Repressive chromatin modification underpins the long-term expression trend of a perennial flowering gene in nature

Nishio, Haruki; Buzas, Diana M.; Nagano, Atsushi J.; Iwayama, Koji; Ushio, Masayuki; Kudoh, Hiroshi

doi:10.1038/s41467-020-15896-4

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Published: 01 May 2020

Repressive chromatin modification underpins the long-term expression trend of a perennial flowering gene in nature

Nature Communications volume 11, Article number: 2065 (2020) Cite this article

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Abstract

Natural environments require organisms to possess robust mechanisms allowing responses to seasonal trends. In Arabidopsis halleri, the flowering regulator AhgFLC shows upregulation and downregulation phases along with long-term past temperature, but the underlying machinery remains elusive. Here, we investigate the seasonal dynamics of histone modifications, H3K27me3 and H3K4me3, at AhgFLC in a natural population. Our advanced modelling and transplant experiments reveal that H3K27me3-mediated chromatin regulation at AhgFLC provides two essential properties. One is the ability to respond to the long-term temperature trends via bidirectional interactions between H3K27me3 and H3K4me3; the other is the ratchet-like character of the AhgFLC system, i.e. reversible in the entire perennial life cycle but irreversible during the upregulation phase. Furthermore, we show that the long-term temperature trends are locally indexed at AhgFLC in the form of histone modifications. Our study provides a more comprehensive understanding of H3K27me3 function at AhgFLC in a complex natural environment.

Seasonal plasticity and diel stability of H3K27me3 in natural fluctuating environments

Article 31 August 2020

Cold-induced Arabidopsis FRIGIDA nuclear condensates for FLC repression

Article Open access 03 November 2021

Duration of cold exposure defines the rate of reactivation of a perennial FLC orthologue via H3K27me3 accumulation

Article Open access 29 September 2020

Introduction

Organisms precisely operate seasonal biological programs even when short-term environmental fluctuations are intercalated into the long-term change^1,2,3,4. Plants are suitable and ideal systems to study such programs. Not only are most plants long-lived (i.e. perennial) but also, unlike animals, plants are sessile and are composed of repeating, readily replaceable, units. Precision in timing the reproductive transition is critical in plants and it is potentiated by the ubiquitous flowering repressor FLOWERING LOCUS C (FLC) in Brassicaceae^5,6,7,8,9. We previously showed that the flowering repressor FLOWERING LOCUS C (FLC) orthologue in perennial Arabidopsis halleri subsp. gemmifera (hereafter AhgFLC) responds to low temperatures of winter and controls perennial phenology — the AhgFLC expression pattern in a natural habitat is seasonal and can be explained by the past 6-week temperature¹⁰. Furthermore, temperatures greatly fluctuate in the natural environment of A. halleri, and often drop to the vernalisation-effective range (estimated to be less than 10.5 °C in A. halleri¹⁰; 0–16 °C in A. thaliana^3,11,12) in both spring and autumn¹. However, AhgFLC is reactivated in spring as if it is insensitive to the cold, although it is repressed in autumn¹⁰. Thus, we speculated that unexplored properties of AhgFLC regulation would permit the identification of long-term trends after short-term fluctuations were filtered out, allowing spring and autumn to be distinguished and preventing springtime AhgFLC repression.

Arabidopsis thaliana FLC (hereafter AtFLC) undergoes epigenetic silencing during vernalisation — a process in which plants become competent to flower after experiencing a period of prolonged cold of winter — in its life cycle as an annual plant. Prior to experiencing the cold, the transcription start site (TSS) of AtFLC is marked by active histone modifications such as histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 36 trimethylation (H3K36me3), and the gene is actively transcribed^13,14. In response to cold, AtFLC is repressed by at least two temperature-sensitive mechanisms in its regulatory network. In an early phase of cold exposure, the long non-coding antisense transcripts, AtCOOLAIR, are transcribed immediately downstream of the poly-A site of the AtFLC sense transcript, and AtFLC silencing is induced^15,16,17; but see Helliwell et al.¹⁸. The H3K4me3 peak at the TSS of AtCOOLAIR correlates with the AtCOOLAIR transcription peak¹⁴. Cold exposure also induces the expression of VERNALIZATION INSENSITIVE 3 (VIN3), the product of which contains a plant homeodomain (PHD)¹⁹. AtVIN3 and other PHD proteins associate with the core polycomb repressive complex 2 (PRC2) to form the PHD − PRC2 complex at the first intron of AtFLC²⁰. The accumulation of PHD−PRC2 results in the nucleation of the repressive histone H3 lysine 27 trimethylation (H3K27me3) at a region close to the AtFLC TSS^{13,14,20,21,22,23}. These repression processes are likely to be shared among species with annual and perennial life cycles within Brassicaceae^8,24,25.

Distinct transcriptional outcomes at the FLC locus between annual and perennial plants occur on return to the warm after a prolonged cold period. In A. thaliana, H3K27me3 spreads over the gene body of AtFLC^14,20,22 in a DNA replication-dependent manner^13,23, and the silencing is maintained to allow the plants to flower under favourable long-day conditions⁶. The silencing of AtFLC lasts during the rest of the life cycle — flowering and fruiting — in annual A. thaliana⁶. In contrast, the silencing of FLC orthologues is transient in plants with perennial life cycles^{8,10,24,25,26}. For example, the cold-induced repression of PEP1, an FLC orthologue in perennial Arabis alpina, and H3K27me3 accumulation at the locus are reset after return to the warm⁸. However, the detailed dynamics of H3K27me3 at the perennial FLC orthologues in both the upregulation and downregulation phases and the involvement of H3K27me3 in the gene regulation have remained elusive. We hypothesized that H3K27me3 at AhgFLC would provide a molecular basis for robust gene regulation.

A combination of time-series data and mathematical modelling has been successfully used to elucidate properties of the molecular mechanisms underlying the vernalisation process^{3,4,10,22,27,28,29}. In this study, we combine high-frequency molecular phenology data and advanced modelling approaches to elucidate how H3K27me3 is involved in the robust seasonal regulation of AhgFLC. We measure the biweekly seasonal dynamics of AhgFLC H3K27me3 and H3K4me3 levels over 2 years in a natural population of A. halleri in Hyogo, Japan, using a chromatin immunoprecipitation (ChIP) protocol optimised for field samples³⁰. One remarkable advantage of the long-term time-series data obtained in the natural plant population is that it can capture both the upregulation and downregulation phases of AhgFLC expression. The other advantage is that it can capture AhgFLC dynamics in the context of complex natural environments where little is known regarding gene functions^{2,10,27,31,32,33}. The data sets provide a unique opportunity to apply a nonlinear time series analysis to elucidate the dynamics of chromatin modifications in a natural environment — we investigate the causality between AhgFLC histone modifications and expression via empirical dynamic modelling (EDM). We also examine the function of H3K27me3 in the seasonal regulation of AhgFLC using mathematical modelling. These approaches allow us to circumvent the absence of manipulative experimental systems and to determine how histone modifications at different regions in the same locus interact with each other to finally produce the long-term expression trend. Combined with transplant experiments, we find that H3K27me3 at the posterior region of the AhgFLC locus may contribute to the ratchet-like character of the AhgFLC system — reversible during the entire perennial life cycle but irreversible during the upregulation phase in spring.

Results

Seasonal dynamics of AhgFLC mRNA and histone modifications

We quantitatively profiled AhgFLC steady-state mRNA, H3K4me3, and H3K27me3 levels across the entire locus at high temporal resolution over 2 years (Fig. 1a–f). We classified the AhgFLC locus into three regions based on previous reports of A.thaliana^14,22,23,28, namely the nucleation region, the linker region, and the distal nucleation region (Fig. 1a). The nucleation region is known as the region registering quantitative increase in H3K27me3 with cold periods^13,14,22. The distal nucleation region corresponds to the promoter and TSS regions of the antisense AtCOOLAIR transcripts, and the transient enrichment of H3K4me3 in this region correlates with the expression of AtCOOLAIR¹⁴. We confirmed the presence of COOLAIR transcripts in A. halleri (AhgCOOLAIR; Supplementary Fig. 1). We designated the region between the two nucleation regions as the linker region where transient H3K27me3 accumulation occurs, but H3K4me3 is absent¹⁴. We designed amplicons I and II in the nucleation region, amplicons III–V in the linker region, and amplicons VI–VIII in the distal nucleation region (Fig. 1a).

