Mechanistic model of temperature influence on flowering through whole-plant accumulation of FT

We assessed temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences both the leaf production rate and expression of FLOWERING LOCUS T (FT), a photoperiodic flowering regulator, in leaves. The Arabidopsis Framework Model incorporated temperature influence on leaf growth but ignored the consequences of leaf growth on and direct temperature influence of FT expression. We measured FT production in differently aged leaves and modified the model, adding the mechanistic temperature influence on FT transcription, and linking FT to leaf growth. Our simulations suggest that in long days, the developmental timing (leaf number) at which the reproductive transition occurs is influenced by day length and temperature through FT, while temperature influences the rate of leaf production and the time (in days) the transition occurs. Further, we demonstrated that FT is mainly produced in the first 10 leaves in the Columbia ecotype, and that FT accumulation alone cannot explain flowering in conditions in which flowering is delayed. Our simulations supported our hypotheses that: 1) temperature regulation of FT, accumulated with leaf growth, is a component of thermal time, and 2) incorporating mechanistic temperature regulation of FT can improve model predictions in fluctuating temperatures.

. Thermal time is modified by day length, to produce instead incorporated mechanistic temperature influence on FT into the Photoperiodism module. 159 We maintained thermal time control over leaf tissue production in phase two, but modified the 160 SLA and respiration components to improve the response of leaf growth to fluctuating 161 temperatures. Then, rather than running the model in two phases, we called the Phenology and 162 Photoperiodism modules at each time step, considering their outputs FT gene expression per unit 163 of leaf tissue. We used the leaf number, age, and area outputs at each time step to determine the  (Blazquez et al., 2003;Lee et al., 173 2007Lee et al., 173 , 2013Posé et al., 2013). SVP protein levels increased shortly after exposure to cool 174 temperatures (Kinmonth-Schultz et al. 2016), as did the ratio of FLM-β to FLM-δ splice variants 175 (Posé et al., 2013). FLM-β facilitates SVP binding, and SVP and FLM-β protein levels increase 176 with decreasing temperatures (Lee et al., 2013). Both SVP and FLM-β are present at 23 C; a 177 transfer from 23 C to 27 C resulted in SVP decay that occurred within 12 h (Lee et al., 2013). 178 We used a single term to simulate the combined SVP and FLM-β behavior termed "SVP 179 activity". Consistent with the observed behavior of these proteins, we modeled SVP activity to 180 increase in response to a decrease in temperature, as shown below. SVPnew is the newly synthesized protein (nmol/h), VTSVP describes the degree SVP synthesis 184 decreases in response to a temperature increase, the intercept (a) is used to adjust the overall 185 amount of SVP synthesized, T is temperature (˚C), and t is time (t = 0 at sowing). The influence 186 of SVP may decline over time, as cool-temperature suppression of FT disappeared over a two-

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week period ( Figure S2a- In LD 22C /12C-night, FT levels are higher at dawn coinciding with higher CO mRNA and 196 protein in cool nights (Kinmonth-Schultz et al., 2016). While SVP activity may respond to 197 absolute changes in temperature (Lee et al., 2007(Lee et al., , 2013Posé et al., 2013), CO accumulation is 198 induced by rapid changes from warm to cool (Kinmonth-Schultz et al., 2016). The degree of 199 temperature change is likely a factor, as a drop of 10 ˚C (22C /12C-night) yielded more CO  To link FT transcript accumulation to leaf tissue production, the Phenology module is called at

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Here, we removed this function and considered direct FT accumulation. Determining the 297 absolute amount of FT required to induce flowering and whether there are threshold levels of 298 transcription, below and above which flowering time is unaffected, will be a useful future study. 299 We maintained the vernalization component from FM-v1.0 to maintain model flexibility, as 300 vernalization should modify overall levels of FT (Helliwell et al., 2006;Searle et al., 2006). This 301 value falls between zero and one and now modifies the levels of FT produced within the 302 Phenology model rather than modifying the thermal unit accumulation rate.  Respiration, carbon storage, or growth may be altered by temperature in ways not captured in the 322 model. In cold-tolerant woody species, respiration of stem cuttings increased near freezing, 323 rather than following the trend predicted by the Arrhenius function, as did the pool of non-324 structural carbohydrates (NSC) (Sperling et al., 2015). Respiration may also increase at more 13 moderate temperatures in cases where freezing tolerance is induced, as in Arabidopsis at 16 ˚C in 326 light with a low red/far-red ratio (Franklin & Whitelam, 2007). In chrysanthemum, cool 327 nighttime temperatures decreased leaf area while increasing dry weight, by increasing stored 328 starch (Heinsvig Kjaer et al., 2007). FM-v1.0 does not incorporate these complexities nor 329 consider sinks for carbon other than growth, such as NSCs. Therefore, to simulate the relative 330 relationships in leaf area across temperature conditions needed for our study ( Figure S6f), we 331 removed the temperature sensitivity of maintenance respiration and adjusted the Specific Leaf 332 Area (SLA, m 2 g -1 ) to decline with decreasing temperature using observations from Pyl et al.   Table 1). The full LTP+GE variant followed a trend close to that observed, increasing the final 420 leaf number for both cool-night temperature treatments and causing a stronger delay in days to  old plants moved to 12 C in LDs for two, four, or six days (12˚C-2d, -4d, or -6d), then moved to 462 warm, LD conditions. We also grew plants in these conditions. Control plants were moved 463 directly to warm, LD conditions at two weeks.

