Circadian Rhythms of Sense and Antisense Transcription in Sugarcane, a Highly Polyploid Crop
a Highly Polyploid Crop. PLoS
ONE 8(8): e71847. doi:10.1371/journal.pone.0071847
Circadian Rhythms of Sense and Antisense Transcription in Sugarcane, a Highly Polyploid Crop
Carlos Takeshi Hotta 0
Milton Yutaka Nishiyama Jr 0
Glaucia Mendes Souza 0
Samuel P. Hazen, University of Massachusetts Amherst, United States of America
0 Departamento de Bioqu mica, Instituto de Qu mica, Universidade de Sa o Paulo , Sa o Paulo , Brazil
Commercial sugarcane (Saccharum hybrid) is a highly polyploid and aneuploid grass that stores large amounts of sucrose in its stem. We have measured circadian rhythms of sense and antisense transcription in a commercial cultivar (RB855453) using a custom oligoarray with 14,521 probes that hybridize to sense transcripts (SS) and 7,380 probes that hybridize to antisense transcripts (AS).We estimated that 32% of SS probes and 22% AS probes were rhythmic. This is a higher proportion of rhythmic probes than the usually found in similar experiments in other plant species. Orthologs and inparalogs of Arabidopsis thaliana, sugarcane, rice, maize and sorghum were grouped in ortholog clusters. When ortholog clusters were used to compare probes among different datasets, sugarcane also showed a higher proportion of rhythmic elements than the other species. Thus, it is possible that a higher proportion of transcripts are regulated by the sugarcane circadian clock. Thirty-six percent of the identified AS/SS pairs had significant correlated time courses and 64% had uncorrelated expression patterns. The clustering of transcripts with similar function, the anticipation of daily environmental changes and the temporal compartmentation of metabolic processes were some properties identified in the circadian sugarcane transcriptome. During the day, there was a dominance of transcripts associated with photosynthesis and carbohydrate metabolism, including sucrose and starch synthesis. During the night, there was dominance of transcripts associated with genetic processing, such as histone regulation and RNA polymerase, ribosome and protein synthesis. Finally, the circadian clock also regulated hormone signalling pathways: a large proportion of auxin and ABA signalling components were regulated by the circadian clock in an unusual biphasic distribution.
Funding: This work was supported by Fundacao de Amparo a` Pesquisa do Estado de Sao Paulo (FAPESP). GMS is recipient of a Conselho Nacional de
Desenvolvimento Cientfico e Tecnolo gico (CNPq) Productivity Fellowship. CTH was supported by a post-doctorate fellowship from FAPESP. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
The circadian clock is a signalling network that provides
organisms with an endogenous timekeeping mechanism. This
mechanism allows the organisms to organize their metabolism in
time; to anticipate rhythmic environmental changes; to measure
the length of the light and dark phases of the day; and to modulate
internal and external signals according to its temporal context, a
phenomenon called gating . Plants with a circadian clock
period that is similar to the period of environmental rhythms fix
more carbon and have higher water use efficiency than plants with
circadian periods that do not match with the environment .
The circadian clock can be divided in three different parts: the
central oscillator; the input pathways and the output pathways.
The input pathways, primarily regulated by light and temperature,
bring environmental information to the central oscillator.
Phytochromes and cryptochromes are the main photoreceptors involved
in the regulation of the central oscillator. Little is known about the
role of temperature in the circadian clock entrainment. The
central oscillator generates the endogenous rhythms. A recent
model suggested that a repressilator circuit composed of multiple
transcriptional feedback loops is found in the core of the central
oscillator . In this model, expression of CIRCADIAN CLOCK
ASSOCIATED 1 (CCA1) and LATE ELONGATED HYPOCOTYL
(LHY) are inhibited by the PSEUDO-RESPONSE
REGULATOR 5, 7 and 9 (PRR5, PRR7 and PRR9), at the same time that
CCA1 and LHY are inhibitors of the evening complex (EC) .
The EC is a protein complex made of LUX ARRHYTHMO,
EARLY FLOWERING 3 (ELF3) and ELF4 . The EC is
auto-inhibited, completing a loop that was previously called the
evening loop. It also inhibits the PRRs, which are activated by
CCA1/LHY, as part of the previously called the morning loop .
Another component, TIME OF CHLOROPHYLL A/B
BINDING PROTEIN 2 (TOC1), was considered part of the central
loop through the activation of CCA1/LHY but there is evidence
that it actually acts as an expression inhibitor [8,9]. Other
important components of the circadian clock are CCA1 HIKING
EXPEDITION (CHE), GIGANTEA (GI) and ZEITLUPE (ZTL)
. The structure of the plant circadian clock has mostly been
studied in the dicot Arabidopsis, and while most components of
the circadian clock are conserved among plants, they may have
different functions . The output pathways take the temporal
information generated from the central oscillator to regulate many
physiological processes, such as photosynthesis, stomata
movements and organ growth. These processes are regulated through
many mechanisms: control of chromatin structure, changes in
mRNA and protein stability, alternative splicing, and regulation of
transcript levels .
A large proportion of the plant transcriptome is subjected to
circadian control. Experiments using microarrays showed that 6%
to 15% of the Arabidopsis, poplar, maize and rice transcriptomes
are rhythmic under constant environmental conditions [13,16
20]. If plants are under a cycling environment light/dark cycles,
warm/cold cycles or both the proportion of rhythmic transcripts
increases to 30% to 50% [17,19,21]. When all experimental setups
are considered, close to 90% of all detectable Arabidopsis
transcripts are rhythmic in one or more conditions .
Sugarcane is a C4 monocot that stores sucrose in its stem.
Commercial sugarcane varieties are the result of multiple
interspecific hybridizations between Saccharum officinarum and S.
spontaneum, which resulted in a highly polyploid and aneuploid
genome [22,23]. The high level of sucrose accumulated in
sugarcane, together with its yield, make this crop an important
bioenergy feedstock in a world concerned about alternatives to
fossil-based fuels . Even though there is evidence that
sugarcane has not reached its potential yield limit, yearly increases
in sugarcane yield are low and may be plateauing, a trend that
may be reversed by the introduction of new biotechnological tools
One strategy to develop new biotechnological tools to use in
sugarcane improvement is to know more about its genome, gene
networks and physiology . Microarrays have been a successful
strategy to identify genes of interest in sugarcane [27,28]. We have
recently developed a new custom oligoarray with more than
twenty-one thousand elements with probes that hybridize to sense
and antisense sugarcane EST sequences . This array identified
928 differentially expressed probes in the sense direction (SS) and
59 in the antisense direction in sugarcane subjected to water
suppression , adding considerable knowledge to previous
experiments that hybridized the same samples using a 1,545
elements microarray and identified 93 differentially expressed
transcripts probes .
Natural antisense transcripts (NATs) have been shown to
regulate transcription, processing and degradation of their sense
cognate . For example, AtCOOLAIR, a cold-induced NAT,
has been associated with transcriptional silencing of its cognate
AtFLOWERING LOCUS C (AtFLC), but the importance of this as a
trigger for vernalization is still in debate [32,34]. Using tiling
arrays, it was found that 24% of regulated protein coding genes
were controlled by the circadian clock, while 7% of the protein
coding genes have circadian-regulated NATs in Arabidopsis .
Here we show that commercial sugarcane varieties have robust
circadian rhythms driven by a central oscillator that is similar, but
not identical to the Arabidopsis circadian clock. We also show that
the proportion of probes that had rhythmic time courses s higher
than the ones found in other plants and that the transcript levels in
both sense and antisense directions are regulated by the circadian
clock. We also show that a high proportion of probes associated
with the harvesting and storage of energy from light and probes
associated with DNA, RNA and protein synthesis are regulated by
the clock circadian but the former are expressed during the light
phase of the day and the latter are expressed during the dark
phase. Taken together, our data suggest that the circadian clock is
highly active in commercial sugarcane varieties and may be
important to its high productivity and sucrose accumulation.
