www.sciencemag.org/cgi/content/full/science.aaf6005/DC1

Supplementary Materials for

Phytochromes function as thermosensors in Arabidopsis

Jae-Hoon Jung, Mirela Domijan, Cornelia Klose, Surojit Biswas, Daphne Ezer, Mingjun Gao, Asif Khan Khattak, Mathew S. Box, Varodom Charoensawan,

Sandra Cortijo, Manoj Kumar, Alastair Grant, James C. W. Locke, Eberhard Schäfer, Katja E. Jaeger, Philip A. Wigge*

*Corresponding author. E-mail: philip.wigge@slcu.cam.ac.uk

Published 27 October 2016 on Science First Release DOI: 10.1126/science.aaf6005

This PDF file includes: Model description

Gene expression cluster descriptions

Materials and Methods

Figs. S1 to S14

Table S1

Captions for data S1 to S6

References

Other supplementary material for this manuscript includes the following: Data S1 to S6 (zipped folders and Excel files)

2

Model Description Mathematical model and parameter estimation.

To understand the role of temperature and light on hypocotyl growth we constructed a

model of ATHB2 mRNA dynamics. ATHB2 is a proxy for growth and its dynamics are

dependent on PIF4 and phytochrome behaviour (32). The model scheme describing this

interaction is shown in Fig. 4A and it builds upon our original model which described the

behaviour of PIF4 mRNA at ambient temperatures (8). Dynamics of PIF4 mRNA

(variable PIF4m), PIF4 protein (PIF4p), active phytochromes (Phy) and ATHB2 mRNA

(ATHB2) and are dependent on time, t and are described by the following differential

equations:

(1)

(2)

(3)

(4)

Equation (1) from our previous study (8) and additionally depends on the behavior of a

light active protein P and a temperature-sensitive repressor R. Their equations are given

as:

(5)

and

dPIF4m(t)dt

=Vmax

1+ gc ⋅R(t)− mD +mL ⋅L(t)+mp ⋅P(t) ⋅L(t)( ) ⋅PIF4m(t)

dPIF4 p(t)dt

= PIF4m (t)− m1 +mphyPhy(t)"

#$

%

&'⋅PIF4 p(t)

dPhy(t)dt

=10 ⋅L(t) ⋅ 1−Phy(t)( )− b ⋅ 1− L(t)( ) ⋅Phy(t)

dATHB2(t)dt

=V2 ⋅PIF4 p(t)k +PIF4 p(t)

− m2 +m3L(t)( ) ⋅ATHB2(t)

dP(t)dt

= 0.3 1− L(t)( ) 1−P(t)( )−P(t)L(t)

3

(6)

where , and

Several equations (namely, (1), (3) and (4)) also depend on the light term L(t). Light is modelled as a square wave function where

(7)

with light arbitrarily chosen to be on (i.e. 1) between 12 h and 20 h.

Equations (1), (5) and (7) form the original model, and the reader can find the detailed

description of equations and parameter names for the PIF4 mRNA, P and R in (8). We

briefly mention that protein P is light dependent and accumulates in light; equation (5)

comes from (33). The repressor R is a flexible wave function that provides a good proxy

for Evening Complex behaviour (8).

Now we describe the new model equations for phytochromes, PIF4 protein and ATHB2

mRNA in turn. PIF4 protein dynamics (Equation 2) depend on active phytochomes (Phy)

that trigger degradation of PIF4 (11). Here this degradation is represented by a rate mphy.

Phytochrome (Phy) dynamics are dependent on light and saturate at value of 1 when in

constant light. In the dark, they decay at the dark-reversion rate of b. Phytochrome

dynamics (Eq. 3) provide a simplified representation of the phyB model described by

other groups (17,34). In the YHB model equation (3) for Phytochrome dynamics is

replaced by Phy(t)=1 for all time, as we assume that the phytochrome effect is driven by

phyB. In the phyABCDE mutant model, Equation (3) for phytochrome dynamics is

R(t) =

rmin +rmax − rmin

21+ sin 2π

2tincrta

"

#$

%

&'

"

#$$

%

&'' 0 < t ' ≤ tincr

rmax tincr < t ' ≤ tincr + tup

rmin +rmax − rmin

21+ sin 2π

2tincrtb

"

#$

%

&'

"

#$$

%

&'' tincr + tup < t ' ≤ tincr + tup + tdecr

rmin tincr + tup + tdecr < t ' ≤ 24

)

*

+++++

,

+++++

t ' = t − tstart (mod24) ta = t '(mod24)−tincr2

tb = t '(mod24)+ tincr + tup( )+ tdecr2 .

L(t) = 1, 12 < t(mod24)< 200, otherwise

!"#

$#

4

replaced by Phy(t)=0 for all time, t.

ATHB2 dynamics depend on the activity of the PIF4 protein. Since ATHB2 is strongly

repressed in the light independently of the phytochromes (ATHB2 expression is still

repressed in phyABCDE) while PIF4 is expressed in the light (in all backgrounds and

temperatures, Figure S9), a second, light-dependent degradation rate (m3) is introduced.

The parameters for the Ler model and the mutant models were estimated by fitting the

models to the data using Bayesian inference and Markov Chain Monte Carlo technique.

Since PIF4 mRNA levels in phyABCDE mutant background (at the two temperatures) are

comparable to those in the wild-type Ler background (Figure S9), and follow the same

behaviour as the wild-type Col data from (8), for sake of simplicity, the parameters for

Equations (1), (5) and (7) are assumed to be identical at the same temperature in Col, Ler

and phyABCDE backgrounds and are taken from our previous model in (2). These values

are reproduced in Supplementary Table S1. The PIF4 mRNA levels in the YHB mutant

background are approximately half of those in the other backgrounds, so, in the YHB

mutant model, all parameters (in Equations (1), (5) and (7)) are kept as in the other model

variants, except for the rate of PIF4 transcription, Vmax , which is halved.