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Simulating these conditions in the full LTP+GE variant of FM-v1.5, we found little difference in 465 days to bolt between 12˚C-2d and the control and a three-day difference between 12˚C-6d and 466 the control. There was a decline in leaf number from 15 to 14 leaves in plants exposed to 12˚C-467 2d and 12˚C-4d, indicating flowering at a slightly younger developmental age that translated to 468 little difference in days to bolt between the control and 12˚C-2d. In 12˚C-6d, the leaf number 469 increased again to be like the control. In the LTP variant, the leaf number of all three treatments was the same as the control, whereas there was an increase in days to bolt for each consecutive 471 two-days at 12 C, consistent with slowed accumulation of FT due to slower leaf growth. 472 We observed slowed growth (relative to the control) in the cool-temperature treatments. Visible 473 leaf number was significantly lower after four and six days in 12 C (Figure 7a). On day seven, 474 after completion of all cool-temperature treatments, there was a gradient in leaf area across 475 treatments, with plants from 12˚C-6d being the smallest (Figure 7b, S8). We observed a 476 statistically significant delay in the number of days to visible bolt in both 12˚C-4d and12˚C-6d,  (Table 3).

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Incorporating underlying mechanisms could improve model utility for a range of conditions 484 without requiring recalibration (White, 2009;Boote et al., 2013). Here, we found that thermal increasing the degree of predicted difference between the warm-day, cool-night treatments and 490 the control.

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FT was reduced in later-produced leaves (Figure 2). This change in FT expression with 492 developmental age was incorporated into FM-v1.5 using leaf age as a proxy, and caused FT to

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Incorporating such a mechanisminfluenced by climate and developmental agemay aid 514 understanding of how climate influences flowering. As proof of concept, we caused the FT 515 threshold level to change with developmental age (thermal time) (Figure 6). Doing so improved 516 the predictive capacity of FM-v1.5 in constant, cool temperatures.

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SVP, in conjunction with FLM, suppresses FT in response to cool temperatures (Blazquez et al., 518 2003;Lee et al., 2007Lee et al., , 2013. We demonstrated that residual SVP and FLM activity after short-519 term cold exposures could be important for FT regulation. For instance, to mimic observed dusk 520 suppression of FT in warm-day, cool-night temperature cycles, simulated SVP activity decayed 521 slowly after at 12 ˚C night, such that it was higher after 16 hs at 22 ˚C, than it was in constant 22

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˚C conditions. Our model also highlights the need to clarify the degree of temperature influence 523 in FT activation and suppression at a range of temperatures. For example, in FM-v1.5, FT is not 524 induced to observed levels, and induction is not maintained as long, after dawn exposure to 17 ˚C 525 (Figure 3f). It is possible that SVP activation is lower in 17 ˚C, than predicted from our model.  Table S1: Coefficients values for equations used in FM-v1.5.  Tables   Table 1: Observed and simulated days to bolt and leaf number in Columbia-0 (Col-0) and Landsberg erecta (Ler) plants exposed to short-term drops in temperature.   Observed treatments counted significantly different from the control when P<0.05 and the confidence interval of the difference from the control does not contain zero.  , and six (c) weeks old and grown in short days were exposed to long days or short days (d) for three days, then harvested at 16 hours after dawn on the third day to determine FT amount per leaf. The colors in (d) correspond to the colors and ages in panels (a-c). FT levels were determined by absolute copy number and normalized within a replicate. The simulated proportion of FT per unit leaf tissue (cm -2 , solid lines) for each plant age is shown. This value was used in FM-v1.5 as a modifier to adjust the amount of FT produced by each leaf. Percent of the leaf area showing staining in pFT:GUS plants (e). For all, the two cotyledons and first two true leaves were pooled for each sample as they emerge in pairs. Older leaves in the six-week old plants failed to yield 2μg total RNA and were excluded. For each plant inset, asterisk indicates one of each cotyledon pair. The shading of the bar graphs (light to dark) indicates leaf age (oldest, first to emerge, to youngest) and corresponds to the shading in the plant insets. Scale bars = 0.5 cm.

Figure 7:
Growth is slowed and flowering is delayed in plants exposed to 12 ˚C for two, four, or six days, then returned to warm temperatures (24 ˚C), relative to control plants grown continuously in warm-temperatures. (a) Average leaf number of plants recorded at dawn after two, four, or six days in 24 ˚C (control) or 12 ˚C temperature conditions. (b) Relative seedling sizes on dawn of day seven, after completion of all cool-temperature treatments (scale bars = 1cm, 0 = control). Individual images cropped from the same photograph and scaled together (see original image, Figure S9). (c) Relative flowering progression three days after appearance of last