No abstract

For nearly 100 years, we have known that both growth and flowering in plants are seasonally regulated by the length of the day (photoperiod). Intense research focus and powerful genetic tools have propelled studies of photoperiodic flowering, but far less is known about photoperiodic growth, in part because tools were lacking. Here, using a new genetic tool that visually reports on photoperiodic growth, we identified a seasonal growth regulation pathway, from photoperiod detection to gene expression. Surprisingly, this pathway functions in long days but is distinct from the canonical long day photoperiod flowering mechanism. This is possible because the two mechanisms detect the photoperiod in different ways: flowering relies on measuring photoperiod by directly detecting duration of light intensity while the identified growth pathway relies on measuring photosynthetic period indirectly by detecting the duration of photosynthetic metabolite production. In turn, the two pathways then control expression of genes required for flowering or growth independently. Finally, our tools allow us to show that these two types of photoperiods, and their measurement systems, are dissociable. Our results constitute a new view of seasonal timekeeping in plants by showing that two parallel mechanisms measure different photoperiods to control plant growth and flowering, allowing these processes to be coordinated independently across seasons.

Photoperiod is an annual cue measured by biological systems to align growth and reproduction with the seasons. In plants, photoperiodic flowering has been intensively studied for over 100 years, but we lack a complete picture of the transcriptional networks and cellular processes that are photoperiodic. We performed a transcriptomics experiment on Arabidopsis plants grown in 3 different photoperiods, and find that nearly one-third of the known genes show photoperiodic alteration in gene expression. Gene clustering, daily expression integral calculations and cis-element analysis then separate photoperiodic genes into co-expression subgroups that display 19 diverse seasonal expression patterns, opening the possibility that many photoperiod measurement systems work in parallel in Arabidopsis. Then, functional enrichment analysis predicts co-expression of important cellular pathways. To test these predictions, we generated a comprehensive catalog of genes in the phenylpropanoid biosynthesis pathway, overlaid gene expression data and demonstrated that photoperiod intersects with the two major phenylpropanoid pathways differentially, controlling flavonoids but not lignin. Finally, we describe the development of a new app that visualizes photoperiod transcriptomic data for the wider community.

No abstract