GENETIC AND ENVIRONMENTAL CONTROL OF PLANT ARCHITECTURE IN ARABIDOPSIS AND...
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1 GENETIC AND ENVIRONMENTAL CONTROL OF PLANT ARCHITECTURE IN ARABIDOPSIS AND STRAWBERRY By TINGTING ZHANG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012
Transcript of GENETIC AND ENVIRONMENTAL CONTROL OF PLANT ARCHITECTURE IN ARABIDOPSIS AND...
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GENETIC AND ENVIRONMENTAL CONTROL OF PLANT ARCHITECTURE IN ARABIDOPSIS AND STRAWBERRY
By
TINGTING ZHANG
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2012
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© 2012 Tingting Zhang
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To my parents and Jiahan
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ACKNOWLEDGMENTS
I would like to address special thanks to Dr. Kevin Folta, my graduate advisor, for
his guidance, encouragement, and help over the years. He has set an example of
excellence as a researcher, advisor and instructor.
I would like to thank my committee members Dr. Karen Koch, Dr. Balasubramani
Rathinasabapathi, Dr. Bernard Hauser for the advice, support, and help through this
process.
I also thank Dr. Mithu Chatterjee, Dr. Huiyi Wang, Dr. Asha Brunings, Yihai Wang
and all other lab members for their assistance and friendship.
Finally, I especially thank my parents and Jiahan for their love and support.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ........................................................................................... 10
ABSTRACT ................................................................................................................... 13
CHAPTER
1 LITERATURE REVIEW .......................................................................................... 15
Introduction ............................................................................................................. 15
Plant Architecture ................................................................................................... 17 The Endogenous Factors ........................................................................................ 17 The Environmental Factors ..................................................................................... 19
Photoreceptors and Their Functions ....................................................................... 20 Green-Light Responses in Plants and Other Organisms ........................................ 24
Shade Avoidance Syndrome and Its Mechanisms .................................................. 28
2 GREEN-LIGHT-INDUCED SHADE AVOIDANCE SYNDROME IN ARABIDOPSIS ....................................................................................................... 39
Introduction ............................................................................................................. 39 Results .................................................................................................................... 41
Addition of Green Light Induces a Shaded Appearance ................................... 41 The Green Response Persists in cry and phy Mutants .................................... 43
Analysis of Shade-Induced Transcripts ............................................................ 44 Supplemental Green Light Decreases Anthocyanin Accumulation ................... 45 The Green-Induced Shade Avoidance Response Is Attenuated in Shade-
Associated Mutants hat4 and pil1 ................................................................. 46
Discussion .............................................................................................................. 46
Materials and Methods............................................................................................ 53 Plant Materials and Growth Conditions ............................................................ 53
Light Sources and Treatments ......................................................................... 53 Morphological Measurements .......................................................................... 54 Anthocyanin Accumulation Assay .................................................................... 54 RNA Preparation and Real-time qPCR............................................................. 54
3 GREEN LIGHT INTERACTIONS WITH FAR-RED LIGHT IN SHADE RESPONSE ............................................................................................................ 70
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Introduction ............................................................................................................. 70
Results .................................................................................................................... 72 Green Light and Far-Red, Alone, or Together Induces Shade Response in
Wild-Type Arabidopsis Col-0 ......................................................................... 72 The Green Light Interactions with Far-Red Persist/Exaggerate in cry
Mutants ......................................................................................................... 74 Green Light Does Not Induce Excessive Shade Avoidance Syndrome in
hfr1 Mutant .................................................................................................... 74
Green-Induced Shade Avoidance Response is Attenuated in pif4 and pif5 Mutants ......................................................................................................... 75
Neither Green Light and Far-Red, Alone, or Together Induces Typical Shade Response in pif4 and pif5 Mutants ..................................................... 76
Comparative Gene Expression ......................................................................... 76
Discussion .............................................................................................................. 77 Materials and Methods............................................................................................ 82
Plant Materials and Growth Conditions ............................................................ 82
Light Sources and Treatments ......................................................................... 83 Morphological Measurements .......................................................................... 83 RNA Preparation and Real-time qPCR............................................................. 84
4 A STRAWBERRY (FRAGARIA SP) RALF PEPTIDE CONTRIBUTES TO ARCHITECTURE OF THE CANOPY, THE ROOT SYSTEM, AND THE INFLORESCENCE ................................................................................................. 95
Introduction ............................................................................................................. 95
Results .................................................................................................................... 97 FaRALF Isolation and Sequence Analysis ....................................................... 97 There is Limited FaRALF Sequence Variability across Diploid and Octoploid
Strawberries .................................................................................................. 98 Expression Pattern of FaRALF Transcripts ...................................................... 99
The FaRALF Gene Contributes to Architecture of Canopy and inflorescence in Mature Strawberry Plants .......................................................................... 99
The FaRALF Gene Affects Root Development and Acidification of Media Around Roots in Strawberry Seedlings ....................................................... 100
Discussion ............................................................................................................ 101 Materials and Methods.......................................................................................... 104
Isolation of FaRALF from Different Varieties of Strawberry ............................ 104
Phylogenetic Analysis and Accession Numbers ............................................. 104 Generation of Transgenic Plants .................................................................... 105 Root Acidification ............................................................................................ 106 RNA Isolation and Real-time PCR .................................................................. 106
LIST OF REFERENCES ............................................................................................. 115
BIOGRAPHICAL SKETCH .......................................................................................... 126
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LIST OF TABLES
Table page 2-1 TaqMan primer and probe sequences used in real-time qPCR .......................... 56
3-1 TaqMan primer and probe sequences used in real-time qPCR .......................... 85
4-1 Accession numbers and their corresponding genes used for phylogenetic clustering .......................................................................................................... 107
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LIST OF FIGURES
Figure page 1-1 Schematic model of SAM organization. .............................................................. 33
1-2 Structure of phytochromes in plants ................................................................... 34
1-3 Cryptochromes and cofactors in Arabidopsis ..................................................... 34
1-4 Structures of phototropins and a Zeitlupe-family photoreceptor ......................... 35
1-5 The model of shade avoidance syndrom mechanisms ....................................... 36
1-6 Quantum energy distribution of full sunlight and under the shade of leaves. ...... 37
2-1 Supplemental green light induces a shade response in wild-type Arabidopsis Col-0 ................................................................................................................... 57
2-2 Decreasing the red light fluence rate in an RB background does not affect rosette architecture ............................................................................................. 58
