Shade Avoidance Components and Pathways in Adult Plants Revealed by Phenotypic Profiling

Abstract

Author Summary
Because plants depend on light for photosynthesis, neighboring plant shade can be detrimental to survival. Many plants sense and respond to neighbor shade to compete for light. Although shade causes responses throughout the plant (collectively known as the shade avoidance syndrome or SAS), most SAS studies have been limited to single-gene analyses in seedlings. Here we move beyond these analyses by taking a multi-gene, multi-trait study of SAS across developmental stages. Recently, whole-genome studies examining large mutant collections have been exploited to determine the pathways and their interactions that combine to determine complex phenotypes. This type of analysis (phenotypic profiling) typically uses thousands of mutants and robotic phenotyping for assaying many characters in the multitude of mutant lines. In this paper, we develop a directed alternative that allows us to take a similar approach to understanding SAS. To reduce the number of mutants required for such an approach, we used a logical selection procedure to define mutants of interest by over-representation analysis of shade-responsive genes. We found at least three different subgroups of shade responses, and that each subgroup had both shared and separate pathways. Also, we found eighteen novel genes involved in SAS. Therefore, our method is useful for multi-dimensional phenotypic profiling without expensive robots.

Full text

RESEARCH ARTICLE
Shade Avoidance Components and Pathways
in Adult Plants Revealed by Phenotypic
Profiling
Kazunari Nozue, An V. Tat, Upendra Kumar Devisetty, Matthew Robinson, Maxwell
R. Mumbach, Yasunori Ichihashi, Saradadevi Lekkala, Julin N. Maloof*
Department of Plant Biology, University of California, Davis, Davis, California, United States of America
* jnmaloof@ucdavis.edu
Abstract
Shade from neighboring plants limits light for photosynthesis; as a consequence, plants
have a variety of strategies to avoid canopy shade and compete with their neighbors for light.
Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The
SAS includes elongation of a variety of organs, acceleration of flowering time, and additional
physiological responses, which are seen throughout the plant life cycle. However, current
mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we
use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS
networks. We used over-representation analysis (ORA) of shade-responsive genes, com-
bined with previous annotation, to logically select 59 known and candidate novel mutants for
phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance
response. In particular, auxin pathway components were required for shade avoidance re-
sponses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway compo-
nents were only required for petiole and flowering time responses. Our phenotypic profiling
allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate
that logical selection of mutants increased success of phenotypic profiling to dissect complex
traits and discover novel components.
Author Summary
Because plants depend on light for photosynthesis, neighboring plant shade can be detri-
mental to survival. Many plants sense and respond to neighbor shade to compete for light.
Although shade causes responses throughout the plant (collectively known as the shade
avoidance syndrome or SAS), most SAS studies have been limited to single-gene analyses
in seedlings. Here we move beyond these analyses by taking a multi-gene, multi-trait study
of SAS across developmental stages. Recently, whole-genome studies examining large mu-
tant collections have been exploited to determine the pathways and their interactions that
combine to determine complex phenotypes. This type of analysis (phenotypic profiling)
typically uses thousands of mutants and robotic phenotyping for assaying many characters
PLOS Genetics | DOI:10.1371/journal.pgen.1004953 April 15, 2015 1/26
OPEN ACCESS
Citation: Nozue K, Tat AV, Kumar Devisetty U,
Robinson M, Mumbach MR, Ichihashi Y, et al. (2015)
Shade Avoidance Components and Pathways in
Adult Plants Revealed by Phenotypic Profiling. PLoS
Genet 11(4): e1004953. doi:10.1371/journal.
pgen.1004953
Editor: Nathan M. Springer, University of Minnesota,
United States of America
Received: April 1, 2014
Accepted: December 11, 2014
Published: April 15, 2015
Copyright: © 2015 Nozue et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All R scripts for this
paper and raw data are available at https://bitbucket.
org/knozue/sasphenotyping. RNA-seq data in this
study have been deposited in the NCBI SRA (Study
ID PRJNA214254) and the NCBI GEO database
(accession GSE66967).
Funding: We acknowledge financial support of
National Science Foundation Integrative Organismal
Systems (http://www.nsf.gov/div/index.jsp?div=IOS;
IOS-0923752) and United States Department of
Agriculture USDA NIFA project (http://www.csrees.
usda.gov/; CA-D-PLB-7226-H) to JNM. The funders

in the multitude of mutant lines. In this paper, we develop a directed alternative that allows
us to take a similar approach to understanding SAS. To reduce the number of mutants re-
quired for such an approach, we used a logical selection procedure to define mutants of in-
terest by over-representation analysis of shade-responsive genes. We found at least three
different subgroups of shade responses, and that each subgroup had both shared and sepa-
rate pathways. Also, we found eighteen novel genes involved in SAS. Therefore, our meth-
od is useful for multi-dimensional phenotypic profiling without expensive robots.
Introduction
Plant canopy shade limits available light for photosynthesis. Because plants are sessile, this pres-
ents a particular challenge. Perhaps as a consequence plants developed a light-quality sensory
system for canopy shade; perception of foiliar shade and/or reflection from neighbor plants
(“neighbor detection”) can induce the shade avoidance syndrome (SAS) is collection of re-
sponses to canopy shade in plants. These SAS responses can be seen in all developmental stages
from seeds to adult plants [1]. Various plant organs elongate under shade, including the hypo-
cotyl (stem) of young seedlings, and the internodes, and leaf petioles of older plants. Further-
more shade induces upward leaf movement, accelerates flowering time (the developmental
transition form vegetative phase to reproductive phase), suppresses shoot branching, and alters
resource allocation [1]. All of these responses can be helpful for promoting survival when there
is competition for light from neighboring plants.
Foliar shade, which has reduced photosynthetically active radiation (PAR) can be detected
both by cryptochrome photoreceptors due to its reduced intensity of blue light and by phyto-
chrome photoreceptors due to its reduced ratio of red to far-red light [1]. Remarkably, plants
can perceive nearby neighbors even before true shading and the concomitant reduction in
PAR. This type of neighbor detections is possible because, even though PAR is not reduced,
light reflected from neighbors has a reduced ratio of red to far-red light detectable by phyto-
chromes [1,2]. Here we use a neighbor-detection protocol to focus on phytochrome-mediated
responses. Detailed analysis of shade induced hypocotyl elongation has revealed that light per-
ception activates transcription factors (TF) that, in turn, modulate plant hormone pathways to
promote organ growth. For example, the PHYTOCHROME-INTERACTING FACTOR (PIF)
5 TF protein is stabilized under shade and induces transcription of genes important for synthe-
sis of the growth-promoting hormone auxin [3,4]. Another example is that upon shade treat-
ment PIF7 is dephosphorylated and activated to induce YUCCA (YUC) 2, YUC5, YUC8, and
YUC9 auxin biosynthetic genes [5].
Plant hormones regulate many aspects of development and growth. At least five plant hor-
mone pathways are related to SAS; auxin, brassinosteroid (BR), gibberellic acid (GA), ethylene,
and cytokinin (CK). Many auxin or BR responsive genes are induced by end-of-day far-red
treatment (EODFR, a proxy for shade treatment) and both auxin (big and shade avoidance 3
(sav3) / tryptophan aminotransferase of Arabidopsis 1 (taa1)) and BR (rotundifolia3 (rot3))
mutant showed reduced shade-induced or EODFR-induced petiole elongation as well as
shade-induced gene expression [6,7]. There is some evidence that GA and CK are involved in
leaf SAS [8,9,10]. Shade also influences jasmonic-acid (JA) mediated plant immune system
[11] and reduced volatile JA levels [12]. However, the entire network of light signaling and hor-
mone pathways in regulation of shade avoidance are unclear. Also, the extent of shared and
separate pathways for each shade avoidance response is not currently known. In part this is
Shade Avoidance Syndrome in Adult Plants
PLOS Genetics | DOI:10.1371/journal.pgen.1004953 April 15, 2015 2/26
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.