**Fig. 1: **Seasonal dynamics of** ***AhgFLC*** **mRNA and histone modification levels for 2 years in the natural habitat**.**

The seasonal dynamics of H3K4me3 at the nucleation region was similar to that of mRNA (Fig. 1b, c). At the distal nucleation region, H3K4me3 accumulated only during the decreasing phase of mRNA (November–March, Fig. 1d), which might be consistent with the putative role of AhgCOOLAIR in antagonising the AhgFLC sense transcription. The H3K27me3 levels at the nucleation region increased gradually from November to January–February and reached a plateau that lasted until March (Fig. 1e). The more distal a chromatin fragment was from the nucleation region, the longer was the delay in the increase of H3K27me3 levels (Fig. 1e, f). Especially, there was a prominent delay in the H3K27me3 accumulation between amplicons II and III. Therefore, we increased the ChIP resolution in this region during the critical period from November 2013 to March 2014 to reveal how H3K27me3 spreads within this region (amplicons A–D; Supplementary Fig. 2). The H3K4me3 level became lower toward amplicon III (Supplementary Fig. 2b, right). The first delay in the increase of H3K27me3 (Supplementary Fig. 2b, left) also lies within this region. Thus, while H3K27me3 spreads linearly, the speed of the spreading decreases in the vicinity of amplicon III, where the border of the H3K4me3 region lies. This feature was not captured in A. thaliana studies, where the features of the fast H3K27me3 spreading might be masked during swift shift from cold to warm temperatures^14,22,23. In contrast to this gradual transmission of H3K27me3 from the nucleation region to the distal nucleation region in the H3K27me3 accumulation phase, the entire AhgFLC locus experienced a more synchronised decrease in the H3K27me3 levels, both temporally and quantitatively (Fig. 1e, f). We confirmed that these seasonal patterns were reproducible with a second set of validated³⁰ reference genes (Supplementary Fig. 3). Consistent with the function of AhgFLC as a floral repressor, bolting, which is a phenological stage representing the initiation of reproduction, started immediately after AghFLC mRNA reached a minimum value (February–March) and was followed by flowering. Reversion to vegetative growth started in May after the complete activation of AhgFLC (Fig. 1g).

When we plotted the H3K4me3 levels against the distance from TSS along the AhgFLC locus, H3K4me3 at the nucleation region decreased from November to March and increased from March to July; H3K4me3 at the distal nucleation region was present from November to March and absent in other months (Fig. 1h and Supplementary Fig. 4a). For H3K27me3 levels, the nucleation near the TSS and spreading to the rest of the locus occurred from November to March, and the synchronised decrease across the entire locus occurred from March to July (Fig. 1i and Supplementary Fig. 4b). In conclusion, each of the three AhgFLC chromatin regions registers a specific pattern through the perennial phenology during the seasonal progression.

Dependency of mRNA and histone modifications on temperature

To determine the period of past temperature on which AhgFLC mRNA, H3K4me3, and H3K27me3 levels depend, we performed linear regression analyses against the simple moving averages (SMAs) of the daily mean temperature calculated for different window lengths (Fig. 2 and Supplementary Fig. 5). AhgFLC mRNA level was best explained by the temperature SMAs for the past 48 days, which is similar to the previously reported memory period of 6 weeks¹⁰ (Fig. 2a, b). AhgFLC H3K4me3 levels at the enriched amplicons were best explained by the temperature SMAs for the past 44 days at amplicons I and II — which is close to the past-temperature period for mRNA — and for the previous day at amplicons VI, VII and VIII (Fig. 2c). Thus, the past-temperature period for H3K4me3 was considerably shorter at the distal nucleation region than at the nucleation region, suggesting that these regions might possess different thermosensors. This result allowed us to predict that AhgCOOLAIR would respond to past temperature for shorter period than the sense AhgFLC transcript (also see the results of EDM). At all amplicons, the past-temperature periods for H3K27me3 were longer than those for H3K4me3 and mRNA (Fig. 2b–d). AhgFLC H3K27me3 levels were best explained by the temperature SMAs for the past 66, 67, 116, 128, 143, 148, 143 and 140 days at amplicon I, II, III, IV, V, VI, VII and VIII, respectively (Fig. 2d). Therefore, the past-temperature period for H3K27me3 positively depended on the distance from TSS (Fig. 2e). We performed the SMA analyses for the daily maximum and minimum temperatures and obtained similar results to those of the daily mean temperature (Supplementary Fig. 5b–g). In conclusion, the nucleation region and the distal nucleation region respond to different lengths of past temperature, as if they represent distinct functional units.

**Fig. 2: **Linear regression of** ***AhgFLC*** **mRNA and histone modification levels on the simple moving averages of past temperature**.**

Interrelation between AhgFLC mRNA and histone modifications

To infer how AhgFLC mRNA, H3K4me3 and H3K27me3 dynamics might interrelate, we performed two analyses. First, we used Lissajous curves, which are time-series trajectories of two sets of cyclical values delineated by plotting one on the horizontal axis and the other on the vertical axis³⁴ (Supplementary Fig. 6), to determine the temporal sequences of events. The shapes and trajectory directions of Lissajous curves represent the degree and direction of the phase differences between the two values, respectively³⁴. We found that the Lissajous curves between H3K4me3 at amplicon I and H3K27me3 at all amplicons were elliptical, and the time-series trajectories of the Lissajous curves rotated clockwise, representing delayed H3K27me3 dynamics relative to H3K4me3 dynamics (Fig. 3 and Supplementary Fig. 7). The ellipses expanded at amplicons III–VIII relative to those at amplicons I and II, indicating a longer delay for H3K27me3 (Fig. 3 and Supplementary Fig. 7). The Lissajous curves between H3K27me3 and mRNA were elliptical and similar to those between H3K27me3 and H3K4me3 (Supplementary Fig. 8). The trajectories of the Lissajous curves between H3K4me3 and mRNA overlapped during their upward and downward phases at amplicons I and II, suggesting little phase difference in their seasonal dynamics (Supplementary Fig. 9). In contrast, H3K4me3 and mRNA exhibited elliptical shapes at amplicons VI–VIII, and the time-series trajectories of the Lissajous curves rotated anticlockwise, representing the phase advances of H3K4me3 at these amplicons relative to mRNA (Supplementary Fig. 9).

**Fig. 3: **Phase differences between the seasonal dynamics of** ***AhgFLC*** H3K27me3 and H3K4me3 for the first year measurement.**

Next, to investigate the causality between AhgFLC mRNA, H3K4me3 and H3K27me3, we performed an EDM causality test called convergent cross-mapping (CCM)^35,36. First, we determined the optimal embedding dimensions by using simplex projection^35,37 (Supplementary Fig. 10), and then applied CCM to determine the causality between the variables (Fig. 4 and Supplementary Figs. 11, 12). The results of CCM showed that the best cross-map skill (forecasting accuracy measured using correlation coefficients, ρ, which can be an index of causality strength; see Methods for more details)^35,38 between H3K27me3 and H3K4me3 at amplicon I occurred at a lag of two weeks (i.e. the parameter tp = −1 in the EDM function) for both directions (Fig. 4a). The cross-map skills at the time lag were significantly higher than those of seasonal-surrogate time series for both directions (Fig. 4a and Supplementary Fig. 11a). These results suggest that the bidirectional causal interactions between H3K27me3 and H3K4me3 at amplicon I occur with the time delay of approximately two weeks. The same was true for the cross-mappings between H3K27me3 and mRNA (Supplementary Figs. 11b, 12b). In addition, we detected a unidirectional causality from H3K27me3 at amplicon II to that at amplicon III with a time delay of approximately four weeks (i.e. tp = −2; Fig. 4b and Supplementary Fig. 11c), implying that the backward propagation of the modification from amplicon III to II might be retarded.

**Fig. 4: **Empirical dynamic modelling of** ***AhgFLC*** **histone modification and mRNA levels**.**

At the distal nucleation region, the CCM results indicated causality between H3K27me3 and H3K4me3, from H3K27me3 to H3K4me3 at amplicon VII, and from H3K4me3 to H3K27me3 at amplicons VI and VIII (Fig. 4c and Supplementary Figs. 11a, 12a). We expected the causality from H3K27me3 to H3K4me3 at amplicon VII to be direct because no time delay was present (tp = 0). In contrast, the very large time delays in the causality from H3K4me3 to H3K27me3 at amplicons VI and VIII (tp = −4 for amplicon VI, tp = −9 for amplicon VIII) might predict the indirect causality. In addition, a causal link of H3K4me3 between the nucleation region (amplicons I and II) and the distal nucleation region (amplicons VI–VIII) was detected (Fig. 4d and Supplementary Figs. 11d, 12d). These results suggest that the two nucleation regions represent distinct regulatory units where H3K27me3 and H3K4me3 interact and that these units are causally linked via the H3K4me3 interaction.

