The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Abstract

Summary paragraphPredicting a multicellular organism’s phenotype quantitatively from its genotype is challenging, as genetic effects must propagate up time and length scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants1–3. Clock genes are rarely essential but appropriate alleles can confer a competitive advantage4,5 and have been repeatedly selected during crop domestication3,6. Here we quantitatively explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for growth of Arabidopsis thaliana7–9. The model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants. Altered night-time metabolism of stored starch accounted for most but not all of the decrease in whole-plant growth rate. Altered mobilisation of a secondary store of organic acids explained the remaining defect. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.

Multiple circadian clock outputs regulate diel turnover of carbon and nitrogen reserves

“…Starch accumulation was significantly slower in prr7 prr9 and elf3 than in the corresponding wild types (Table S1; P values >0.001). The result for prr7 prr9 contrasts with previous studies, where starch accumulation in prr7 prr9 resembled that in wild‐type Col‐0 (Chew et al, ). After transfer to continuous light, starch accumulation slowed or plateaued in all genotypes (Figure S3A; see also Fernandez et al, ).…”

“…The clock has a pervasive influence on starch degradation (Figures 3, S3, and S4). Our results confirm earlier reports that starch is prematurely exhausted in the short period mutant lhy cca1 (Graf et al, 2010;Scialdone et al, 2013) and incompletely mobilized at dawn in prr7 prr9 (Chew et al, 2017) and gi (Eimert, Wang, Lue, & Chen, 1995;Messerli et al, 2007;Rédei, 1962) and show that starch is incompletely mobilized in toc1 and elf3. Graf et al (2010) proposed that the circadian clock sets the rate of degradation such that starch is almost but not completely exhausted at dawn.…”

“…Thus, the slow starch accumulation in elf3 and prr7 prr9 is consistently associated with decreased or abolished ELF3 expression, whereas there is no consistent relation with PRR7 or PRR9 expression. The idea that slow starch accumulation in prr7 prr9 is a secondary effect would also explain why starch accumulation is not always slowed down in prr7 prr9 (Chew et al, ).…”

Photosynthetic Entrainment of the Circadian Clock Facilitates Plant Growth under Environmental Fluctuations: Perspectives from an Integrated Model of Phase Oscillator and Phloem Transportation

“…Numerous mathematical models on growth of various plants have been developed (e.g., Thornley and Johnson, 1990 ; Chew et al, 2014 , 2017 ; Feller et al, 2015 ; Barillot et al, 2016a , b ). Among them, two multiscale models of A. thaliana include the circadian clock sub-model and are able to quantitatively predict growth (Chew et al, 2014 , 2017 ).…”

A multi-model Framework for the Arabidopsis life cycle

“…The Framework Model (FMvl) represented vegetative growth of Arabidopsis in lab conditions (Chew et al , 2014b), starting from four independent models that represent photosynthesis and carbon storage (Rasse and Tocquin, 2006), plant structure and carbon partitioning among organs (Christophe et al , 2008), flowering phenology (Chew et al , 2012) and the circadian clock gene circuit and its output to photoperiodic flowering (Salazar et al , 2009). Later updates focussed on plant phenotypes controlled by the clock, such as tissue elongation and starch metabolism (FMv2; Chew et al , 2017), or temperature and organ-specific inputs to flowering (Kinmonth-Schultz et al , 2018). The Framework Models align with community efforts to link understanding of crop plant processes at multiple scales, for benefits in agriculture (Wu et al , 2016; Zhu et al , 2016).…”