because each SAS mutant has been tested under different experimental conditions making it
difficult to compare the phenotypic consequences of each mutant.
Phenotypic profiling of genetic mutants with high-throughput phenotyping is a powe rful
method to tease out complex gene networks [13]. Systematic phenotypic profiling of multiple
traits originated with bacterial studies, followed by studies on single eukaryotic cells such as
yeast and cultured animal cells [14]. Multi-dimensional phenotypic profiling of gene-perturbed
multi-cellular organisms had been done both in invertebrates and vertebrates [15,16,17]. In
plants phenotypic profiling of recombinant inbred lines or natural population has been con-
ducted for QTL analysis or genome-wide association studies [18], but it has not yet been ap-
plied to induced mutants in plants.
Advances of automatic and robotic technologies made it possible to conduct high-through-
put phenotyping. In plant, high-throughput robotic phenotyping systems had been reported
(reviewed in [19,20]), but its use in research has only recently been published [21]. Profiling of
multiple phenotypes in selected gene-perturbed plants has been repo rted in root epidermal cell
patterning study [22] and red-light signaling [23,24], which showed effectiveness of reverse-
genetics approaches to recover mutants of interest from transcriptome data.
Here we extend this approach to multiple phenotypes to develop a systems-level understand-
ing of shade avoidance. We took advantage of a newly developed semi-automated leaf shape
measurement system for throughput measurement of shade avoidance in leaves [25]. Further-
more, we aimed to narrow down candidate genes involved in SAS to be screened instead of
screening of entire knockout mutant collection or mutagenized population. For this purpose, we
selected candidate mutants based on over-representation analysis of shade-responsive genes in
leaves. Our broad phenotypic profiling of hypocotyl, leaf, and flowering time in selected mutants
has allowed us to dissect the complex shade avoidance syndrome network.
Results and Discussion
Shared and separate gene sets are enriched in shade-responsive genes
in Arabidopsis seedling and leaf/apical region
To begin to define the genes required for SAS in leaf/apical region, after the seedling stage we
performed an expression profiling experiment to find gene induces or repressed by simulated
shade. Previous SAS expression profiling experiments have used microarrays and focused on
seedlings or specifically on the petiole or leaf blade after EODFR treatment [6,7,26]. To obtain a
broader view of expression changes in older plants, we harvested leaf and shoot apex tissue and
used RNAseq (statistics are presented in S1 Table) since the most common Arabidopsis micro-
array (Affymetrix ATH1) only covers about 70% of defined genes in the transcriptome. We
compared gene expression in samples treated with simulated shade (white light supplemented
with far-red light to achieve R/FR of 0.5 and 80–100 μE PAR) for 1 hour or 4 hours to untreated
control samples (R/FR = 1.9 and 80–100 μE PAR) and found a total of 164 and 97 genes to be
differentially expressed (FDR <0.001; S2 Table see Materials and Methods). Most known shade
induced genes in leaves were found in our list (bold text in S2 Table, 1 hour after onset of shade
treatment) [3,4,7,27,28,29]. There is a high correlation of expression fold changes by shade treat-
ment between our data and published microarray data in leaf [6](S1 Fig), confirming that our
RNA-seq based transcriptional analysis is reliable. Significant correlation of shade-responsive
genes between seedlings and leaves indicates common mechanisms exist between these two or-
gans (S1 Fig). In addition we identified 38 (1 hour treatment) and 19 (4 hour treatment) genes
not present on the Arabidopsis ATH1 microarray, including the known shade induced gene
PHYTOCHROME-INTERACTING-FACTOR-3 LIKE I (PIL1)[3,30], while 50 (1 hour) and 68
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(4 hour) genes were on ATH1 but not previously found as shade-responsive genes in EODFR
treated petiole or leaf (S2 Table).
GO enrichment analysis showed that plants respond to 1 hour and 4 hour shade treatment
differently (Tables 1 and 2). Two GO terms common to both time points were GO:0009733 (re-
sponse to auxin stimulus) and GO:0009753 (response to JA stimulus). Known shade avoidance
related genes were also enriched in the 1 hour treatment, but not in the 4 hour treatment. Plant
immune related pathways (GO:0009611 (response to wounding) and GO:0009617 (response to
bacterium), which are related to the JA pathway, are enriched in the 4 hour treatment.
It has been described that some plant hormone pathways, such as auxin, BR, and GA, are in-
volved in SAS [1]. To gain a better understanding of involvement of hormone pathways in
SAS, over-representation analysis (ORA) was done to test if any hormone-responsive genes
were enriched among the shade-regulated genes (Table 3). Consistent with ORA with previous
microarray data of leaf upon EODFR treatment, auxin, BR, and JA pathways were enriched
[6]. In addition, we found that ethylene and abscisic acid (ABA) pathways were enric hed in our
data sets. Involvement of ethylene in SAS was suggested because shade increases ambient ethyl-
ene levels in Sorghum [31] and tobacco [32]. However ethylene production is required only for
early response to shade in petiole and stem, but not response in leaf angle [32], although ethyl-
ene induces leaf hyponasty [33], hypocotyl elongation [34], and stem elongation [35]. Involve-
ment of ABA in SAS is known for shade-suppressed branching [36], but has not been reported
to be involved in th e shade-avoidance responses examined in this paper. Interestingly leaves of
four-day shade treated tomato plants have increase level of ethylene precursor and ABA [37].
In summary, the significant differences in the shade-responsive transcriptome at these two
time-points reflected dynamic temporal changes of early SAS signaling cascade.
Table 1. GO category analysis of 1 hour shade-responsive genes in leaf/apical region.
category Term over_represented_pvalue over_represented_padjust value
GO:0009733 response to auxin stimulus 2.70e-41 6.70e-38
GO:0010583 response to cyclopentenone 7.49e-07 6.61e-04
GO:0009753 response to jasmonic acid stimulus 8.00e-07 6.61e-04
GO:0009641 shade avoidance 3.03e-06 1.88e-03
GO:0009630 gravitropism 5.48e-06 2.72e-03
GO:0009741 response to brassinosteroid stimulus 7.92e-06 3.27e-03
Plants were grown on soil under simulated sun (R/FR = 1.9) and long-day (16/8 hours) conditions for two weeks and then were transferred to simulated
shade (R/FR = 0.5) or left in the sun condition. Above-ground parts excluding hypocotyl were collected at 1 hour shade treatment in leaf. Terms with
adjusted p-value by Benjamini & Hochberg method [135] <0.01 were selected from GOseq analysis.
doi:10.1371/journal.pgen.1004953.t001
Table 2. GO category analysis of 4 hour shade-responsive genes in leaf/apical region.
category Term over_represented_pvalue over_represented_padjust value
GO:0009753 response to jasmonic acid stimulus 5.27e-10 1.31e-06
GO:0009611 response to wounding 2.29e-09 2.84e-06
GO:0055114 oxidation-reduction process 2.75e-07 2.27e-04
GO:0080167 response to karrikin 2.08e-06 1.10e-03
GO:0009733 response to auxin stimulus 2.21e-06 1.10e-03
GO:0009617 response to bacterium 2.92e-06 1.21e-03
Same as Table 1 except 4 hour shade treatment instead of 1 hour shade treatment
doi:10.1371/journal.pgen.1004953.t002
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Next we asked if the previous seedling shade transcriptome data [7] shows the same trends
as our data. We found that there were both common and specific GO terms between the hypo-
cotyl and our leaf/apical region data sets (Table 1, 2, and 4). Of the common terms, we focused
on GO:0009733 (response to auxin stimulus), GO:0009741 (response to BR stimulus), and
Table 3. Over-representation analysis of shade-responsive genes and hormone responsive genes.