To test whether the difference in Ler data at the two temperatures could be partly

attributed to the difference in the phytochrome dark reversion dynamics, we built Ler

models at the two temperatures where the dark reversion rate parameter b was chosen to

be different across the two datasets, while the rest of the parameters (except Vmax in YHB)

were chosen to be common to all datasets (Ler, phyABCDE and YHB). We remind the

reader that in the phyABCDE and YHB models Equation (3) is replaced by a constant

function Phy(t)=0 and Phy(t)=1, respectively at all times, t. This means that the mutant

models can be simplified, and have fewer parameters. In the phyABCDE model there are

only five parameters (k, V2, m1, m2 and m3), while the YHB model includes the additional

parameter mphy.

Due to the nested structure of the model variants, we decided to parameterize the

phyABCDE model first, followed by the YHB and then the Ler model. For the

5

phyABCDE model, we inferred the parameter V2 (the other parameters were assumed).

Then we fit YHB model to the data (while other parameters are as determined from

phyABCDE model) and inferred the phytochrome dependent degradation of PIF4 protein

(mphy). Finally, in order to test whether the differences seen in the Ler data could be

attributed to the decay rate b, the rate was chosen to be represented by parameters, bj

where superscript represents the decay rate of the 22°C Ler model and that

of the 27 °C Ler model.

The model parameters are estimated from the normalised data, where all ATHB2 data

presented are normalised with respect to the maximum level of ATHB2 expression in 22

°C Ler. In the original model (from our previous study in (2)), the PIF4 expression was

normalised with respect to maximum level of PIF4 expression in 22 °C Col-0. The

nonnormalised version of model (for both PIF4 mRNA and ATHB2) can be easily

obtained by just rescaling the Vmax and k (they have to be multiplied by the value for the

maximum level of PIF4 in 22 °C) and by rescaling V2 (it has to be multiplied by the

value for the maximum level of ATHB2 in 22 °C Ler).

In the Bayesian approach, for data and parameters the prior distribution and

likelihood are used to compute a posterior probability for the parameters,

. Here the data is grouped with representing 22°C

time series data and representing 27°C data. Our parameters for the three model

variants are: , and , respectively. Uninformative prior

distributions and hyperparameters were chosen, specifically,

and .

In our model, the likelihood follows a Gaussian distribution, i.e.,

where for each time point , are the experimental values at for the dataset

(j=1,2) and are the estimated values obtained from the set of parameters . The

j =1 j = 2

d θ π (θ )

π (d |θ )

π (θ | d)∝π (d |θ )p(θ ) d = {d j} d1

d 2

θ = {V2} θ = {mphy} θ = {b1,b2}

π (V2 ) = π (mphy ) =Uniform(0,100) π (b1) = π (b2 ) =Uniform(0,1)

π (d j |θ )∝ exp |!dij − di

j |2σ 2

#

$%

&

'(

ti dij ti d j

!dij θ

6

posterior probability is A Metropolis Hastings

algorithm (35) with random walks for all parameters was used to explore the posterior.

For each model variant the dataset was run with 3 parallel chains with 50,000 iterations

each. The parallel chains were pooled together after a burn in of 10,000 iterations and

thinning with acceptance of each 80th iteration. We confirmed that the posterior

distribution had converged by visual inspection of trace plots and autocorrelation plots

that showed good mixing of chains and low autocorrelation. These diagnostic tools for

assessing convergence are provided with our code.

Preliminary runs of the phyABCDE model (with parameters V2, k, m1 and m2) indicated

that there is strong correlation between several pairs of the parameters when the

remaining two are held fixed. Hence, we decided to fix k, m1 and m2 at arbitrary values.

For simplicity, we assumed the degradation of PIF4 protein (m1) to be comparable to

degradation of PIF4 mRNA, and we chose k to be simply 1. We chose the degradation of

ATHB2 mRNA (m2) to be very low, since ATHB2 expression in phyABCDE in nighttime

shows an almost linear growth with time. We verified that these assumptions are not

strong, since sensitivity analysis (described below) showed that the results carry over

when different values of these three parameters are chosen.

Table S1 shows the parameter summary statistics for the Ler, phyABCDE and YHB

models at 22°C and 27°C. The dark reversion rates of the Ler model at 22°C and 27°C

are different with median(b1)=0.3337966 and median(b2)=0.4509969. The difference of

rates was also confirmed by the rejection of the null hypothesis of the Smirnov-

Kolmogorov two-sample test. The half lives of model phytochrome Phy were computed

and stand at t1/2=2.09103h in 22°C, and t1/2=1.531002h in 27°C. We verified that these

results hold when the parameters m1, m2 and k are varied. Each parameter was allowed to

take the values of 0.001, 0.01, 0.1, 1 and 10 in turn while the other two were kept at their

assumed values (see Table S1). Discarding the models that were a poor qualitative fit for

phyABCDE data (i.e ones where phyABCDE model at 22ºC and 27ºC showed a

temperature differential) or that were a poor fit for YHB data, we found that the remaining

π (θ | d)∝π (d1 |θ )π (d 2 |θ )π (θ ).

7

models suggest a difference in the two dark reversion rates, with comparable half lives

(b1 and b2 are in the ranges [0.281,0.445] (3 decimal places (d.p.)) and [0.331, 0.810] (3

d.p.), respectively). We confirmed that the same qualitative features are observed when

V2, mphy, b1 and b2 take values at the end of their inferred range (2.5th and 97.5th

percentile, Table S1). Though, as expected, in the cases where b1> b2 (i.e. the model has

b1 take it’s 97.5th percentile value while b2 takes it’s 2.5th percentile value) are the

temperature differential in WT model is very small and occurs only in last couple of

hours of darkness (For the cases where b1< b2 the temperature differential (the difference

in area under the curves for ATHB2 expression at 27ºC and 22ºC)) is in the range [2.495,

9.227] (3 d.p.) while in the four cases where b1>b2 the range is [0.295, 0.563] (3 d.p.)).

Additionally, to verify how much temperature dependence of the Phy decay contributes

to the fit of the model to the data, we fitted the Ler models at 22°C and 27°C to Ler data

assuming that they share the decay rate b identical at the two temperatures (i.e. b=b1=b2).