2-3 Supplemental green light induces a shade response in Arabidopsis cry1cry2 mutant ................................................................................................................ 59
2-4 Supplemental green light effects are maintained in photoreceptor mutants ....... 60
2-5 Shade-avoidance related genes expression levels in wild-type (Col-0) plants grown in various amounts of green light. ............................................................ 61
2-6 Shade-avoidance related genes expression levels in A) cry1cry2, B) cry1, and C) cry2 mutants grown in different light treatments. .................................... 63
2-7 Shade-avoidance related genes expression levels in wild-type (Col-0), cry1cry2, cry1, cry2 plants grown in RB light condition. .................................... 64
2-8 Supplemental green light decreases anthocyanin accumulation in wild-type Arabidopsis (Col-0). ............................................................................................ 65
2-9 Green light reverses blue-induced anthocyanin accumulation in lettuce. ........... 66
2-10 Supplemental green light does not induce a shade response in Arabidopsis hat4 mutant......................................................................................................... 67
2-11 Supplemental green light does not induce a shade response in Arabidopsis pil1 mutant. ......................................................................................................... 68
2-12 A model depicting green-light influence in far-red independent shade avoidance responses. ......................................................................................... 69
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3-1 Green light and far-red additively induce shade response in wild-type Arabidopsis Col-0. .............................................................................................. 86
3-2 Green light and far-red additively induce shade response in the Arabidopsis cry1cry2 mutant. ................................................................................................. 87
3-3 Green-light-induced shade avoidance symptom is not enhanced in the Arabidopsis hfr1 mutant. ..................................................................................... 88
3-4 Green-light-induced shade response is limited in the Arabidopsis pif4 mutant. .. 89
3-5 Green-light-induced shade response is limited in the Arabidopsis pif5 mutant. .. 90
3-6 Shade avoidance response induced by green and far-red is absent in the Arabidopsis pif4 mutant. ..................................................................................... 91
3-7 Shade avoidance response induced by green and far-red is absent in the Arabidopsis pif5 mutant. ..................................................................................... 92
3-8 Shade-responsive gene expression levels in wild-type (Col-0) plants grown in various green and far-red light conditions. .......................................................... 93
3-9 Shade avoidance-related gene expression levels in cry1cry2 mutants grown in different light treatments. ................................................................................ 94
4-1 Analysis of RALF family genes. A) Alignment of FaRALF amino acid sequence along with six close related RALFs using ClustalW. ........................ 109
4-2 ClustalW alignment of the FaRALF isolated from different strawberry variaties. ........................................................................................................... 110
4-3 Relative expression of the FaRALF transcript in various strawberry tissues. ... 111
4-4 The plant architecture of FaRALF RNAi transgenic plants. .............................. 112
4-5 The flower morphology of FaRALF RNAi lines. ................................................ 113
4-6 FaRALF contributes to root development and acidification of root adjacent media in strawberry seedlings. ......................................................................... 114
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LIST OF ABBREVIATIONS
# Number
µmol Micromolar
A Absorbance
aa Amino acid
ABA Abscisic acid
AM Axillary meristem
Arabidopsis Arabidopsis thaliana
At Arabidopsis thaliana
B Blue light
BR Brassinosteroids
C Celsius
CaMV Cauliflower mosaic virus
CaSO4 Calcium sulfate
CCE Cryptochrome C-terminal extension
cDNA Complementary DNA
cm Centimeter
Col Columbia
cry Cryptochrome
CT Threshold cycle
Cys Cysteine
CZ Central zone
d Days
DNA Deoxyribonucleic acid
Fa Fragaria
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FAD Flavin adenine dinucleotide
FMN Flavin mononucleotide
FR Far-red
g Grams
G Green light
GA Giberellins
GFP Green florescent protein
h Hours
HCl Hydrochloric acid
HDZip Homeodomain leucine zipper
IBA indole-3-butyric acid
L Liter
Le Lycopersicon peruvianum
LED Light-emitting diodes
LOV Light, oxygen, voltage domain
m Meter
MAP Mitogen-activated protein
mg miligram
mm Millimeter
MS Murashige and Skoog
Mt Medicago trunculata
MTHF 5,10-methenyltetrahydrofolate
Na Nicotiana attenuata
nm Nanometer
OC Organizing center
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P P-value
PAR Photosynthetivally active radiation
PCR Polymerase chain reaction
Pfr Far-red-absorbing phytochrome
pH Potential Hydrogen
phot Phototropin
PHR Photolyase-homologous region
phy Phytochrome
PPF Photosynthetic photon flux
Pr Red-absorbing phytochrome
PZ Peripheral zone
qPCR Quatitative polymerase chain reaction
R Red light
R/FR Red to far-red ratio
RALF Rapid Alkalinization Factors
RAM Root apical meristem
RM Rib meristem
RNAi RNA interference
s Second
SAM Shoot apical meristem
Sl Solanum lycopersicum
spp. Species
UV Ultraviolet
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
GENETIC AND ENVIRONMENTAL CONTROL OF PLANT ARCHITECTURE IN
ARABIDOPSIS AND STRAWBERRY
By
Tingting Zhang
August 2012
Chair: Kevin M. Folta Major: Horticultural Science
Plant architecture is important for plant biology, and also contributes to economic
value of various crops. Plant architecture can be used to help identify and separate
different plant species. Economically, plant architecture is a key determinant in planting
density, disease resistance, and yield. Plant architecture is determined by a
combination of genetic and environmental factors. For example, plants grown in shade
exhibit extensive remodeling of architecture and transcriptomes to accommodate growth
in photosynthetically challenging conditions. The conspicuous changes in morphology
include petiole elongation, leaf hyponasty, and decrease in chlorophyll content.
Together these changes are termed “shade avoidance syndrome”. The shade response
has been shown to be induced by either low blue light acting through cryptochromes
blue light receptors, or by a low ratio of red to far-red light acting through phytochromes.
The work herein tests the hypothesis that parallel symptoms can be induced by
enrichment of green light in the plant’s environment. The results has demonstrated that
green light induces shade responses through a separate light signaling pathway from
far-red based mechanisms, and that green light promotes independent effects on gene
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expression in Arabidopsis. In addition, we showed that green light and far-red
wavelengths have synergistic effect on induction of shade symptoms and shade-
associated transcripts. Furthermore, data indicate that the green and far-red shade
sensing and response systems likely converge at PIF4 and PIF5, two proteins required
for red and far-red light signaling. These trials demonstrate how environmental factors
shape plant architecture.
Genetic factors also control plant form. Canopy architecture is important in many
crops, particularly in members of the Rosaceae where breeders select particular plant
forms for horticultural reasons. A gene controlling the canopy shape of strawberry
(Fragaria spp.) was functionally characterized, and shown to have multiple roles in
strawberry biology. The encoded protein is a Rapid Alkalinization Factor (RALF).
Functional characterization revealed that the small peptide contributes to rosette
architecture, and development of flowers and roots in strawberry.
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CHAPTER 1 LITERATURE REVIEW
Introduction
Plant architecture is controlled by genetic factors, but it also is highly influenced by
environmental factors such as light (Reinhardt and Kuhlemeier, 2002). Because plants
are sessile organisms, their survival depends on an exquisite sensitivity to change in
their ambient environment. Light not only provides plants with energy for metabolism, it
is also a source of information about the surrounding environment. Incident irradiation
can supply important environmental information that includes light quantity (fluence
rate), quality (spectral composition), duration (photoperiod), and direction (phototropism)
(Chen et al., 2004; Spalding and Folta, 2005). Changes in these factors are more the
rule than the exception. Alterations such as transient cloud cover, or long term presence
of adjacent plants, require plant acclimation for optimal light capture. Optimal
acclimation to the light environment requires continual adjustment of gene expression,
physiology, and architecture. It is therefore not surprising that plants utilize information
from discrete sections of the light spectrum to guide these adaptive responses.
The research detailed herein is aimed to identify effects of discrete light qualities,
especially green light, on control of plant architecture and other processes in physiology
and development. The work also seeks to further expand our understanding of plant
photobiology by the analysis of how green wavelengths of light are sensed and
integrated. This study is innovative in that it focuses on exploring the green sensory
pathway, which is an emerging topic in photobiology.
