bioRxivPlant BiologyPreprint
Defining chromatin state transitions predicts a network that modulates cell wall remodeling in phosphate-starved rice shoots
M. Foroozani, S. Zahraeifard, D.-H. Oh, G. Wang, M. Dassanayake, A. P. Smith
Abstract
Phosphorus (P) is an essential plant macronutrient vital to fundamental metabolic processes. Plant-available P is low in most soils, making it a frequent limiter of growth. Declining P reserves for fertilizer production exasperates this agricultural challenge. Plants modulate complex responses to fluctuating P levels via global transcriptional regulatory networks. Although chromatin structure plays a substantial role in controlling gene expression, the chromatin dynamics involved in regulating P homeostasis have not been determined. Here we define distinct chromatin states across the rice genome by integrating multiple aspects of chromatin structure, including the H2A.Z histone variant, H3K4me3 modification, and nucleosome positioning. In response to P starvation, 40% of all protein-coding genes exhibit a transition from one chromatin state to another at their transcription start site. Several of these transitions are enriched in subsets of genes differentially expressed by P deficiency. The most prominent subset supports the presence of a coordinated signaling network that targets cell wall structure and is regulated in part via a decrease of H3K4me3 at the transcription start site. The P-starvation induced chromatin dynamics and correlated genes identified here will aid in enhancing P-use efficiency in crop plants, benefitting global agriculture.\n\nOne sentence summaryCombining data for three components of chromatin structure from control and phosphate-starved rice shoots reveals specific chromatin state transitions that correlate with subsets of functionally distinct differentially-expressed genes.
- organism: plant (other)
- assay: DNA methylation
- assay: ChIP-seq
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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