We illustrated the entire causal network of mRNA, H3K4me3, and H3K27me3 based on the CCM results (Fig. 4e). The H3K27me3 interactions between adjacent amplicons were consistent with the previously assumed linear propagation of histone modifications between adjacent nucleosomes³⁹ and did not contradict the chromatin looping-driven spreading model^40,41.

Next, we detected causality from temperature to each of mRNA, H3K4me3, and H3K27me3, indicating that temperature affects these variables either directly or indirectly (Supplementary Fig. 13). Consistent with the results of the linear regression analyses against the temperature SMAs (Fig. 2c), we detected a direct causality (tp = 0) from temperature to H3K4me3 at the distal nucleation region (Supplementary Fig. 13d, g, h). Taking into consideration the results of the Lissajous curve analyses, change in the H3K4me3 level at the distal nucleation region would be the first event driven by an external driver (i.e. temperature) in the seasonal AhgFLC regulation. Given that we quantified the histone modification levels using pooled samples from multiple individuals (see Methods for more details), the common environmental driver would cause a coupled response between multiple plant individuals within the population.

Function of the H3K27me3 and H3K4me3 interaction

To explore the function of H3K27me3 at AhgFLC in a natural environment, we constructed a mathematical model predicting the H3K27me3, H3K4me3 and mRNA dynamics at the tissue level from natural temperatures. We modelled H3K27me3 and H3K4me3 at the two nucleation regions because these regions are the distinct regulatory units where H3K27me3 and H3K4me3 interact (Fig. 4 and Supplementary Figs. 11, 12). We described the H3K27me3 states using combinations of unmodified (U) and modified (M) states (U_NU_D, M_NU_D, M_NM_D and U_NM_D; the first letter with a subscript (N) indicates the state of the nucleation region, while the second letter with a subscript (D) indicates the state of the distal nucleation region). The probabilities that an AhgFLC locus in a single cell is in these four states were designated as u_Nu_D, m_Nu_D, m_Nm_D and u_Nm_D, respectively (u_Nu_D + m_Nu_D + m_Nm_D + u_Nm_D = 1), which can be considered as the proportion of cells in these states in a tissue composed of a large number of cells. We derived differential equation models from stochastic models (Supplementary Note 1) without violating the assumption that FLC is regulated in cis^17,42. For ease of derivation of differential equation models, we assumed that the H3K27me3 state, the H3K4me3 state at the nucleation region, and the H3K4me3 state at the distal nucleation region are stochastically independent (see the Methods section and Supplementary Note 1 for the evaluation of this assumption). We included the U_NM_D state because we observed that H3K27me3 at the nucleation region began to decrease slightly (1–2 time points) earlier than that at the other regions, which was especially clear in the measurements conducted during the second year (Supplementary Fig. 7). Based on the seasonal dynamics of H3K27me3 at AhgFLC (Fig. 1e, f), we assumed unidirectional circular transitions between the four states: U_NU_D → M_NU_D → M_NM_D → U_NM_D → U_NU_D (Fig. 5a). We also assumed that the U_NU_D → M_NU_D and M_NU_D → M_NM_D transitions are induced by cold and warm temperatures, respectively, in accordance with the vernalisation process in A. thaliana^13,14,22,23. We described the H3K4me3 states at the two nucleation regions separately by unmodified (U) and modified (A) states because different sets of thermosensors were assumed to be present between these regions (Fig. 2c). We assigned U_N and A_N for the nucleation region [the proportions of cells: u_N and a_N, respectively (u_N + a_N = 1)], and U_D and A_D for the distal nucleation region [the proportions of cells: u_D and a_D, respectively (u_D + a_D = 1); Fig. 5a]. Based on the results of CCM (Fig. 4) and previous models^22,28,43,44, we assumed three feedback regulations between H3K27me3 and H3K4me3 [a_N, (m_Nu_D + m_Nm_D), and (m_Nm_D + u_Nm_D); red letters in Fig. 5a]. AhgFLC mRNA level was modelled by linear regression with the H3K4me3 level at the nucleation region (Fig. 5a). We compared the full model (model 1 with all three feedbacks) and the models without each of the three feedbacks (model 2, 3 and 4, respectively; Fig. 5a).

**Fig. 5: **Mathematical modelling of** ***AhgFLC*** **histone modification and mRNA levels**.**

In model 1, the simulated AhgFLC H3K27me3 levels agreed well with the observed data (Fig. 5b), indicating that our simple model explains the major dynamics over multiple weeks. During the cold season (November–February), the M_NU_D state was predominant, and M_NM_D showed a slower increase depending on the M_NU_D increase and intermittent warm temperatures (Fig. 5c) — temperature was occasionally higher than 10 °C even in the middle of winter (Supplementary Fig. 3a). In response to the temperature increase after March, M_NM_D became predominant and transitioned swiftly into U_NU_D, without U_NM_D ever reaching a high level (Fig. 5c), indicating that H3K27me3 is removed almost simultaneously at the two nucleation regions. In model 1, the simulated AhgFLC H3K4me3 levels agreed well with the observed data (Fig. 5d).

Next, we evaluated how the mutual regulation of H3K27me3 and H3K4me3 affects the seasonal AhgFLC mRNA dynamics by comparing the full model (model 1) with the other three models. First, model 1 was compared with model 2, which lacks the feedback effect of H3K4me3 on the transition from M_NM_D to U_NM_D, assuming that the transition occurs at a constant rate and replacing ‘κ a_N’ with ‘κ’ (Fig. 5a). Model 1 explained the long-term mRNA dynamics in the natural environment (Fig. 5e). However, model 2 failed to recapitulate the mRNA dynamics during autumn−spring, and an earlier decrease during December−January and an earlier increase during February−March were observed (Fig. 5e). In the model without the feedback from H3K27me3 to H3K4me3 at the nucleation region (model 3) and the distal nucleation region (model 4), the mRNA dynamics were similar to that in model 1 (Supplementary Fig. 14). These results suggested that the feedback from H3K4me3 to H3K27me3 at the nucleation region is required for AhgFLC mRNA to respond to long-term temperature trends.

Ratchet function of H3K27me3 at the distal nucleation region

Next, we investigated the mechanism required to distinguish spring from autumn despite the similarity of temperature profiles. To reveal whether AhgFLC transcription is insensitive to cold in spring, we transferred naturally growing plants to a constant 4 °C chamber during the springtime upregulation of the gene. In three-time transplantations in March and April, the mRNA level did not decrease over a period of 48 days, indicating that AhgFLC is insensitive to the cold (Fig. 6a). The entire AhgFLC locus was covered by H3K27me3 in spring (Fig. 1e, f), and a direct causality from H3K27me3 to H3K4me3 was detected at amplicon VII (Fig. 4c). Thus, we speculated that H3K27me3 at amplicon VII maintains the low level of H3K4me3 at amplicon VII (thus the repressed state of the antisense transcription) to prevent unseasonal downregulation of AhgFLC. In support of this idea, in the March transfer when the locus was fully covered by H3K27me3 (Fig. 6b, c), the H3K4me3 levels at the nucleation region (amplicons I and II) and the distal nucleation region (amplicon VII) remained steady (Fig. 6d). In contrast, in plants from the July transfer when the locus was devoid of H3K27me3 (Fig. 6e), the mRNA level decreased, indicating that AhgFLC eventually recovered the sensitivity to long-term cold (Fig. 6a). During this response, the H3K4me3 level at the nucleation region did not show a clear decrease, but that at the distal nucleation region (around TSS of antisense transcript, AhgCOOLAIR) increased (Fig. 6f). Interestingly, AhgFLC mRNA level increased for the first 3–4 days after the transfer to the cold chamber on 26 March and 10 April, and for the first 12 days after the cold transfer on 24 April and 3 July (Fig. 6a). This indicates that the AhgFLC locus retains the memory of warm temperatures for a period correlating with the amount of warm that plants have experienced. When plants were transferred to a 24 °C chamber in March for the control treatment, AhgFLC mRNA level gradually increased in a similar manner to that under the natural condition (Fig. 6a). During this transfer to the warm, the H3K27me3 levels decreased at all the amplicons, and the H3K4me3 level increased at the nucleation region (Fig. 6g, h), which was similar to the dynamics observed in the natural population (Fig. 1c–f). Taken together, these results suggest that H3K27me3 stabilises the H3K4me3 level at the distal nucleation region, and prevents unseasonal downregulation of AhgFLC in spring despite the presence of temperature fluctuations.