1 hour shade treatment 4 hour shade treatment
regulated by hormone UP DOWN UP DOWN
IAA UP 4.59e-75 1 1.2e-06 0.138
IAA DOWN 1 0.0642 0.152 1
BR UP 0.000269 1 1 1
BR DOWN 1.04e-10 1 0.00355 1
GA UP 1111
GA DOWN 0.00148 1 1 1
JA UP 0.0083 0 8.34e-18 0.0567
JA DOWN 1.9e-12 1 0.000406 1
CK UP 0.00259 1 1 1
CK DOWN 1111
ABA UP 0.0258 0.431 1.31e-07 1
ABA DOWN 0.00141 0.252 0.0064 1
ACC UP 1.58e-05 1 0.000106 1
p-values were calculated by GOseq [129] and adjusted by Benjamini & Hochberg correction [135].
doi:10.1371/journal.pgen.1004953.t003
Table 4. GO category analysis of 1 hour shade-responsive genes in hypocotyl.
category Term over_represented_pvalue
GO:0009719 response to endogenous stimulus 1.72e-38
GO:0010033 response to organic substance 1.46e-32
GO:0009725 response to hormone stimulus 9.18e-32
GO:0009733 response to auxin stimulus 6.02e-28
GO:0042221 response to chemical stimulus 3.04e-24
GO:0050896 response to stimulus 1.23e-17
GO:0009741 response to brassinosteroid stimulus 5.38e-16
GO:1901700 response to oxygen-containing compound 2.03e-12
GO:0033993 response to lipid 7.38e-10
GO:0097305 response to alcohol 1.25e-09
GO:0009639 response to red or far red light 2.94e-09
GO:0014070 response to organic cyclic compound 4.45e-09
GO:0065007 biological regulation 1.62e-08
GO:0009612 response to mechanical stimulus 2.55e-08
GO:0009641 shade avoidance 5.70e-08
GO:0010200 response to chitin 2.78e-07
GO:0010243 response to organic nitrogen 4.77e-07
GO:0009416 response to light stimulus 8.17e-07
GO:0009314 response to radiation 2.87e-06
GO:0009628 response to abiotic stimulus 4.51e-06
Same as Table 1 except 1 hour shade treatment in hypocotyl instead of 1 hour shade treatment in leaf. Expression data is from [7]. Terms with p-
value<1e-5 were selected from amiGO analysis.
doi:10.1371/journal.pgen.1004953.t004
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GO:0009641 (shade avoidance). Among the leaf/apical region- specific terms GO:0009753 (re-
sponse to JA stimulus) is of particular interest because the role of JA pathways in morphologi-
cal aspects of SAS is not fully understood. Based on our ORA we chose to include mutants of
genes in these categories in our phenotypic profiling (see below).
Leaf phenotype profiling of fifty-nine mutant lines
We hypothesized that shade-responsive genes and/or pathways are required for proper SAS be-
cause among the 34 causal genes in known hypocotyl SAS mutants 11 (TAA1, PIN3, YUC2,
YUC5, YUC8, YUC9, BIM1, GAI, PHYB, PAR1, HAT3) of them have shade-responsive tran-
scripts (S3 Table). Based on our differentially expressed gene list and previous knowledge, we
chose 59 mutant lines encompassing 59 mutant genes (although some lines have more than
one mutant gene) from nine categories (auxin, GA, JA, BR, light signal ing, shade avoidance,
flowering time, leaf size, and unknown shade responsive genes) (Fig 1). We prioritized auxin
and JA pathways because both pathways were enriched in both time points of our transcrip-
tome analysis (see above).
Petiole elongation is an important component of SAS, but most leaf phenotype measurement
software does not report petiole and blade length. Phenotyping of mutants/overexpressors with
a pathway of interest is a direct method to test if the given gene in the pathway is involved in
phenotype of your interest. At the first step towards high throughput petiole and blade pheno-
typing, we developed LeafJ, an ImageJ plug-in, which is more accurate and faster measurement
system than manual method [25].
To normalize petiole elongation between genotypes with different leaf size, we calculated
ratio of petiole length to blade length. St atistical significance between Col under sun treatment
and shade treatment effects was examined by a mixed effects model (Fig 2). Mutants that
showed a statistically different response to shade when compared to the corresponding wild-
type (P < 0.05; see Methods) were considered to have a significant SAS phenotype. In Col, we
could detect significant shade-induced petiole elongation and an increase of the petiole length
to leaf blade length ratio (S2F Fig and S3 Table), but not in other leaf blade parameters (length,
width, and area) ( S2C –S2E Fig). Some other studies have reported that leaf area does increase
or decrease upon shade treatment; the differe nces between these studies and ours may be due
to differences in plant growth conditions or because the developmental stage of our leaves
might be too young to show these responses [7, 38,39,40]. Comparing the kinetics of leaf devel-
opment under both light condition could tell us when shade-responsive organ elongation hap-
pens and should be examined in future studies.
Screening these 59 mutant lines, we found 33 mutants that showed differences in at least
one trait from the background ecotype (Fig 2, and S3 Table). These 33 mutants include genes
in the auxin, jasmonic acid, and light signaling pathways. Involvement of auxin in petiole elon-
gation upon far-red light treatment has been previously reported [5], whereas there have not
been any reports of JA involvement in petiole shade avoidance. Details of these mutants will be
discussed below. It is important to note that we assay a leaf series from each plant where some
leaves are still expanding and others are mature. As a consequence, the mutations that we have
identified could be affecting leaf development itself or developmenta l timing (the proportion of
expanding to expanded leaves).
Shade-induced acceleration of flowering time of fifty-nine mutant lines
Compared to other SAS phenotypes, only seven mutant lines for acceleration of flowering time
have been described; phytochrome mutants (phytochromeB (phyB) phyD, phyB/D/E) [41,42],
known flowering time mutants (constans (co) and gigantea (gi)) [43], a circadian clock
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Fig 1. Mutants used in SAS phenotypic profiling. Shade-induced genes are shown in magenta bold and shade-repressed are shown in green.
Background genotype for almost all mutants is Col (other genotypes are show in parentheses).
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Fig 2. Phenotypic profiling of 59 mutants/overexpressors. For hypocotyl phenotype, plants were grown
under continuous simulated sun conditions (R/FR = 1.3) for four days and further grown for three days under
either simulated sun or simulated shade (R/FR = 0.5). For leaf phenotypes, plants were grown under long day
conditions (16 hour light/8 hour dark) with approximately 90 μE PAR (R/FR = 1.9). Two week old plants were
further grown for 12 days under either simulated sun (R/FR = 1.9) or simulated shade (R/FR = 0.5). For
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component (early flowering 3 (elf3)) [44], an auxin-related mutant (big1)[45], and a mediator
complex mutant (phytochrome and flowering time 1 (pft1)) [46]. Our SAS phenotypic profiling
provided additional mutants for shade-accelerated flowering. In our condition shade treatment
accelerated flowering time about 10% in Col, which is less effective than previously reported
(35–40% [42,43,47]). This small acceleration is because our shade treatment started when plants
were already close to flowering in long day conditions. Perhaps because of this we observed a
strong inverse correlation between flowering time shade response and flowering time in sun
(S3H Fig; genotypes that flowered later in sun were more responsive to shade). Given this corre-
lation we defined two categories flowering time shade response mutants. One category (“flower-
ing.time” in Fig 2) consists of mutants whose log2 shade response is different from Col-0. The
second category (“flowering.time.resid” in Fig 2) are those mutants whose response is signifi-
cantly different from that expected based on their flowering time in the sun (see Methods). Even
though our experimental conditions were suboptimal for detecting acceleration of flowering
time and we could not reproduce reduced responses to shade of pft1, we did find six new flower-
ing time SAS mutants; cryptochrome (cry) 1 cry2 (cry1/2), altered-tryptophan regulation 4 (atr4),
reveille 8/lhy-cca1-like 5 (rve8/lcl5), coronatine insensitive 1 (coi1), myc2/jasmonate insensitive1
(jin1), and jasmonate-zim-domain protein 5 (jaz5) (Fig 2, and S3 Table). Details of these mu-
tants will be discussed below.