In recent years, different roles for green wavebands in plants and other organisms
have been reported by multiple groups (Spalding and Folta, 2005; Folta and Maurunich
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2007). Green light transiently induces stem elongation in the etiolated seedling (Folta,
2004), decreases plastid transcript accumulation (Dhingra et al., 2006), and modulates
stomatal aperture (Talbott et al., 2003). Green light also influences leaf inclination
(Mullen et al., 2006) and hypocotyl length of Arabidopsis (Arabidopsis thaliana)
seedlings (Sellaro et al., 2010). Results of our research herein integrate our
understanding of plant responses to the green portion of spectrum into the complex light
signaling networks. Overall, work presented here further expands our knowledge in light
sensing systems and regulation of plant architecture.
Arabidopsis thaliana (denoted in the text by its familiar name, “Arabidopsis”) and
strawberry (Fragaria spp.) were used as experimental organisms in work presented
here. Arabidopsis serves as a model plant for the physiology and genetics, and has
played a tremendous role in our understanding of photomorphogenesis. Its fully-
sequenced plant genome and ample genetic tools greatly contribute to decoding
molecular mechanisms of photobiology (Chory, 2010). Strawberry (Fragaria spp.) is an
economically important crop with a short growth cycle and compact growth habit. The
rapidly expanded sequence resources, efficient genetic transformation capacities, and
diversity in germplasm make strawberry an excellent plant system for research. The
genetic and environmental influences on plant architecture are well established in
Arabidopsis. However, little is known about the factors controlling these processes in
strawberry, despite the importance of canopy architecture to plant selection by
strawberry breeders.
The work in this study tests the role of light and genetic factors in contributing to
control of canopy architecture in these two different plant systems. For light studies, a
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narrow-bandwidth LED-based light platform was used. The half bandwidth of LED light
is only about 20nm, which prohibits interference caused by other wavelengths of light,
allowing isolation of the effect of particular light qualities.
Plant Architecture
Plant architecture is regulated by both genetic and environmental factors. The
endogenous regulation involves meristem determinacy and differentiation, phyllotaxis,
as well as stem, petiole and inflorescence elongation. The environmental effectors
include light, temperature, humidity, nutrition and others (Wang and Li, 2008). In
addition to being a simple method of identifying and classifying plant species, plant
architecture is also of great economic importance. Crop plant architecture influences
various aspects of crop production, such as planting density, light harvest, disease
resistance, and lodging. Crops with desirable architecture have higher yields as well as
quality. In 1960s, the Green Revolution substantially improved the grain yield with the
innovation of semidwarf wheat and rice cultivars (Peng et al., 1999). Planting density
also influences architecture and even production, as decreased spacing allows for more
plants that then have to compete for light and other resources (Franklin, 2008).
Therefore, in the past decades, extensive research has focused on the environmental
factors and endogenous mechanisms that regulate the plant architecture using both
model plant Arabidopsis and crop plants such as maize and tomato (Reinhardt and
Kuhlemeier, 2002).
The Endogenous Factors
Plant architecture is mainly determined by the plant’s genetic profile. The plant
body is composed of the above-ground and underground parts, which are determined
by the shoot apical meristem (SAM) and root apical meristem (RAM), respectively. The
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SAM harbors pluripotent stem cells and forms all aerial architecture including leaves,
branches and flowers (Wang and Li, 2008). In Arabidopsis, the SAM consists of three
functionally distinct zones: central zone (CZ), the peripheral zone (PZ) and rib meristem
(RM)(Figure 1-1). The CZ is at the tip of the SAM and responsible for the indeterminate
growth and plant development. The PZ and RM are both developed from CZ. The PZ,
located on the sides of the meristem, regulates differentiation of leaf and flower
primordia, whereas RM is beneath the CZ and can generate cells of the stem (Gordon
et al., 2009).
The initial patterns of plant architecture are established in meristematic regions.
Genetic studies have identified that SAM activities are largely mediated by a CLAVATA-
WUSCHEL (CLV-WUS) feedback loop. The CLAVATA1(CLV1) is a transmembrane
receptor kinase in cells of the RM (Shea and Ackers, 1985).The CLAVATA3 (CLV3), a
glycopeptide secreted from cells of the CZ, activates receptor kinase signaling to
repress WUS expression to cells of the organizing center (OC), a subset of cells of the
RM. The WUSCHEL (WUS), a homeodomain transcription factor, positively regulates
CLV3 expression in overlying cells of the CZ (Bintu et al., 2005). Beyond its function in
the loop, WUS is demonstrated as a central hub integrating the regulatory signals from
different pathways to regulate SAM activities (Wang and Li, 2008).
Phytohormones are also central in the regulation of plant architecture. Cytokinins,
plant-specific hormones, function in cell division and the transition of undifferentiated
stem cells to differentiation (Riou-Khamlichi et al., 1999). Auxin contributes to apical
dominance, as well as axillary meristem (AM) initiation and development, which are key
determinants of plant architecture (Benkova et al., 2003).
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Gibberellins (GA) and brassinosteroids (BR), play important roles in plant height
determination and organ expansion. The analysis of dwarf mutants and the molecular
studies revealed that defects in genes for both GA biosynthesis and sensing/signaling
pathways affect plant height. For example, the Reduced height (Rht) gene in wheat, an
ortholog gene of a negative GA-response gene GAI in Arabidopsis (Winkler and
Freeling, 1994; Peng et al., 1997), and the GA 20-oxidase gene (Os20ox2) of the GA
biosynthetic pathway in rice semidwarfing gene (sd1) are the “Green Revolution” genes
that greatly improved grain productivity (Monna et al., 2002).
The Environmental Factors
In addition to genetic determinants, plant growth and development is strongly
influenced by ambient environment. To acclimate to the ever-changing environment, a
plant integrates complex external stimuli and exhibits strong adaptive plasticity to
compete and to survive.
Light plays a critical role in plant architecture and other developmental processes.
In higher plants, light controls plant form at different levels. In aerial tissues, light affects
foliage inclination angle and expansion. In a broad canopy, light affects branching
frequency, foliage distribution and biomass allocation (Niinemets, 2010). For example,
in shade, maple (Acer pseudoplatanus L.) develops an umbrella-like crown, that
captures light more efficiently as planting density increases (Petritan et al, 2009).
Important light-mediated architectural adaptations involve seedling de-etiolation, shade
avoidance syndrome, phototropism, photoperiodic movements and perhaps flowering.
The shade avoidance syndrome describes a typical plant acclimation response to
shaded environmental conditions, including petiole elongation, leaf hyponasty and
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reduced leaf area (Franklin, 2008). These attributes can negatively affect crop yield and
product quality in high-density plantings (Hornitschek et al., 2009).
Temperature is another important external cue. Plants adapt to daily and seasonal
temperature changes. The perception of temperature also helps plants control the
timing of developmental transitions and improve the resistance to temperature extremes
(Heggie and Halliday, 2005). Temperature compensation of the circadian clock is a
good example. Circadian clock components maintain accurate rhythms with a 24 h
period in a wide range of physiological temperatures (Gould et al., 2006). Recent
articles reported that Arabidopsis plants grown in identical light conditions exhibited
phenotypes comparable to shade avoidance at higher temperature (28oC), while lower
temperatures (16oC) resulted in a dwarf and compact rosette (Atkin et al., 2006;
Franklin, 2009). Under high humidity and temperature conditions, the common bean
(Phaseolus vulgaris L.) presents enhanced vegetative development, that causes the
erect lines to become prostrate.