**Fig. 6: **Lack of** ***AhgFLC*** **cold responses in spring and the role of H3K27me3 at the distal nucleation region**.**

Discussion

The sessileness and perenniality of our study species enabled us to obtain high-frequency molecular phenology data for gene expression and histone modifications in a natural environment. By integrating the data, advanced modelling approaches, and manipulative experiments, we showed that H3K27me3-mediated chromatin regulation at AhgFLC provides two properties that are required for robust gene regulation in a fluctuating natural environment.

The first property is the ability to filter out short-term temperature fluctuations by the feedback regulation from H3K4me3 to H3K27me3 at the nucleation region. The seasonal dynamics of AhgFLC H3K27me3 were delayed relative to those of mRNA and H3K4me3, and the CCM results indicated a causality from mRNA and H3K4me3 to H3K27me3 in addition to the typical consequence of H3K27me3 on transcriptional repression. Thus, the regulation of H3K27me3 depends on transcription at an endogenous FLC locus, consistent with the finding that experimental manipulations of transcription triggered changes in the H3K27me3 level at the AtFLC transgene⁴⁵. Our mathematical modelling showed that the feedback regulation from H3K4me3 to H3K27me3 allows AhgFLC expression to follow the long-term temperature trends. Recent studies have shown that multiple thermosensors are distributed in the regulatory unit of AtFLC as well as that of AtVIN3 (refs. ^3,4). Our model that captured AhgFLC dynamics also had four temperature inputs. In this study, we analysed H3K4me3 as an active modification at the AhgFLC locus to model dynamics at the two nucleation regions. Given that H3K36me3 has been reported to be antagonistic to H3K27me3 at all locations across AtFLC¹⁴, modelling the seasonal dynamics of H3K36me3 at AhgFLC should be addressed in future studies.

The second property is the ratchet-like character of the AhgFLC system, i.e. reversible in the entire perennial life cycle, but irreversible during upregulation phase. Because upregulation of FLC after winter is essential for a perennial life cycle, this irreversibility is relevant to perennial rather than annual species of Arabidopsis. The transplant experiments showed that AhgFLC expression becomes insensitive to prolonged cold when the entire locus is covered by H3K27me3 in spring. Although the H3K4me3 level at the distal nucleation region increased after the plants were transferred to a cold condition in summer, the level remained low after the cold transfer in spring. We confirmed the presence of AhgCOOLAIR transcripts of which the TSS/promoter corresponds to the distal nucleation region. Thus, our results imply that the spreading of H3K27me3 to the distal nucleation region represses AhgCOOLAIR and renders the sense transcription insensitive to the prolonged cold in spring. In contrast, when H3K27me3 is absent from the distal nucleation region during summer–winter, H3K4me3 at the region responds to cold within a short period. Interestingly, the timing of H3K27me3 accumulation at the distal nucleation region of AhgFLC corresponds to that of AhgFLC upregulation (compare Fig. 1b, f), which might repress unseasonal AhgCOOLAIR transcription in spring. In any case, our data have suggested a role for H3K27me3 at the FLC posterior region — that allows the irreversible upregulation of perennial FLC in the presence of springtime temperature fluctuations — in addition to the maintenance of the silenced FLC state in annual plants^{13,14,20,22,23}.

Furthermore, we have shown that long-term seasonal trend of temperature is indexed locally at AhgFLC in the form of histone modifications. The H3K27me3 levels were correlated with longer past temperatures when they were measured at a greater distance from the TSS along the locus. This suggests that temporal environmental information is transformed into the spatial pattern of H3K27me3 accumulation along the locus. The collinearity between seasons and consecutive chromatin segments observed at AhgFLC are remarkably similar to the collinearity between the spatiotemporal sequences of gene activation and the physical order of the genes observed at the HOX gene loci in Drosophila^46,47.

In conclusion, our study provides a more comprehensive understanding of H3K27me3 function at the AhgFLC locus to achieve the robust seasonal expression in a complex natural environment. Our findings provide the basic background for future studies on ecological and agricultural aspects, e.g. predicting phenological shifts and developing robust crops in a changing climate. In animals, H3K27me3 mediates long-term gene regulation in organismal responses that occur over weeks, e.g. development^48,49,50 and carcinogenesis^51,52,53. Therefore, time-series analyses on a broad range of species — not only Brassicaceae but also other plant families and animals — will reveal the evolutionary conservation of the H3K27me3 functions in robust gene regulation under natural conditions.

Methods

Field sampling of leaves for seasonal analysis

Field sampling of leaves was conducted at 2-week intervals for 2 years (from 25 September 2012 to 16 September 2014) in a natural population of A. halleri at the Omoide-gawa River, Naka-ku, Taka-cho, Hyogo Prefecture, Japan (35°06′ N, 134°55′ E; altitude 190–230 m). On each date, sampling started at 12.00 and was completed within one and a half hours (by 13.30).

To evaluate the dynamics of histone modifications at the level of the entire population, we sampled 40 leaves from 40 plants (1 leaf per plant) and made four ChIP biological replicates by pooling ten leaves for each replicate (1 g per replicate) because a large amount of leaf tissue was needed for the ChIP experiments. Sampling was conducted along a stream in a sampling area of ~20 m × 300 m. On each sampling date, the area was divided into 10 sections, and 4 plants were sampled from each section (>2 m between plants). Pooled 10 leaves were from ten sections (one leaf per section). Forty plants were newly selected on each sampling date. The total number of seasonal ChIP samples was 200 (50 time points × 4 replicates).

We analysed leaf samples because they are the most accessible plant tissues that are available year-round in the field¹⁰. One fully expanded young leaf was harvested from each plant (~0.1 g per plant). During the vegetative phase, we sampled rosette leaves. During the flowering period (from 30 April 2013 to 11 June 2013 for the first year and from 28 April 2014 to 10 June 2014 for the second year), we sampled cauline leaves (leaves on a flowering stem) because the rosette leaves had started to senesce after the reproductive transition. Once all four replicates were obtained, samples were fixed with 1% formaldehyde in PBS in the field. Vacuum infiltration was conducted twice, for 5 min each, at ambient temperature. To quench the cross-linking reaction, glycine was added to a final concentration of 125 mM and vacuum infiltration was conducted for an additional 5 min.

For RNA samples, we obtained four biological replicates from additional individuals on each sampling date. One small young leaf (~0.01 g) was harvested for each replicate. Harvested leaves were preserved in RNAlater Stabilization Solution (AM7021; Thermo Fisher Scientific, Waltham, MA, USA), kept on ice during transfer to the laboratory, and then stored at −20 °C.

ChIP-qPCR

For ChIP experiments, we modified the protocol of Gendrel et al.⁵⁴ as described below. Chromatin was extracted using extraction buffer 1–3, and sonicated eight times for 15 s each using a Q700 Sonicator (Qsonica, Newtown, CT, USA) at 10% power output. After centrifugation, the supernatant was diluted in ChIP dilution buffer (up to 3.0 ml per 1 g sample). For pre-clearing of this chromatin lysate, it was incubated with Dynabeads Protein G (Thermo Fisher Scientific) at 4 °C for 1 h with rotation. The chromatin lysate was then incubated with antibody for 5 h. Antibody dilutions were as follows: 1:500 for anti-H3K27me3 (07-449; Millipore, Billerica, MA, USA) and anti-H3K4me3 (07-473; Millipore) and 1:1000 for anti-histone H3 (ab1791; Abcam, Cambridge, UK). By the incubation with Dynabeads Protein G at 4 °C for 2 h, the immune complexes were collected. The beads were then washed with low salt, high salt, LiCl, and TE buffers in this order. By incubating the washed beads with elution buffer at 65 °C for 15 min, immunoprecipitated chromatin was eluted. Each sample was then heated at 65 °C for 12 h to reverse formaldehyde cross-linking, and incubated wih proteinase K at 45 °C for 1 h. After phenol/chloroform extraction, DNA was ethanol-precipitated, and resuspended in 50 μl of TE buffer. qPCR was performed in duplicate by using the appropriate primers (Supplementary Table 1). To collect data, 7300 System SDS Software v1.3 was used. We analysed eight regions along the AhgFLC locus: amplicons I–VIII. Amplicons I and II were selected within the previously defined nucleation region^14,22,23,28; amplicons III–V were selected within the gene body with a distance of more than 1 kbp between them; amplicons VI–VIII were selected around the 3′ end of the locus. The absolute amount of H3K4me3/H3K27me3 ChIP DNA, expressed as percentage of input, was divided by the absolute amount of H3 ChIP DNA, expressed as percentage of input at the same region. In addition, as internal controls, AhgSTM and AhgFUS3 were used for H3K27me3 ChIP, and AhgACT2 and AhgPP2AA3 were used for H3K4me3 ChIP³⁰. Both AhgFLC H3K27me3 and H3K4me3 levels are presented on a linear scale.

