Back

Gibberellin Signaling Through Rga Suppresses Gcn5 Effect On Stamen Elongation Of Arabidopsis Flowers

Balouri, C.; Poulios, S.; Tsompani, D.; Spyropoulou, Z.; Ketikoglou, M.-C.; Kaldis, A.; Doonan, J. H.; Vlachonasios, K.

2024-05-03 plant biology
10.1101/2024.04.30.591935 bioRxiv
Show abstract

Histone acetyltransferases (HAT) modify the amino-terminal tails of the core histone proteins via acetylation, regulating chromatin structure and transcription. The GENERAL CONTROL NON-DEREPRESSIBLE 5 (GCN5) is a HAT that specifically acetylates H3K14 residues. GCN5 has been associated with cell division and differentiation, meristem function, root, stem, foliar and floral development, and plant environmental response. The flowers of gcn5-6 plants display reduced length of stamen and exhibit male sterility relative to the wild-type plants. We show these effects may arise from gibberellin (GA) signaling defects. The signaling pathway of bioactive GAs depends on the proteolysis of their repressors, DELLA proteins. The DELLA protein, REPRESSOR OF GA (RGA), represses plant growth, inflorescence, flower and seed development. Our molecular data indicate that GCN5 is required for activation and H3K14 acetylation of genes involved in the late stages of GA biosynthesis and catabolism. We studied the genetic interaction of RGA and GCN5; RGA can partially suppress GCN5 action. The reduced elongation of the stamen filament of gcn5-6 mutants is reversed in the rga-t2;gcn5-6 double mutants. This mechanism involved suppressing the GCN5 effect on the expression and histone acetylation in GAI-locus by RGA. Interestingly, RGA and RGL2 do not suppress ADA2b function, suggesting that ADA2b acts downstream in GA signaling and is distinct from GCN5 activity. In conclusion, we propose that the action of GCN5 on stamen elongation is mediated partially by RGA and GA signaling.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.