The value of b inferred from Ler data is median(b)=0.4078159. The other summary

statistics are as follows: the 2.75th, 25th, 75th and 97.5th percentiles are 0.3213224,

0.3796184, 0.4323124 and 0.4813988, respectively.

The simulated ATHB2 expression for both versions of Ler models (i.e. the models with

the temperature sensitive and insensitive dark reversion) is shown in Fig. S13. Our model

with the temperature sensitive Phy decay rate shows a greater temperature differential in

the nighttime which more closely recapitulates the experimental data.

All computations were performed in R (www.R-project.org) with the ODE model written

in Rcpp (36). The code is provided with the paper.Some of the plots were produced in

ggplot2 package (37) and MATLAB.

Gene Expression Cluster Descriptions

We used clustering to analyse gene expression over 24 h at 22 and 27ºC in Ler,

phyABCDE and YHB (see Materials and Methods for details). This results in 20 distinct

clusters of 25 or more genes for genes that are differentially expressed in response to

8

temperature. We have uploaded the individual gene lists for each cluster as well as the

GO enrichment terms. We describe these clusters in general terms below:

Cluster 1

1030 genes. This cluster shows the largest perturbation in YHB, where they become

highly expressed in the evening. These genes are slightly more expressed in conditions

that favour phytochrome activity (low temperature) and are slightly repressed in the

phyABCDE background.

Main GO Terms:

metabolic process, mitotic spindle assembly, activation of APC-Cdc20 complex activity,

exit from mitosis, regulation of transcription from RNA polymerase III promoter, mitotic,

metaphase/anaphase transition of mitotic cell cycle, mitotic cell cycle, regulation of

transcription from RNA polymerase I promoter, mitotic, positive regulation of

transcription from RNA polymerase I promoter during mitosis, positive regulation of

transcription from RNA polymerase III promoter during mitosis.

Cluster 2

722 genes. This cluster shows a very similar pattern to Cluster 1.

Main GO Terms:

cellular process, translational elongation, translational termination, mitochondrial

translation, cytoplasmic translation, viral translation, N-terminal peptidyl-proline

dimethylation involved in translation, maintenance of translational fidelity, plastid

translation, peptide biosynthetic process.

Cluster 3

507 genes. As with Cluster1 and 2, the genes in this group are more expressed in YHB,

and repressed compared to Ler in phyABCDE.

Main GO Terms:

metabolic process, cell killing, killing of cells of other organism, disruption of cells of

other organism, cytolysis in other organism, killing of cells in other organism involved in

symbiotic interaction, isopentenyl adenine biosynthetic process, discadenine biosynthetic

process, secretion by cell, zeatin biosynthetic process.

9

Cluster 4

890 genes. This cluster is more strongly expressed during the day in Ler, compared to

night-time. The cluster becomes more highly expressed in the presence of YHB, and less

expressed in phyABCDE.

Main GO Terms:

metabolic process; photosynthesis, dark reaction; cellular metabolic process;

photosynthesis; photosynthetic electron transport in photosystem I; photosynthetic

electron transport chain; photosynthesis, light reaction; single-organism metabolic

process; oxidation-reduction process; electron transport chain

Cluster 5

215 genes. Overall, this cluster has similar properties to Cluster 4 in terms of its

expression.

Main GO Terms:

metabolic process; anthocyanin-containing compound metabolic process; response to

fungus; anthocyanin biosynthetic process involved in anthocyanin accumulation in

response to UV light; regulation of transcription from RNA polymerase II promoter in

response to iron ion starvation; anthocyanin-containing compound biosynthetic process;

cellular response to jasmonic acid stimulus; cellular response to iron ion starvation;

response to jasmonic acid; response to herbivore.

Cluster 6

397 genes. This cluster shows broadly higher expression at 27ºC compared to 22ºC in all

backgrounds, although the degree of induction is highest in YHB.

Main GO Terms:

cellular process; cell killing; multi-organism process; biological adhesion; response to

stimulus; single-organism process; behavior; locomotion; biological phase; biological

regulation

Cluster 7

125 genes. This cluster is broadly similar in its expression to that of Cluster 6.

10

Main GO Terms:

metabolic process; protein C-linked glycosylation; protein S-linked glycosylation; protein

glucuronylation; protein glycosylation at cell surface; protein glycosylation in cytosol;

protein glycosylation in endoplasmic reticulum; protein glycosylation in Golgi; protein

phosphate-linked glycosylation; protein galactosylation

Cluster 8

84 genes. This cluster is broadly similar in its expression to that of Cluster 7.

Main GO Terms:

metabolic process; photosynthesis, light harvesting in photosystem I; photosynthesis,

light harvesting; protein-chromophore linkage via peptidyl-S-4-hydroxycinnamyl-L-

cysteine; peptide cross-linking via L-seryl-5-imidazolinone glycine; protein-chromophore

linkage via peptidyl-N6-retinal-L-lysine; peptide cross-linking via 2-imino-glutaminyl-5-

imidazolinone glycine; protein-chromophore linkage via peptidyl-cysteine; protein-

chromophore linkage via peptidyl-N6-3-dehydroretinal-L-lysine; peptide cross-linking

via 3-(2-methylthio)ethyl-6-(4-hydroxybenzylidene)-5-iminopiperazin-2-one

Cluster 9

1474 genes. These genes tend to be more repressed in expression in phyABCDE,

particularly at 27ºC.

Main GO Terms:

metabolic process; MAPK cascade; stress-activated protein kinase signaling cascade;

cytoplasmic pattern recognition receptor signaling pathway; signal transduction

downstream of smoothened; I-kappaB kinase/NF-kappaB signaling; protein kinase A

signaling; hippo signaling; TRAF-mediated signal transduction; NIK/NF-kappaB

signaling

Cluster 10

478 genes. These genes are relatively repressed in Ler at night-time at 27ºC compared to

22ºC. These genes are strongly repressed in phyABCDE.