RNA extraction and RT-qPCR

RNA was extracted using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) and quantified using Qubit Fluorometer and Qubit RNA HS Assay Kits (Thermo Fisher Scientific). cDNA was synthesised using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). qPCR was performed in duplicate by using the appropriate primers (Supplementary Table 1). To collect data, 7300 System SDS Software v1.3 was used. Normalisation was done using a standard cDNA sample produced from laboratory-grown non-vernalised plants. AhgFLC mRNA level was normalised with the mRNA level of either AhgACT2 or AhgPP2AA3 (ref. ³⁰) and is presented on a common logarithmic scale.

Plant phenology

On each sampling date for the seasonal analysis, we recorded the growth stages of the 40 plants whose leaves were harvested for ChIP samples. During the reproductive phase, we classified the plants into three sequential stages, i.e. bolting, flowering, and reversion, and recorded the number of plants in each stage. Bolting was defined as the stage when flowering stems were longer than 5 mm but did not present open flowers. Flowering was defined as the stage with visible white petals after the flowers had opened. Reversion was defined as the stage when leaves formed at the tip of the bolt at the end of flowering. During reversion, plants form aerial rosettes at the most distal end of the flowering stem and then generate roots at the same node, facilitating the establishment of vegetative (clonal) offspring. We defined the generation of roots at the aerial rosettes as the start of the next clonal generation and the end of the reversion stage.

Cloning of AhgCOOLAIR

RNA extraction was performed as described above from the leaves of mature A. halleri plants grown for 7 and 21 days at 4 °C. We applied two cold periods to capture the transient expression of putative AhgCOOLAIR. Five micrograms of RNA were used for SuperScript III (18080044; Thermo Fisher Scientific) cDNA synthesis of polyA transcripts. PCR amplification was performed on 1:50 cDNA dilution using TaKaRa Ex Taq (RR001A; Takara Bio, Kusatsu, Shiga, Japan) using the primers listed in Supplementary Table 1. PCR products from the two stages were pooled, cleaned using a QIAEX II Gel Extraction Kit (20021; Qiagen), and then cloned into pGEM-T-Easy-Vector-Systems (A1360; Promega, Madison, WI, USA). Individual clones were sequenced by Eurofins Genomics (Tokyo, Japan).

Dependency of mRNA and histone modifications on temperature

For linear regression analyses, we used the air temperature data recorded by the Japan Meteorological Agency at the meteorological station in Nishiwaki, Hyogo Prefecture, Japan (35°00′ N, 135°00′ E; altitude 72 m), which is the nearest station to our field site. To represent the past-temperature trend, we calculated the SMAs of the daily mean, maximum, or minimum air temperature with different window lengths. For example, for the 1-d SMA, we used the temperature of the day before each sampling date, and for the 1-week SMA, we averaged the temperature for the seven days before each sampling date. By using the temperature SMA as the explanatory variable and either AhgFLC H3K27me3, H3K4me3, or mRNA level as the response variable, we performed linear regression analyses by using the lm function of R v3.2.1. The coefficient of determination (R² value) was calculated to estimate the goodness-of-fit of the linear regression models for the observed data. All variables were transformed into logarithmic scale (base 10) before the linear regression analyses.

Phase shift analysis

We used the idea of Lissajous curve analysis³⁴ to visualise phase differences between AhgFLC mRNA and histone modifications (H3K4me3 and H3K27me3). The observed values of AhgFLC mRNA and histone modifications were normalised, setting the minimum value to 0 and the maximum value to 1, each for the first year (25 September 2012–10 September 2013) and the second year (24 September 2013–16 September 2014).

Empirical dynamic modelling

EDM is a nonlinear time series analysis used to recover the system dynamics from empirical time series without assuming any set of equations governing the system^35,36,55. EDM is rooted in state space reconstruction (SSR), i.e. lagged coordinate embedding of time series⁵⁶. The Takens’ theorem proves that reconstructing the system dynamics in a state space is possible by substituting the time lags of the observable variables for the unknown variables. The information in the unobserved variables is encoded in the observed time series (if they are dynamically coupled), and thus a single time series can be used to reconstruct the original state space. The number of time lags used in SSR is the number of dimensions that are necessary to resolve the original attractor (i.e. embedding dimension E)⁵⁵. Before EDM, AhgFLC histone modifications and mRNA levels were standardised by setting the mean and standard deviation for the two-year data of each variable to 0 and 1, respectively. Air temperature was smoothened by the smooth.spline function of R setting spar, a smoothing parameter, to 0.5, because AhgFLC histone modification levels at most of the tested regions and the mRNA level were explained by long-term temperature trends (Fig. 2 and Supplementary Fig. 5).

CCM³⁵, an EDM causality test, was used to infer the causal relationship between the histone modifications and mRNA levels of AhgFLC. A detailed algorithm of CCM is described elsewhere^35,57. Briefly, CCM explores the signature of a causal variable in the time series of an effect variable by testing whether correspondence exists between their reconstructed state space. For example, if variable X unidirectionally affects variable Y, information of the causal variable X is encoded in the time series of the affected variable Y. Therefore, predicting the state of X is possible using the information encoded in Y (i.e. cross-mapping). In this study, cross-mapping from one variable to another was performed using simplex projection³⁷. In the simplex projection, a set of neighbouring points of Y at time t in a reconstructed state-space are used to identify their time-corresponding points of X. If the time-corresponding points of the nearest neighbours of Y are also neighbours of X, then it is possible to predict X by using cross-mapping. Cross-map skill can be evaluated using the Pearson’s correlation coefficient (ρ) between predicted and observed values, and it is a proxy for causal relationships.

Time delays in causation can be considered by introducing a time lag into the cross-mapping³⁶. In the case of unidirectional causality, an effect variable should predict the past values of a causal variable better than the future values because a causal variable cannot influence the past values of an effect variable³⁶. By changing the time to prediction (tp) from −8 to 4 (where negative and positive tp values represent the past and future points, respectively), we determined the best tp value that showed the highest cross-map skill for each cross-mapping. For cross-mappings in which the best tp values were negative, indicating true causality, we adopted two criteria to identify the significant causal relationships for the determined tp. First, the cross-map skill for the observed data should be higher than those for its seasonal-surrogate time series (i.e. null models with the same level of seasonality) at the maximum library size (i.e. the number of points in a state space). Seasonal-surrogate time series were used to avoid the misidentification of causality owing to synchronization between the histone modifications and mRNA levels of AhgFLC driven by seasonality^58,59. Second, cross-map skill should show convergence, i.e. it should be improved with an increase of library size. As the number of points in the state space increases, the trajectory defining the attractor fills in, which results in closer nearest neighbours in the state space. The convergence is an index of one-to-one correspondence between the attractors, which can be another practical criterion for causality. We set ‘(cross-map skill at final library size) – (cross-map skill at initial library size) > 0.1’ as a criterion of convergence.

The embedding dimension (E), i.e. how many time lags are used to reconstruct the state space, should be carefully determined because the results of CCM are sensitive to the choice of E. By changing the E value from 1 to 24, we determined the E value that showed the minimum values of the root mean squared error (RMSE) of the prediction by using univariate simplex projection for each variable (Supplementary Fig. 10 and 13a). The three processes, i.e. CCM, seasonal-surrogate time series generation, and the best E value determination, were performed using the rEDM v0.6.9 package of R using the rEDM::ccm, rEDM::make_surrogate_data, and simplex functions, respectively (https://doi.org/10.5281/zenodo.1081784)³⁶. The results were visualised using the ggplot2 package of R⁶⁰.

