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. 2015 Feb 26;10(2):e0116973.
doi: 10.1371/journal.pone.0116973. eCollection 2015.

A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network

Felipe Leal Valentim  1 , Simon van Mourik  2 , David Posé  3 , Min C Kim  3 , Markus Schmid  3 , Roeland C H J van Ham  1 , Marco Busscher  1 , Gabino F Sanchez-Perez  4 , Jaap Molenaar  5 , Gerco C Angenent  6 , Richard G H Immink  1 , Aalt D J van Dijk  7
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A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network

Felipe Leal Valentim et al. PLoS One. .

Abstract

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.

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Conflict of interest statement

Competing Interests: Roeland van Ham is currently employed by Keygene N.V. Upon submission he is not aware of patents or patent applications covering the content of this manuscript to which he or his current employer is involved as inventor or applicant. This statement does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Network of flowering time integrator genes.
Green indicates expression in leaf tissue, blue in meristem tissue. Red arrows represent repression, blue arrows activation. Most interactions were taken as given based on literature information, but for regulation of LFY by AGL24 and SOC1, different ways of combining the two inputs were tested (indicated by the light blue arrows). Dashed arrow represents FT transport. Junction symbol next to AP1 indicates cooperativity predicted for regulation of AP1 by LFY. As indicated, AP1 expression is used as a marker for the moment of the floral transition. This network was used to fit expression time-course data and to predict the effect of perturbations. Gene names are given in full in the text.
Fig 2
Fig 2. Experimental and simulated expression time-course of the genes in the integration network model.
Gene expression was measured by qRT-PCR (shown as dots) of wild type plants grown under long-day conditions at 23°C (average and standard deviation are shown). The continuous lines show the simulated gene expression using the parameters estimated by data fitting. Note that FLC and SVP are not regulated by other components of the network and hence are present as input factors only, and their expression level is not simulated by the model. qRT-PCR data for FT was obtained from leaves; for the other genes, qRT-PCR data was obtained from meristem enriched material.
Fig 3
Fig 3. Model predictions and experiments in various mutant backgrounds.
(A) Predicted vs. experimentally observed flowering time for mutants used in training the model (black) and for double mutants used for validation (red). Wild type flowering time is indicated in green. RL, rosette leaves: the more rosette leaves, the later flowering. (B) Prediction of expression changes; total change in expression over the simulated time-course is calculated, normalized against wild type; absolute value is reported to focus on the magnitude of the predicted expression change. Horizontal axis, mutants; vertical axis, genes for which expression change in mutant background is simulated. Note that FLC and SVP are not regulated by other genes in the model and hence, their expression level does not change upon any mutation. For comparison between predictions and experiments, see Figures C and D in S1 File.
Fig 4
Fig 4. Effect of knockout mutations (agl24, soc1 and soc1/agl24) on LFY expression and on flowering time.
(A) Number of rosette leaves counted at the onset of flowering for wild type and mutants. The plants were grown in long-day conditions at 23°C. (B-C) LFY expression in wild type and mutants from simulations (B) or microarray experiments (C). The simulations show the expression time-course over 20 days after germination; the microarray data consist of four time-points after transfer of plants grown in short-day to long-day conditions. (D) Effect of efficiency by which LFY expression is activated by AGL24 (β6) and SOC1 (β7), on predicted flowering time. Flowering time, predicted flowering time for given values of parameters. Blue boxes in heatmap indicate best-fit model parameters and the two mutants soc1 and agl24; arrows point from wild type model to mutants.
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This work was supported by a Max Planck postdoctoral fellowship to DP, an EMBO Long-Term postdoctoral fellowship to MCK, a Deutsche Forschungsgemeinschaft (DFG) grant (ERA-PG “BloomNet”, SCHM1560/7-1) to MS and the Max Planck Society, a Netherlands Organisation for Scientific Research (NWO) VENI grant (863.08.027) to ADJvD, the SYSFLO Marie Curie Initial Training Network (FLV), and by the Netherlands Consortium for Systems Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. RvH is employed by Keygene N.V. Keygene N.V. provided support in the form of salary for author RvH, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section.