Other environmental factors, such as nutrition and salinity, also have impacts on
plant architecture. Tomato plants (Solanum lycopersicum L. “Marmara”) showed
decreased plant height, stem internode length, leaf area, and number of leaflets per leaf
in salinity stressed environments (Najla et al., 2009).
Photoreceptors and Their Functions
Light is regarded as one of the most important external cues controlling plant
architecture, therefore this topic has drawn the interest of plant biologists for centuries.
The critical influence of light in plant growth and development led to analyses of
different light responses dating back to the 19th century. Investigations have extended
from physiological to biochemical and molecular-genetic methods (Kami et al., 2010).
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For the purposes of this work, the accepted nomenclature for plant photosensory
pathways (Parks and Quail, 1993) will be implemented. Examples are as follows:
GENE, mutant gene, PROTEIN, and chromoprotein.
In plants, light is sensed by a series of photoreceptors and transduced through
associated signaling networks. As a model plant for mechanistic analyses of
photomorphogenesis, Arabidopsis thaliana, has contributed greatly to discovering
photoreceptors and signaling systems (Chory, 2010). Photoreceptors are typically
chromoproteins consisting of an apoprotein and a chromophore, or in one case
chromophores (Rockwell et al., 2006). There are at least three main families of sensory
photoreceptors: phytochromes (Franklin and Quail, 2010), cryptochromes and Light
Oxygen Voltage (LOV)-domain photoreceptors, the latter including the phototropins
(Demarsy and Fankhauser, 2009)(Figures 1-2,1-3 and 1-4). The phytochromes are red
and far-red (600-750 nm) light-sensing pigments that are comprised of an apoprotein
and a tetrapyrrole chromophore, phytochromobilin. These exist in two interconvertable
forms, noted as Pr and Pfr. Absorption of red light converts the biological inactive form,
Pr, to the active form, Pfr. An equilibrium is established between red and far-red
absorbing forms that dictates downstream gene expression patterns and ultimately
physiology. In phytochrome signaling, the Pfr form of phytochrome translocates to the
nucleus (Nagatani; Sakamoto and Nagatani, 1996), and has been shown to further
compartmentalize to subnuclear foci, referred to as photobodies (Chen and Chory,
2011). Active phytochromes physically interact with the basic helix-loop-helix (bHLH)
transcription factors, PHYTOCHROME INTERACTING FACTORS (PIFs), and lead to
their degradation via the 26S proteosome (Al-Sady et al., 2006; Shen et al., 2007).
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Phytochromes play a central role in adaptation to the light environment, seed
germination, de-etiolation, sensing of shade, flowering, and many other plant processes.
Arabidopsis has five distinct phytochromes, phyA through phyE (Whitelam et al., 1993;
Shinomura et al., 1996; Franklin et al., 2003).
Ultraviolet (UV)-A and blue light (320-500 nm) signals are received via
cryptochromes (crys) and phototropins (phots) (Sullivan and Deng, 2003). In addition,
additional LOV-domain proteins such as ZEITLUPE (ZTL), FLAVIN-BINDING KELCH
REPEAT F-BOX 1 (FKF1) and LOV KELCH PROTEIN 2 (LKP2) have been shown to
undergo a light-driven photocycle and are likely acting as blue-light photoreceptors
(Somers and Fujiwara, 2009). The crys regulate a range of physiological and
developmental processes in plants, and the circadian clock in animals (Liu et al., 2011).
In Arabidopsis, the cry1 and cry2 receptors contain an apoprotein consisting of the N-
terminal photolyase-homologous region (PHR) domain and the cryptochrome C-terminal
extension (CCE) domain. The chromophores flavin adenine dinucleotide (FAD) and
5,10-methenyltetrahydrofolate (MTHF) bind non-covalently to the PHR domain (Lin et
al., 1995; Liu et al., 2011)(Figure 1-3). In Arabidopsis cry1 and cry2 initiate signals that
affect blue-induced de-etiolation, meristem activity, inhibition of hypocotyl elongation,
root growth, stomatal opening, photoperiodic flowering, shade avoidance and
entrainment of the circadian clock (Lin and Shalitin, 2003). The cry3 receptor (CRY-
DASH) belongs to the photolyase superfamily, and it possibly functions in repair roles
within the mitochondria and chloroplasts (Kleine et al., 2003).
Phototropins and the ZEITLUPE family use LOV domains to perceive blue light. In
Arabidopsis, phototropins include phot1 and phot2, that are composed of two LOV
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domains (LOV1 and LOV2), an N-terminal phosphorylation domain and a C-terminal
Ser/Thr kinase domain. In the dark state, the photosensor holds its non-covalently
bound flavin mononucleotide (FMN) chromophore in the LOV domain (Figure 1-4). Blue
light activation leads to the formation of a cystenyl adduct between the LOV domain and
the chromophore, resulting in a conformational change of the protein. This
conformational change makes the kinase domain physically accessible, enhancing
kinase activity (Kleine et al., 2003; Christie, 2007). Phototropins mediate a variety of
responses in plants that share a common theme of optimizing photosynthetic activity
(Folta and Spalding, 2005). Responses such as phototropism, stomatal opening, leaf
expansion, leaf position, and chloroplast accumulation are controlled by both phot1 and
phot2 with some functional overlap (Christie, 2007), whereas chloroplast avoidance
response and nuclear positioning are mediated almost exclusively by high-fluence rate
blue light activation of phot2 (Demarsy and Fankhauser, 2009).
Other LOV-domain containing sensors are ZTL, LKP2, and FKF1, which are
composed of the LOV domain, an F-box domain and six Kelch repeats. The LOV
domain binds FMN and modulates the ZTL family members interaction with GIGANTEA
(GI) and their SCF-type (Skp1, Culin, and F-box) ubiquitin E3 ligase activity. FKF1
forms a complex with GI, and leads to the derepression of CONSTANS (CO), a central
regulator of flowering time. Blue light actives the formation of the ZTL and GI complex,
which restrict the degradation of components of the circadian oscillator by ZTL
(Demarsy and Fankhauser, 2009; Moglich et al., 2010). Blue-light photoreceptors can
also respond to UV (especially UV-A and UV-C) (Jenkins, 2009). Recent studies
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uncovered a discrete photoreceptor for UV-B (282-320 nm) termed UV RESISTANCE
LOCUS 8 (UVR8) (Rizzini et al., 2011).
Green-Light Responses in Plants and Other Organisms
Different light qualities have specific effects on plant growth and development, and
the effects of red, far-red, blue, and UV-A have been the subjects of extensive study.
However, mechanisms by which green wavelengths affect photomorphogenesis are just
starting to be unveiled, even though many responses to green light have been reported
(Klein, 1992; Folta and Maruhnich, 2007). Recent results from our laboratory and others
have shown clear, yet unexpected effects of green light that affect seedling and mature
plant physiology (Folta, 2004; Dhingra et al., 2006; Mullen et al., 2006; Banerjee et al.,
2007; Bouly et al., 2007). Based on this emerging work it is clear that green light
conditionally antagonizes red and blue light-regulated processes, and is transduced
either by cryptochromes or a novel light-sensing system, depending on the response in
question.