Mathematical modelling

We assumed that H3K27me3 at the AhgFLC locus can be classified into four states, i.e. U_NU_D (the nucleation region and the distal nucleation region are not modified), M_NU_D (only the nucleation region is modified), M_NM_D (the nucleation region and the distal nucleation region are modified) and U_NM_D (only the distal nucleation region is modified) — the proportions: u_Nu_D, m_Nu_D, m_Nm_D, and u_Nm_D, respectively (Fig. 5a). Based on the observed dynamics of H3K27me3 at the AhgFLC locus (Fig. 1e, f), we assumed the transition of H3K27me3 at the locus to be unidirectional, i.e. U_NU_D → M_NU_D → M_NM_D → U_NM_D → U_NU_D (Fig. 5a). We described the H3K4me3 states by U_N and A_N for the nucleation region (the proportions: u_N and a_N), and U_D and A_D for the distal nucleation region (the proportions: u_D and a_D; Fig. 5a). To link the tissue-level observations in experiments with the locus-level H3K27me3 and H3K4me3 states, we derived differential equation models from stochastic models that assumes the AhgFLC regulation in cis (Supplementary Note 1). We confirmed that the results were similar between the original stochastic model and the differential equation model (Supplementary Fig. 15). We explain the derived differential equation models below.

Based on previous reports of vernalisation processes in A. thaliana^13,14,22,23, we assumed that the transitions U_NU_D → M_NU_D and M_NU_D → M_NM_D are induced by cold and warm temperatures, represented by the functions of temperature, μ(T) and ν(T), respectively. These functions are given by the logistic equations of the form

$$\mu \left( T \right) = \frac{\zeta }{{1 + \exp \left( {\alpha \left( {T - \theta _1} \right)} \right)}}$$

(1)

$$\nu \left( T \right) = \frac{\eta }{{1 + \exp \left( { - \beta \left( {T - \theta _2} \right)} \right)}}$$

(2)

Based on the results of EDM (Fig. 4 and Supplementary Figs. 11, 12), we assumed that the M_NM_D → U_NM_D transition is induced by H3K4me3 at the nucleation region (a_N), and that the U_NM_D → U_NU_D transition occurs at a constant rate (λ). Thus, the time derivatives of the H3K27me3 states at the AhgFLC locus are given by

$$\frac{{du_{\mathrm{N}}u_{\mathrm{D}}}}{{dt}} = \lambda \,u_{\mathrm{N}}m_{\mathrm{D}} - \mu \left( T \right)u_{\mathrm{N}}u_{\mathrm{D}}$$

(3)

$$\frac{{dm_{\mathrm{N}}u_{\mathrm{D}}}}{{dt}} = \mu \left( T \right)\,u_{\mathrm{N}}u_{\mathrm{D}} - \nu \left( T \right)m_{\mathrm{N}}u_{\mathrm{D}}$$

(4)

$$\frac{{dm_{\mathrm{N}}m_{\mathrm{D}}}}{{dt}} = \nu \left( T \right)\,m_{\mathrm{N}}u_{\mathrm{D}} - \kappa \,a_{\mathrm{N}}\,m_{\mathrm{N}}m_{\mathrm{D}}$$

(5)

$$\frac{{du_{\mathrm{N}}m_{\mathrm{D}}}}{{dt}} = \kappa \,a_{\mathrm{N}}\,m_{\mathrm{N}}m_{\mathrm{D}} - \lambda \,u_{\mathrm{N}}m_{\mathrm{D}}$$

(6)

The observed H3K27me3 levels at the nucleation region and the distal nucleation region were represented by the mean levels of amplicons I and II, and VI–VIII, respectively. Before the mathematical model was optimised, the summed proportion of M_NU_D and M_NM_D (m_Nu_D + m_Nm_D) and that of M_NM_D and U_NM_D (m_Nm_D + u_Nm_D) were multiplied by the two-year maximum of the H3K27me3 levels observed at the nucleation region and the distal nucleation region, respectively, to represent the simulated H3K27me3 levels at these regions.

Based on the seasonal dynamics, we assumed that H3K4me3 at the nucleation region and the distal nucleation region accumulates in response to warm [ξ(T)] and cold [τ(T)] temperatures, respectively. These functions are given by the logistic equations of the form

$$\xi \left( T \right) = \frac{\iota }{{1 + \exp \left( { - \gamma \left( {T - \theta _3} \right)} \right)}}$$

(7)

$$\tau \left( T \right) = \frac{\rho }{{1 + \exp \left( {\varepsilon \left( {T - \theta _4} \right)} \right)}}$$

(8)

Based on the results of EDM (Fig. 4 and Supplementary Figs. 11, 12), we assumed that H3K4me3 at the distal nucleation region negatively affects that at the nucleation region, and vice versa. We also assumed that H3K27me3 negatively affects H3K4me3 at the two nucleation regions. Thus, the time derivatives of the H3K4me3 states at the AhgFLC locus are given by

$$\frac{{da_{\mathrm{N}}}}{{dt}} = \xi \left( T \right)(1 - a_{\mathrm{D}})u_{\mathrm{N}} - \varphi \left( {m_{\mathrm{N}}u_{\mathrm{D}} + m_{\mathrm{N}}m_{\mathrm{D}}} \right)a_{\mathrm{N}}$$

(9)

$$\frac{{da_{\mathrm{D}}}}{{dt}} = \tau \left( T \right)\left( {1 - a_{\mathrm{N}}} \right)u_{\mathrm{D}} - \psi \left( {m_{\mathrm{N}}m_{\mathrm{D}} + u_{\mathrm{N}}m_{\mathrm{D}}} \right)a_{\mathrm{D}}$$

(10)

The observed H3K4me3 levels at the nucleation region and the distal nucleation region were represented by the mean levels of amplicons I and II, and VI–VIII, respectively. Before the mathematical model was optimised, the proportion of A_N (a_N) and A_D (a_D) were multiplied by the two-year maximum of the H3K4me3 levels observed at the nucleation region and the distal nucleation region, respectively, to represent the simulated H3K4me3 levels at these regions.

The simulation was performed by the ode function of the deSolve package of R. The parameters in the model were optimised by fitting the simulated H3K27me3 and H3K4me3 levels to the observed values. Fitting was conducted by minimising the residual sum of squares between the model output and the observed data in two steps. First, 1,000 parameter sets were randomly chosen from the uniform distribution, and the best parameter set which fitted the observed data was selected. Using the parameter set as the initial values, we then optimised the parameters by using the simulated annealing (SANN) method⁶¹ using the optim function of R. The first values of the observed data (25 September 2012) were used as the initial state of the H3K27me3 and H3K4me3 levels in the simulation. Explanations and the determined values of the parameters are listed in Supplementary Table 2. For the simulation, we used air temperature data recorded every 10 min at the meteorological station nearest to our field site (Nishiwaki, Hyogo; Supplementary Fig. 3a). The temperature data was interpolated by the approxfun function of R before the simulation.

In the optimisation of the parameters, we set the upper limits of all parameters to 100 to avoid divergence of the values. Based on the comparison between air temperature data (Supplementary Fig. 3a) and the seasonal dynamics of AhgFLC H3K27me3 and H3K4me3 at the nucleation region and the distal nucleation region (Fig. 1c–f), we set the threshold temperatures θ₁−θ₄ to 5, 10, 15 and 5 °C, respectively.

The observed AhgFLC mRNA level was modelled via linear regression with the observed H3K4me3 level at the nucleation region (Fig. 5a). The equation is given by

$${\mathrm{log}}_{10}\left( {RNA} \right) = \sigma \,{\mathrm{log}}_{10}\left( K4 \ at \ NR \right) + \omega$$

(11)

Using the determined regression coefficient and intercept, AhgFLC mRNA level in each model was reproduced from the simulated H3K4me3 level at the nucleation region. The mRNA level was standardised to make the seasonal amplitude even between models.