A significant proportion of the sugarcane transcriptome
Three month old sugarcane plants (RB855453) were entrained
in a 12 h light/12 h dark photoperiod and transferred to
continuous light for 24 h before being harvested every 4 h for
48 h. RNA extracted from the harvested samples was hybridized
in 44 k custom oligoarrays . Of the 14,119 probes with
positive signals for transcripts in the sense direction (SS), 75.7%
were above the noise levels and were considered expressed
(10,6916541; mean 6 SD; n = 12). Of the 6,575 probes for
transcripts in the antisense direction (AS) with positive signal,
19.7% were considered expressed (1,2976450; n = 12). Only time
courses of probes considered expressed in at least 10 of the 12
arrays were analysed for the presence of circadian behaviour. The
dataset of expressed probes corresponded to 9,931 SS (70.3%) and
665 AS (10.1%) (Table 1), a similar number to what was found in
previous experiments .
Three different methods were used to identify time courses that
showed rhythms with a circadian period (20 h to 28 h): COSOPT,
JTK_CYCLE and Fishers G-test (Figure 1) . COSOPT
considered rhythmic (p,0.05) a total of 3,024 SS time courses
(30.4%) and a total of 136 AS time courses (20.5%). Fishers G-test
considered rhythmic (p,0.05) a total of 3,259 SS time courses
(32.8%) and a total of 163 AS time courses (24.5%). Finally,
JTK_CYCLE considered rhythmic 3,461 SS (34.9%) and 169 AS
(25.4%) time courses. As each algorithm has its own biases, in
order to reduce the number of false positives and false negatives,
only the time courses that were considered as having a circadian
rhythm by more than one method were counted. In total, 3,189 SS
(32.1%) and 146 AS (22.0%) were considered circadian clock
regulated. In order to compare these results with previous studies
that used similar experimental conditions, we have reanalysed one
maize, one rice and two Arabidopsis datasets [17,18,20,39]. The
maize dataset had 4.1% of their probes considered circadian clock
regulated, the rice dataset had 2.1%, the Arabidopsis (Covington;
C) dataset had 10.7%, and the Arabidopsis (Edwards; E) dataset
had 12.4% (Figure S1; Table 2).
COSOPT was used to estimate the period of circadian time
courses. The circadian SS time courses had a mean period of
25.062.50 h (mean 6 SD; n = 3161) and the circadian AS time
courses had a mean period of 24.662.63 h (n = 144). The phases
of the circadian time courses were estimated using JTK_CYCLE.
The results were divided into six groups according to their
estimated time of peak. Circadian time courses were assigned to all
phases. Most of the circadian SS time courses (29.2%) had a peak
12 h after the subjective dawn (zeitgeber time 12; ZT12), while most
of the circadian AS time courses (28.1%) had a peak at early
morning (ZT4) (Figure 1B and D).
*compared to the number of probes;
**compared to number of probes expressed in #10 time points.
More ortholog clusters are circadian-regulated in
sugarcane than in other plants
Our oligoarray was designed based on the sugarcane expressed
sequence tag (SUCEST) database. The SUCEST database has
43,141 putative transcripts known as sugarcane assembled
sequences (SAS) . Every probe that passed quality controls
and was unique to one SAS was used, minimizing any selection
bias. However, there are several SAS within SUCEST that may
correspond to the same coding gene, either because they represent
different alleles, different paralogs, or because they are fragments
of the same gene that were not assembled together (Figure S2).
Furthermore, each probe was not designed to differentiate among
alleles or duplications of the same gene. Thus, it was possible that
the large proportions of circadian-regulated probes that we had
identified did not correspond to a higher proportion of
circadianregulated transcripts. In order to address this issue, we have
selected 9 enzyme models associated with sucrose metabolism and
compared their time courses among the different datasets. Eight of
the sugarcane enzyme models had at least one rhythmic probe. In
contrast, four to six Arabidopsis enzyme models and one to three
rice and maize enzyme models had at least had at least one
rhythmic probe (Table 3).
We also have clustered together orthologs and inparalogs of the
species we have circadian datasets and only compared those that
were common among then. An ortholog catalogue of 18,611
ortholog clusters was created using a combination of InParanoid
circadian probes in
HAYSTACK + COSOPT
clusters in array
dataset/method of analysis
The number of circadian probes and the total number of probes (in parenthesis) was determined for each enzyme model in different datasets and different methods of
analysis. CJF: analysis using COSPOT, Fishers G-test and JTK-CYCLE; C-CJF: Covington dataset; E-CJF: Edwards dataset; D: data taken from DIURNAL (http://diurnal.
1probes that peak at ZT0;
2cells in red represent probes that peak at ZT8;
3probes that peak at ZT16;
4probes that peak at ZT20.
with MultiParanoid [41,42]. InParanoid was used to identify
orthologs and inparalogs in pairwise proteome comparisons of five
different species: 39,021 proteins from sugarcane, 36,338 proteins
from sorghum, 106,046 proteins from maize, 51,258 proteins from
rice, and 35,386 proteins from Arabidopsis . MultiParanoid
was used to merge InParanoid pairwise comparisons into
multispecies ortholog clusters . Thus, an ortholog cluster is made of
all the orthologs and inparalogs identified among the plant species
used. Inparalogs are genes in which duplication happened after
the speciation event, in contrast to outparalogs. A caveat of this
analysis is that all the paralogs generated from duplication events
that occurred after the split from Arabidopsis and the grasses
common ancestor were considered inparalogs . However, the
incorporation of these paralogs to ortholog clusters should
minimize the problem with inaccurate or incomplete sequences
assemblies, a necessity when using data from a species whose
genome that has not been completely sequenced like the sugarcane
We have validated the ortholog clusters by comparing the
manual annotation of 9 genes associated with sucrose metabolism
(Table S1). Of the 178 annotated genes that were grouped in 28
ortholog clusters, only 2 were false positives and did not
correspond to the annotated enzyme model. There were 31
incomplete SAS that were not assigned ortholog clusters but could
be annotated manually. Among the other species, there were 17
genes that were false negatives and should be included in an
ortholog cluster but were not included in any.
The ortholog catalogue allowed us to associate the probes of
each array to an ortholog cluster. This way, we can use the probes
for ortholog clusters that are common to a pair of arrays,
eliminating any selection biases that exist in each array. For
example, there were 246 ortholog clusters present in both
sugarcane dataset and maize dataset. Of these, 114 (46.3%) were
considered circadian in sugarcane dataset and 29 (11.8%) were
considered circadian in the maize dataset (Figure 2). In total, the
sugarcane array had 5,490 ortholog clusters, 2,027 of which
(36.9%) were considered circadian, a greater proportion than the
other plant datasets (Table 2).
We also compared if the phase ortholog clusters was conserved
in different species. A pairwise comparison using circadian
orthologs groups that were common to a pair of arrays showed
that the highest coefficient of determination (r2) was between the
two Arabidopsis datasets (r2 = 0.72). Among the comparisons that
used the sugarcane database, the highest r2 was between sugarcane
and maize (r2 = 0.67) (Table 4).
Antisense transcripts are controlled by the circadian
There was little overlap between the 3,189 SS and 146 AS
transcripts identified as circadian clock regulated (Figure 3A).
This would suggest little correlation between SS and AS
expression. However, when the Spearmans rank correlation
coefficient (r) was calculated for each of the 428 SS/AS
transcript pairs, 36% of the pairs had a positive correlation
(Figure 3B). If only the 207 SS/AS pairs that had at least one
transcript considered circadian clock regulated were
considered, 44.9% of the pairs had a positive correlation. The r
coefficient is a non-parametric measure of the dependence
between two variables. Values of r above 0.56 indicate a
significant positive correlation between the SS and the AS.
Values bellow 20.56 indicate a significant negative correlation.
The distribution of Spearmans rank correlation coefficient
among AS/SS pairs in the oligoarray was bimodal (Figure 3B).