Main GO Terms:

metabolic process; single-organism metabolic process; anaerobic phenylalanine

11

oxidation; ethanol oxidation; NADH oxidation; hypoxanthine oxidation; xanthine

oxidation; NADH regeneration; NADPH regeneration; ent-kaurene oxidation to

kaurenoic acid

Cluster 11

449 genes. Transcripts in this group are mildly induced by 27ºC during daytime

compared to 22ºC in Ler. They are strongly induced in phyABCDE during daytime

compared to Ler.

Main GO Terms:

metabolic process; DNA-dependent DNA replication; DNA strand elongation involved in

DNA replication; DNA replication; mitochondrial mRNA modification; RNA

phosphodiester bond hydrolysis; replication of extrachromosomal circular DNA;

mitochondrial RNA modification; RNA-dependent DNA replication; DNA synthesis

involved in DNA replication

Cluster 12

552 genes. These transcripts show similar behaviour to those in cluster 11.

Main GO Terms:

cellular process; rRNA processing; ribosome biogenesis; ncRNA processing; rRNA 3'-

end processing; maturation of LSU-rRNA; maturation of 5.8S rRNA; rRNA metabolic

process; exonucleolytic trimming involved in rRNA processing; rRNA modification

Cluster 13

436 genes. Genes in this cluster are induced in response to temperature with a peak of

expression 1 h after dawn. The expression of this cluster is largely unaffected by changes

in phytochrome activity.

Main GO Terms:

cellular process; cellular response to heat; heat acclimation; filamentous growth of a

population of unicellular organisms in response to heat; response to temperature stimulus;

response to heat; response to stress; response to high light intensity; cellular response to

high light intensity; photoinhibition

12

Cluster 14

161 genes. Genes in this cluster are most expressed in phyABCDE at the beginning of the

night at 27ºC.

Main GO Terms:

cell killing; biological_process; cellular process; reproductive process; growth; multi-

organism process; DNA modification; DNA alkylation; macromolecule modification;

meiotic DNA double-strand break formation involved in reciprocal meiotic

recombination

Cluster 15

747 genes. This cluster is inducible by warmer temperature in Ler during night-time,

constitutively repressed in YHB, and strongly up-regulated in phyABCDE.

Main GO Terms:

biological_process; signal transduction by protein phosphorylation; protein

phosphorylation; protein autophosphorylation; phosphorylation; inhibitory G-protein

coupled receptor phosphorylation; common-partner SMAD protein phosphorylation; I-

kappaB phosphorylation; JUN phosphorylation; regulation of translational initiation by

eIF2 alpha phosphorylation

Cluster 16

1189 genes. This cluster is similar to Cluster 15.

Main GO Terms:

metabolic process; mRNA splicing, via spliceosome; mRNA processing; RNA

processing; mRNA 3'-end processing; polycistronic mRNA processing; mRNA splicing,

via endonucleolytic cleavage and ligation; 7-methylguanosine mRNA capping;

chloroplast mRNA processing; mitochondrial mRNA processing.

Cluster 17

307 genes. Broadly similar to cluster 16.

Main GO Terms:

metabolic process; regulation of transcription from RNA polymerase II promoter in

response to hypoxia; intrinsic apoptotic signaling pathway in response to hypoxia;

13

cellular response to decreased oxygen levels; positive regulation of proteasomal

ubiquitin-dependent protein catabolic process involved in cellular response to hypoxia;

response to hypoxia; cellular response to hypoxia; seed development; cellular response to

stress; embryo development ending in seed dormancy

Cluster 18

607 genes. Broadly similar to cluster 16.

Main GO Terms:

metabolic process; arginine transport; histidine transport; lysine transport; pH elevation;

regulation of cellular pH; establishment or maintenance of cytoskeleton polarity;

regulation of pH; pH reduction; monovalent inorganic cation homeostasis

Cluster 19

133 genes. Broadly similar to Cluster 16.

Main GO Terms: response to stimulus; defense response to other organism; defense

response to bacterium; multicellular organismal reproductive process; induced systemic

resistance; cellular response to alcohol; seed dormancy process; innate immune response;

cellular response to lipid; defense response to bacterium, incompatible interaction

Cluster 20

641 genes. Genes in this cluster are specifically upregulated at night in Ler in response to

27ºC. These genes tend to show more expression during night-time in phyABCDE.

Main GO Terms:

cellular process; actin ubiquitination; isopeptide cross-linking via N6-glycyl-L-lysine;

protein monoubiquitination; protein autoubiquitination; circadian regulation of calcium

ion oscillation; histone ubiquitination; protein modification by small protein conjugation;

protein polyubiquitination; protein ubiquitination involved in ubiquitin-dependent protein

catabolic process.

Materials and Methods Plant materials and growth conditions (Fig. 1, 2 and 3)

14

The elf3-1, lux-4, and phyB-9 mutants have been described previously (8). The

phytochrome mutants in Ler backgrounds phyB-1, phyBDE, phyABDE, and phyABCDE

were provided by K. Franklin. The PHYB and YHB transgenic plants in the phyAB

backgrounds were obtained from J. C. Lagarias (19). The PHYB::PHYB-myc, which was

used in chromatin immunopurification sequencing (ChIP-seq) experiments, was

constructed by amplifying a 8.2 kb genomic fragment of PHYB including its promoter

with primers 6469 (5’-TTGCCTAGGCATAAGTGATTCTTAGAGATTG) and 6472

(5’-AAAGGCGCGCCATATGGCATCATCAGCATCATG). The PCR product was

digested using AvrII and AscI restriction enzymes and then cloned into the modified

6xmyc-pBA vector, in which the cauliflower mosaic virus (CaMV) 35S promoter was

deleted. The PHYB::PHYB-FLAG (PHYB-FLAG), which was also used in ChIP-seq

experiments, was generated by cloning the PHYB genomic sequence into pENTR vector

(ThermoFisher) according to the manufacturer’s procedure, in which primers 5257 (5’-

CACCGGACTAACAGGAGAAACAGATCAGGTAC) and 5268 (5’-

ATATGGCATCATCAGCATCATGTCAC) were used for PCR. The resultant entry

plasmid was recombined with LR clonase into the Gateway binary vector pJHA212K

containing a C-terminal 3xFLAG tag. The binary constructs were transformed into the

phyB-9 mutant by floral dipping method. The PHYB-MYC and PHYB-FLAG transgenic

plants were isolated by Basta and Kanamycin selection respectively, and propagated to

obtain single insertion lines rescuing the long hypocotyl phenotype in the phyB-9. The

ABO/A- transgenic line used for dark reversion measurements has been described

previously (38, 39).