Support for this hypothesis extends back through diverse reports of green light
effects over the last 50 years (Klein, 1992; Folta, 2005). Several studies have described
specific effects on plant form, function or content that appear to be related to
illumination with green (520-550 nm) wavebands. In 1957, Frits Went concluded that
green wavebands were inhibitory to plant growth. For example, tomato seedlings grown
in green-depleted conditions gained more biomass than those provided with a complete
spectrum (Went, 1957). In 1965, Klein et al. described what appeared to be green-
specific alterations in plant architecture by using colored films that depleted or increased
the prevalence of green wavebands (Klein et al., 1965). Recent studies further
demonstrate the role of green light in plant growth and development. Green-light
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exposure reversibly decreases stomatal conductance in lettuce (Kim et al., 2004a),
while the combination of green light with blue and red light enhances lettuce growth
(Kim et al., 2004b). Studies in Vicia faba and Arabidopsis thaliana have identified a role
for green wavebands as a modulator of stomatal aperture, reversing the blue light
response (Frechilla et al., 2000). Stomatal opening stimulated by blue light is reversed
by green light in both pulse and continuous illumination experiments. The opposition is
fluence-rate dependent and full reverse requires the 2:1 ratio of green/blue. In the
nonphotochemical quenching1 (npq1) mutant, blue-specific stomatal opening was not
observed in high-fluence conditions and the opening under low-fluence light is reversed
by far-red light instead of green light. The results indicate that zeaxanthin plays an
important role in modulating the blue-induced stomata aperture (Frechilla et al., 2000;
Talbott et al., 2003). It was also observed that monochromatic green light induces
changes in Arabidopsis leaf position that are independent of phytochromes and
cryptochromes (Mullen et al., 2006). In sunflower, both green monochromatic light and
light transmitted through its own canopy induce the opening of abaxial stomata, while
adaxial stomata remain unresponsive (Wang et al., 2008). The time to heading in wheat
is accelerated by green light in a fluence-rate-dependent manner (Kasajima et al.,
2008), and an action spectrum shows a peak at 540-550 nm (Kasajima et al., 2009).
This peak is consistent with that described as maximum for stomatal opening reversal
(Frechilla et al., 2000), green-induced dormancy maintenance in ryegrass and green-
driven inhibition of elongation in cress roots during gravitropic bending (Klein, 1979;
Tanada, 1982). These findings represent some of the physiological alterations brought
on by green light illumination, alone, or in concert with other wavelengths. The
26
alignment of action spectra maxima provides independent evidence that all of these
responses are likely initiated from a common receptor.
Green light effects are not only reported in plants, but also in other organisms. In
cyanobacteria, The Orange Carotenoid Protein (OCP), a photoactive protein that
includes a carotenoid as chromophore, functions as a photoreceptor of blue-green light.
High intensity illumination induces OCP to transform reversibly between a dark-stable
orange (human perceived color) form and a red (human perceived color) “active” form
(Wilson et al., 2008). In addition, Hirose et al. indicated that cyanobacteriochrome,
CcaS, is a green light receptor, which undergoes photoconversion between a green-
absorbing form and a red-absorbing form (Hirose et al., 2008).
Genetic studies have demonstrated that some of the responses to green light are
attributable to cryptochromes. Green light has been demonstrated to reverse blue-
mediated inhibition of hypocotyl elongation and anthocyanin accumulation in
Arabidopsis seedlings (Banerjee et al., 2007; Bouly et al., 2007; Sellaro et al., 2010).
Green wavebands also inhibit blue-induced flowering induction, FLOWERING LOCUS T
(FT) expression and cry2 degradation (Banerjee et al., 2007). All of these blue-green
reversible responses are mediated by cryptochrome receptors, as they fail to persist in
cry mutants. The mechanism of blue-green cryptochrome reversibility has been
proposed to be based on switching between oxidative chromophore states, changing
from a semiquinone active form and reduced inactive form of the chromophore,
(Banerjee et al., 2007; Bouly et al., 2007) or autophosphorylation of cryptochromes
caused by a photolyase-like cyclic electron shuttle (Liu et al., 2010). In these cases
green light reverses the course of plant physiology by interrupting the signaling status of
27
the cryptochrome blue light receptors, demonstrating that at least some green
responses are cryptochrome dependent.
In other cases the blue light driven cryptochrome response cannot be reversed by
green light. Dormancy maintenance in imbibed annual ryegrass (Lolium rigidum) seeds
also is likely cryptochrome mediated, yet green light does not reverse the blue response
(Goggin et al., 2008). Instead, green illumination effects are comparable to the effects of
blue light. The authors conclude that either the green response is cryptochrome
mediated in seeds (not reversing cryptochrome, but working in the same direction), or is
mediated by an independent, non-phytochrome receptor. The action spectrum for the
response suggests the latter. Similar phenomena have been observed in our laboratory,
as green light acts in parallel to enhance cryptochrome-mediated stem growth
responses during early photomorphogenic growth (Wang et al., unpublished).
The effects of specific wavelengths and genetic factors can be precisely described
by monitoring the growth of the hypocotyl during photomorphogenesis. The dark-grown
seedling hypocotyl growth rate is extremely sensitive to transition to the light
environment. Red, blue and far-red light suppress hypocotyl elongation (Parks et al.,
2001). However, when a short, single pulse of green light is given to a dark-grown
seedling it transiently elongates at a rate that eclipses the dark rate. This increase in
growth rate persists in cry, phy and phot receptor mutant backgrounds (Folta, 2004).
This finding, in conjunction with the fact that the response is the opposite of normal light
responses mediated by characterized receptors, suggests that the green response is
driven by a novel photosensor. Using the green-induced growth kinetics as a guide,
microarray experiments (performed precisely at the peak of green-light response)
28
presented changes in the transcriptome that accompanied the green-light-induced
increase in growth rate (Dhingra et al., 2006). Two classes of transcripts were
significantly affected. The first class is similar to those induced by phyA, reinforcing the
dictum that phyA is a sensitive receptor for all visible wavelengths. These included
ELIP, HY5 and PKS transcripts.
The second class of transcripts decreased following green light treatment.
Surprisingly these were plastid resident transcripts, especially those encoding proteins
destined to support the photosynthetic apparatus such as psaA, rbcL and psbD
(Dhingra et al., 2006). These transcripts have long been known to be induced by light,
namely red or blue light. Here the green light system drives their abundance down in
contrast to the action of other light qualities.
Together the cryptochrome-dependent and the cryptochrome-independent green
light responses share a common theme of opposing light-driven physiological or
developmental responses.
Shade Avoidance Syndrome and Its Mechanisms
In the canopy or within high plant densities, the relative red-to-far-red ratio
dramatically decreases. While red and blue light are efficiently screened from incident
light by photosynthetic pigments, far-red and green light pass through and are scattered
(Klein, 1992). Far-red light is abundant in the understory. Plants grown in shade exhibit
extensive remodeling of transcriptomes and architecture to accommodate growth in
photosynthetically challenging conditions (Smith and Whitelam, 1997; Kim et al., 2005;
Vandenbussche et al., 2005). The conspicuous changes in morphology include petiole
elongation, leaf hyponasty, leaf area reduction, chlorophyll content decrease, and
enhanced apical dominance (Franklin, 2008).