Separate and overlapping pathways for three different shade avoidance
responses
To investigate how many of the genes under study are required for normal shade avoidance re-
sponse in both the leaf and the hypocotyl, we assayed hypocotyl SAS in the same 59 mutant
panel (S3A Fig, Fig 1, and S3 Table). Among the eighteen previously reported hypocotyl SAS
mutants tested, we observed altered SAS phenotypes in six mutants (28%). This relatively low
validation rate is probably due to differences in growth conditions such as day length, the ratio
of red to far -red used for sun and shade, or whether shade was applied throughout the day or
simulated by EODFR. In addition to previously described SAS mutants, we discovere d seven
mutant lines with previously unknown hypocotyl SAS defects (gibberellin 20-oxidase (ga20ox)
1 ga20ox2 (ga20ox1/2), phytochrome rapidly regulated (par) 2–1, indole-3-acetic acid inducible
(iaa)6iaa19 (iaa6/19), light-regulated zinc finger protein 1 (lzf1), spat ula (spt), elongated hypo-
cotyl 5 (hy5), cry1/2, and atr4).
Our mutant phenotypic profiling also revealed that two different indices for one phenotype
(“petiole length” and ratio of “petiole blade length ratio” for SAS in petiole; “flowering.time”
and “ flowering.time.resid” for SAS in flowering time) were clustered together, two elongation
phenotypes (hypocotyl elongation and petiole elongation) were clustered while the two
flowering time phenotype plants were grown in the same condition with leaf phenotyping and days after
stratification at bolted was used for flowering time index. For phenotype clustering heatmap, differences
between shade and sun values (except flowering time) were normalized and centered on Col (i.e., Col
value = 0) and visualized with color coding (magenta indicates larger response than Col while green indicates
reduced response relative to Col). For flowering time, responses to shade were normalized against flowering
time under sun condition to eliminate strong dependencies of response on flowering time under sun condition
(see S2H Fig for normalized data and S2G Fig for non normalized data). Asterisks (*) from hypocotyl.length
to flowering.time indicate a significant difference from corresponding wild type (p-value<0.05). Although
SPT_ox appeared to be a SAS mutant in flowering time (residual method, S3H Fig), it was eliminated from
SAS mutants in flowering time (residual method) because its background genotype (Ler) also showed a
similar shift from the regression line. Colors of mutant names correspond to groups found in Fig 1. Clustering
of mutants according to its phenotype is shown in the dendgrogram on the right. Clustering of traits is show on
the top dendrogram. Known phenotypes for hypocotyl, petiole, or flowering time are shown in colored boxes
(yellow-green for less response, blue for normal response, and magenta for exaggerated response).
doi:10.1371/journal.pgen.1004953.g002
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elongation phenotypes and flowering time were distinct (top dendrogram in Fig 2). Examining
which genes have mutant phenotypes for each trait revealed that there are common and sepa-
rate pathways for each shade avoidance response (Fig 2 and S3 Table). Auxin-related genes are
required for all responses; phytochromes and three TFs (LZF1, HY5, and PIL1) were involved
in both hypocotyl and leaf responses; cryptochromes are involved in both hypocotyl and flow-
ering-time response; and JA related pathways were involved in petiole and flowering time re-
sponses. Twelve genes were required specifically for petiole response and two genes were
required specifically for hypocotyl response (Fig 2 and S2 Table). Our results also showed that
our strategy was powerful for identifying new genes required for these shade avoidance re-
sponses, including genes in light, auxin, JA, and BR pathways (S2 Table). Details of those genes
will be discussed in following sections.
Differences in the sets of genes required for shade avoidance response in hypocotyls and
leafs are consistent with GO analysis of shade-responsive genes in hypocotyl or leaf tissue
(Table 1, 2, and 4). For example GO:0009753 (response to JA stimulus) is only found in leaf
data sets and we found that mutants affecting the JA pathway only affected SAS phenotypes in
adult plants. There is a previous report of JA affecting seedling SAS, but this was under ex-
tremely low R:FR (0.068) and JA was found to act by modulating phyA signaling [48]. Under
the more moderate low R:FR conditions used in this study and for the seedling microarray as-
says [ 7], phyA is not involved (Fig 2) and phyB is the major receptor for shade. Thus, under
moderate shade conditions, JA is likely to affect adult rather than seedling SAS.
Our findings that pathways of flowering time in response to shade were different from those
of hypocotyl are consistent with previous data. For example, altered ARABIDPSIS THALIANA
HOMEOBOX PROTEIN 2 expression changed shade-avoidance responses in hypocotyl [49]
but not in flowering time [43]. In addition, supressor of phytochrome a-105 (spa1 / 2/3/4) qua-
druple mutant and constitutive photomorphogenesis 1 (cop1) show shade-induced acceleration
of flowering time, but did not show shade-induced hypocotyl elongation [50].
Updated shade avoidance syndrome pathways
Among 59 tested mutant lines we could detect 33 mutants with defects in at least one shade
avoidance response, including 20 new mutant lines (Fig 2). A schematic diagram of SAS signal-
ing pathways is shown in Fig 3. SAS phenotypes with mutants used in this study and known SAS
mutants are summarized in S3 Table. Below we discuss details of the pathways corresponding to
each mutant category in Fig 1 and then discuss phenotypic clustering of SAS mutants (Fig 2).
Light signaling pathway. We simulated canopy shade solely by changing the R/FR ratio,
which is perceived by phytochromes. Five PHY genes in Arabidopsis thaliana have partially
overlapping functions [51]. Our phenotypic profiling showed PHYB is the major photoreceptor
and PHYA has minor function with PHYB in the SAS responses of hypocotyl and leaf in low
R/FR condition because phyA mutant phenotypes could be observed only in a phyB mutant
background, consistent with previous data [1](S3 Table, S3A, S3B, and S3F Fig). The situation
is different for flowering time where PHYB does not dominate the response, instead it is redun-
dant with PHYD and PHYE [47]. This is consistent with our data that phyB and even phyA/B
showed normal shade acceleration of flowering time (Fig 2).
We found that the CRY blue-light photoreceptors were important for proper hypocotyl and
flowering time SAS under simulated canopy shade. At first this is surprising because our simu-
lated sun and shade conditions only have altered R/FR ratios but have the same blue irradiance.
However, CRY1 and CRY2 blue light photoreceptors of Arabidopsis thaliana are known to in-
teract genetically with phytochrome signaling in other photomorphogenic responses
[52,53,54,55,56,57,58,59,60]. There are numerous molecular events known to occur upon
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phytochrome activation that may explain functional interaction between phytochromes and
cryptochromes. One example is light-dependent PHYB-CRY1 interaction [61] and light depen-
dent CRY-COP1 E3 ligase interaction [62]. Recent studies showed COP1 and its interacting
proteins SPA1/2/3/4 are also required for SAS in hypocotyl [50,63,64], so that COP1-SPAs sys-
tem could be a key component of phytochrome-cryptochrome interaction observed in our data.
