Information integration and decision making in flowering time control

Abstract

In order to successfully reproduce, plants must sense changes in their environment and flower at the correct time. Many plants utilize day length and vernalization, a mechanism for verifying that winter has occurred, to determine when to flower. Our study used available temperature and day length data from different climates to provide a general understanding how this information processing of environmental signals could have evolved in plants. For climates where temperature fluctuation correlations decayed exponentially, a simple stochastic model characterizing vernalization was able to reconstruct the switch-like behavior of the core flowering regulatory genes. For these and other climates, artificial neural networks were used to predict flowering gene expression patterns. For temperate plants, long-term cold temperature and short-term day length measurements were sufficient to produce robust flowering time decisions from the neural networks. Additionally, evolutionary simulations on neural networks confirmed that the combined signal of temperature and day length achieved the highest fitness relative to neural networks with access to only one of those inputs. We suggest that winter temperature memory is a well-adapted strategy for plants' detection of seasonal changes, and absolute day length is useful for the subsequent triggering of flowering.

Fig 1. Flowering time regulation in Arabidopsis thaliana.

In Arabidopsis Thaliana, long days promote the expression of FLOWERING LOCUS T (FT). The vernalization process also promotes its expression by turning-off its repressor FLOWERING LOCUS C (FLC).

Fig 2. Idealized gene expressions.

The idealized expression levels of FLC and FT for Arabidopsis perennials in the northern hemisphere.

Fig 3. Temperature dynamics in Cologne.

The temperature dynamics consist of the seasonal changes and the daily temperature fluctuations, which were fitted by a second order Fourier series. The dynamical data were obtained by averaging 93 years of temperatures in temperate city Cologne. The first day in the plot was January 1st.

Fig 4. Reconstruct the switch behavior of FLC.

(a): The vernalization was simplified as birth-death process for actively modified cells. n stands for the number of active cells, the production rate βT(t) depends on the temperature T(t) and λ is the degradation rate. Solving the model led to the distribution p(n, t) which is parameterized by β and λ; (b): For the data from Cologne, the autocorrelation in daily temperature decays exponentially, the bootstrapping was performed by using block length of 50 days; (c): The probability p(n, t) distribution over number of active cells (in total 60 cells in the simulation) on 1st January and 15th April; (d): The switching FLC expression behavior was constructed from the probabilities of having less than 30 active cells over two years, with green areas for potential flowering seasons.

Fig 5. Three years of test temperatures.

Three years of temperatures were used to test the regression models. In the second year, one can observe a long temperature spike starts from late October to early November. This is in correspondence with a predicted local minimum in Fig 6.

Fig 6. Predicted expression patterns by neural networks.

(a): Cologne, fitting result of the idealized FLC expressions using 42 days of temperature as the input features gave a local minimum in September due to similarity between spring and autumn; (b): Cologne, fitting result from 42 days of temperature and 2 days of day lengths with eliminated local minima; (c): Kahului, fitting result from 42 days of temperatures; (d): Kahului, fitting result from 42 days of temperature and 2 days of day lengths.

Fig 7. Evolution simulation.

Distributions of group offspring proportions for mutation rates of 0, 0.05, and 0.2, for group sizes of 10, 50, and 200 individuals. “B” stands for group with access to both temperature and day length, “T” for temperature and “D” for day length.