29
In the past decade, the molecular mechanisms of far-red induced shade
avoidance signaling have received considerable attention. Shade avoidance response
is primarily mediated by phyB, while phyD and phyE act redundantly on its suppression
(Stamm and Kumar, 2010). However, phyA moderates shade avoidance, antagonizing
the amplitude of phyB,D,E-mediated response. Multiple red to far-red ratio-regulated
genes, controlled by phytochromes have been indentified and provide a means to
examine the mechanisms responsible for the green-induced effects. Several transcripts
pivotal to far-red responses were examined. The ARABIDOPSIS THALIANA
HOMEOBOX PROTEIN 2 (HAT4) and PIF3-like1 (PIL1) genes are strongly induced
during shade avoidance responses to far-red light and regarded as shade marker genes
(Devlin et al., 2003). As a member of the family of homeodomain leucine zipper (HDZip)
transcription factors, HAT4 binds DNA via a 9-bp sequence, CAATNATTG (Henriksson
et al., 2005; Ciarbelli et al., 2008; Ruberti et al., 2011). Analysis of multiple
phytochrome-deficient mutants revealed that HAT4 expression is redundantly
suppressed by phyB and phyE (Franklin et al., 2003). The PIL1 gene encodes a bHLH
transcription factor and is a member of PIF3 transcription factor family. It was recently
found to play an important negative role in long-term shade avoidance syndrome in a
phyB background, aside from its effect on shade stimulation (Roig-Villanova et al.,
2006).
PIFs are a family of bHLH transcription factors involved in light signaling pathways,
and are demonstrated to bind to phytochromes. Photoactivated phytochromes act to
target PIFs at Active Phytochrome Binding (APB) domains, inducing rapid
phosphorylation of PIFs and their subsequent degradation via the 26S proteosome
30
(Shen et al., 2007; Lorrain et al., 2008). The PIF4 and PIF5 genes have been shown to
promote shade avoidance responses. In the canopy, where enriched far-red light would
be expected to convert phyB from its Pfr form to its Pr form, the stability of PIF4 and
PIF5 would be enhanced, and the expression of shade-related genes would result
(Lorrain, 2008).
The gene, Long Hypocotyl in Far Red1 (HFR1), which encodes a bHLH
transcription factor, is a negative regulator in shade avoidance syndrome, and prevents
excessive responses to shade (Sessa et al., 2005). Recently, genetic and biochemical
approaches were used to demonstrate that HFR1 interacts with PIF4 and PIF5 by
forming non-DNA binding bHLH heterodimers (Hornitschek et al., 2009). In addition, the
up-regulated expression levels of GAI, IAA29, ACS8 and CKX5 in an hfr1 background
link shade avoidance responses with phytohormone signaling (Stamm and Kumar,
2010).
The actual changes in plant form are a consequence of changes in growth
regulators. Auxins, gibberellins, ethylene, brassinosteroids, cytokinins and jasmonates
have all been implicated in the mechanisms of shade avoidance responses (Pierik et
al., 2004; Robson et al., 2010). The shade-induced elongation growth is caused by the
increase of auxins, ethylene or gibberellin, and the decrease of cytokinin. Auxin plays
an essential role in shade avoidance responses (Devlin et al., 2003). Under shade, the
auxin synthesis metabolic pathway in Arabidopsis is activated via aminotransferase,
which is encoded by TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1
(TAA1) (Tao et al., 2008). The auxin transport-related genes, PIN-FORMED3 (PIN3)
and PIN7 are also regulated under far-red enriched condition (Devlin et al., 2003). The
31
PHY RAPIDLY REGULATED 1 (PAR1) and PAR2, atypical bHLH transcription factors,
negatively regulate auxin-induced gene expression. They are also rapidly up-regulated
in shade and repress the shade avoidance response via the interaction with auxin
signaling (Roig-Villanova et al., 2007).
Gibberellin induces elongation growth in the shade avoidance response through
DELLA proteins. DELLA proteins bind to PIF4, a positive regulator of shade avoidance,
preventing expression of downstream genes (de Lucas et al., 2008; Feng et al., 2008).
In Arabidopsis, low R: FR light conditions promote gibberellin biosynthesis, which
results in the degradation of DELLA proteins. Therefore, PIF4 is released and up-
regulates the expression of transcripts associated with elongation growth (Alabadi and
Blazquez, 2009). Cytokinins, cooperating with other major phytohormones, such as
gibberellins and auxins, control the shade response. For instance, auxin induces
cytokinin breakdown in pre-procambial cells of developing leaf primordial via cytokinin
oxidase, CKX6 (Carabelli et al., 2007). Pierik demonstrated that ethylene was involved
in the low blue-induced shade avoidance in tobacoo via auxin signaling pathways
(Pierik et al., 2004; Pierik et al., 2009). The pathways of auxin and gibberellin signaling
appear to operate in parallel, while ethylene seems to be upstream of auxin signalling
under shade conditions (Pierik et al., 2009). In sum, the mechanisms of shade
avoidance can be synthesized into a cogent model (Figure 1-5).
In a dense canopy, green light, like far-red light, also passes through plant tissue
with greater efficiency than red or blue light. This causes the red to far-red ratio to
decrease and the green light portion, or green to blue ratio to change in the dense
canopy environment (Figure 1-6). Green light may also contribute to the shade
32
avoidance syndrome, a hypothesis supported by numerous preliminary observations.
This hypothesis was formally tested as the basis for this dissertation research, first by
examining the effects of green light on the shade avoidance syndrome and their
possible relation to the far-red sensing system. The effects of green wavebands on
whole-plant aspects of shade avoidance were examined. Next the genetic mechanisms
of green light-induced shade avoidance were examined, as well as their relationship
with far-red induced shade responses. The transcript levels of the genes mentioned are
well-described molecular signatures of the shade response, and thereby constitute
interesting targets for further analysis to compare and contrast the effects of far-red and
green light that have similar effects on morphology.
While these laboratory experiments may seem to only expand our fundamental
understanding of light-mediated processes in model plants, they do have profound
implications in broader contexts. Identification of mechanisms underlying green-induced
shade avoidance may be ultimately advantageous to agriculture because if we better
understand the genetics and environmental factors controlling density-related
responses in crops, plants may be grown more productively. These findings may be the
basis for more productive planting schemes and management plans that optimize
agricultural land, thus benefiting crop-based economies (Hornitschek et al., 2009).
33
Figure 1-1. Schematic model of SAM organization. Red area represents the central
zone (CZ), blue area represents the organizing center (OC). The peripheral zone (PZ) can be subdivided into the inner PZ [IPZ] in pink and the outer PZ [OPZ] in green. The yellow parts are organ primordial (OP). Modified from Perales and Reddy, 2011.
34
Figure 1-2. Structure of phytochromes in plants. NTE, plant-specific amino-terminal
extension; PLD, PAS-like domain; GAF, a domain distantly related to PAS; PHY, a domain specific to phytochromes; HKRD, histidine kinase related domain; HisKA, histidine kinase A domain-related; HisK-ATPase, histidine kinase ATPase superfamily domain. Modified from Sharrock, 2008.
Figure 1-3. Cryptochromes and cofactors in Arabidopsis. The highest conservation
domain in crys is the PHR. This region binds the FAD and MTHF cofactors non-covalently. Cry1 and cry2 carry an additional domain at the C-terminal end that varies in length and sequence. The DAS domain is conserved in plant cryptochromes. Modified from Klar et al., 2006.