Although the cry1/2 double showed reduced SAS in hypocotyl and reduced acceleration of flow-
ering time, the mode of action for these two traits are different. cry1/2 hypocotyls showed consti-
tutive shade avoidance, while cry1/2 showed constitutive exaggerated “sun” phenotype for
flowering time, indicating that the interaction between phytochrome and cryptochrome signal-
ing are different between these two traits. Possible mechanisms of the differences could be relat-
ed to photoperiod-dependent flowering time regulation by CRY2 [65]. CRY2 interacts with SPA
in blue-light dependent manner, which prevents CO degradation by COP1-SPAs [66]. Also
CRY activates a subset of bHLH TFs in a blue light dependent manner; these bHLHs in turn ac-
tivate the flowering time master gene, FT [67]. Since cryptochromes are required to sense de-
pleted blue light under canopy shade [68,69], plants may have evolved a cross-talk system
between phytochrome and cryptochrome signaling systems to coordinate shade avoidance syn-
dromes response in natural condition.
Fig 3. Schematic representation of proposed signal transduction for shade-avoidance syndrome.
PIFs represent PIF3, PIF4, PIF5, PIF7, PIL1, and SPT. JAZs represents JAZ5 and JAZ10. Black lines
represent genetic interactions, blue lines show direct interactions, and yellow lines show hormone
biosynthesis/metabolism. Lines without arrowheads are for interactions where directionality is unknown.
Dashed lines show hypothetical interactions. SCL13 is omitted because of its unknown function within
this context.
doi:10.1371/journal.pgen.1004953.g003
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Among TFs in light signaling pathways that we tested (Fig 1), our data showed that HY5,
LZF1, PIL1, and SPT were required in common between hypocotyl and petiole responses,
while PIF1 , PIF3, PIF4, PIF5, PAR1, LONG HYPOCOTYL IN FAR-RED (HFR1), SALT TOLER-
ANCE (STO), and SALT TOLERANCE HOMOLOG2/ B-BOX DOMAIN PROTEIN 21 (STH2/
BBX21) were required in petiole response and PAR2 was required in hypocotyl.
Among them SPT is of particular interest because of its unique spatial expression pattern at
the boundary between the leaf blade and petiole, where petiole cell proliferation occurs [70].
Thus the spatial expression pattern of SPT could explain the reduced SAS petiole elongation
phenotype of spt-11. Also the severe allele of SPT mutant (spt-11) showed less shade avoidance
responses than mild allele (spt-12)(S3 Table), consistent with the reported differences of peti-
ole length and leaf blade area phenotype between spt-11 and spt-12 [70]. SPT regulates gynoeci-
um development by activating genes involved in shade avoidance [71]. Similar mechanisms of
SPT regulation of shade avoidance are likely conserved in shade-induced petiole elongation.
It is well recognized that PIFs are growth-promoting proteins whose activities are modulat-
ed by a variety of environmental factors [72]. In addition to PIF4 and PIF5, our data showed
that both PIF1 and PIF3 are new positive regulators of SAS in petiole (Fig 2). PIF1 is known to
regulate hypocotyl growth [72,73] as well as non-growth related processes such as chlorophyll
biosynthesis [74]. PIF3 is also known to regulate hypocotyl growth (reviewed in [75]), but the
role of either gene in regulating adult plant growth is not known. We confirmed the pif3 result
by examining a second allele, pif3-3 [76], and found that pif3-3 also had a significant petiole
SAS defect (S4 Fig;p<0.01). In hypocotyl growth, PIF1/3/4/5 regulate overlapping and specific
target genes [77], and it seems likely that many of these targets also contribute to petiole SAS.
We found that a knock-out mutant of a PP2C-type phosphatase gene, MISREGULATED IN
DARK (MIDA) 9, showed reduced petiole shade response. MIDA9 has been shown to be in-
volved in hook formation and its expression is regulated by PIF3 [23], consistent with our find-
ing that pif3 mutant also showed reduced petiole shade response. We previously found that
MIDA9 is induced by PIF4 and/or PIF5 during hypocotyl growing phase [78], suggesting that
MIDA9 is involved in growth-control networks. Another link is inactivation of H
+
-ATPase ac-
tivity (required for cell wall loosening) via dephosphorylation of H
+
-ATPase by MIDA9 phos-
phatase 2C-typeD [79]. It would be interesting to know if other PIFs and SPT controls
MIDA9 expression.
Auxin pathways. Previous studies have implicated auxin as being important in hypocotyl
and leaf SAS [80]. Here we extend those studies to identify specific auxin signa ling components
important for SAS in various organs. Consistent with prior studies [4,5,6,7], both our transcrip-
tome data and mutant phenotypic profiling showed auxin pathways were involved in hypocot-
yl and leaf shade avoidance responses. By assaying many auxin pathway genes we can begin to
assign shade avoidance functions to specific components.
Our data showed that components of auxin pathway required for each response are differ-
ent. For example, two different gene families in one auxin biosynthesis pathway contribute dif-
ferently in three separate shade avoidance responses. Although TAA1 protein catalyzes auxin
biosynthesis in the same pathway as YUC proteins [81,82], TAA1 was required only for hypo-
cotyl shade avoidance response, while YUC2/5/8/9 genes were required for all shade avoidance
responses (Fig 2; see Müller-Moulé et al., submitted, for a more detailed analysis). These differ-
ences could be explained by lower gene expression level of TAA1 in leaves, in contrast of ubiq-
uitous expression of YUC2/5/8/9 (S5 Fig). Among shade-induced AUX/IAA family genes (see
S2 Table), we tested the function of IAA6, and IAA19. The iaa6/19 double showed a defect only
in hypocotyl and leaf SAS, but not in acceleration of flowering time. Therefore IAA6/IAA19 are
required for response to shade in elongation. Similar to other auxin-related phenotypes, these
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shade-induced AUX/IAA genes likely function redundantly with other family members in the
other two responses [83].
From transcriptome data it is not clear whether the auxin pathway is involved in accelera-
tion of flowering time by shade, but phenotypic profiling indicates its involvement in flowering
time control in response to shade. An auxin overproducing mutant (atr4)[84] and reduced
auxin level mutant (yuc2/5/8/9 and sav3/taa1) showed opposite flowering responses, support-
ing a role of auxin in this phenomenon (Fig 2).
How are light signaling and auxin pathways connected? All available data suggested that
shade increases active auxin content by inducing auxin biosynthesis gen es (YUCs) [80].
Among the PIFs, connection of PIF4/5/7 and SPT to auxin has been reported [4,5,78,85,86,87].
At least some SAS traits are reduced in single or multiple pif4/5/7 mutants (summarized in S3
Table). In our study, yuc2/5/8/9 mutant is defective in all three shade-avoidance responses,
which is consistent with upregulation of YUC2/5/8/9 genes by PIF7 [5] and YUC5/8/9 by PIF5
[4]. Interaction of TERMINAL FLOWER 2
(TFL2) protein with IAA6 and IAA19 could be an-
other pathway to link SAS and auxin [88], although we were not able to test TFL2 in this study.
Nevertheless, tfl2 showed reduced shade avoidance (EODFR response) in hypocotyl [88].
JA pathway. Evidence showing a link between light and JA pathways has been accumulat-
ing for some time (reviewed in [89]). For example JA mediated defense responses are attenuat-
ed in shade [11]. This effect is mediated by COI1-JAZ10-dependent, salicylic acid-independent
mechanisms [90]. Previous work also implicated JA signaling in phyA mediated responses to
very low R:FR [48]. In tomato, stems of plants treated with shade for four days showed reduced
JA levels [ 37]. Our transcriptome ORA with hormone responsive genes also shows shade-at-
tenuation of the JA pathway (Table 3).