This suggested that there were two types of control of AS
expression: one that regulated both SS and AS expression and
one that regulated AS expression independently of SS
expression. RuBisCo activase (SCBGLR1044D06.g) was an
example of SAS with SS and AS time courses very similar to
each other (Figure 3C). On the other hand, a urease
(SCSGLR1045A02.g) had its SS and AS time courses with
completely opposite phases (Figure 3D). A glucose 1-phosphate
adenylyltransferase (SCVPCL6061A06.g) and a violaxanthin
de-epoxidase (SCVPHR1095C07.g) were examples of SAS that
had their AS and SS time courses with phases a few hours
apart (Figure 3E and F).
Components of circadian clock have strong rhythms
Arabidopsis circadian clock genes were used to identify putative
orthologs in SUCEST. Rice circadian clock genes were used to
confirm the identified orthologs. There was only one sequence
found to be similar to both AtCCA1 and AtLHY. ScLHY (ScMYB19;
SCCCLR1048E10.g) had strong rhythms that peaked at ZT1
(Figure 4A). Validation using qPCR showed that the ScLHY
expression levels at its peak were 44.8 to 63.4 times higher than
expression levels at its trough (Figure S3). Two ScTOC1 probes
(SCCCSB1002H04.g and SCEPLB1042B08.g) were present in the
array, peaking at ZT13 and ZT14 respectively (Figure 4B). These
probes were designed from two different SAS that match different
portions of one gene (Figure S2). ScGI (SCJFAD1014B07.b)
peaked at ZT12, while ScPRR3 (SCACLR1057G02.g), ScPRR59
(SCCCLR1077F09.g) and ScPRR7 (SCACLR1057C07.g and
SCCCST3001B11.g) peaked at ZT10 (Figure 4C to F). ScPRR7
and ScPRR59 expression in the AS direction was detected and
Arabidopsis (C) 0.54 (14)
Arabidopsis (E) 0.40 (17)
In parenthesis is the number of ortholog clusters pairs used in each comparison.
they correlated well with the expression in the SS direction
(Figure 4E and F). Sugarcane candidates for AtFIO1
(SCBGLR1096A01.g), AtELF3 (SCEZLB1009F09.g), and
AtLUX (SCMCST1052A09.g) [43,44] were also identified and
showed circadian expression, with peaks at ZT10, ZT16 and
ZT12, respectively (Figure 4G to I). No sugarcane candidates
for AtELF4and AtCHE [4,6,45] were identified in the SUCEST
database or in our unpublished draft of the sugarcane genome.
In sugarcane, as in other monocots, the sequences with highest
identity to AtELF4 are more similar to Arabidopsis ELF4-like
(AtEFL) than AtELF4 [46,47]. Likewise, the sequences with
highest identity to AtCHE are more similarity to other
Arabidopsis TCP-family transcription factors than AtCHE.
We also have identified putative sugarcane photoreceptors: four
red-light receptors PHYTOCHROME, ScPHYA
(SCCCCL3080H06.g), ScPHYB (SCQSLR1040D12.g), ScPHYC-1
(SCCCLR1065C10.g) and ScPHYC-2 (SCVPRT2081G11.g); two
CRYPTOCHROME, ScCRY1 (SCAGST3138B05.g, SCCCRZ1C02F07.g and
SCBFRZ3009A01.g) and ScCRY2 (SCRFST1042F05.g and
SCSBAD1050G03.g); two ZEITLUPE, ScZTL-1
(SCCCLR1C07F05.g) and ScZTL-2 (SCCCLB1025H12.g); one
PHOTOTROPIN, ScPHOT (SCCCRZ3001D06.g); and one ultraviolet-B receptor
ScUVR8 (SCSBAD1054F04.g and SCBGFL4056D06.g) [48,49].
The expression of all putative sugarcane photoreceptors but
ScPHYB and UVR8 were considered circadian (Figure 5).
ScPHYA had a peak at ZT17 while both ScPHYC had a peak
at ZT12 (Figure 5A to C). The three ScCRY1 probes for the
same gene showed closely related profiles and all peaked
between ZT22 and ZT0, showing that the arrays show technical
reproducibility (Figure 5C). ScCRY2 had a peak at ZT17
(Figure 5D). The two ScZTL peaked at ZT20 and ZT2 while
the ScPHOT peaked at ZT8 (Figure 5F to H).
Outputs of the circadian clock cluster by function
We have identified important biological processes in plants that
have a high proportion of rhythmic probes and compared
their phases. Some biological processes had a noticeable
number of rhythmic probes that clustered around the same
phase. For example: photosynthesis, carbohydrate metabolism,
hormone signalling and genetic information processing
(Table 5). Other important processes that had many rhythmic
probes were: lipid metabolism, amino acid metabolism,
nitrogen metabolism and flowering regulation (Figure S4).
Indeed, a term enrichment analysis of the annotation of the
rhythmic probes had Protein Metabolism, Others and
RNA metabolism, as enriched function categories among
the circadian SS (Table S2) and Porphyrin and chlorophyll
metabolism, Protein metabolism and Cytoskeleton and
vesicle transport (Table S3) as enriched function categories
among the circadian AS, if the Unknown and No match
categories are not considered.
One important feature of the outputs of the circadian clock is
that transcripts that are associated with the same biological process
are co-regulated. For example, probes associated with
lightdependent reactions of photosynthesis had a tendency to peak
between ZT0 and ZT6, while probes associated with the carbon
accumulation and fixation genes had a tendency to peak between
ZT20 and ZT02 (Figure 6). We have used median and median
absolute deviation (MAD) in order to describe the phase of a
population of probes associated with the same biological process.
These parameters are more resistant to outliers than mean and
standard deviation. For example, an outlier probe that has a phase
of ZT18 may have a great impact in the average of a group of
probes with phase of ZT0. MAD is a measure of the population
variability, it is defined as the median of the difference between
each value and the median of whole the population. This way, half
of the values in the population are within one MAD of the median.
All of the eight probes involved in the light harvesting complex
were considered rhythmic and peaked at ZT661.5 (median 6
MAD; n = 8; Figure 6A). Three of the 7 probes of the photosystem
I peaked at ZT460.0 (n = 3; Figure 6C), while five of the 11
probes of the photosystem II peaked at ZT060.0 (n = 5), with one
outlier peaking at ZT10 (Figure 6E). Moreover, four of five probes
associated with chlorophyll synthesis peaked at ZT362.0 and 8 of
9 (n = 4) probes associated with carotenoid and xanthophyll
synthesis peaked at ZT060.0 (n = 8; Figure 6B). Twelve probes
associated with the CO2 accumulation processes of C4 plants were
considered circadian and peaked at ZT062.0 (with two outliers, at
ZT12 and at ZT18; n = 12) (Figure 6D), and 9 probes associated
with the Calvin cycle at ZT062.0 (n = 9; Figure 6F).
Another important feature of circadian clocks is that the phase
of some transcripts is similar to the phase of the different
physiological processes that happen during the course of a day. For
example, sugar metabolism has probes associated with sucrose and
starch synthesis peaking in the subjective morning (ZT261.0,
n = 7; and ZT361.0, n = 6, respectively); sucrose degradation
during the subjective evening (ZT860.0; n = 2); and starch
degradation during the early subjective night (ZT1563.0; n = 5)
(Figure 7A to D). Curiously, starch branching probes also peaked
during the early subjective night (ZT1162.0; n = 2) while starch
debranching probes peaked during the late subjective night
(ZT2161.0; n = 4) (Figure 7E and F). Photosynthesis and sucrose
metabolism are linked together by the upper part of the glycolysis/
gluconeogenesis pathway (ZT2263.5; n = 11), that has a phase
that matches the phases of the carbon fixation and sucrose
synthesis pathways. On the other hand, probes associated with the
lower part of the glycolysis/gluconeogenesis pathway have a phase
(ZT1865.0; n = 5) that matches the transcripts for citric acid cycle
(ZT1764.0; n = 7).
