Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu 520-2113, Japan.
Proc Natl Acad Sci U S A. 2010 Jun 22;107(25):11632-7. doi: 10.1073/pnas.0914293107. Epub 2010 Jun 7.
Plants flower in particular seasons even in natural, fluctuating environments. The molecular basis of temperature-dependent flowering-time regulation has been extensively studied, but little is known about how gene expression is controlled in natural environments. Without a memory of past temperatures, it would be difficult for plants to detect seasons in natural, noisy environments because temperature changes occurring within a few weeks are often inconsistent with seasonal trends. Our 2-y census of the expression of a temperature-dependent flowering-time gene, AhgFLC, in a natural population of perennial Arabidopsis halleri revealed that the regulatory system of this flowering-time gene extracts seasonal cues as if it memorizes temperatures over the past 6 wk. Time-series analysis revealed that as much as 83% of the variation in the AhgFLC expression is explained solely by the temperature for the previous 6 wk, but not by the temperatures over shorter or longer periods. The accuracy of our model in predicting the gene expression pattern under contrasting temperature regimes in the transplant experiments indicates that such modeling incorporating the molecular bases of flowering-time regulation will contribute to predicting plant responses to future climate changes.
即使在自然波动的环境中,植物也会在特定的季节开花。温度依赖性开花时间调控的分子基础已被广泛研究,但对于基因表达如何在自然环境中受到控制知之甚少。如果植物没有对过去温度的记忆,那么在自然嘈杂的环境中检测季节将非常困难,因为在几周内发生的温度变化通常与季节性趋势不一致。我们对自然生境中多年生拟南芥 AhgFLC 这一与温度相关的开花时间基因的表达进行了为期两年的普查,结果表明,该开花时间基因的调控系统能够提取季节线索,就好像它记住了过去 6 周的温度一样。时间序列分析表明,AhgFLC 表达的变化中,有多达 83%仅可以通过前 6 周的温度来解释,而与较短或较长时期的温度无关。我们的模型在移植实验中预测对比温度条件下基因表达模式的准确性表明,这种结合开花时间调控分子基础的建模方法将有助于预测植物对未来气候变化的反应。