Transplant experiments

We conducted transplant experiments on 26 March, 10 April, 24 April and 3 July in 2018 during seasonal increases in temperature (Supplementary Fig. 16a). On each date, six plants from the natural population were transferred to a cold growth chamber [4.5 °C ± 0.5 °C (SD) 12 h light/12 h dark cycles]. On 26 March, we transferred additional six plants to a warm growth chamber [23.3 °C ± 0.9 °C (SD) 12 h light/12 h dark cycles]. For RNA samples, we harvested one small young leaf (~0.01 g) from each plant at six different times (the sampling schedule is shown in Supplementary Fig. 16b, c) and obtained three biological replicates by pooling two leaves on each sampling date. The harvested leaves were preserved in RNAlater Stabilization Solution (Thermo Fisher Scientific) on ice, kept at 4 °C for one day and then stored at −20 °C. For ChIP samples, we harvested one fully expanded young leaf (~0.1 g) from each plant at four different times (the sampling schedule is shown in Supplementary Fig. 16b, c) and obtained three biological replicates by pooling two leaves on each sampling date. Samples were fixed with 1% formaldehyde in PBS in the field on the first sampling date and later in the laboratory.

Statistical analysis

All the descriptions of statistical analyses are provided in the figure legends and Methods section.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data supporting the findings of this work are available within the paper and its Supplementary Information file. A reporting summary for this Article is available as a Supplementary Information file. The data sets generated and analysed during the current study are available from the corresponding author upon request. The source data underlying Figs. 1b–g, 2a–d, 3–5a–d, 6a and c–h, as well as Supplementary Figs. 2b, 3c–g and 7 are provided as a Source Data file.

Code availability

The R codes used in this study are available at https://github.com/hnishio/Nishio_Nat.Commun_script.

References

Kudoh, H. & Nagano, A. J. in Evolutionary Biology: Exobiology and Evolutionary Mechanisms (ed Pontarotti, P.) 195–215 (Springer, 2013).
Kudoh, H. Molecular phenology in plants: in natura systems biology for the comprehensive understanding of seasonal responses under natural environments. New Phytol. 210, 399–412 (2016).
Article CAS PubMed Google Scholar
Hepworth, J. et al. Absence of warmth permits epigenetic memory of winter in Arabidopsis. Nat. Commun. 9, 639 (2018).
Article ADS PubMed PubMed Central CAS Google Scholar
Antoniou-Kourounioti, R. L. et al. Temperature sensing is distributed throughout the regulatory network that controls FLC epigenetic silencing in vernalization. Cell Syst. 7, 643–655.e9 (2018).
Article CAS PubMed PubMed Central Google Scholar
Michaels, S. D. & Amasino, R. M. FLOWERING LOCUS C encodes a novel MADS domain protein that acts as a repressor of flowering. Plant Cell 11, 949–956 (1999).
Article CAS PubMed PubMed Central Google Scholar
Sheldon, C. C., Rouse, D. T., Finnegan, E. J., Peacock, W. J. & Dennis, E. S. The molecular basis of vernalization: The central role of FLOWERING LOCUS C (FLC). Proc. Natl Acad. Sci. USA 97, 3753–3758 (2000).
Article ADS CAS PubMed PubMed Central Google Scholar
Lin, S.-I. et al. Differential regulation of FLOWERING LOCUS C expression by vernalization in cabbage and Arabidopsis. Plant Physiol. 137, 1037–1048 (2005).
Article CAS PubMed PubMed Central Google Scholar
Wang, R. et al. PEP1 regulates perennial flowering in Arabis alpina. Nature 459, 423–427 (2009).
Article ADS CAS PubMed Google Scholar
Schiessl, S. V., Quezada-Martinez, D., Tebartz, E., Snowdon, R. J. & Qian, L. The vernalisation regulator FLOWERING LOCUS C is differentially expressed in biennial and annual Brassica napus. Sci. Rep. 9, 14911 (2019).
Article ADS PubMed PubMed Central CAS Google Scholar
Aikawa, S., Kobayashi, M. J., Satake, A., Shimizu, K. K. & Kudoh, H. Robust control of the seasonal expression of the Arabidopsis FLC gene in a fluctuating environment. Proc. Natl Acad. Sci. USA 107, 11632–11637 (2010).
Article ADS CAS PubMed PubMed Central Google Scholar
Wollenberg, A. C. & Amasino, R. M. Natural variation in the temperature range permissive for vernalization in accessions of Arabidopsis thaliana. Plant. Cell Environ. 35, 2181–2191 (2012).
Article PubMed Google Scholar
Duncan, S. et al. Seasonal shift in timing of vernalization as an adaptation to extreme winter. eLife 4, e06620 (2015).
Article PubMed Central Google Scholar
Finnegan, E. J. & Dennis, E. S. Vernalization-induced trimethylation of histone H3 lysine 27 at FLC is not maintained in mitotically quiescent cells. Curr. Biol. 17, 1978–1983 (2007).
Article CAS PubMed Google Scholar
Yang, H., Howard, M. & Dean, C. Antagonistic roles for H3K36me3 and H3K27me3 in the cold-Induced epigenetic switch at Arabidopsis FLC. Curr. Biol. 24, 1793–1797 (2014).
Article CAS PubMed PubMed Central Google Scholar
Swiezewski, S., Liu, F., Magusin, A. & Dean, C. Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target. Nature 462, 799–802 (2009).
Article ADS CAS PubMed Google Scholar
Csorba, T., Questa, J. I., Sun, Q. & Dean, C. Antisense COOLAIR mediates the coordinated switching of chromatin states at FLC during vernalization. Proc. Natl Acad. Sci. USA 111, 16160–16165 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Rosa, S., Duncan, S. & Dean, C. Mutually exclusive sense–antisense transcription at FLC facilitates environmentally induced gene repression. Nat. Commun. 7, 13031 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Helliwell, C. A., Robertson, M., Finnegan, E. J., Buzas, D. M. & Dennis, E. S. Vernalization-repression of Arabidopsis FLC requires promoter sequences but not antisense transcripts. PLoS ONE 6, e21513 (2011).
Article ADS CAS PubMed PubMed Central Google Scholar
Sung, S. & Amasino, R. M. Vernalization in Arabidopsis thaliana is mediated by the PHD finger protein VIN3. Nature 427, 159–164 (2004).
Article ADS CAS PubMed Google Scholar
De Lucia, F., Crevillen, P., Jones, A. M., Greb, T. & Dean, C. A PHD-Polycomb Repressive Complex 2 triggers the epigenetic silencing of FLC during vernalization. Proc. Natl Acad. Sci. USA 105, 16831–16836 (2008).
Article ADS PubMed PubMed Central Google Scholar
Sung, S., Schmitz, R. J. & Amasino, R. M. A PHD finger protein involved in both the vernalization and photoperiod pathways in Arabidopsis. Genes Dev. 20, 3244–3248 (2006).
Article CAS PubMed PubMed Central Google Scholar
Angel, A., Song, J., Dean, C. & Howard, M. A Polycomb-based switch underlying quantitative epigenetic memory. Nature 476, 105–108 (2011).
Article CAS PubMed Google Scholar
Yang, H. et al. Distinct phases of Polycomb silencing to hold epigenetic memory of cold in Arabidopsis. Science 357, 1142–1145 (2017).
Article ADS CAS PubMed Google Scholar
Castaings, L. et al. Evolutionary conservation of cold-induced antisense RNAs of FLOWERING LOCUS C in Arabidopsis thaliana perennial relatives. Nat. Commun. 5, 4457 (2014).
Article ADS CAS PubMed Google Scholar
Kiefer, C. et al. Divergence of annual and perennial species in the Brassicaceae and the contribution of cis-acting variation at FLC orthologues. Mol. Ecol. 26, 3437–3457 (2017).
Article CAS PubMed PubMed Central Google Scholar
Kemi, U. et al. Role of vernalization and of duplicated FLOWERING LOCUS C in the perennial Arabidopsis lyrata. New Phytol. 197, 323–335 (2013).
Article CAS PubMed Google Scholar
Satake, A. et al. Forecasting flowering phenology under climate warming by modelling the regulatory dynamics of flowering-time genes. Nat. Commun. 4, 2303 (2013).
Article ADS PubMed CAS Google Scholar
Angel, A. et al. Vernalizing cold is registered digitally at FLC. Proc. Natl Acad. Sci. USA 112, 4146–4151 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Berry, S., Dean, C. & Howard, M. Slow chromatin dynamics allow polycomb target genes to filter fluctuations in transcription factor activity. Cell Syst. 4, 445–457.e8 (2017).
Article CAS PubMed PubMed Central Google Scholar
Nishio, H. et al. From the laboratory to the field: assaying histone methylation at FLOWERING LOCUS C in naturally growing Arabidopsis halleri. Genes Genet. Syst. 91, 15–26 (2016).
Article CAS PubMed Google Scholar
Wilczek, A. M. et al. Effects of genetic perturbation on seasonal life history plasticity. Science 323, 930–934 (2009).
Article ADS CAS PubMed Google Scholar
Nagano, A. J. et al. Annual transcriptome dynamics in natural environments reveals plant seasonal adaptation. Nat. Plants 5, 74–83 (2019).
Article PubMed Google Scholar
Ito, T. et al. Seasonal stability and dynamics of DNA methylation in plants in a natural environment. Genes 10, 544 (2019).
Article CAS PubMed Central Google Scholar
O’Shea, P. in The Measurement, Instrumentation, and Sensors Handbook (CRC Press, 1999).
Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).
Article ADS CAS PubMed MATH Google Scholar
Ye, H., Deyle, E. R., Gilarranz, L. J. & Sugihara, G. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep. 5, 14750 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).
Article ADS CAS PubMed Google Scholar
BozorgMagham, A. E., Motesharrei, S., Penny, S. G. & Kalnay, E. Causality analysis: identifying the leading element in a coupled dynamical system. PLoS ONE 10, e0131226 (2015).
Article PubMed PubMed Central CAS Google Scholar
Hodges, C. & Crabtree, G. R. Dynamics of inherently bounded histone modification domains. Proc. Natl Acad. Sci. USA 109, 13296–13301 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Erdel, F. & Greene, E. C. Generalized nucleation and looping model for epigenetic memory of histone modifications. Proc. Natl Acad. Sci. USA 113, E4180–E4189 (2016).
Article CAS PubMed PubMed Central Google Scholar
Erdel, F. How communication between nucleosomes enables spreading and epigenetic memory of histone modifications. BioEssays 39, 1700053 (2017).
Article CAS Google Scholar
Berry, S., Hartley, M., Olsson, T. S., Dean, C. & Howard, M. Local chromatin environment of a Polycomb target gene instructs its own epigenetic inheritance. eLife 4, e07205 (2015).
Article PubMed Central CAS Google Scholar
Satake, A. & Iwasa, Y. A stochastic model of chromatin modification: cell population coding of winter memory in plants. J. Theor. Biol. 302, 6–17 (2012).
Article MathSciNet CAS PubMed MATH Google Scholar
Dodd, I. B., Micheelsen, M. A., Sneppen, K. & Thon, G. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell 129, 813–822 (2007).
Article CAS PubMed Google Scholar
Buzas, D. M., Robertson, M., Finnegan, E. J. & Helliwell, C. A. Transcription-dependence of histone H3 lysine 27 trimethylation at the Arabidopsis polycomb target gene FLC. Plant J. 65, 872–881 (2011).
Article CAS PubMed Google Scholar
Lewis, E. B. A gene complex controlling segmentation in Drosophila. Nature 276, 565–570 (1978).
Article ADS CAS PubMed Google Scholar
Akam, M. The molecular basis for metameric pattern in the Drosophila embryo. Development 101, 1–22 (1987).
CAS PubMed Google Scholar
Müller, J. et al. Histone methyltransferase activity of a Drosophila Polycomb group repressor complex. Cell 111, 197–208 (2002).
Article PubMed Google Scholar
Ross, P. J. et al. Polycomb gene expression and histone H3 lysine 27 trimethylation changes during bovine preimplantation development. Reproduction 136, 777–785 (2008).
Article CAS PubMed Google Scholar
Liu, X. et al. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 537, 558–562 (2016).
Article ADS CAS PubMed Google Scholar
Kotake, Y. et al. pRB family proteins are required for H3K27 trimethylation and Polycomb repression complexes binding to and silencing p16^INK4α tumor suppressor gene. Genes Dev. 21, 49–54 (2007).
Article CAS PubMed PubMed Central Google Scholar
Kondo, Y. et al. Gene silencing in cancer by histone H3 lysine 27 trimethylation independent of promoter DNA methylation. Nat. Genet. 40, 741–750 (2008).
Article CAS PubMed Google Scholar
Ngollo, M. et al. Global analysis of H3K27me3 as an epigenetic marker in prostate cancer progression. BMC Cancer 17, 261 (2017).
Article PubMed PubMed Central CAS Google Scholar
Gendrel, A.-V., Lippman, Z., Martienssen, R. & Colot, V. Profiling histone modification patterns in plants using genomic tiling microarrays. Nat. Methods 2, 213–218 (2005).
Article CAS PubMed Google Scholar
Chang, C.-W. W., Ushio, M. & Hsieh, C.-H. Empirical dynamic modeling for beginners. Ecol. Res. 32, 785–796 (2017).
Article Google Scholar
Takens, F. in Dynamical Systems and Turbulence (eds Rand, D. A. & Young, L.-S.) 366–381 (Springer, 1981).
Tsonis, A. A., Deyle, E. R., Ye, H. & Sugihara, G. in Advances in Nonlinear Geosciences (ed Tsonis, A. A.) 587–600 (Springer, 2018).
Deyle, E. R., Maher, M. C., Hernandez, R. D., Basu, S. & Sugihara, G. Global environmental drivers of influenza. Proc. Natl Acad. Sci. USA 113, 13081–13086 (2016).
Article CAS PubMed PubMed Central Google Scholar
Ushio, M. et al. Fluctuating interaction network and time-varying stability of a natural fish community. Nature 554, 360–363 (2018).
Article ADS CAS PubMed Google Scholar
Wickham, H. ggplot2 (Springer-Verlag, 2016).
Bélisle, C. J. P. Convergence theorems for a class of simulated annealing algorithms on ℝ^d. J. Appl. Probab. 29, 885–895 (1992).
Article MathSciNet MATH Google Scholar

