Peer review of: Repressive chromatin modification underpins the long-term expression trend of a perennial (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

This is an interesting paper showing a very large and impressive dataset with extensive analysis that 
is well placed in the context of the recent literature. The data is consistent with current knowledge and 
there are also some surprising and exciting findings. In most cases, the conclusions are well supported 
by the data in a clear way. I do comment on one case (comment on lines 257-259, 267-269) where 
the connection between the results and conclusion is not clear in the text. There is also a possible 
error in the conceptual underpinning of the mathematical model (see comment on Fig.5a below) that 
needs to be addressed. I also include some more minor comments below. 

Comments on scientific content: 

line 110: The authors say K4 "followed" the mRNA, but actually K4 seems to drop earlier in Fig.1b,c. 

line 149: what do the authors mean by the period being “colinear”? 

line 169: What is meant by the word "advanced" in this sentence? Is this related to the above point? 
In general, for this section on Lissajous curves, what has been learned here that could not have been 
learned from a detailed inspection of Fig. 1? 

lines 208-212: Is the UM state necessary for the model? Is it reasonable? Also based on line 117 
earlier, the drop in K27 is synchronised, which seems not to support the existence of this state. 

Fig.5a: The transition MM->UM depends on transcription but according to the way it is defined in the 
model, transcription in trans is assumed to affect this transition. This means that transcription from 
other loci (and also in other cells) is somehow affecting the removal of K27 at the MM loci. Previous 
work has shown links to transcription but this is “in cis”, meaning at the same locus. 

lines 228-238: I don't really understand this paragraph. More intuition would be helpful. Furthermore, 
the model with/without feedback does not seem to be fully defined anywhere. 

lines 247-248: What is this speculation based on? If it is based on Fig.4c, that is not clear in the text 
and it should be stated and explained. 

line 254: Did K4 really decrease? It seems to be noisy (going first up then down), without a clear 
trend. 

line 255-257: K4 in distal nucleation region is reported to respond to cold earlier than at the 
nucleation region, but in Fig.1 it seems to respond later. 

lines 257-259, 267-269: What is the evidence of the H3K27 role here? There is no reference to the 
CCM results; is that the evidence? I would instead suggest postponing speculation about H3K27 (and 
COOLAIR, as you already have done) to the discussion section. 

SMA analysis: This analysis is using only mean temperatures. But in A. thaliana it was shown that 
daily maxima and minima are key in temperature sensing for vernalization. Could you comment on 
this choice? 

EDM-CCM analysis: This is described clearly and seems reasonable but, for full disclosure, I am not an 
expert on this type of modelling. 

line 328-329: How was it completed in half an hour if it started at 12 and finished at 13:30? Not 1.5h? 

Fig.4c: If this panel is important, you could make it a bit bigger and clearer. 

lines 230-233: Sentence unclear. 

Comments on language and typographical errors: 

line 30: “Furthermore, we show that the long-term…” 
line 80: “revealed” -> “measured”? 
line 95: “enables” 
lines 148: “best explained” (no "the") 
line 172: “performed a causality” 
line 229: “affects” 
line 235: “best explained” (no "the") 
line 238: “to the long-term” 
line 242: “similarity” 
line 303: “fluctuations” 

Reviewer #2 (Remarks to the Author): 