During the subjective night, there were a significant number
of probes that were associated with the processing of genetic
information, while there were a significant number of probes
associated with photosynthesis and the use of fixed carbon
during the subjective day (Table 5). Probes associated with
histones and their regulatory proteins peak at ZT1462.0
(n = 23), probes associated with ribosome biogenesis peak very
tightly at ZT1260.7 (n = 27), and a very large number of probes
associated with the synthesis of new proteins peak at ZT1462.0
(n = 119).
Figure 5. Time courses of probes associated with the perception of light. Z-score normalized time courses of rhythmic probes for sugarcane
transcripts associated with the perception of light. Expression levels were measured using oligoarrays. Different probes for the same sugarcane genes
are represented separately. (A) PHYTOCHROME A (ScPHYA), (B) ScPHYB, (C) ScPHYC, (D) CRYPTOCHROME1 (ScCRY1), (E) ScCRY2, (F) PHOTOTROPIN
(ScPHOT) (G) ZEITLUPE (ZTL-1), (H) ZTL-2, (I) ScUVR8. White boxes represent periods of subjective day and light grey boxes represent periods of
We also found evidence of control of hormone signalling by the
circadian clock (Figure 8A and B). Five auxin synthesis and
transport probes peaked between ZT14 and ZT18. Accordingly,
four AUX/IAA transcripts and two ARF probes that are part of
the auxin response pathways peak between ZT14 and ZT17.
However, there were two ARF probes, two AUX/IAA probes and
one auxin receptor probe that peaked between ZT7 andZT10,
suggesting that there were two times during the day when auxin
signalling was at a peak. Probes associated with ABA signalling
were also regulated by the circadian clock in more than one phase
(Figure 8C and D). One probe involved in ABA synthesis had a
peak in the subjective early morning (ZT2), while two had a peak
during the subjective night (ZT14 and ZT17). On the other hand,
probes associated with ABA response peak close to subjective
dawn (ZT20 to ZT4) and close to subjective dusk (ZT10 to ZT12).
Ethylene and brassinosteroid signalling also had rhythmic probes
that may suggest that the whole pathway may be controlled by the
circadian clock (Figure S5). We found no evidence of significant
control of the circadian clock over cytokinin and gibberellin
Commercial sugarcane is the product of the hybridization of
two polyploid species, Saccharum officinarum and S. spontaneum, that
resulted in 8 to 10 copies of each gene in its genome [22,50].
However, the sugarcane circadian clock is still capable of
generating periods close to 24 h. There is evidence of sugarcane
circadian clock control of over 32.1% of the probes for expressed
sense transcripts (SS) and 22.0% of the probes for expressed
antisense transcripts (AS) in plants grown under light cycles and
constant temperature (Table 1). In this study, we were unable to
design probes that could distinguish among paralogs, mainly
because we lack the genomic resources. This means that each
rhythmic probe has at least one transcript that is controlled by the
circadian clock. At least three scenarios could happen that may
bias the proportion of rhythmic probes: i) several different genes
may hybridize the same probe at the same time, which could mask
rhythms of paralogs that have very different phases; ii) only one
rhythmic transcript among several others that hybridize to the
same probe may be enough to make the probe to be marked as
rhythmic; and iii) a rhythmic transcript may hybridize to more
than one probe. These issues will only be completely clarified
whenever the sugarcane genome, with all its copies, is sequenced
and the transcription levels of different copies of the same gene can
We have tested whether bias could explain the large proportion
of rhythmic probes in our dataset. A selection of nine enzyme
models associated with sucrose metabolism showed that sugarcane
had more enzyme models with at least one rhythmic probe than
Light harvesting complex
Photosynthetic electron transport
Carotenoid and xanthophyll synthesis
C4 carbon accumulation
Amino acid metabolism
Genetic Information Processing
Histones and histone regulation
The number of SS and AS probes considered circadian compared to the total number of transcripts expressed in the pathway (in parenthesis) and the phase of the
circadian SS transcripts (median 6 MAD).
*two populations with distinct phases were detected.
the other datasets. We also grouped probes into ortholog groups
that were common among the compared datasets and sugarcane
had more rhythmic ortholog clusters than the compared datasets
(Table 2). Thus, it is possible that the sugarcane circadian clock
controls a high proportion of the sugarcane transcriptome. This
could be the result of the multiple intra and interspecific
hybridizations and intensive selection for desired agronomic traits
that increased ploidy and aneuploidy levels in this plant. In
particular, the hybridization between S. officinarum and S.
spontaneum could result in the enrichment of the
circadiancontrolled genes of each species. It has been observed that
patterns of gene expression may drastically change as result of
hybridization and increased ploidy in Brassica napus and Arabidopsis
thaliana . Ploidy and hybridity effects in the circadian clock
have been associated with increase of vigour in Arabidopsis
Comparison among similar datasets from other species showed
that there is some overlap between circadian transcripts among
different species. However, when the phase of these genes where
compared, the coefficient of determination varied accordingly to
the phylogenetic distance between the species, showing that the
control of the plant circadian clock over its outputs is a process that
is constantly changing, possibly to increase the resonance of the
plants rhythms with the environmental rhythms.
Based on the available transcripts and available sugarcane
sequences, the sugarcane circadian clock has similar components
to the circadian clock of Arabidopsis but it also has differences: i)
only one of the MYB-like transcription factor AtCCA1/AtLHY has
been identified so far; ii) only four PSEUDO RESPONSE
REGULATOR were found, while Arabidopsis has five; iv) ScPRR3,
ScPRR59 and ScPRR7 peak at the same time of the day and not in
slightly different phases; iv) ScFIO, ScZTL-1 and ScZTL-2 are
rhythmic; and v) no sequences similar to AtELF4 and AtCHE could
be found (Figure 3 and 4). AtELF4, AtLUX and AtELF3 have been
shown to form the evening complex, which is essential to the
maintenance of the circadian clock function [3,6]. These
differences, however, did not impair the sugarcane circadian
clock, which suggests that the interactions between the
components of the sugarcane circadian clock may also show differences
from Arabidopsis. It is worth noting that the presence of common
components of the core oscillator between sugarcane and
Arabidopsis does not necessarily mean that they will have the
same function. It is also possible that there are more differences in
the sugarcane circadian clock as we did not try to identify elements
of the circadian clock that are present in sugarcane, or grasses, but
not in Arabidopsis.
One important question is how the circadian clock relays time
information to other pathways. We have found 111 transcription
factors, of a total of 356 identified in the SUCEST database,
regulated by the circadian clock (31.2%), including ScCCA1 and
ScTOC1 . Moreover, a large number of probes associated with
histone regulation, spliceosome function, RNA degradation and
transport are circadian clock regulated (Table 5). These are
mechanisms known to be involved in the control of transcript
levels by the circadian clock [14,15,35,5559]. Another important
regulatory mechanism is the expression of natural antisense
transcripts (NATs). NATs have been shown to regulate their
cognate transcript levels through gene methylation, direct control
of transcription, alternative splicing and transcript stability [30
33]. Widespread antisense transcription has been described in
Arabidopsis [35,60]. In this work, not only have we found AS that
were co-regulated with their SS cognates, but AS that had
expression levels that did not correlated with their SS cognates, as
well (Figure 3). The circadian AS time courses had a tendency to
Figure 7. Rhythmic probes associated with sugar storage. Z-score normalized time courses of rhythmic probes for transcripts associated with
sugar storage pathways were separated into (A) sucrose synthesis (light green); (B) sucrose degradation (dark green); (C) starch synthesis (dark red);
(D) starch degradation (cyan); (E) starch branching (red); and starch debranching (light blue). White boxes represent periods of subjective day and
light grey boxes represent periods of subjective night.
peak towards subjective dawn, while the circadian SS time courses
had a tendency to peak towards the subjective middle of the day, a
difference that was also observed using tiling arrays in Arabidopsis
. AS/SS pairs have been associated with gene plasticity and
responsiveness to stimuli, which could also be a strategy to increase
the amplitude of circadian rhythms . Most of the circadian AS
time courses identified (68%) did not have the time courses of their
SS cognate considered circadian. These circadian AS may
regulate the translation activity of their cognate SS in order to
confer circadian behaviour in the protein level . Some AS/SS
pairs show small changes in phase, such as violaxanthin
deepoxidase (SCVPHR1095C07.g) and glucose 1-phosphate
adenylyltransferase (SCVPCL6061A06.g) (Figure 3E and F). It is
possible that these AS act controlling the expression of their SS
cognate, helping to regulate the timing of their expression [31,32] .
