Epigenetic modifications play pivotal roles in regulating plant adaptive responses to viral infection and various other stresses. However, how viral infection rewires and shapes chromatin-based epigenetic regulatory networks in crops with contrasting resistance remains unclear. To this end, we investigated the consequences of epigenetic variations in resistant and susceptible soybean cultivars following soybean mosaic virus (SMV) infection. SMV infection mediates the depletion of 24-nucleotide small interfering RNAs (24-nt siRNAs) in susceptible cultivars and induces the accumulation of 24-nt siRNAs in resistant cultivars. Twenty-four-nucleotide siRNA-dependent DNA methylation variable regions are preferentially enriched in euchromatic CHH contexts, and highly variable DNA methylation regions in heterochromatic long terminal repeat (LTR) retrotransposons are independent of 24-nt siRNAs. Moreover, SMV infection triggers extensive chromatin remodelling in susceptible cultivar, where the depletion of 24-nt siRNAs is related to reduced chromatin accessibility. Conversely, SMV infection mildly remodels chromatin accessibility at heterochromatic LTR retrotransposons in the resistant cultivar. Variations in 24-nt siRNA levels and DNA methylation in upstream regions of autophagy-related genes in susceptible cultivars may influence their expression. Our work provides insights into SMV-triggered divergent epigenetic regulatory networks in soybeans with contrasting resistance and provides a valuable foundation for investigating gene regulatory programmes based on epigenetic variations.

Agent-based modeling (ABM) is a unique tool for understanding social mechanisms and emergent phenomena. The paper presents an empirically grounded agent-based model that simulates how stakeholders embedded in flood governance networks facilitate community loss-sharing and post-flood recovery. The model is designed and calibrated using extensive empirical data from communities in Guangzhou, China. Modeled agents include multi-level government agencies, NGOs, private sector entities, and local clans, among others. The model integrates core processes (rainfall and flood impacts, network-based loss sharing and recovery, and the implementation of resilience measures) with modules for trust evolution and resource constraints. The purpose of this model is to evaluate the effects of different network structures, inter-stakeholder trust, and the diffusion of flood resilience measures on community flood resilience, and to advance the understanding of how resilience emerges as a macro-level attribute from micro-level interactions. Innovations are twofold: First, it moves beyond static analysis to simulate the dynamic, network-based collaborative processes among diverse institutional stakeholders; Second, it implements a process-based framework to measure community robustness and adaptivity, using these metrics to evaluate overall community resilience to floods. Key parameters, derived from literature and empirical research, were validated and tested via sensitivity analysis. The model serves as an accessible tool for researchers and practitioners interested in stakeholder collaborations in community-level climate governance and identifying optimal intervention strategies. • The model is described using the ODD protocol. • Validation, sensitivity analysis, and the number of minimum simulation runs are explained. • Complete NetLogo code and a brief user guide are provided.