Conserved gene- and network-level thermal memory intervals in two divergent perennial crucifers in nature
Endo, Y.; Nishio, H.; Oguchi, T.; Yamane, K.; Eseese, V. F.; Frederica, C. F.; Kudoh, H.; Buzas, D. M. M.
Show abstract
Some biological responses persist long after the initial stimulus has disappeared--a phenomenon known as cellular memory. In its long-term form, cellular memory often reflects interactions between cis-acting chromatin states and diffusible trans-acting regulators, which are experimentally difficult to separate in vivo. A key challenge is to develop a quantitative framework that captures cellular memory length even without prior mechanistic knowledge. The FLOWERING LOCUS C (FLC) gene illustrates this problem and opportunity: a Polycomb/Trithorax cis-acting chromatin switch at FLC produces bistable ON/OFF transcriptional states, while trans-acting factors such as VIN3 and FT modulate transitions between those states. In laboratory studies, FLC memory is classically viewed as persistence of repression or activation after the inducing signal disappears; field studies additionally show that FLC integrates fluctuating temperatures over past intervals, revealing time-integrative mode of memory. To quantify such long-term effects systematically, we formalized the thermal memory interval (TMI), the time window of past environmental cues that best predicts current gene expression--as a transferable metric. We applied TMI to the VIN3-FLC-FT module in two perennial Brassicaceae with divergent life histories: Arabidopsis halleri subsp. gemmifera, an established ecological model, and Eutrema japonicum, introduced here to identify features generalizable beyond a single species. Using long-term expression and meteorological data, TMIs distinguished spring versus autumn FLC states and revealed distributed memory across the VIN3-FLC-FT network, with intervals from 1-150 days, extending previously reported FLC timescales. While TMIs require dense time-series data and do not by themselves reveal molecular mechanism, they offer a robust, quantitative, and generalizable framework: TMIs can be extended to other genes and to alternative environmental or physiological variables, enabling direct, comparative quantification of cellular memory across genes, species, and contexts.
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