The sugarcane circadian transcriptome has shown some
properties found in similar works, such as the clustering of
transcripts with similar function, the anticipation of daily
environmental changes and the temporal compartmentation of
metabolic processes. Clustering can be observed in the light
harvesting reactions of photosynthesis, in which probes associated
with light harvesting complex and photosystem I cluster very
tightly between ZT4 and ZT6, when sunlight is usually at its peak
(Figure 6). Curiously, probes for synthesis of pigments precede the
expression of pigment binding proteins. On the other hand, probes
associated with the carbon fixing part of photosynthesis, including
RuBisCo activase, tended to cluster between ZT22 and ZT2, in
the beginning of the subjective day (Figure 3).
Probes for sucrose and starch metabolism are co-ordinated
during the day following the timing of the carbon fixing pathways
(Figure 7). There is a strong association between the peaks of
probes associated with sucrose synthesis, including sucrose
phosphatase synthase (SPP), with the peaks of transcript for
carbon fixation and the upper portion of
glycolysis/gluconeogenesis, indicating that one of the destinations of the newly fixed
carbon is sucrose (Figure 9). Probes for starch synthesis cluster a
few hours later. Less predominantly, two probes for sucrose
degradation, including a neutral invertase (NI), cluster close to the
beginning of the subjective night, around ZT8. As with sucrose
and starch synthesis, starch degradation starts a few hours later. It
has been shown that both the levels of probes associated with
starch metabolism and their respective enzymatic activities cycle
during the day but their protein levels remain constant [62,63].
Variations of transcript levels may be important to maintain the
levels of enzyme activity instead of the total amount of the
enzymes. Similarly, pigment concentration in the leaves does not
vary during the day (Figure S6). However, rhythms in the
transcripts associated with photosynthetic pigment synthesis may
follow rhythms in their damage due to photosynthetic activity,
which would help maintain their concentration constant. Indeed,
plants that have a circadian clock period that matches the period
of environmental rhythms accumulate more chlorophyll than
plants that are arrhythmic or have mismatched circadian clocks
Our results suggest that sugarcane has distinct metabolic profiles
during the subjective day and the subjective night. It is possible
that this is a way to optimize energy and carbon allocation for two
energy intensive tasks: carbon assimilation and genetic processing
. During the subjective day, there was dominance of probes
associated with photosynthesis-related processes such as carbon
fixation and carbohydrate metabolism. During the subjective
night, there was dominance of probes associated with genetic
processing, such as DNA replication, histone regulation and RNA
polymerase, ribosome and protein synthesis. Transcripts for amino
acid synthesis and sucrose, starch and lipid degradation also peak
during the subjective night, providing energy and substrates for
these processes during this period (Figure S4).
Not only does the circadian clock regulate a major part of the
sugarcane metabolism, but its signalling pathways as well. The
modulation of a signalling pathway by the circadian clock is called
gating. We have found that a large proportion of probes of auxin
and ABA signalling components were regulated by the circadian
clock. In Arabidopsis, most transcripts of auxin signalling peaked
during the subjective day, while the peak response to exogenous
auxin was at subjective dawn [39,65] (Table 5). ABA had a peak of
response in the middle to the end of the subjective day . In
contrast, sugarcane may have two phases of auxin and ABA
response (Figure 8). This suggests that the polyploidy in sugarcane
have led to sub-functionalization, either at expression or at
functional level, and even to neofunctionalization [69,70]. It is
possible that different parts of the sugarcane leaf are sensitive to
these hormones in different times of the day. Another explanation
is that sugarcane activates auxin and ABA signalling daily as a
basal mechanism, synthesizing and transporting these hormones at
a specific time of the day but also has other times of the day where
it is more sensitive to acute auxin and ABA synthesis in response to
stresses like drought, cold or wounding.
Commercial sugarcane is the result of many rounds of
hybridization and selection that resulted in a hybrid genome that
contains variable numbers of S. officinarum and S. spontaneum genes.
Despite the intense selection for the most productive individuals,
the circadian clock is still capable of generating self-sustained
rhythms. The circadian clock has been shown to confer survival
advantages, as it allows for the anticipation of rhythmic
environmental events, the temporal compartmentation of
metabolic processes, the optimization of starch accumulation, and the
gating of environmental signals [1,2,71]. It is possible that those
traits allowed the commercial sugarcane become the important
crop it is today.
Materials and Methods
Plant growth condition and harvesting
The commercial variety of sugarcane RB855453 (Saccharum
hybrid), propagated from stem cuttings, was grown in a 1:2
compost:sand mix under a 12 h light (100 mmol photons
m22 s21)/12 h dark photoperiod at 2562uC for three months.
Plants were transferred to continuous light in the beginning of the
experiment. Twenty-four hours later, all the leaves from 9 different
plants were harvested every 4 h for 48 h in a total of 12
timepoints (between 48 h and 68 h under constant light) and frozen in
liquid nitrogen. The plants were separated into three groups: two
for each technical replicate of the oligoarrays and one for
validation using RT-qPCR. There was no visual evidence of
chlorosis in sugarcane leaves after 72 h under constant light
conditions. There was also no significant difference in the levels of
chlorophyll a, chlorophyll b and carotenoids levels throughout the
experiment (One-way ANOVA; Figure S6).
Total RNA extraction, labelling and hybridization were
performed as described . Briefly, frozen samples were
pulverized, and then total RNA was extracted using Trizol
(Invitrogen), treated with DNase I (Life Technologies) and purified
with RNeasyH Mini Kit (Qiagen). Sample labelling was done
following the Two-Colour Microarray-Based Gene Expression
Analysis Protocol (Quick Amp Labelling) and hybridization was
performed in a custom 4644 k oligoarray (Agilent) using a
equimolar pool of all samples as a reference. The oligoarray was
designed using an in house pipeline that selected specific sequences
from 14,521 Sugarcane Assembled Sequences (SAS) from the
Sugarcane EST Project (SUCEST). All the SAS from SUCEST
that had specific 60-mer probes that fit the quality controls were
represented in the array. The quality controls used for the60-mer
probe were: 1) the G% must be less than 50%; 2) CG content must
be between 35% and 55%; 3) melting temperature must be
between 68uC and 76uC; 4) homopolymers must be smaller than
6 bp. Probe specificity was assayed using BLASTN between the
selected probes and the SUCEST. The first hit must had 100%
identity with the target SAS. The following hit had a score bit
lower than 42.1 and coverage of 35% or less, without gaps . A
total of 14,521 probes that hybridize to sense transcripts and 7,380
probes that hybridize to antisense transcripts were used in
duplicate in the oligoarray. The arrays were scanned with a
GenePix 4000B scanner (Molecular Devices, Sunnyvale, CA,
USA) using the suggested Agilent Scan Settings. Two
hybridizations were made for each time-point using independently prepared
samples and dye swaps. The arrays were validated with RT-qPCR
using a third set of samples, as described previously . We have
tested eight transcripts considered rhythmic and one considered
arrhythmic. All these transcripts had similar time courses either
using oligoarrays or RT-qPCR. However, the arrhythmic time
course did not show a significant correlation using Spearmans
rank correlation coefficient (r) (Figure S3). Of the 108 individual
points, 88% were validated. The complete dataset can be found at
the Gene Expression Omnibus public database (GEO) under the
accession number: GSE42725.
