offers unprecedented insights into the chronobiology of stress-induced developmental plasticity. These findings provide a vital genomic resource and theoretical foundation for future breeding programs aimed at engineering climate-resilient Brassica napus cultivars through targeted genetic manipulation of temporal stress responses.
References
1. Chen, Z., Gao, P., Lee, Y., Barthelemy, J., Zhou, L., & Wang, L. (2025, June). Optimizing Efficiency and Visual-Textual Alignment for LLM-Based Radiology Report Generation. In 2025 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
2. Zhang, W., Luo, Z., Gu, C., Ma, J., Cao, Y., Yuan, W., & Jin, Y. (2025). Bridging the compression-precision paradox: A hybrid architecture for clinical EEG report generation with guaranteed measurement accuracy. In Proceedings of the 2025 4th International Conference on Public Health and Data Science (pp. 201–207).
3. Lai, H., Yip, H. C., Gong, Y., Chan, K. F., Leung, K. K. C., Chan, M. S., ... & Chiu, P. W. Y. (2024). MFGE8 in exosomes derived from mesenchymal stem cells prevents esophageal stricture after endoscopic submucosal dissection in pigs. Journal of nanobiotechnology, 22(1), 143.
4. Sha, L., Wang, Y., Meng, P., Deng, Y., Chen, T., Zhang, X., ... & Xu, Q. (2025). Pharmacological inhibition of PSPH reduces serine levels and epileptic seizures. Nature Chemical Biology, 21(11), 1742-1753.
5. Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.
6. Gao, X. L., & Wang, M. (2026). Enhancing cancer immunotherapy antibody discovery: Genetic algorithms and risk-aware protein modeling. Available at SSRN 6033537.
7. Lyu, M., Torii, R., Liang, C., Zhang, X., Wang, X., Li, Q., ... & Chen, D. (2025). Predictive computational framework to provide a digital twin for personalized cardiovascular medicine. Communications Medicine, 5(1), 370.
8. Lyu, M., Torii, R., Liang, C., Li, Q., Wang, X., Ventikos, Y., & Chen, D. (2025). In silico comparison of two non-invasive pre-procedural virtual coronary revascularisation techniques for personalised cardiovascular medicine. Computer Methods and Programs in Biomedicine, 109046.
9. Yu, X., Zhang, Y., & Wang, H. (2025, July). Subjective Sleep Gains in Empty Nose Syndrome: A Cautionary Note on the Need for Objective Validation. In International Forum of Allergy & Rhinology (Vol. 15, No. 7, pp. 761-762).
10. Verhoven, Bret, et al. "Attenuating Ischemia and Reperfusion Injury Using NAD+-Loaded Nanoparticles in Mouse Kidneys." Transplantation Direct 12.1 (2025): e1890.
11. Yin, Li, et al. "Targeted NAD+ Delivery for intimal Hyperplasia and Re-endothelialization: a novel anti-restenotic therapy approach." bioRxiv (2024): 2024-02.
12. Sinha, D., Tong, Y., Zepeda, M. A. F., Hanstad, G., Nelson, E. C., Theisen, C. O., ... & Gamm, D. M. (2023). Silica nanocapsules as a nonviral delivery platform for iPSC-RPE.Investigative Ophthalmology & Visual Science,64(8), 774-774.
13. Kabra, Meha, et al. "Nonviral base editing of KCNJ13 mutation preserves vision in a model of inherited retinal channelopathy." The Journal of Clinical Investigation 133.19 (2023).
14. Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., ... & Cheng, Y. (2024). Cooperative Atomically Dispersed Fe–N4 and Sn–N x Moieties for Durable and More Active Oxygen Electroreduction in Fuel Cells. Journal of the American Chemical Society, 146(49), 33569-33578.
15. Wang, Y. (2025, August). AI-AugETM: An AI-augmented exposure–toxicity joint modeling framework for personalized dose optimization in early-phase clinical trials. In 2025 19th International Conference on Complex Medical Engineering (CME) (pp. 182-186). IEEE.
16. Wu, Y., Wei, J., Wang, S., & Rus, V. (2026, May). Persona-Conditioned Generation of Patient Self-Reports from EHRs. In Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026) (Vol. 11, No. 16, pp. 10792-10801).
17. Huang, Y., He, W., Zhao, M., McClements, D. J., Xu, Y., Li, L., ... & Li, C. (2025). Unraveling the health contributions of five key bioactives in virgin olive oil: A dose-based comparative review. Trends in Food Science & Technology, 105274.
18. Ma, H., Dai, X., Wang, T., He, Z., Du, R., Pei, H.,... & Zhao, K. (2025). Investigation of uridine in anti-aging-related diseases based on network pharmacology, molecular docking and cell experimental validation. Engineered Science, 35, 1572.
19. Yang, Z., Guo, D., Fu, Y., Qi, L., Zhu, T., Ma, X.,... & Zhao, K. (2025). Investigating the Fracture Treatment Mechanism of Dipsacus asper Based on Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation. Engineered Science, 38, 1856.
20. Sun, X., Hu, X., Wei, J., & An, H. (2025). Uncovering leading compounds for alzheimer’s disease treatment: mendelian randomization and virtual screening insights into plasma protein modulation. Biological Research, 58.
21. Zhao, Y., Bian, H., Mu, M., Uddin, M. R., Li, Z., Li, X., ... & Xu, M. (2024, October). Cryosam: Training-free cryoet tomogram segmentation with foundation models. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 124-134). Cham: Springer Nature Switzerland.
22. Wang, Z., Zhang, X., Wang, L., Fu, S., Wu, J., Xiong, J., & Huang, S. (2024). Concentrations and short-term health effects of VOCs in explored subway, bus and taxi in Beijing, China. Atmospheric Environment, 339, 120855.
23. Shen, Q., Sun, Z. H., Xu, Y. M., Hu, Q. B., Zhang, W. H., & Huang, S. A. (2025). Associations between complete-blood-count-derived inflammatory markers and gastric ulcer: A cross-sectional study. World Journal of Gastroenterology, 31(38), 111298.
24. Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410.
25. Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10), 931–934.
26. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., et al. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1, 18.
27. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.
28. Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367–1372.
29. Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Single-cell transcriptomics meets deep learning. Nature Methods, 16(11), 1083–1091.
30. Xing, Z. (2026, May). Design of High-Bandwidth DDR5 MRDIMM Memory Modules for AI Servers. In 2026 IEEE 6th International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 307-311). IEEE.
31. Jiang, J., Rahbarimanesh, A., & Maqbool, R. (2026). Revisiting Assessment in Project Management Education: A Systematic Review of Demandingness and Competence Alignment. Project Leadership and Society, 100233.
32. Xu, Y., Shao, K., Ishikawa, I., Hashimoto, Y., Logothetis, N., & Shen, Z. (2025). A data-driven framework for Koopman semigroup estimation in stochastic dynamical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 35(10).
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