While it is known that shade attenuates JA responses, we used JA pathway mutants (Fig 1)
to probe which JA components are required for SAS. Because PHYA is not important for the
moderately low R/FR used in our study (see “light signaling pathway” section above) this en-
ables us to determine whether JA is important for the typical PHYB mediated shade responses
examined here. Our phenotypic profiling of these mutants showed that the JA pathway is in-
volved in shade avoidance responses in leaf and flowering time, but we did not find significant
effects on hypocotyl response (Fig 2, and S3 Table). This is consistent with GO analysis of tran-
scriptomes of hypocotyl and juvenile plants (Table 1, 2, and 4) where JA GO categories were
over-represented in the leaf/apical region but not in the hypocotyl data set. Reduced JA mediat-
ed plant immunity by shade is found in adult plants, but it is not known if this is true for hypo-
cotyl. Comparison of shade-responsive genes between hypocotyl and leaf/apical region and
comparison of phenotypic profiling predict that JA mediated plant immunity in hypocotyl is
not affected by shade. In conclusion, we found that interaction of JA pathways and SAS net-
work were not unidirectional but bidirectional.
Interestingly, partially different subsets of JA pathway genes are required for normal leaf
and flowering shade response: COI1 and JASMONATE RESISTANT 1 (JAR1) for leaf response
and COI1 and JAZ5 for flowering time response. What might drive these differences? JAR1 en-
codes a protein that catalyzes conjugation of Ile to JA to produce active JA, which is required
for JA mediated immunity [89]. Therefore one possibility is that JA-Ile is the active comp onent
for leaf response while other active JA-related compou nds such as OPDA or cis-jasmone [89],
could be the active components for flowering time. Another possibility is that JAZ5 acts in a
flowering time specific pathway.
It is interesting that JA biosynthesis mutants (allene oxide synthase (aos
) and
12-oxophytodienoate-reductase 3 (opr3)) still retained weak shade-avoidance responses al-
though a JA receptor mutant (coi1-16) had a defect of shade avoidance responses in petiole and
flowering time (Fig 2). The fact that opr3 mutants retaining JA biosynthesis in certain
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condition [91] may explain this. Alternatively, native JA related compounds released from
neighbor plants in our experiments might partially rescue our JA biosynthesis mutants.
MYC2 is a basic helix-loop-helix (bHLH) TF important for JA mediated immune responses
that acts redundantly with its homologs, MYC3 and MYC4 [92]. Mutation in MYC2 slightly re-
duced SAS in adult plants (Fig 2, and S3 Table), raising the possibility that the MYC2/3/4 re-
dundancy is also true for SAS. PHYTOCHROME AND FLOWERING TIME 1 (PFT1) encodes
the conserved MED25 subunit of the mediator complex, which is involved in JA-mediated im-
mune system as well as light-signaling pathways [46, 93,94,95]. Our hypothesis that pft1 mutant
impairs SAS was not confirmed because pft1 did not show detectable differences compared
with Col in any phenotypes we tested.
Our phenotypic profiling data showed less contribution of JA related-genes to shade-induced
hypocotyl elongation than expected from previous data. For example, it was previously reported
that exogenous MeJA application inhibited hypocotyl growth [96] and that this effect was influ-
enced by PHYB [97]. Another examples is that coi1 mutants showed an increases response to
low R/FR [48]. The inconsistency with our results could be due to differences between exoge-
nous JA application in the previous study versus the use of JA pathway mutants in our study, or
due to differences in light conditions: previous studies used continuous monochromatic red
light or extremely low R/FR (0.068), while our study used low R/FR (0.5). Therefore how light
quality influences JA-dependent SAS is of future interest.
How JA pathways modulate petiole elongation is also unclear. Possible mechanis m is pro-
motion of PIF activities by binding of JAZ proteins to DELLA proteins that suppress PIF pro-
tein activities) [98]. Another link between JA and growth is that exogenous MeJA delays the
start of endoreduplication cycle [99]. Interaction of these factors is of future interest.
Gibberellic acid pathways. The GA pathway is involved in shade-induced hypocotyl and
petiole elongation as shown by GA biosynthesis inhibitor treatments and a GA deficient mu-
tant (ga requiring 1 (ga1-3)) [ 8]. The GA signaling DELLA gene family is reported to have a
weak effect in hypocotyl SAS and none in petiole SAS [8]. We used another GA deficient mu-
tant line (ga20ox1/2)[78,100] and a quadruple DELLA mutant line (dellaQ)[101] to test if GA
is involved in other SAS traits (Fig 2). Similar to ga1-3, ga20ox1/2 showed reduced response to
shade in hypocotyl (Fig 2). In contrast to ga1-3, ga20ox1/2 showed shade-induced petiole elon-
gation even though both organs were about 60% of wild-type size (S3A and S3B Fig). In agree-
ment of previous studies, dellaQ showed very weak influence on shade avoidance responses of
hypocotyl and petioles in our condition (Fig 2, S3A, and S3B Fig). Both GA deficient and GA
signaling mutants showed normal shade response for flowering time (Fig 2 and S3 Table), sug-
gesting that GA pathway is not involved in flowering time acceleration by shade.
Circadian clock pathways. We found that over-expression of RVE8, a circadian clock
component, caused moderately exaggerated shade responses in flowering time (Fig 2). This is
consistent with the finding that natural variation of ELF3 modulates flowering time response
to shade [44]. Recent studies showed direct targets of RVE8 TF [102]. Among them PSEUDO
RESPONSE REGULATOR5(PRR5), another circadian clock component, is of interest because
PRR5 is activated by RVE8 [102] and knocking out PRR5 caused exaggerated shade avoidance
response in petiole possibly through elevation of PIF4 and PIF5 expression levels [103]. Also it
has been shown that circadian modulation of hypocotyl response to shade is mediated by circa-
dian clock regulation of PIL1 expression [30]. However , our shade avoidance assay for flower-
ing time showed pif4/5 and pil1 had normal flowering time shade responses. It would be
interesting to test if a RVE8—PRR5 pathway contributes to shade response in flowering time.
Novel component. Our mutant analysis defined a new components of shade avoidance re-
sponses; a potassium channel gene (POTASSIUM CHANNEL IN ARABIDOPSIS THALIANA
1, KAT1). The KAT1 gene encodes a potassium channel gene, which is locally expressed in
Shade Avoidance Syndrome in Adult Plants
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guard cells [104,105]. KAT1 potassium channel is inactivated upon ABA treatment and medi-
ates stomata closure [106]. KAT1 is thought to be important for vegetative growth [107], but its
molecular mechanisms are unknown. Recently ABA was shown to be involved in suppression
of branching by shade [36]. It is not clear if ABA pathways were involved in shade avoidan ce
responses described in this paper, although ABA responsive genes were enriched in shade re-
sponsive genes (see above). Interestingly KAT1 gene expression is induced by auxin [108,109],
which may be related to its role in petiole SAS.
Phenotypic-clustered SAS mutants may share common functions
In C. elegans, multiple phenotypic profiling showed that mutants with similar phenotypic pro-
files function in shared pathways [16]. In our phenotypic clusters (Fig 2), two flowering time
mutant lines (gi-2 and co-9) cluster together. These are known from prior studies to act in pho-
toperiodic induction of flowering, and to have reduced shade response for flowering time but
not petiole elongation [42] (cluster 7), showing proof of concept. In our data, clusters that con-
sist of SAS mutants are of particular interest (clusters 9, 10, 11, and 12) because they could indi-
cate shared membership in sub-networks of the shade avoidance pathway. Therefore, the
following genes likely function in common sub-networks: IAA6, IAA19, PIL1, LZF1, HY5, and
SPT in cluster 9 (hyp and pet), and PAR1, KAT1, PIF4, PIF5, MIDA9, PIF3, and JAR1 in cluster
10 (pet only). Opposite effects of mutations were found in cluster 12; mutation caused reduced
responses in hypocotyl and petiole, while its caused exaggerated responses in flowering time.
Molecular networks within these clusters are of future interests.