Data extraction and normalization. Data was extracted
using Feature Extraction software version 184.108.40.206 (Agilent
Technologies) using the protocol GE2-v5_95_Feb07. A background
signal correction and a non-linear LOWESS normalization were
applied to each dataset . Signals that were distinguishable
from the local background signal were used as an indication that
the correspondent transcript was expressed and only transcripts
that were considered expressed in 10 of the 12 time-points were
further analysed. The time course for each transcript considered
expressed was normalized by Z-score .
Identification of rhythmic transcripts. Each time course
was analysed by three different algorithms to identify rhythmic
behaviour: COSOPT, Fishers G-test and JTK_CYCLE. Only
time courses that were considered rhythmic by two or more
algorithms were considered circadian. Both COSOPT and
JTK_CYCLE measure how close a time course fits to a series of
cosine waves with different phases and period lengths between
20 h and 28 h [37,38]. Fishers G-test is an algorithm that detects
rhythmic time courses using Fourier transform . The period
was estimated using COSOPT and the phase was estimated using
Term enrichment analysis. Each SAS in the oligoarray was
automatically and manually annotated and categorized in thirty
functional categories. Term enrichment analysis was done using
the GeneMerge tool , which compares the frequency of each
category among the circadian SAS and compares with the
frequency of each category in all the SAS contained in the whole
Identification of ortholog clusters. Ortholog clusters were
generated using InParanoid and MultiParanoid [41,42].
InParanoid was used to make pairwise proteome comparisons of 39,021
sugarcane proteins predicted from 43,141 SAS , sorghum
(36,338 proteins), maize (106,046 proteins), rice (51,258 proteins)
and Arabidopsis (35,386 proteins). This algorithm uses a
hexanucleotide-based Markov chain in order to model coding
regions even when they have sequencing errors that induce frame
shifts or indels . MultiParanoid was used to create 18,611
multi-species ortholog clusters through the aggregation of results
from InParanoid comparisons . We used: confidence score
. = 0.05, score cutoff . = 40 bits, sequence overlap cutoff
. = 0.5, group merging cutoff . = 0.5, and the BLOSUM80
matrix, as parameters in this analysis.
Pairwise comparison between arrays. In order to make
pairwise comparisons between two arrays, we have converted
each annotated probe to its respective ortholog cluster. Only the
ortholog clusters that were found in both arrays were used. If an
ortholog cluster contained more than one probe in the array, it
was still counted once. In these cases, if one probe was
considered circadian, the whole ortholog cluster was labelled as
circadian. Then, the list of common orthologous clusters was
compared for common circadian ortholog clusters. The ortholog
clusters considered circadian in both arrays were separated for
phase analysis. The phase of each ortholog cluster was
determined by the median of all circadian probes assigned for
that ortholog cluster. The difference between a pair of phases
was always minimized. For example, if a pair of ortholog
clusters have phases ZT2 and ZT22, the comparison was made
between ZT26 and ZT22.
Figure S1 Identification of rhythmic probes in other
datasets. (A) A maize dataset , (B) a rice dataset and (C
D) two Arabidopsis circadian datasets [20,39] were reanalysed
using our analysis pipeline. Venn diagrams showing the number of
transcripts in each dataset that were considered rhythmic by three
algorithms: JTK_CYCLE, COSOPT and Fishers G-test.
Figure S2 More than one sugarcane assembled
sequences (SAS) may align to a same gene model. The
SbTOC1 CDS (1,732 bp) was blasted against the Sugarcane EST
database and two different SAS were selected: SCEPLB1042B08.g
(381 bp) and SCCCSB1002H04.g (1,256 bp).
Figure S3 Real-time PCR validation of array time
courses. Z-score normalized expression levels from the arrays
(darker colour) and from real-time PCR (lighter colour) for (A)
ScCCA1 (SCCCLR1048E10.g), (B) ScTOC1 (SCCCSB1002H04.g
and SCEPLB1042B08.g), (C) ScGI (SCJFAD1014B07.b), (D)
ScPRR3 (SCACLR1057G02.g), (E) ScPRR59
(SCCCLR1077F09.g), (F) ScPRR7 (SCACLR1057C07.g), (G) ScPP2C
(SCEPRZ1010E06.g), (H) ScPSI (SCQGLR2025B12.g), and (I)
ScCAB2 (SCUTST3086G11.g). Spearmans rank correlation
coefficient (r) between the array and real-time PCR time courses
is shown. Significant correlations were marked with a * (p.0.56 or
p,20.56). White boxes represent periods of subjective day and
light grey boxes represent periods of subjective night.
Figure S4 Rhythmic probes associated with several
pathways. Z-score normalized time courses of rhythmic probes
for transcripts associated with the photosynthetic pathway were
separated into (AB) amino acid metabolism; (CD) lipid
metabolism; (E) nitrogen metabolism; and (F) flowering regulation.
Lines in different colours indicate transcripts with contrasting
phases. White boxes represent periods of subjective day and light
grey boxes represent periods of subjective night.
Figure S5 Rhythmic probes associated with several
hormone signalling pathways. Z-score normalized time
courses of rhythmic probes for transcripts associated with the
photosynthetic pathway were separated into (A) brassinosteroids
signalling (green) and (B) ethylene signalling (red). Lines in
different colours indicate transcripts with contrasting phases.
White boxes represent periods of subjective day and light grey
boxes represent periods of subjective night.
Figure S6 Levels of photosynthetic pigments
remained constant in constant light. Leaf pigments were
extracted using chilled 80% acetone and measured using a
spectrophotometer. Briefly, 100 mg of frozen ground leaf tissue
was homogenized in 10 ml 80% acetone for 72 h, protected
from light at 4uC. Samples were measured in sealed 96-well
plates, to avoid acetone evaporation, using a plate
spectrophotometer (BMG Labtech). Absorbance was measured at 480 nm
(A488), 645 nm (A645) and 663 nm (A663). Pigments
concentrations were calculated using the Arnon equations and then
normalized for 80% acetone volume used and the amount of
tissue used in weight.
Table S1 Ortholog clusters validation using enzymes
involved in sucrose synthesis and degradation. The
ortholog clusters generated using InParanoid and MultiParanoid
were compared with genes annotated manually. The number of
false positives is the number of sequences that were present in an
ortholog cluster but did not match the other genes. The number of
false negatives is the number of sequences that were manually
annotated as an enzyme but were not present in any ortholog
We thank Michael E. Hughes (Yale School of Medicine) for providing the
algorithms used to identify circadian transcripts, Monalisa Carneiro
Sampaio (Universidade Federal de Sao Carlos) for providing plant
materials, and the reviewers that gave us insightful suggestions for the
improvement of this work. Original and supplemental data to this article
can be found at http://sucest-fun.org/wsapp.
Conceived and designed the experiments: CTH GMS. Performed the
experiments: CTH. Analyzed the data: CTH MYN. Contributed
1. Hotta CT , Gardner MJ , Hubbard KE , Baek SJ , Dalchau N , et al. ( 2007 ) Modulation of environmental responses of plants by circadian clocks . Plant, cell & environment 30 : 333 - 349 .
2. Dodd AN , Salathia N , Hall A , Kevei E , Toth R , et al. ( 2005 ) Plant circadian clocks increase photosynthesis , growth, survival, and competitive advantage. Science 309 : 630 - 633 .
3. Pokhilko A , Fernandez AP , Edwards KD , Southern MM , Halliday KJ , et al. ( 2012 ) The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops . Mol Syst Biol 8 : 574 .
4. Kolmos E , Nowak M , Werner M , Fischer K , Schwarz G , et al. ( 2009 ) Integrating ELF4 into the circadian system through combined structural and functional studies . HFSP J 3 : 350 - 366 .
5. Dixon LE , Knox K , Kozma-Bognar L , Southern MM , Pokhilko A , et al. ( 2011 ) Temporal repression of core circadian genes is mediated through EARLY FLOWERING 3 in Arabidopsis . Curr Biol 21 : 120 - 125 .