FLC is a potent regulator of flowering time in both annual and perennial species within the 
Brassicacae. In this manuscript the authors have carried out a comprehensive analysis of FLC 
expression and chromatin environment, in the perennial Arabidopsis halleri, over a 2 year period 
under natural conditions. Modelling demonstrated that there is a bi-directional relationship between 
transcription and H3K37me3 modification of AhgFLC. In addition, marking of AhgFLC chromatin with 
H3K27me3 allows reversible FLC expression over the life cycle of this perennial plant but ensures that 
transcription of AhgFLC is insensitive to cold temperatures during the up-regulation phase in spring. 
This is a nice piece of work using plants that are grown under natural conditions over two years, as 
well as experiments that perturb the normal seasonal changes in temperature allowing the authors to 
gain some novel insights into the regulation of the much studied FLC locus over the natural life cycle. 
These experiments were done in a perennial species, and so contribute to our understanding of how 
the perennial life cycle is regulated. I have a few criticisms of the manuscript which I have outlined 
below: 
1. I am concerned by the use of Arabidopsis as an abbreviation in both the title and throughout the 
text as this I misleading. While A. halleri is a member of the Arabidopsis genus its life cycle is very 
different from that of A. thaliana with which most readers will be more familiar. For clarity I strongly 
recommend that the authors stick with the nomenclature A. halleri throughout. 
2. P7-8, “Function of AhgFLC H2K27me3 in the seasonal gene regulation”; in this section the author 
use a mathematical model to predict H3K27me3 and mRNA dynamics under seasonal variation in 
temperature. They assume that transcription enhances the removal of H3K27me3 and that there are 
unidirectional circular transitions between the four modification states (UU to MU to MM to UM to UU). 
The predicted H3K27me3 levels agree well with observed data leading the authors to conclude that 
their “simple model explains the major dynamic over weeks without previously assumed interaction 
with active modifications”. I may be missing something but as transcriptional activity is closely linked 
with the level of H3K4me3 at the nucleation region (see Fig 1 b,c) I do not think that the authors can 
exclude the possibility that it is the level of H3K4me3, rather than transcription per se, that influences 
H3K27me3 levels. The Lissajous plots for HK4me3 (amplicons 1 & 2) against mRNA support my 
comments. 
3. I strongly recommend that the authors provide Lissajous curves for the correlation between 
H3K4me3 (at the nucleation region) and H3K27me3. 
4. I suggest that the authors define more explicitly the abbreviations UU, MU, MM and UM so that it is 
clear what they refer to. Initially I wondered if the two letters referred to the status of H3 at the two 

FLC alleles, or to the two different molecules of H3 in a single nucleosome but finally decided that the 
first letter probably refers to the status of H3 modification in the nucleation region and the second 
letter to the linker region. 
5. The significance of the second conclusion – that H3K27me3 at the distal nucleation region prevents 
downregulation of AhgFLC during cold weather in spring is relevant to perennial rather than annual 
species of Arabidopsis. This needs to be clearly stated as it is the seasonal upregulation of FLC after 
winter that is essential for a perennial life cycle. 
6. P8, line 236; Here the authors refer to Fig 5e, but there is no such figure in the version I received. 
7. One wonders why the authors did not check COOLAIR transcription (P10 lines 293-299) given that 
they had RNA available to do this. 
8. I don’t really understand what the authors are proposing in the last sentence – please explain or 
delete. 
Minor comments: 
P9 line 281; I suggest replacing the word ‘innate” with “endogenous” 

Reviewer #3 (Remarks to the Author): 

The manuscript by Nishio and colleagues describes an exceptional data set: the authors have 
conducted a long term (2-years) field study on a natural population of the perennial plant species 
Arabidopsis halleri and collected in a fine-grained time course samples for chromatin 
immunoprecipitation and expression analysis. The authors focussed on the kinetics of A. halleri FLC, 
measuring expression and the dynamics of H3K27me3 and H3K4me3 across the locus. The authors 
then analysed their data by mathematical approaches that include linear regression comparison 
between measured traits and windows of past temperature, empirical dynamic modelling using 
established causality test and ODE-based modelling of chromatin states. Despite the real interesting 
dataset, the modelling approach is a little disappointing in terms of novelty and doesn’t seem to give 
full justice to the great data set. Maybe the authors can go back to the model, or explain the points 
listed below to my satisfaction. 
FLC is a well-studied locus in the annual model A. thaliana, but has also been studied in perennial 
Brassica species. The modelling approaches confirmed existing models and predictions on the A. 
thaliana FLC locus concerning down-regulation of the gene in response to cold temperatures 
(vernalization). Novel aspects that are typical to perennial FLC regulation would be a better 
understanding of the processes involved in FLC upregulation after flowering. The authors describe a 
distal nucleation region, located downstream of FLC that may correspond to the promoter/TSS of the 
anti-sense transcript COOLAIR. Unfortunately, the ODE approach excludes the H3K4me3 modification, 
which fluctuates at the distal nucleation region; therefore the observation is not actually addressed in 
the mathematical model. 
Major points: 
1. Paragraph on co-linearity between regions and temperature medians across windows. Everything is 
quite coherent until the conclusion. Different temperature sensors are implied for both nucleation 
regions. The proximal region appears to react to a short-term trend while TSS nucleation and 
spreading integrates long-term trends. That is rather interesting and could mean that if cold periods 
are interrupted often enough, the COOLAIR promoter cannot fire anymore to prevent FLC 
upregulation, possibly because a specific transcription factor is not present. But why go from there to 
the statement “—we conclude that the H3K27me3 levels are indexing the seasonal temperature 
throughout the year…” where is the novelty in this statement? 
2. Analysis of Lissajou’s curves for H3K4me3 at the distal nucleation region suggests that H3K4me3 
dynamics advance FLC expression levels. CCM analysis of causality is well discussed for the TSS 
nucleation region and spreading regions, for these regions there is limited novelty. For the exciting 
part, the fact that both nucleation regions are independent of each other, the description remains too 
vague (Line 194 ff). The causality analysis for both marks at the distal is unclear when looking at the 