Mutants affecting of flowering time regulation under simulated sun
We observed many genotypes that showed altered flowering time in sun condition (S6 Fig). In
this section, we will discuss about known components and then novel components in flowering
time pathways.
Known components. As reported known knock out of master regulators of flowering time
(CO and GI) showed late flowering in our long-day condition [110,111,112]. Light signal ing
has been known to modulate flowering time [113]. Phytochromes affect flowering time by
post-transcriptional regulation and cryptochromes affect flowering time by both transcription-
al and post-transcriptional regulation [62]. We observed that phyB, phyA/B, and cry1/2 mu-
tants showed flowering time phenotype as reported. Early flowering phenotype of hy5 hy5
homolog (hyh) is also reported [114]. PIF4 has been known to induce a master regulator of
flowering time, FT gene, upon raised temperature [115]. pif4/5 mutant showed late flowering
time, so that it is likely that PIF4 and/or PIF5 induce FT gene expression in our condition.
GA is essential for flowering time control. The GA biosynthesis double mutant (ga20ox1/2)
showed delayed flowering in our experiment, consistent with earlier reports [100]. However,
this mutant showed normal shade effects on flowering time (see above).
The circadian clock is also important for flowering time control. As reported, we found op-
posite phenotypes of rve8 (early flowering) and RVE8-OX (late flowering) [116].
New components. It is surprising that auxin was involved in flowering time regulation,
since it is not incorporated into current models of flowering time pathways (reviewed in
[113]). However, exogenous auxin has been reported to delay flowering time, perhaps due to
induced damage on plants [117]. This result is consistent with our auxin biosynthesis mutant
flowering time data; overproduction delayed flowering time (atr4) while reduced production
accelerated flowering time (yuc2/5/8/9 and taa1). These results suggest that it is necessary to re-
investigate the involvement of auxin pathways in flowering time.
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It has been shown that coi1 mutants flowered early [48,98]. We observed that other JA mu-
tants also show altered flowering time. Not only JA biosynthesis mutants (aos and opr3) and
JA receptor mutant (coi1-16), but also three JA signaling component mutants (JAZ1 RNAi,
jaz5-1, myc2) showed early flowering phenotypes under sun condition. Early flowering of myc2
is inconsistent with a previous report showing its late flowering phenotype [118] possibly due
to different photoperiods. It will be interesting to investigate how JA pathways regulate flower-
ing time. One possibility is that JA pathways interact with GA pathways in control of flowering,
similar to their interaction in growth control [98].
Our data showed that two SPT knock out mutants (spt-11 and spt-12), PAR1 RNAi, sth2,
and two KAT1 knock out mutants (kat1-1 and kat1-2) showed early flowering phenotype,
while SCL13 anti-sense line 1 (scl13 as1) and hfr1 showed late flowering phenotype. Early flow-
ering of spt-11 has been reported [119] and that was confirmed by another allele (spt-12) in our
study. Early flowering phenotype of PAR1 RNAi, sth2, and kat1-1 have not been previously re-
ported and their connections to the flowering time pathway are unknown.
Conclusion
Here we showed that RNA-seq followed by phenotypic profiling is a powerful approach for
elucidating complex SAS pathways and discovery of new SAS components. A similar approach
was successful in searching new components of de-etiolation of seedlings, a developmental
stage with a simple architecture [24]. Our study expanded this approach to show th e transcrip-
tome-based discovery of new mutants are also effective for complex syndrome by multiple
phonotypic profiling.
After our phenotypic profiling, an additional SAS mutant line has been reported which con-
tained genes that were also in our shade-responsive genes. Specifically, we found that BR EN-
HANCED EXPRESSION 3 (BEE3), a bHLH TF, was induced by shade (S2 Table). It was
recently showed that the bee1 bee2 bee3 triple mutant has altered hypocotyl SAS [120], perhaps
due to the altered BR signaling in this triple [121]. This example is add itional evidence that our
strategy is effective to find novel SAS mutants. Further analysis is needed for elucidating inter-
actions between these genes and/or pathways. Our approaches are straightforward and cost ef-
fective, so that these should be applicable to other cases in general.
We found that the effects of some mutations were context-dependent (only found for some
organs or developmental stages) whereas others were ubiquitous. Those mutat ions that affect
all organs points to shared mechanisms underlying the SAS in different organs. The mutations
that have context-dependent effects could indicate unique genes functioning in the different
organs or more quantitative differences in the relative importance of the components in differ-
ent organs. Regardless the fact that we did find organ-specific effects suggests that we need to
be cautious when generalizing conclusions from hypocotyl studies.
Materials and Methods
Light condition
For simulated sun condition, white light (cool-white fluorescent light) was supplemented with
far-red light (provided by LEDs (Orbitec, inc) to obtain R/FR = 1.86. For simulated shade con-
dition, white light was supplemented with far-red LEDS to obtain R/FR = 0.52. Both condition
had 80–100 μE of Photosynthetically Active Radiation (PAR). Plants were grown under long
day condition (16 hour light/8 hour dark) at constant temperature (22°C). For hypocotyl ex-
periments, seedlings were grown under simulated sun (R/FR = 1.3) or simulated shade condi-
tion (R/FR = 0.5) with combination of LED lights (Quantum Devices Snap-Lite) [122].
Ambient light spectrum was measured by Black-Comet (StellarNet, Florida).
Shade Avoidance Syndrome in Adult Plants
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Plant materials
Arabidopsis seed stocks used in this study are listed in Fig 1. To confirm genotypes of T-DNA
insertion lines ordered from Arabidopsis Biological Resource Center (ABRC), genomic DNA
was extracted (DNeasy Plant Mini kit, Qiagen) and subject to genomic PCR. cDNA was synthe-
sized by direct mRNA extraction [123] and quantitative PCR (qPCR) was done with homemade
SYBR green master mix with the iCycler Multicolor real-time PCR detection system (Bio-Rad).
For kat1 mutants, standard RT-PCR was done. Primers used for genomic PCR and (q)RT-PCR
and their results were summarized in S4 Table. Arabidopsis seeds were imbibed with water on
filter papers and stored them at 4°C for four days. Three days after stratification under sun con-
dition, three germinated seeds were transferred to soil in a well of 5x10 well flat. Fourteen days
after stratification, excess seedlings were removed to leave one well-grown plant per pot and the
flats were transferred to either sun or shade condition. For hypocotyl growth measurements,
seeds were grown on vertical square plates [124] with 1/2 MSMO, 5 mM 2-(N-morpholino)
ethanesulfonic acid (MES, pH = 5.8, Sigma), and 0.8% agar (Sigma). Each plate was divided into
three rows and two columns and in six spaces five or six seeds of six genotypes were sown. Ge-
notype positions were randomized in repeated sets. 4593 seedling images were taken by a scan-
ner and hypocotyl length was measured by ImageJ (http://rsb.info.nih.gov/ij/)[125].
RNA-seq library preparation and sequencing
For RNA extraction, plants were treated with shade starting at ZT 4 or left in the sun. We pre-
pared two replicates of each sample at 1 hour and 4 hours after sun and shade treatment and
five plants were pooled for each replicate. Cotyledons, hypocotyls, and roots were removed
from the samples, leaving leaves and apical tissue. Total RNA from the plants was extracted
using RNeasy Plant Mini kit (Qiagen) with DNAse treatment (Qiagen). Five μg total RNA was
used to construct mRNA library using mRNA-Seq-8 sample Prep kit (Illum ina). The resulting
cDNA libraries were sequenced by Illumina GAIIx with 40 bp single end mode. Basic statistics
of mapping results are given in S1 Table.