6. Nusinow DA , Helfer A , Hamilton EE , King JJ , Imaizumi T , et al. ( 2011 ) The ELF4-ELF3-LUX complex links the circadian clock to diurnal control of hypocotyl growth . Nature 475 : 398 - 402 .
7. Helfer A , Nusinow DA , Chow BY , Gehrke AR , Bulyk ML , et al. ( 2011 ) LUX ARRHYTHMO encodes a nighttime repressor of circadian gene expression in the Arabidopsis core clock . Curr Biol 21 : 126 - 133 .
8. Gendron JM , Pruneda-Paz JL , Doherty CJ , Gross AM , Kang SE , et al. ( 2012 ) Arabidopsis circadian clock protein, TOC1, is a DNA-binding transcription factor . Proc Natl Acad Sci U S A 109 : 3167 - 3172 .
9. Huang W , Perez-Garcia P , Pokhilko A , Millar AJ , Antoshechkin I , et al. ( 2012 ) Mapping the core of the Arabidopsis circadian clock defines the network structure of the oscillator . Science 336 : 75 - 79 .
10. McClung CR ( 2011 ) The genetics of plant clocks . Adv Genet 74 : 105 - 139 .
11. Imaizumi T , Tran HG , Swartz TE , Briggs WR , Kay SA ( 2003 ) FKF1 is essential for photoperiodic-specific light signalling in Arabidopsis . Nature 426 : 302 - 306 .
12. Lu SX , Knowles SM , Webb CJ , Celaya RB , Cha C , et al. ( 2011 ) The Jumonji C domain-containing protein JMJ30 regulates period length in the Arabidopsis circadian clock . Plant Physiol 155 : 906 - 915 .
13. Harmer SL ( 2000 ) Orchestrated Transcription of Key Pathways in Arabidopsis by the Circadian Clock . Science 290 : 2110 - 2113 .
14. Filichkin SA , Mockler TC ( 2012 ) Unproductive alternative splicing and nonsense mRNAs: A widespread phenomenon among plant circadian clock genes . Biol Direct 7 : 20 .
15. Staiger D , Green R ( 2011 ) RNA-based regulation in the plant circadian clock . Trends Plant Sci 16 : 517 - 523 .
16. Covington MF , Maloof JN , Straume M , Kay SA , Harmer SL ( 2008 ) Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development . Genome Biol 9 : R130 .
17. Filichkin SA , Breton G , Priest HD , Dharmawardhana P , Jaiswal P , et al. ( 2011 ) Global profiling of rice and poplar transcriptomes highlights key conserved circadian-controlled pathways and cis-regulatory modules . PloS one 6: e16907.
18. Khan S , Rowe SC , Harmon FG ( 2010 ) Coordination of the maize transcriptome by a conserved circadian clock . BMC plant biology 10: 126.
19. Michael TP , Mockler TC , Breton G , McEntee C , Byer A , et al. ( 2008 ) Network discovery pipeline elucidates conserved time-of-day-specific cis-regulatory modules . PLoS Genet 4 : e14 .
20. Edwards KD , Anderson PE , Hall A , Salathia NS , Locke JC , et al. ( 2006 ) FLOWERING LOCUS C mediates natural variation in the high-temperature response of the Arabidopsis circadian clock . Plant Cell 18 : 639 - 650 .
21. Blasing OE , Gibon Y , Gunther M , Hohne M , Morcuende R , et al. ( 2005 ) Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in Arabidopsis . Plant Cell 17 : 3257 - 3281 .
22. D'Hont A , Glaszmann JC ( 2005 ) Unravelling the genome structure of polyploids using FISH and GISH; examples of sugarcane and banana . Cytogenet Genome Res 109 : 27 - 33 .
23. D'Hont A , Glaszmann JC ( 2001 ) Sugarcane genome analysis with molecular markers, a first decade of research . Proc Int Soc Sugar Cane Technol 24 : 556 - 559 .
24. Dal-Bianco M , Carneiro MS , Hotta CT , Chapola RG , Hoffmann HP , et al. ( 2012 ) Sugarcane improvement: how far can we go? Curr Opin Biotechnol 23 : 265 - 270 .
25. Waclawovsky AJ , Sato PM , Lembke CG , Moore PH , Souza GM ( 2010 ) Sugarcane for bioenergy production: an assessment of yield and regulation of sucrose content . Plant Biotechnol J 8 : 263 - 276 .
26. Hotta CT , Lembke CG , Domingues DS , Ochoa EA , Cruz GMQ , et al. ( 2010 ) The Biotechnology Roadmap for Sugarcane Improvement . Tropical Plant Biology 3 : 75 - 87 .
27. Rocha FR , Papini-Terzi FS , Nishiyama MY Jr, Vencio RZ , Vicentini R , et al. ( 2007 ) Signal transduction-related responses to phytohormones and environmental challenges in sugarcane . BMC genomics 8: 71.
28. Papini-Terzi F , Rocha F , Nicoliello R ( 2005 ) Transcription profiling of signal transduction-related genes in sugarcane tissues . DNA Research 38 : 27 - 38 .
29. Lembke CG , Nishiyama MY Jr, Sato PM , de Andrade RF , Souza GM ( 2012 ) Identification of sense and antisense transcripts regulated by drought in sugarcane . Plant Mol Biol 79 : 461 - 477 .
30. Lapidot M , Pilpel Y ( 2006 ) Genome-wide natural antisense transcription: coupling its regulation to its different regulatory mechanisms . EMBO Rep 7 : 1216 - 1222 .
31. Modarresi F , Faghihi MA , Lopez-Toledano MA , Fatemi RP , Magistri M , et al. ( 2012 ) Inhibition of natural antisense transcripts in vivo results in gene-specific transcriptional upregulation . Nat Biotechnol 30 : 453 - 459 .
32. Swiezewski S , Liu F , Magusin A , Dean C ( 2009 ) Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target . Nature 462 : 799 - 802 .
33. Luo C , Sidote DJ , Zhang Y , Kerstetter RA , Michael TP , et al. ( 2012 ) Integrative analysis of chromatin states in Arabidopsis identified potential regulatory mechanisms for Natural Antisense Transcript production . Plant J.
34. Helliwell CA , Robertson M , Finnegan EJ , Buzas DM , Dennis ES ( 2011 ) Vernalization-repression of Arabidopsis FLC requires promoter sequences but not antisense transcripts . PloS one 6: e21513.
35. Hazen SP , Naef F , Quisel T , Gendron JM , Chen H , et al. ( 2009 ) Exploring the transcriptional landscape of plant circadian rhythms using genome tiling arrays . Genome Biol 10 : R17 .
36. Hughes ME , DiTacchio L , Hayes KR , Vollmers C , Pulivarthy S , et al. ( 2009 ) Harmonics of circadian gene transcription in mammals . PLoS genetics 5: e1000442.
37. Panda S , Antoch MP , Miller BH , Su AI , Schook AB , et al. ( 2002 ) Coordinated transcription of key pathways in the mouse by the circadian clock . Cell 109 : 307 - 320 .
38. Hughes ME , Hogenesch JB , Kornacker K ( 2010 ) JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets . J Biol Rhythms 25 : 372 - 380 .
39. Covington MF , Harmer SL ( 2007 ) The circadian clock regulates auxin signaling and responses in Arabidopsis . PLoS Biol 5 : e222 .
40. Vettore AL , da Silva FR , Kemper EL , Souza GM , da Silva AM , et al. ( 2003 ) Analysis and Functional Annotation of an Expressed Sequence Tag Collection for Tropical Crop Sugarcane . Genome research 13 : 2725 - 2735 .
41. Berglund AC , Sjolund E , Ostlund G , Sonnhammer EL ( 2008 ) InParanoid 6: eukaryotic ortholog clusters with inparalogs . Nucleic Acids Res 36 : D263 - 266 .