graph and not actually described in the text. 
3. The distal nucleation regions may be controlled independently and H3K4me3 appears to be 
influencing H3K27me3 (or the other way around). However, the mathematical model is limited to the 
H3K27me3 modification. The dynamics of H3K27me3 at FLC for both annual and perennial models 
have been presented before, it remains unclear wherein the novelty in the approach can be found. 
4. Distal H3K27me3 as stabilizer of H3K4me3. The presented shift data are very interesting. However, 
the hypothesis that distal H3K27me3 stabilizes H3K4me3 comes out of the blue, it has not been 
discussed as a result of the modelling (see comments above). The hypothesis seems to ignore the 
conclusion from above that the temperature sensor in independent for both nucleation regions as well 
as assumes a causality that was not actually presented by CCM (or not clearly discussed). The 
conclusion “..H3K27me3 antagonized H3K4me3 at the distal region, which enables irreversible 
upregulation of AhgFLC in spring …” is not made clear to the reader. 
5. Discussion 296: If H3K27me3 renders the locus insensitive to the cold; how can the distal region 
have a short term temperature memory? 
Smaller points: 
1. Introduction 54ff discussion on the role of COOLAIR. The function of COOLAIR in FLC repression is a 
bit more controversial as the authors suggest. Data from Helliwell et al. 2011 should be included in 
the introduction. 
2. Note that not all manuscripts listed in line 66 do actually discuss or model DNA-replication. For e.g. 
Angel et al. 2011 excluded replication-based spreading from the mathematical model. 
3. The last summary sentence 93 is far from clear. It is in part very general, it may also be wrong as it 
is not shown that H3K27me3 is actually causal for the lack of temperature responsiveness. 
4. 287 inputs not input 

Reviewer #4 (Remarks to the Author): 

This manuscript describes the correlation between AhgFLC histone modifications and expression by 
using nonlinear time series analyses and the function of H3K27me3 in the 
seasonal regulation of AhgFLC by using mathematical modelling. The causality between H3K27me3 
and expression of FLC was resolved by using EDM-CCM. EDM-CCM was previously used for 
mathematical modeling of ecological variations (Sugihara et al, 2012). 

The main conclusion was that antagonistic relationship between H3K4me3 and H3K27me3 at “distal 
nucleation” region enables irreversible upregulation of AhgFLC which overcomes temperature 
fluctuations. 

Mathematical modeling itself is very robust and could be very useful to address the different effects of 
histone modifications on the perennial behavior of FLC orthologs in natural setting. But there are a few 
shortcomings authors need to address. 