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Acknowledgements

We thank T. Kawagoe and J. Sugisaka for support during field work and K. Yagi for support during laboratory work. This study was supported by JSPS Grant-in-Aid for Scientific Research (S) 26221106, JST CREST JPMJCR15O1 and a Cooperative Research Grant from the Plant Transgenic Design Initiative, Gene Research Center, T-PIRC, University of Tsukuba.

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Center for Ecological Research, Kyoto University, Otsu, 520-2113, Japan
Haruki Nishio, Atsushi J. Nagano, Masayuki Ushio & Hiroshi Kudoh
Tsukuba-Plant Innovation Research Center and Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, 305-8572, Japan
Diana M. Buzas
Faculty of Agriculture, Ryukoku University, Seta Oe-cho, Otsu, 520-2194, Japan
Atsushi J. Nagano
Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan
Koji Iwayama
PRESTO, Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan
Koji Iwayama & Masayuki Ushio
Hakubi Center, Kyoto University, Yoshida-honmachi, Kyoto, 606-8501, Japan
Masayuki Ushio

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Haruki Nishio
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Diana M. Buzas
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Atsushi J. Nagano
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Koji Iwayama
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Masayuki Ushio
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H.N., D.M.B., A.J.N. and H.K. designed the research. H.N. and D.M.B. performed the experiments. H.N. and M.U. performed the empirical dynamic modelling. H.N. and K.I. performed the simulation. H.N., D.M.B. and H.K. wrote the paper.

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Correspondence to Haruki Nishio or Hiroshi Kudoh.

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Nishio, H., Buzas, D.M., Nagano, A.J. et al. Repressive chromatin modification underpins the long-term expression trend of a perennial flowering gene in nature. Nat Commun 11, 2065 (2020). https://doi.org/10.1038/s41467-020-15896-4

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Received: 19 July 2019
Accepted: 01 April 2020
Published: 01 May 2020
DOI: https://doi.org/10.1038/s41467-020-15896-4

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Abstract

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Introduction

Results

Seasonal dynamics of AhgFLC mRNA and histone modifications

Dependency of mRNA and histone modifications on temperature

Interrelation between AhgFLC mRNA and histone modifications

Function of the H3K27me3 and H3K4me3 interaction

Ratchet function of H3K27me3 at the distal nucleation region

Discussion

Methods

Field sampling of leaves for seasonal analysis

ChIP-qPCR

RNA extraction and RT-qPCR

Plant phenology

Cloning of AhgCOOLAIR

Dependency of mRNA and histone modifications on temperature

Phase shift analysis

Empirical dynamic modelling

Mathematical modelling

Transplant experiments

Statistical analysis

Reporting summary

Data availability

Code availability

References

Acknowledgements

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Contributions

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