42. Alexeyenko A , Tamas I , Liu G , Sonnhammer EL ( 2006 ) Automatic clustering of orthologs and inparalogs shared by multiple proteomes . Bioinformatics 22 : e9 - 15 .
43. Herrero E , Kolmos E , Bujdoso N , Yuan Y , Wang M , et al. ( 2012 ) EARLY FLOWERING4 recruitment of EARLY FLOWERING3 in the nucleus sustains the Arabidopsis circadian clock . Plant Cell 24 : 428 - 443 .
44. Kim J , Kim Y , Yeom M , Kim JH , Nam HG ( 2008 ) FIONA1 is essential for regulating period length in the Arabidopsis circadian clock . Plant Cell 20 : 307 - 319 .
45. Pruneda-Paz JL , Breton G , Para A , Kay SA ( 2009 ) A functional genomics approach reveals CHE as a component of the Arabidopsis circadian clock . Science 323 : 1481 - 1485 .
46. Khanna R , Kikis EA , Quail PH ( 2003 ) EARLY FLOWERING 4 functions in phytochrome B-regulated seedling de-etiolation . Plant Physiol 133 : 1530 - 1538 .
47. Murakami M , Tago Y , Yamashino T , Mizuno T ( 2007 ) Comparative Overviews of Clock-Associated Genes of Arabidopsis thaliana and Oryza sativa . Plant and Cell Physiology 48 : 110 - 121 .
48. Rizzini L , Favory JJ , Cloix C , Faggionato D , O' Hara A , et al. ( 2011 ) Perception of UV-B by the Arabidopsis UVR8 protein . Science 332 : 103 - 106 .
49. Kim WY , Fujiwara S , Suh SS , Kim J , Kim Y , et al. ( 2007 ) ZEITLUPE is a circadian photoreceptor stabilized by GIGANTEA in blue light . Nature 449 : 356 - 360 .
50. Grivet L , Arruda P ( 2002 ) Sugarcane genomics: depicting the complex genome of an important tropical crop . Curr Opin Plant Biol 5 : 122 - 127 .
51. Higgins J , Magusin A , Trick M , Fraser F , Bancroft I ( 2012 ) Use of mRNA-seq to discriminate contributions to the transcriptome from the constituent genomes of the polyploid crop species Brassica napus . BMC genomics 13: 247.
52. Miller M , Zhang C , Chen ZJ ( 2012 ) Ploidy and Hybridity Effects on Growth Vigor and Gene Expression in Arabidopsis thaliana Hybrids and Their Parents . G3 (Bethesda) 2: 505 - 513 .
53. Ni Z , Kim ED , Ha M , Lackey E , Liu J , et al. ( 2009 ) Altered circadian rhythms regulate growth vigour in hybrids and allopolyploids . Nature 457 : 327 - 331 .
54. Yilmaz A , Nishiyama MY Jr, Fuentes BG , Souza GM , Janies D , et al. ( 2009 ) GRASSIUS: a platform for comparative regulatory genomics across the grasses . Plant Physiol 149 : 171 - 180 .
55. Perales M , Mas P ( 2007 ) A functional link between rhythmic changes in chromatin structure and the Arabidopsis biological clock . The Plant cell 19 : 2111 - 2123 .
56. Hong S , Song H-R , Lutz K , Kerstetter Ra , Michael TP , et al. ( 2010 ) Type II protein arginine methyltransferase 5 (PRMT5) is required for circadian period determination in Arabidopsis thaliana . Proceedings of the National Academy of Sciences 5 : 1 - 6 .
57. Sanchez SE , Petrillo E , Beckwith EJ , Zhang X , Rugnone ML , et al. ( 2010 ) A methyl transferase links the circadian clock to the regulation of alternative splicing . Nature.
58. Wang X , Wu F , Xie Q , Wang H , Wang Y , et al. ( 2012 ) SKIP Is a Component of the Spliceosome Linking Alternative Splicing and the Circadian Clock in Arabidopsis . Plant Cell 24 : 3278 - 3295 .
59. Lidder P , Gutierrez RA , Salome PA , McClung CR , Green PJ ( 2005 ) Circadian control of messenger RNA stability. Association with a sequence-specific messenger RNA decay pathway . Plant Physiol 138 : 2374 - 2385 .
60. Ietswaart R , Wu Z , Dean C ( 2012 ) Flowering time control: another window to the connection between antisense RNA and chromatin . Trends Genet 28 : 445 - 453 .
61. Carrieri C , Cimatti L , Biagioli M , Beugnet A , Zucchelli S , et al. ( 2012 ) Long non-coding antisense RNA controls Uchl1 translation through an embedded SINEB2 repeat . Nature.
62. Lu Y , Gehan JP , Sharkey TD ( 2005 ) Daylength and circadian effects on starch degradation and maltose metabolism . Plant Physiol 138 : 2280 - 2291 .
63. Smith SM , Fulton DC , Chia T , Thorneycroft D , Chapple A , et al. ( 2004 ) Diurnal changes in the transcriptome encoding enzymes of starch metabolism provide evidence for both transcriptional and posttranscriptional regulation of starch metabolism in Arabidopsis leaves . Plant Physiol 136 : 2687 - 2699 .
64. Piques M , Schulze WX , Hohne M , Usadel B , Gibon Y , et al. ( 2009 ) Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis . Mol Syst Biol 5 : 314 .
65. Dowson-Day MJ , Millar AJ ( 1999 ) Circadian dysfunction causes aberrant hypocotyl elongation patterns in Arabidopsis . Plant J 17 : 63 - 71 .
66. Legnaioli T , Cuevas J , Mas P ( 2009 ) TOC1 functions as a molecular switch connecting the circadian clock with plant responses to drought . EMBO J 28 : 3745 - 3757 .
67. Robertson FC , Skeffington AW , Gardner MJ , Webb AA ( 2009 ) Interactions between circadian and hormonal signalling in plants . Plant Mol Biol 69 : 419 - 427 .
68. Mizuno T , Yamashino T ( 2008 ) Comparative transcriptome of diurnally oscillating genes and hormone-responsive genes in Arabidopsis thaliana: insight into circadian clock-controlled daily responses to common ambient stresses in plants . Plant Cell Physiol 49 : 481 - 487 .
69. Roulin A , Auer PL , Libault M , Schlueter J , Farmer A , et al. ( 2012 ) The fate of duplicated genes in a polyploid plant genome . Plant J.
70. Wang Y , Wang X , Paterson AH ( 2012 ) Genome and gene duplications and gene expression divergence: a view from plants . Ann N Y Acad Sci 1256 : 1 - 14 .
71. Gibon Y , Pyl ET , Sulpice R , Lunn JE , Hohne M , et al. ( 2009 ) Adjustment of growth, starch turnover, protein content and central metabolism to a decrease of the carbon supply when Arabidopsis is grown in very short photoperiods . Plant Cell Environ 32 : 859 - 874 .
72. Cheadle C , Vawter MP , Freed WJ , Becker KG ( 2003 ) Analysis of microarray data using Z score transformation . The Journal of molecular diagnostics : JMD 5 : 73 - 81 .
73. Castillo-Davis CI , Hartl DL ( 2003 ) GeneMerge-post-genomic analysis, data mining, and hypothesis testing . Bioinformatics 19 : 891 - 892 .
74. Vettore AL , da Silva FR , Kemper EL , Souza GM , da Silva AM , et al. ( 2003 ) Analysis and functional annotation of an expressed sequence tag collection for tropical crop sugarcane . Genome Res 13 : 2725 - 2735 .
75. Rohwer JM , Botha FC ( 2001 ) Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data . Biochem J 358 : 437 - 445 .
76. Mockler TC , Michael TP , Priest HD , Shen R , Sullivan CM , et al. ( 2007 ) The DIURNAL project: DIURNAL and circadian expression profiling, model-based pattern matching, and promoter analysis . Cold Spring Harb Symp Quant Biol 72 : 353 - 363 .