1. Although authors defined “distal nucleation” based on the peaks of both H3K4me3 and H3K27me3, 
it is expected them to be as the region is the promoter regions of coolair anti-sense transcripts. 
Transient enrichment of H3K4me3 at this region was very well documented and correlates with the 
expression of coolair (not FLC). This aspect was not addressed. 
2. Previous studies showed that H3K36me3, but not H3K4me3, anti-correlates with H3K27me3. 
Unfortunately, authors did not examine the level of H3K36me3. Inclusion of this modification may 
result in different conclusion of the modeling. 

Reviewer #5 (Remarks to the Author): 

My expertise lies in mathematical modeling and time series analysis. Please note this when reading 
my review. 

Kudoh and coworkers present results of a field study in which the dynamics of methylation at the floral 
repressor locus C in a natural Arabidopsis halleri population was investigated. Over a period of two 
years, samples of Arabidopsis halleri leaves were taken every two weeks and analyzed by molecular 
biology. In this way, the authors were able to create a unique dataset containing time series of 
transcription and methylation. The time series were then analyzed using modern methods of time-
series analysis (convergent cross-mapping). In particular, dynamic coupling between variables 
("causality") was tested. As a result, the authors present an explanation for the seasonal dynamics of 
gene expression at the flowering repressor locus. This explanation is formalized by a mathematical 
model. Further empirical evidence is given by transplantation experiments. 

MAIN COMMENTS 

(1) I found that an excellent study. In general, the manuscript is well-written (but see my comment 
below). The most novel aspect seems to me to be the use of nonlinear time series analysis to 
elucidate the methylation dynamics in natural populations. 

(2) The authors need to describe in more detail how sampling was conducted. It is not clear from the 
methods whether (i) 40 plants were randomly selected at the beginning of the field study, and only 
leaves from these 40 individuals were taken during the entire two-year sampling period, or (ii) 40 
plants were randomly selected every two weeks, so that the removed leaves came from different 
plants on different sampling days. This distinction is critical (see next two comments). 

(3) If the sampling had taken place as described in (2)(i), there are 40 independent time series data 
sets describing the methylation/transcription dynamics of 40 different plant individuals. Each of the 40 
data sets would deserve its own analysis, and these results should be shown. 

(4) If the sampling had taken place as described in (2)(ii), there is one time series data set describing 
the methylation/transcription dynamics of the whole Arabidopsis halleri population (and NOT of 
Arabidopsis halleri plant individuals). This is because samples were taken randomly from the 
population. The reconstructed attractor would therefore describe the population wide methylation 
dynamics. This would suggest that transcription/methylation within the population is dynamically 
coupled. The authors would need to discuss if results are better explained by a common external 
driver (e.g., temperature) or some hidden coupling between plant individuals (e.g., plant volatiles). 

(5) The mathematical model is reasonable and intuitive. I have only minor comments. 
Please explain better what it meant with model with feedback and model without feedback. 
Please explain in more detail how simulations were conducted. What software was used? 
The model consists of a set of ordinary differential equations. Time is assumed to be continuous. 
Please explain how the discrete temperature data were incorporated. 

(6) This is a great study. Please try to make it more appealing to a general audience. 
The manuscript would benefit from 1-2 general paragraphs in the introduction as well as an outlook in 
the discussion. 
The authors could highlight the advantages of their plant system over animal systems. I can not 
imagine an animal system in which the methylation dynamics in a natural population can be examined 
similarly well. 
In systems biology, the circadian oscillator has become a major system for studying gene regulation. 
After reading the manuscript, I think that the seasonal dynamics of flowering loci may play a similarly 
prominent role in the study of methylation dynamics in natural populations. 

SPECIFIC COMMENTS 

Line 2-3. Please try to find a better title. The current title is not very appealing for a general audience. 
Please try to avoid abbreviations such as „H3K27me3“. 

Line 27. Better to use „short-term FLUCTUATIONS of temperature“ instead of „short-term NOISES of 
temperature“. The term „noise“ has a precise mathematical definition. „Fluctuation“ is more general 
and includes cycles, random fluctuations (noise), and deterministic chaos. 

line 329. Change 13:30 to 12:30. 

line 330-340. Please split the paragraph in two. 

line 339-340. Please clarify the meaning of cross-linking.