For assays, Arabidopsis seeds were sterilized and sown on ½ X Murashige and

Skoog-agar (MS-agar) plates at pH 5.7. Sterilized seeds were stratified for 3 days at 4ºC

in the dark and allowed to germinate for 24 hours at 22ºC under cool-white fluorescent

light at 170 µmol/m2s. The plates were then transferred to short-day conditions (8 h light

and 16 h dark) at different temperatures for assays. For hypocotyl length measurement, 8

or 9 day-old seedlings were photographed and analyzed using ImageJ software

(http://rsbweb.nih.gov/ij/).

Gene expression by RNA-seq (Fig. 2)

15

Seedlings of the indicated genotypes were grown at the specified temperatures and

sampled at intervals over the diurnal cycle: ZT = 0, 1, 4, 8, 12, 16, 20 and 22 h. For Ler

27ºC at ZT=12 the sample was lost, so some of the analyses relying on this sample omit

this time-point. Total RNA was isolated from 30 mg of ground seedlings using the

MagMAX-96 Total RNA Isolation kit (Ambion, AM1830), following the manufacturer’s

instructions. RNA quality and integrity was assessed on the Agilent 2200 TapeStation.

Library preparation was performed using 1 µg of high integrity total RNA (RIN>8) using

the TruSeq RNA Library Preparation Kit v2 (Illumina, RS-122-2101 and RS-122-2001),

following manufacturer’s instruction. The libraries were sequenced on a HiSeq2000

using paired-end sequencing of 100 bp in length at the Beijing Genomics Institute (BGI)

sequencing centre. Analysis of sequence reads: First, adapters were trimmed

with Trimmomatic-0.32. Then, Tophat was used to map to the ler_0.v7 annotated

genome-- duplicates were removed and the read counts were normalised by genome-wide

coverage. Raw counts were determined by HTseq-count, and cufflinks was used to

calculate Fragments Per Kilobase Million (FPKM), which was then converted into

Transcripts Per Million (TPM).

Analysis of RNA-seq data (Fig. 2) by clustering, PCA and Venn diagram

Data preprocessing - For the clustering and PCA visualizations presented in Fig 2A and

2C, we used an ‘expression-filtered’ dataset. Genes that were not-differentially expressed

(see below) or had low expression variation (coefficient of variation = standard deviation

of expression/mean expression < 0.4) were first filtered out of the dataset, leaving 11143

genes left in the analysis.

Differential expression analysis - We performed differential expression analysis on the

full data set that included all 24689 measurable genes (mean counts per million > 0.001).

To do this, we utilized the generalized linear model functionality of the edgeR package.

For each gene, expression was modeled as a negative binomial random variable, whose

mean was given by the equation exp(light*b1 + temperature*b2 + genotypeYHB*b3 +

genotypephyA-E*b4). Here light, temperature, genotypeYHB, and genotypephyA-E were all

binary indicator variables where light = 1 if the growth chamber lights were on when the

16

sample was obtained (0 if the lights were off), temperature = 1 if the sample was in a

27ºC chamber, genotypeYHB = 1 if the sample was from the YHB mutant, and

genotypephyA-E if the sample was from the phyABCDE mutant. The bi represent regression

coefficients. Differential expression with respect to any of the modeled terms was

assessed using a likelihood ratio test. The genes were considered differentially expressed

if their regression coefficients varied significantly from zero; the significance threshold

was set so that the false discovery rate would not surpass 0.01 using the method of

Benjamini and Hochberg (40) on the p-values obtained from the likelihood ratio test.

Clustering - Clustering was performed using the method described in

(http://research.microsoft.com/apps/pubs/default.aspx?id=67239). This allowed us to

model the number of clusters without any prior restrictions. The algorithm found 23

distinct clusters in the data, however 3 of these clusters contained fewer than 25 genes

and were discarded from the visualization.

PCA - Principal components analysis was performed using the ‘pca’ function in

MATLAB on the expression-filtered dataset, using genes as features. Specifically, we

first calculated each gene’s read counts per million (CPM) as a measure of its expression

in each sample (Calculated by read count for each transcript divided by total number of

read counts in sample and multiplying this ratio by 106). These values were then z-score

transformed across all samples presented in the PCA in order to center the expression of

each gene and normalize their variance. This ensures that the patterns observed in the

PCA are representative of the entire transcriptome and are not driven by a few highly

expressed and/or highly variable genes.

Venn diagram - Venn diagram was created based on the differential expression analysis

described above.

ChIP-seq experimental details (Fig. 3)

Seedlings were grown under short day (SD) conditions (8 hours light, 16 hours dark) on

MS agar plates at 17ºC for 11 days. Seedlings were shifted at the end of the light period

(ZT=8) to 27ºC onto pre-warmed plates while control plants were maintained at 17ºC.

17

After 2 h of darkness (ZT=10), 3 g seedlings for each treatment were fixed under vacuum

for 20 min in 1xPBS (10 mM PO43−, 137 mM NaCl, and 2.7 mM KCl) containing 1%

Formaldehyde (F8775 SIGMA). The reaction was quenched by adding glycine to a final

concentration of 62 mM. Chromatin immunoprecipitation (ChIP) was performed as

described (28), with the exception that 100 µl of anti-c-Myc agarose affinity gel antibody

was used (A7470 SIGMA-Aldrich) per sample. Sequencing libraries were prepared

using TruSeq ChIP Sample Preparation Kit (Illumina IP-202-1024) and samples

sequenced on an Illumina HiSeq system (BGI). Sequence reads were analysed using in-

house pipelines.

For the MNase ChIP, 3 g seedlings were flash frozen and ground with mortar and pestle.