35
Figure 1-4. Structures of phototropins and a Zeitlupe-family photoreceptor. In both
classes of photoreceptors the LOV domain binds to FMN and functions as light sensor. Phototropins have two FMN-binding LOV domains in the N-terminal end (LOV1 and LOV2) and a serine/threonine kinase domain in the C terminal part (KD). The Ja-helix (Ja) connects LOV2 and KD. Zeitlupe family photoreceptors harbor one LOV domain at the N-terminus, an F-Box motif and six Kelch repeats (KELCH) in the C-terminal region. Modified from Demarsy and Fankhauser, 2009.
.
36
Figure 1-5. The model of shade avoidance syndrom mechanisms. The model shows
that multiple parts of the spectrum and their associated receptors coordinate with hormones to contribute to the shade avoidance syndrome. Genes are shown as boxes, and hormones are in ellipse. Arrows indicate positive effects (accumulation of transcript and/or protein and hormone level, respectively; activation through interaction, etc.), and blocked arrows indicate negative effects. Modified from Stamm and Kumar, 2010.
.
37
Figure 1-6. Quantum energy distribution of full sunlight and under the shade of leaves.
Light conditions were measured at noon in mid-April in Gainesville, FL (29.67° N), using a StellarNet spectroradiometer. Adapted from Folta and Maruhnich, (2007).
38
The results in Chapter 2 have been published in Plant Physiology and Plant
Signaling and Behavior. The journal URL (http://www.plantphysiol.org or
http://www.plantcell.org) is cited here.
Copyright American Society of Plant Biologists
Copyright Plant Signaling and Behavior
39
CHAPTER 2 GREEN-LIGHT-INDUCED SHADE AVOIDANCE SYNDROME IN ARABIDOPSIS
Introduction
Plant survival depends on an exquisite sensitivity to changes in the ambient
environment. Incident irradiation constitutes an important package of environmental
information, as light quantity, quality and duration all have important effects on plant
growth and development (Chen et al., 2004; Spalding and Folta, 2005; Kami et al.,
2010). For instance, the relative ratio of red to far-red light is an important indicator of
shade or high plant density, as far-red light is readily transmitted through plant tissues in
the canopy while red light is absorbed (Smith and Whitelam, 1997; Ballare, 1999; Kim et
al., 2005; Vandenbussche et al., 2005). Plants grown in enriched far-red or low-blue-
light environments exhibit “shade avoidance syndrome” a genetic program that alters
plant form and gene expression to best suit the spectral shift induced by shade (Stamm
and Kumar, 2010; Keuskamp et al., 2011). Like shade-abundant far-red light, green
light also passes through plant tissue with greater efficiency than red or blue light (Klein,
1992); also Figure 1-6). In this chapter, an adjustable LED lighting system was used to
test the hypothesis that green light also informs the plant of shade conditions and
induces adjustments in morphology characteristic of shade-avoidance.
Green light responses can be divided into cryptochrome-dependent responses
and cryptochrome-independent responses. Blue-light responses have been shown to be
opposed by green light acting through the neutral semiquinone flavin of the receptor’s
chromophore (Banerjee et al., 2007; Bouly et al., 2007; Liu et al., 2010) or
autophosphorylation of cryptochromes caused by a photolyase-like cyclic electron
shuttle (Liu et al., 2010). This blue-green reversibility has been described for stem
40
elongation and flowering acting through cryptochromes. Sellaro et al (2010) recently
reported the hypocotyl length of Arabidopsis seedlings decreased along with the
increase of blue/green ratios (Sellaro et al., 2010). Other green light effects are
independent of known sensory systems. Green light induces transient stem elongation
in the etiolated seedling (Folta, 2004) and also drives a decrease in steady-state
transcript accumulation of various plastid transcripts (Dhingra et al., 2006). Whether
cryptochrome dependent or cryptochrome independent, either mechanism describes
effects of green wavebands that oppose blue-light responses.
These studies may be expanded to other biologically relevant contexts where
plants may be subject to an enriched green environment. Such a state exists within a
canopy or in plots of high plant density (Ballare, 1999; Vandenbussche et al., 2005).
While red and blue light are efficiently filtered from incident light by photosynthetic
pigments, far-red and green light pass through and are scattered (Klein, 1992; Franklin,
2008). Far-red light is abundant in the understory shifting the red to far-red (R/FR) ratio.
Plants grown under a low R/FR ratio exhibit extensive remodeling of the transcriptome
and body plan to accommodate growth in photosynthetically challenging conditions
(Smith and Whitelam, 1997; Kim et al., 2005; Vandenbussche et al., 2005). The
conspicuous changes in morphology include elongation of the petioles and a hyponastic
deviation in their orientation, presumably to position photosynthetic surfaces above
adjacent foliage (Kozuka et al., 2005). In the past decade, the molecular mechanisms of
far-red-induced shade avoidance signaling have been well described. Multiple R/FR
ratio-regulated genes, controlled by phytochromes, have been identified. HAT4 and
PIL1 are direct targets of the phytochrome signaling system that are induced during
41
shade avoidance responses. The accumulation of these two transcripts is quickly and
reversibly regulated by simulated shade (Carabelli et al., 1996; Salter et al., 2003). A
description of the behavior of these genes in response to an enriched green light
environment may also be informative, especially in delineating similarities and
differences between far-red and green induced shade responses.
The experiments presented in this chapter utilize narrow-bandwidth visible LED
light mixtures to test the effect of green light on rosette architecture. The study utilizes
Arabidopsis thaliana plants, chosen for their compact growth, well-characterized shade
responses, and availability of photoreceptor mutants. The molecular mechanism
underlying the response to green light was examined using mutants and by evaluation
of changes in gene expression compared to far-red-mediated shade responses. The
results indicate that while plants maintained under blue and red light exhibit the normal
prone rosette architecture, addition of green light to the mix paradoxically induces a low-
light growth habit resembling that found under shaded conditions.
Results
Addition of Green Light Induces a Shaded Appearance
A narrow-bandwidth LED-based light platform was used to test the hypothesis that
green light could induce shade effects in plants grown under blue and red light.
Arabidopsis seeds were planted on soil, stratified, and then germinated and grown
under white light for three weeks. Plants were then transferred to experimental
conditions. In the first three treatments, red and blue light fluence rates were kept
constant and two fluence rates of green light were added. The baseline treatment for
comparison is 50 µmol m-2 s-1 red and 40 µmol m-2 s-1 blue light (RB). Green light was
added to the RB background at 10 µmol m-2 s-1 (RBg) and 40 µmol m-2 s-1 (RBG) to test
42
if green-induced effects were fluence-rate dependent. A fourth treatment was conducted
at 40 µmol m-2 s-1 green light (as in RBG) while decreasing red light (rBG) to keep
photosynthetically active radiation (PAR) identical to other treatments. Examples of
representative wild-type Arabidopsis (Col-0) plants grown under the different light
treatments are presented in Figure 2-1A. The morphological adaptations to an added
green light environment were conspicuous in RBG and rBG conditions within five days
of transfer. Plant morphology was similar to that of plants subjected to low red, high far-
red environments, presenting the hallmarks of shade-avoidance response while being
grown under enriched green light environment (Figure 2-1).
Analysis of a series of morphological parameters, including leaf angle, leaf length,
leaf blade length, petiole length and leaf blade area, were measured in the third pair of
true leaves. Eight to ten plants were measured in three independent biological
replicates, with similar results observed over many independent trials in different growth
chambers. The most conspicuous differences between RB and RBG plants were leaf
angle (Figure 2-1B) and petiole length as a function of total leaf length (Figure 2-1C).