Differential expression analysis and over-representation analysis (ORA)
Reads after sorting according to barcodes were subjected to removal of adaptor contamination
by custom Perl scripts. Reads were mapped by TopHat [126]toArabidopsis reference genome
using known annotation (TAIR10). Differentially expressed genes were extracted by edgeR pack-
age [127] in R statistical environment [128](FDR<0.001). ORA was done by GOseq package
[129] in R statistical environment. GO analysis was done by using GO category database package
from Bioconductor (org.At.tair.db and ANNOTATE package). For ORA of hormone responsive
genes custom categories were used as defined in Supplemental Table S9 in [130] and Supplemen-
tal S1 Table in [131]. GO analysis of shade-responsive genes in hypocotyl [7] was done using the
GO Web site (http://amigo.geneontology.org/cgi-bin/amigo/term_enrichment;[132]).
Phenotype measurement and analysis
For scoring leaf phenotypes, 26 day old plants were dissected and leaf images were recorded
by a flatbed scanner (Epson, Perfection V700 PHOTO). Scanned images were measured
using I mageJ [125]andtheLeafJplugin[25] to determine petiole length, leaf blade length,
leaf blade width, and leaf blade area. Days to bolting was scored to measure flowering time.
Leaf phenotypes (petiole length, leaf blade length, leaf blade width, leaf blade area) were mea-
sured from 10 sets of experiments with 1268 plants in total. For flowering time (days to bolt-
ing) measurement, 1950 plants were measured in total. Each phenotype was fitted by mixed
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PLOS Genetics | DOI:10.1371/journal.pgen.1004953 April 15, 2015 17 / 26

effects model, i.e.
trait ¼ plant þ treatment þ plant : treatment þðtreatmentjsetÞþε
where p lant is a mutant/overexpressor, treatment is sun or shade condition, plant:treatment
is interaction of “plant” and “treatment”, (treatment|set) is the random effect associated with
the treatment in set of experiments, and ε is the error. The m odel was applied to each trait to
calculate coefficient (“sun” value). For leaf traits where we measured across multiple leaves
(from leaf 3 to leaf 6) for a given trait we treated leaf as a random effect, using the
following model
trait ¼ plant þ treatment þ plant : treatment þð1jleafÞþðtreatmentjsetÞþε
Mutants were considered to have a defect in SAS when the plant:treatment term was sig-
nificant (P<0.05), indicating that the genotype of the p lant (mutant versus wild-type) affect-
ed the response to shade.
For flowering time, days to bolting was log2 transformed. We found that acceleration of flow-
ering time by shade treatment was strongly correlated with days to bolting in sun condition, i.e.,
late flowering mutants had more shade-accelerated flowering time than Col (S2G Fig). To ad-
dress this issue we regressed flowering time shade response on average sun flowering time for
each genotype and calculated the residuals from the regression [122]. These residuals represent
the amount of flowering time shade response that was not predicted by the sun flowering time.
The residuals for shade treated plants were then used in the mixed effects model (S3H Fig).
residuals ¼ plant þð1jsetÞþε
The lme4 (R package version 1.0–6) [133] and lmerTest [134] packages in R was used for
these analyses. All phenotyping data is summarized in S5 Table.
Heatmaps for phenotypic clustering were drawn after scaling each trait data and centered
at Col.
All R scripts for this paper and raw data are available at https://bitbucket.org/knozue/
sasphenotyping.
Accession numbers
RNA-seq data in this study have been deposited in the NCBI SRA (Study ID PRJNA214254) and
the NCBI GEO database (accession GSE66967). Mutants used in this study are listed in Fig 1.
Supporting Information
S1 Fig. Comparison of different platform and tissues with fold-changes of shade-responsive
gene expression. Current RNA-seq data (juvenile plants under low R: FR labeled as “Nozue”)
and microarray (leaf or petiole treated with EODFR [6] labeled as “Kozuka”), and hypocotyl
treated with low R:FR (1 hour [7,26] (labeled as “Tao” and “Sessa.1h”, 4 days [26] labeled as
“Sessa.4d”)).
(PDF)
S2 Fig. Model checking for each trait. (A) hypocotyl, (B) petiole length, (C) leaf blade length,
(D) leaf blade width, (E) leaf blade area, (F) petiole length/leaf blade length ratio, (G) flowering
time, and (H) flowering time (log2 transformed).
(PDF)
S3 Fig. Graphs of each trait. (A) hypocotyl, (B) petiole length, (C) leaf blade length, (D) leaf
blade width, (E) leaf blade area, (F) petiole length/leaf blade length ratio, (G) flowering time
Shade Avoidance Syndrome in Adult Plants
PLOS Genetics | DOI:10.1371/journal.pgen.1004953 April 15, 2015 18 / 26

(log2 transformed) and (H) flowering time (log2 transformed residuals). Error bars in (A) to
(G) represent standard errors. Genotype names in (H) indicate lines whose flower ing time
shade response differs significantly from prediction by regression (p < 0.05).
(PDF)
S4 Fig. Col and pif3-3 petiole length. Plants were grown in simulated sun and shade using our
standard conditions. Three independent experiments were performed and a total of 20 to 41
plants were examined per treatment/genotype combination. pif3-3 has a significantly reduced
response to shade (p<0.01 for genotype X treatment interaction in linear regression ).
(PDF)
S5 Fig. Expression pattern of TAA1 and YUC2/5/8/9. Developmental expression pattern was
obtained from eFP browser [136].
(TIF)
S6 Fig. Heatmap with absolute values. Values were normalized and centered on Col (i.e., Col
value = 0) and visualized with color coding (magenta indicates larger value than Col while
green indicates smaller value relative to Col). Colors of asterisks indicate genetic background of
each mutant, i.e., Col (white), Ws (yellow), and Ler (light blue).
(TIF)
S1 Table. Statistics of RNA-seq data. In each condition two biological replicates are shown in
a and b.
(XLSX)
S2 Table. A complete list of shade-responsive genes in leaf/apical region of plants. Blue let-
ters; shade-induced genes, pink letters; shade-repressed genes, yellow box; no probes on ATH1
microarray. Bold text; known shade-responsive genes. Comparison with transcriptome data
from EODFR treated leaf blade and petiole [5] are also shown.
(XLSX)
S3 Table. Summary of SAS phenotypes with known SAS mutants and mutants used in this
study.
(XLSX)
S4 Table. List of primers used in genotyping and (q)RT-PCR and their results. (Nearly) ho-
mozygous lines are shown in light blue.
(XLSX)
S5 Table. Complete phenotype data after applying mixed effects models.
(CSV)
Acknowledgments
We thank Arabidopsis Biological Resource Center (ABRC), Judith Bender, Cordelia Bolle,
Giltsu Choi, Joanne Chory, Katayoon Dehesh, Christian Fankhauser, Wim Grunewald, Nicho-
las P. Harberd, Stacey Harmer, Peter Hedden, Daniel Kliebenstein, Chentao Lin, Jaime F. Mar-
tínez-García, David Smyth, Hirokazu Tsukaya, Detlef Weigel, Shu-Hsing Wu, and Yunde
Zhao for providing seeds listed in Fig 1. We also thank Patricia Mueller-Moule for bulking
seeds, Cody Markelz for stimulating discussion, and Natalie Gath, Grace Pan, Ji-hoon Lee,
Amanda Schrager, Leonela Carriedo, Matthew Jones, Huy Tran, Ian Andrew-Hanna Knox,
Mary Zhang, and Cody Markelz for sample preparation and collection.
Shade Avoidance Syndrome in Adult Plants
PLOS Genetics | DOI:10.1371/journal.pgen.1004953 April 15, 2015 19 / 26

Author Contributions
Conceived and designed the experiments: JNM KN YI. Performed the experiments: KN MR
MRM SL UKD. Analyzed the data: AVT KN JNM. Contributed reagents/materials/analysis
tools: AVT UKD KN JNM. Wrote the paper: KN JNM.
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