Summary Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep‐learning approaches, we developed a Smart Model for Epigenetics in Plants (SMEP) to predict six types of epigenomic modifications: DNA 5‐methylcytosine (5mC) and N6‐methyladenosine (6mA) methylation, RNA N6‐methyladenosine (m6A) methylation, and three types of histone modification. Using the datasets from the japonica rice Nipponbare, SMEP achieved 95% prediction accuracy for 6mA, and also achieved around 80% for 5mC, m6A, and the three types of histone modification based on the 10‐fold cross‐validation. Additionally, > 95% of the 6mA peaks detected after a heat‐shock treatment were predicted. We also successfully applied the SMEP for examining epigenomic modifications in indica rice 93‐11 and even the B73 maize line. Taken together, we show that the deep‐learning‐enabled SMEP can reliably mine epigenomic datasets from diverse plants to yield actionable insights about epigenomic sites. Thus, our work opens new avenues for the application of predictive tools to facilitate functional research, and will almost certainly increase the efficiency of genome engineering efforts.
Both genetic and epigenetic information must be transferred from mother to daughter cells during cell division. The mechanisms through which information about chromatin states and epigenetic marks like histone 3 lysine 27 trimethylation (H3K27me3) are transferred have been characterized in animals; these processes are less well understood in plants. Here, based on characterization of a dwarf rice (Oryza sativa) mutant (dwarf-related wd40 protein 1, drw1) deficient for yeast CTF4 (CHROMOSOME TRANSMISSION FIDELITY PROTEIN 4), we discovered that CTF4 orthologs in plants use common cellular machinery yet accomplish divergent functional outcomes. Specifically, drw1 exhibited no flowering-related phenotypes (as in the putatively orthologous Arabidopsis thaliana eol1 mutant), but displayed cell cycle arrest and DNA damage responses. Mechanistically, we demonstrate that DRW1 sustains normal cell cycle progression by modulating the expression of cell cycle inhibitors KIP-RELATED PROTEIN 1 (KRP1) and KRP5, and show that these effects are mediated by DRW1 binding their promoters and increasing H3K27me3 levels. Thus, although CTF4 orthologs ENHANCER OF LHP1 1 (EOL1) in Arabidopsis and DRW1 in rice are both expressed uniquely in dividing cells, commonly interact with several Polycomb complex subunits, and promote H3K27me3 deposition, we now know that their regulatory functions diverged substantially during plant evolution. Moreover, our work experimentally illustrates specific targets of CTF4/EOL1/DRW1, their protein–proteininteraction partners, and their chromatin/epigenetic effects in plants.
The timing of floral transition is critical for reproductive success in flowering plants. In long-day (LD) plant Arabidopsis, the floral regulator gene FLOWERING LOCUS T (FT) is a major component of the mobile florigen. FT expression is rhythmically activated by CONSTANS (CO), and specifically accumulated at dusk of LDs. However, the underlying mechanism of adequate regulation of FT transcription in response to day-length cues to warrant flowering time still remains to be investigated. Here, we identify a homolog of human protein arginine methyltransferases 6 (HsPRMT6) in Arabidopsis, and confirm AtPRMT6 physically interacts with three positive regulators of flowering Nuclear Factors YC3 (NF-YC3), NF-YC9, and NF-YB3. Further investigations find that AtPRMT6 and its encoding protein accumulate at dusk of LDs. PRMT6-mediated H3R2me2a modification enhances the promotion of NF-YCs on FT transcription in response to inductive LD signals. Moreover, AtPRMT6 and its homologues proteins AtPRMT4a and AtPRMT4b coordinately inhibit the expression of FLOWERING LOCUS C, a suppressor of FT. Taken together, our study reveals the role of arginine methylation in photoperiodic pathway and how the PRMT6-mediating H3R2me2a system interacts with NF-CO module to dynamically control FT expression and facilitate flowering time.
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