Resulting powder was fixed in 1% Formaldehyde (F8775 SIGMA) at room

temperature. Fragmentation was done using micrococcal nuclease according to

manufacturer’s instructions (NEB, M0247S). ChIP was performed as described (28),

using 100 µl anti-flag M2 Affinity Gel (SIGMA-ALDRICH, A2220) per sample.

Libraries were prepared using NEBNext® Ultra™ II DNA Library Prep Kit for

Illumina® (NEB, E7645L) with a size selection step as described (26). Libraries were

sequenced in-house on an Illumina NextSeq 500 system.

Analysis of ChIP-seq data (Fig. 3)

The ChIP-seq data was mapped against the TAIR10 Arabidopsis genome using Bowtie2,

and the peaks were identified with Model Based Analysis of ChIP-seq data (MACS2).

Specifically, phyB peak enrichment in Figure 3B refers to the MACS2-calculated log-

fold change over Input. De novo motif analysis was performed with Homer2, using

shuffled sequence as a background. The ChIP-seq data was visualized with the

Integrated Genome Viewer (IGV). Figure 3F illustrates the direct overlap in binding sites

between PIFs (rows) and phyB binding sites (columns), as determined by the bedtools

intersect function. The order of the columns was determined as follows: firstly, the

columns were separated by whether phyB was bound at 17ºC only, 27ºC only, or at both

temperatures. Within each category, columns with no overlap with PIFs were placed on

the left, and columns with 100% PIF overlap were placed on the right. The order of

columns with 1 to 3 overlaps with PIFs was determined by hierarchical clustering

(default parameters from R heatmap).

18

Modelling of the network (Fig. 4)

Described above.

Assaying dark reversion rate for phyB (Fig. 4)

4-day-old etiolated Arabidopsis thaliana seedlings overexpressing PHYB in a phyA-201

mutant background (ABO/A-) were irradiated with red light (LED; Λmax=656 nm; 20

µmol/m2s) for 10 min in order to photoconvert the maximal amount of phyB into the Pfr

form (87%). Subsequently seedlings were transferred to darkness at different

temperatures (12, 17, 21 or 27°C) for up to 8 h. At the respective time-points seedlings

were harvested, placed into cuvettes and Pfr/Ptot ratios were measured in a dual

wavelength spectrophotometer as described (29).

19

Fig. S1. Progressive loss of phytochrome function impairs thermoresponsiveness of elongation growth in seedlings Images (top) and hypocotyl length measurements for various phytochrome mutants in the Ler background after 8 days of growth under SD (data for Ler-0, phyB-1 and phyABCDE is from Fig. 1C, D). Scale bars, 5 mm.

12ºC 17ºC 22ºC 27ºC

02468

10121416

Hyp

ocot

yl le

ngth

(mm

)

Ler−

0

phyb−1

phybde

phyabde

phyabcde

02468

10121416

Hyp

ocot

yl le

ngth

(mm

)

Ler−

0

phyb−1

phybde

phyabde

phyabcde

02468

10121416

Hyp

ocot

yl le

ngth

(mm

)

Ler−

0

phyb−1

phybde

phyabde

phyabcde

02468

10121416

Hyp

ocot

yl le

ngth

(mm

)

Ler−

0

phyb−1

phybde

phyabde

phyabcde

Ler-0phyB

-1

phyAB

CD

EphyA

BD

EphyB

DE

Ler-0phyB

-1

phyAB

CD

EphyA

BD

EphyB

DE

Ler-0phyB

-1

phyAB

CD

EphyA

BD

EphyB

DE

Ler-0phyB

-1

phyAB

CD

EphyA

BD

EphyB

DE

20

Fig. S2 Constitutively active phyB (YHB) substantially reduces the warm temperature response. Top, pairs of plants expressing PHYB (left) or Y276H-mutated PHYB in the phyAB backgrounds (PHYB and YHB respectively) at different temperatures imaged after 9 days of growth. Lower panels, hypocotyl length measurements at 17, 22 and 27ºC.

0

2

4

6

8

10

Hyp

ocot

yl le

ngth

(mm

)

Ler−0 PHYB #14 YHB #4 YHB #5

22ºC27ºC17ºC

22ºC 27ºC17ºC

Ler-0PHYB #14

YHB #4YHB #5

Ler-0PHYB #14

YHB #4YHB #5

Ler-0PHYB #14

YHB #4YHB #5

21

●

●

−20

24

6

Cluster13 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

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−20

24

6

Cluster13 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster13 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster13 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster13 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster13 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

22

●

●

−20

24

6

Cluster15 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

Cluster15 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster15 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster15 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster15 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster15 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

23

●

●−2

02

46

Cluster16 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

Cluster16 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster16 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster16 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster16 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster16 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

24

Fig. S3 Temperature responsive gene clusters peak at different times of day. Representation of variation in gene expression by Z-score for selected temperature responsive gene clusters (Fig. 2). Cluster 13 is more highly expressed at 27ºC, but this response requires light, and these genes are most responsive in the early morning (ZT = 1). By contrast, genes in Clusters 15, 16 and 20 show peak thermal responsiveness at ZT = 0. For the clusters that respond to temperature in the darkness, YHB (denoted yhb) has the overall effect of reducing gene expression induction, while the opposite occurs in phyABCDE (denoted phyq). This result is consistent with phytochromes being transcriptional repressors with an activity that declines during the night at a rate proportional to temperature. Vertical grey bars represent transitions between light and dark. Vertical grey bars represent transitions between light and dark.

●

●

−20

24

6

Cluster20 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

Cluster20 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster20 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

phyq −Cluster20 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster20 : 22° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

●

●

−20

24

6

yhb −Cluster20 : 27° C

time

Z−score

ZT−4 ZT0 ZT4 ZT8 ZT−12 ZT−8

25

Fig. S4. PhyB predominantly associates with the promoters of genes expressed during the night in response to temperature. Pie-chart showing the proportion of genes whose promoters are bound by phyB (Fig. 3) in each of the temperature responsive gene expression clusters (Fig. 2). The time of day associated with the expression of these clusters is indicated by color-coding.

