Ammari, M. | Search bioRxiv

bioRxiv2026

Dynamic histone hyperacetylation shapes environmentally responsive chromatin states

Transcription factors (TFs) orchestrate environmental responses by activating target genes, yet how they reshape epigenome architecture to coordinate gene expression remains poorly understood. We previously identified SIENA (Stimulus-Induced ENhancer Acetylation) domains as large regions of jasmonic acid (JA)-induced H3K9 hyperacetylation surrounding MYC2 TF binding sites in Arabidopsis and tomato. However, the mechanisms underlying the formation of SIENA domains (SIENAs) and their functional significance remained unknown. Here, we show that SIENAs also form at major JA-responsive genes and gene clusters in soybean, extending this phenomenon to an evolutionarily distant crop species. Comprehensive chromatin profiling revealed that SIENAs accumulate multiple histone acetylation marks, including H3K9ac, H3K27ac, H3K56ac, H2BK20ac, and H2A.Zac, establishing them as regions of broad histone hyperacetylation. Pharmacological disruption of proteasomal turnover and histone acetylation dynamics compromised SIENA formation. Chromatin accessibility analyses further showed that inducible accessibility within SIENAs is tightly associated with MYC2 binding sites, supporting a model in which MYCs nucleate localized chromatin reprogramming events. Together, our findings establish SIENAs as MYC2-dependent chromatin-organizing domains and identify histone hyperacetylation as a central feature of MYC2-mediated gene activation.

Plant Biology↗

bioRxiv2022

Genome-scale host-pathogen prediction for non-medical microbes.

BackgroundNetwork studies of host-pathogen interactions (HPI) are critical in understanding the mechanisms of pathogenesis. However, accessible HPI data for agriculturally important pathogens are limited. This lack of HPI data impedes network analysis to study agricultural pathogens, for preventing and reducing the severity of diseases of relevance to agriculture. ResultsTo rapidly provide HPIs for a broad range of pathogens, we use an interolog-based approach. This approach uses sequence similarity to transfer known HPIs from better studied host-pathogen pairs and predicts 389,878 HPIs for 23 host-pathogen systems of relevance to US agriculture. Each predicted HPI is qualitatively assessed using co-localization, infection related processes, and interacting domains and this information is provided as a confidence indicator for the prediction. Evaluation of predicted HPIs demonstrates that the host proteins predicted to be involved in pathogen interactions include hubs and bottlenecks in the network, as reported in curated host proteins. Moreover, we demonstrate that the use of the predicted HPIs adds value to network analysis and recapitulates known aspects of host-pathogen biology. Access to the predicted HPIs for these agricultural host-pathogen systems is available via the Host Pathogen Interaction Database (HPIDB, hpidb.igbb.msstate.edu), and can be downloaded in standard MITAB file format for subsequent network analysis. ConclusionsThis core set of interolog-based HPIs will enable animal health researchers to incorporate network analysis into their research and help identify host-pathogen interactions that may be tested and experimentally validated. Moreover, the development of a larger set of experimentally validated HPI will inform future predictions. Our approach of transferring biologically relevant HPIs based on interologs is broadly applicable to many host-microbe systems and can be extended to support network modeling of other pathogens, as well as interactions between non-pathogenic microbes.

bioinformatics↗