Back

A frosty genetic screen unmasks a major regulatory role for SHORT VEGETATIVE PHASE of flowering in response to a cold snap

Edwards, A.; Thordal-Christensen, H.; Wenkel, S.

2024-03-23 plant biology
10.1101/2024.03.20.585907 bioRxiv
Show abstract

The control of flowering in plants is intricately governed by a combination of internal and environmental signals, with temperature playing a critical role. Thus, Arabidopsis thaliana plants display temperature-dependent variations in flowering time. As unexpected periods of cold temperatures can occur at any time, plants have evolved mechanisms to detect such cold snaps and to respond by delaying flowering. Plants are more tolerant to cold temperatures in the vegetative stage, while flowers are more sensitive and have reduced reproductive success due to damage to floral structures and gametes. At the molecular level, delayed flowering can be caused by repressing the FLOWERING LOCUS T (FT) gene, and several MADS box transcription factors have been shown to repress FT expression in response to cold and in this way prevent flowering. Here, we employed a forward genetic screen aimed at understanding the effect of a cold snap on the transition to flowering. We germinated a population of A. thaliana EMS M2 plants at 20{degrees}C and then gradually lowered the temperature to 10{degrees}C and selected early flowering mutants. Using whole-genome sequencing, we identified seven mutant alleles of the SHORT VEGETATIVE PHASE (SVP) gene. This finding establishes a central role for SVP in repressing flowering in response to a cold snap and provides novel alleles, several of which affect splice junctions. Our research thus presents valuable insights into the nuanced molecular mechanisms governing temperature-responsive flowering in Arabidopsis and sheds light on the dynamic interplay between SVP and environmental cues.

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.