134

68910111213

15

16

1718

19

20

daydawnnightother

26

Fig. S5. PhyB ChIP-seq peak signal is higher for peaks that overlap with PIF binding sites. PhyB peak signal is measured as fold-enrichment over Input (as calculated by MACS2). Each phyB peak at 17ºC and 27ºC was separated into two groups: peaks that overlap with at least one PIF binding site, as identified in (17) and those that do not.

17C, no PIF bs 17C, PIF bs 27C, no PIF bs 27C, PIF bs

23

45

67

8

phyB

pea

k fo

ld−e

nric

hmen

t

27

Fig. S6 Average gene expression does not change across the time-course but phyB targets are significantly affected by high temperature in phyABCDE at night. (A) Across all genes with TPM>1, the log-fold difference in TPM across temperatures or between phyABCDE (abbreviated phya-e) and Ler-0 does not vary across the time-course. (B) Expression does change among genes near phyB bound sites. Note: (B) was also depicted in Figure 3D, but here this figure is redrawn to have the same y-axis scale as subfigure (A) for easier visual comparisons.

ZT1 ZT4 ZT8 ZT16 ZT20 ZT22 ZT24

−4−2

02

4

time

log⎛ ⎝⎜27

C le

r−0

TPM

22C

ler−

0 TP

M⎞ ⎠⎟

ZT1 ZT4 ZT8 ZT16 ZT20 ZT22 ZT24

−4−2

02

4

time

log⎛ ⎝⎜22

C p

hy a−e

TPM

22C

ler−

0 TP

M⎞ ⎠⎟

ZT1 ZT4 ZT8 ZT16 ZT20 ZT22 ZT24

−4−2

02

4

time

log⎛ ⎝⎜27

C le

r−0

TPM

22C

ler−

0 TP

M⎞ ⎠⎟

ZT1 ZT4 ZT8 ZT16 ZT20 ZT22 ZT24−4

−20

24

time

log⎛ ⎝⎜22

C p

hy a−e

TPM

22C

ler−

0 TP

M⎞ ⎠⎟

A

B

28

Fig. S7 Top de novo motif results from homer2. Motif predictions for the region directly over 17ºC phyB ChIP-seq peaks (top) and the subset of sequences that do not overlap with the PIF peaks identified from (23) (bottom).

De novo motif finding (Homer) for 17C phyB peaks with shuffled sequence as background

De novo motif finding (Homer) for 17C phyB peaks with no overlap with PIF ChIP-seq peaks with shuffled sequence as background

29

Fig. S8 PhyB ChIP-seq compared to PIF binding sites at three additional loci. PhyB binding near Bbx29 has different strengths at 17ºC and 27ºC (A), but there is only a slight change in PhyB signal strength near ATHB-4 (B). There is no phyB peak near LUX (C) at 27ºC.

AT5G54470.1

[0 - 156]

[0 - 156]

22,113,000 bp 22,114,000 bp 22,115,000 bp 22,116,000 bp 22,117,000 bp

4,742 bp

Chr5

AT2G44900.1,AT2G44900.1-Protein AT2G44910.1,AT2G44910.1-Protein AT2G44920.1 AT2G44925.1

[0 - 90]

[0 - 90]

18,516 kb 18,518 kb 18,520 kb 18,522 kb 18,524 kb 18,526 kb 18,528 kb

13 kb

Chr2

PhyB 17C

PhyB 27C

Genes

PIF1

PIF3

PIF4

PIF5

PhyB 17C

PhyB 27C

Genes

PIF1

PIF3

PIF4

PIF5

AT3G46630.1 AT3G46640.3

[0 - 56]

[0 - 56]

17,181,000 bp 17,182,000 bp 17,183,000 bp 17,184,000 bp 17,185,00

4,124 bp

Chr3

PhyB 17C

PhyB 27C

Genes

PIF1

PIF3

PIF4

PIF5

A

B

C

30

Fig. S9 PhyB target genes expression dynamics across over a 24 h short day. This figure extends the results in Figure 3C to all time-points sampled.

−2 0 2 4

−20

24

ZT=1 (1 hrs of day)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.441R = 0.501

−2 0 2 4

−20

24

ZT=4 (4 hrs of day)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.428R = 0.405

−2 0 2 4

−20

24

ZT=8 (8 hrs of day)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.325R = 0.271

−2 0 2 4

−20

24

ZT=16 (8 hrs of night)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.594R = 0.638

−2 0 2 4

−20

24

ZT=20 (12 hrs of night)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.629R = 0.817

−2 0 2 4

−20

24

ZT=22 (14 hrs of night)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.633R = 0.698

−2 0 2 4

−20

24

ZT=24 (16 hrs of night)

log

27C ler−0 TPM

22C ler−0 TPM

log 22

C p

hya−

e TP

M

22C

ler−

0 TP

M

R = 0.737R = 0.717

31

Fig. S10. PhyB-FLAG X-ChIP validates ChIP of phyB-myc and demonstrates high resolution peaks that allow direct motif identification. (A) Analysis is the same as Figure 3F, except an additional colour-bar was added representing the peaks found in phyB-FLAG (note: no Input control for this ChIP)—green indicates that the peak was found at both 17ºC and 27ºC in the phyB-FLAG ChIP-seq, blue indicates that it was found in 17ºC only, orange indicates it was found at 27ºC only, and white indicates that this peak was not found in the phyB-FLAG ChIP-seq replicates. Clearly, if a phyB peak overlaps with PIF binding sites, it is more likely to be found at both 17ºC and 27ºC in the phyB-myc ChIP-seq experiment. (B) Peaks present at the ATHB2 promoter by X-ChIP. The use of MNase in the X-ChIP protocol allowed for much smaller fragments to be sequenced, enabling higher resolution peaks (Compare the coverage of ATHB2 by X-ChIP with Fig. 3G). High sequencing depth (average ~40 million reads per ChIP) enabled detection of weak peaks over PIF binding sites that were not detected in the previous experiment.

PIF1

PIF3

PIF4

PIF5

PhyB-FLAGPhyB-myc

AT4G16770.1 AT4G16780.1

[0 - 104]

[0 - 104]

9,434 kb 9,436 kb 9,438 kb 9,440 kb 9,442 kb 9,444 kb 9,446 kb 9,448 kb 9,450 kb

17 kb

Chr4

PIF1

PIF3

PIF4

PIF5

PhyB-myc peaks

Genes

17C PhyB-FLAG

27C PhyB-FLAG

A B

32

Fig. S11. Temperature doesn’t alter PHYB gene expression or protein levels

(A) Expression of PHYB in the 24 h RNA-seq time-course. PHYB levels are not significantly different at 22ºC or 27ºC

(B) Protein levels of phyB-myc determined by immunoblot. C refers to the positive control, samples were harvested for short day grown seedlings after 4 h of darkness. Seedlings were grown for 12 h at the designated temperatures before harvesting. Upper panels were incubated with anti-myc and lower panels with anti-H3 (loading control)

33

Figure S12. Relative expression of PIF4 and ATHB2. The data is normalized by

maximum expression at 22°C in Ler.

Col−0 Ler phyabcde YHB

● ● ● ●●

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0

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3

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1

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AT

HB

2P

IF4

0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20Time (h)

Rela

tive

expre

ssio

n

Temp●

●

2722

34

Figure S13. Simulated ATHB2 levels at the two temperatures (orange lines for 22ºC and red lines for 27ºC) for the models without temperature−sensitive phy decay rate (dashed lines) and models with temperature−sensitive phy decay rate (filled line). The 22ºC and 27ºC data are plotted as dots and triangles, respectively.

35

Figure S14. PhyB dark reversion is temperature dependent. The proportion of Pfr

compared to total Phytochrome (Ptot) was measured as a function of time at five different

night-time temperatures (blue dots: measured values). A line of best fit has been

calculated for the data (red line).

time (h)0 5 10

Pfr/P

tot

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Model: exp(-b*t) b=0.80792 95% CI: (0.85257,0.76328)

Temp=27°Cdatafitted curve

time (h)0 5 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Model: exp(-b*t) b=0.38809 95% CI: (0.40942,0.36676)

Temp=22°Cdatafitted curve

time (h)0 5 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Model: exp(-b*t) b=0.221 95% CI: (0.24691,0.19509)

Temp=17°Cdatafitted curve

time (h)0 5 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Model: exp(-b*t) b=0.12556 95% CI: (0.13784,0.11327)

Temp=12°Cdatafitted curve

time (h)0 5 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Model: exp(-b*t) b=0.056819 95% CI: (0.067751,0.045886)

Temp=4°Cdatafitted curve

36

Parameter Description Value (Percentiles: 2.75th, 25th, 75th and 97.5th)

Reference

phytochrome dark reversion rate at 22°C in Ler background

0.3314860 (0.1930727, 0.2977509,0.3655068, 0.4516095)

inferred from Ler 22°C data

phytochrome dark reversion rate at 27°C in Ler background

0.4527408 (0.3554816, 0.4226371, 0.4808054, 0.5776337)

inferred from Ler 27°C data

phytochrome dependent degradation of PIF4 protein

3.788774 (2.583933, 3.292977, 4.541803, 9.306151)

inferred from YHB data

maximal ATHB2 mRNA transcription rate

0.2965539 (0.2453419, 0.2783617, 0.3136994, 0.3434396)

inferred from phyae data

maximum level of PIF4 mRNA repressor activity at 22°C

1 assumed

phytochrome independent degradation rate of PIF4 protein

0.1 assumed

light independent degradation rate of ATHB2 mRNA

0.001 assumed

light dependent degradation rate of ATHB2 mRNA

10 assumed

PIF4 mRNA transcription rate in Ler and phyabcde models

1.206761 Box et al. 2015

PIF4 mRNA dark degradation 0.3473129 Box et al.

2015

PIF4 mRNA additional light degradation

0.6071956 Box et al. 2015

PIF4 mRNA P-dependent degradation

4.923235 Box et al. 2015

maximum level of R repressor activity at 22°C

261.1502 Box et al. 2015

maximum level of R repressor activity at 27°C

53.21953 Box et al. 2015

start time of repressor coming up at 22°C

19.57348 Box et al. 2015

start time of repressor coming up at 27°C

19.23680 Box et al. 2015

duration time of PIF4 mRNA repressor R up at 22°C

11.19309 Box et al. 2015

duration time of PIF4 mRNA repressor R up at 27°C

9.26984 Box et al. 2015

transition time for repressor to increase

2 Box et al. 2015

transition time for repressor to decrease

2 Box et al. 2015

maximum level of the repressor 1 Box et al.

2015

Minimum level of the repressor 0.001 Box et al.

2015 Table S1. Model parameters and summary statistics. In the YHB model, the PIF4

mRNA transcription rate (Vmax) is halved.

b1

b2

mphy

V2k

m1m2

m3

VmaxmD

mL

mP

gc1

gc2

tstart1

tstart2

tup1

tup2

tincrtdecr

rmaxrmin

37

Legends for Data S1 to S6

Data S1

An Excel file containing the normalized read values (Transcripts Per Million; TPM)

for gene expression over a 24 h timecourse at 22 and 27ºC. A separate tab is present

for each genetic background: Landsberg erecta, phyABCDE and PHYB YHB (Fig. 2).

Data S2

An Excel file listing the genes in each of the 20 clusters depicted in Fig. 2.

Data S3

An Excel file listing the enriched Gene Ontology (GO)-terms for each of the 20

clusters depicted in Fig. 2.

Data S4

A list of the peaks detected as bound by phyB by ChIP-seq (Fig. 3)

Data S5

Scripts used to analyse the Illumina generated sequence reads for RNA- and ChIP-

seq. (Fig. 2 and 3).

Data S6

The code used to model the PIF-phytochrome temperature network (Fig. 4).

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2. D. S. Battisti, R. L. Naylor, Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009). Medline doi:10.1126/science.1164363

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