Leaf angle is reported as the absolute angle of the third pair of true leaves. Therefore,
increasing inclination results in a lower value. The leaf angle in RBg plants decreased
only 2% (3.5 degrees) compared to that of control (RB) plants. However, the leaf angles
of RBG and rBG plants decreased 19% (25.9 degrees) and 13% (17.8 degrees),
respectively (P < 0.05). These results indicate that addition of green light induced a
change in leaf orientation of wild-type Arabidopsis plants.
The ratio of petiole length to total leaf length was also affected by the addition of
green light to the constant RB background (Figure 2-1C). The data are presented as
43
petiole length as a function of total leaf length, because it is a dependable indicator of
the phenomenon among all genotypes studied. The petiole represented about 33% of
the total leaf length under RB or RBg conditions. Under RBG or rBG conditions the
petiole increased to 40% of the total leaf length (significant at p<0.05).
In the analyses presented here the fluence rate of RB was kept constant and G
was added. Thus, increasing the G component yielded a simultaneous increase in total
fluence rate. To determine whether the changes seen were due to an increase in the
total fluence rate, the fourth light treatment was designed. This treatment maintained B
and G as in the RBG treatment, and the R component was decreased so that rBG
approached the fluence rate to RB, keeping PAR equivalent in both conditions. The
effects observed in rBG plants were similar to those observed in RBG plants. To further
test the possibility that the shade avoidance responses of rBG plants were due to the
reduced R component, plants grown under RB and rB conditions were compared. The
results demonstrate that lowering the red component between RB and rB conditions did
not affect rosette architecture (Figure 2-2).
The Green Response Persists in cry and phy Mutants
Various light-induced changes in plant morphology have been ascribed to green
light. Green light responses are either cryptochrome dependent (Banerjee et al., 2007;
Bouly et al., 2007) or persist in all mutant backgrounds tested, suggesting an unknown
receptor (Folta, 2004; Dhingra et al., 2006). To test if the morphological changes
observed are mediated by a known class of light sensors, the experiments in Figure 2-1
were repeated using cry and phy mutant plants. The cry1cry2 mutants exhibited a
response similar to wild-type plants (Figure 2-3A). Compared to RB condition, the leaf
angle decreases 9% in RBg, 14% in RBG and 21% in rBG (Figure 2-3B). Similarly,
44
plants in RB and RBg conditions exhibited petioles that measured 38% of their total leaf
length, while under RBG and rBG conditions the percentages of petiole to total leaf
length increased to 42% and 46%, respectively (Figure 2-3C).
Additional experiments were conducted under conditions that enhanced the effect
of the treatment on petiole elongation. In these experiment plants were grown under 70
µmol m-2 s-1 red and 20 µmol m-2 s-1 blue light (RB), or identical conditions
supplemented with 20 µmol m-2 s-1 green light (RBG). The petiole and leaf length
(Figure 2-4) were measured for the second true leaves of these mutants. Removal of
phyA and phyB or cry1 and cry2 receptors consistently and significantly amplified the
effects of green light, even though the mutation itself resulted in an exaggeration of
petiole length compared to wild-type plants. The phyAphyBcry1 triple mutant was also
tested and maintained the green light response. Addition of green wavebands resulted
in an additional increase in petiole length.
Analysis of Shade-Induced Transcripts
To further explore the mechanism of green-induced shade avoidance and also test
the relationship between green and far-red responses mediated by phytochromes, the
expression of genes known to be affected by far-red light was quantified using real-time
qPCR. The transcripts associated with HAT4, PIL1, and PHYB are strongly induced by
phytochrome under low R/FR conditions (Devlin et al., 2003). Plants were treated in the
same four light conditions as used in Figure 1 and then total RNA was prepared and
analyzed as described in Materials and Methods. At least two independent biological
replicates were tested, providing consistent gene expression patterns. In wild-type
plants the relative steady-state transcript level of both HAT4 and PIL1 did not increase
in the enriched green light environment. Instead a marked decrease in HAT4 mRNA
45
was observed. Consistent with shade symptoms, the PHYB transcript increased in
abundance in RBg and RBG conditions. HY5, a transcript strongly affected by light,
was included for comparisons along with eIFα, a transcript that is not expected to
change between conditions (Figure 2-5).
Due to the known influence of green light via cryptochromes, gene expression
patterns were also assessed in the cry1cry2 mutant background (Figure 2-6A). In
cry1cry2 plants the addition of green light caused an increase in HAT4 and PIL1
transcript levels, a pattern consistent with far-red treatment, even in the absence of far-
red light. This trend is the opposite of what was observed for wild-type Arabidopsis
seedlings. The strong induction of PHYB and HY5 was also not observed. To further
determine whether CRY1, CRY2, or both together contribute to the changes in gene
expression, cry1 and cry2 single mutant plants were grown in the same experimental
light conditions and analyzed. The single mutants exhibited a HAT4 and PIL1
accumulation pattern similar to the cry1cry2 mutant, indicating that both CRY1 and
CRY2 affect the green-specific responses and their effects are synergistic (Figure 2-6B,
6C). The effects of the mutations on basal gene expression (RB conditions) were not
always identical in the cry mutant backgrounds (Figure 2-7). While HAT4 levels are
similar in cry mutants and WT plants, while PIL1 levels are significant higher in cry1cry2
and cry1 mutants. These differences should be considered when interpreting the data
in Figure 2-6.
Supplemental Green Light Decreases Anthocyanin Accumulation
As noted earlier, green light negates blue-light-induced anthocyanin accumulation
(Bouly et al, 2007). In this study, it was observed that mature Arabidopsis plants grown
in red and blue conditions with supplemental green light contained visibly less
46
anthocyanin than those in red and blue conditions alone. Anthocyanins were quantified
and the results are presented in Figure 2-8. As the green component increases, the
amount of anthocyanin decreases, approaching half the level present in red + blue
conditions alone. The same principles were tested in ‘Red Sails’ lettuce, a lettuce
variety that has a wide linear dose-response accumulation of pigments in response to
blue light (Folta and Price, unpublished). In ‘Red Sails’ lettuce, anthocyanin
accumulates dramatically along with the increasing fluence rates of blue light (Figure 2-
7A). As in Arabidopsis, green light also reverses this blue-induced response.
Compared to lettuce grown in blue light alone, the levels of anthocyanin were sharply
lower than when grown in blue + green or green alone (Figure 2-9B). These results are
consistent with the anthocyanin accumulation data presented by Bouly (2007) and add
an additional example of how green light antagonizes other light-induced responses in
mature plants across species.
The Green-Induced Shade Avoidance Response Is Attenuated in Shade-Associated Mutants hat4 and pil1
Because transcript accumulation was affected by green light in a cryptochrome-
dependent manner, it was important to examine if there were differences in plant shade
responses to green light in the associated mutants. The hat4 and pil1 mutants were
treated in the same light conditions used in Figure 2-1. Wild-type plants were used as
positive control of green-responses. Neither mutant exhibited shade avoidance
responses in green-enriched conditions (Figures 2-10 and 2-11).
Discussion
When sunlight is filtered by a foliar canopy, red and blue light are selectively
reduced, resulting in an enriched environment of far-red light. Careful examination of
47
the spectrum transmitted through leaves shows that along with the strong decrease in
R/FR ratio there is an overall decrease in the fluence rate and an enrichment of green
wavebands relative to blue and red (Folta and Maruhnich, 2007; Franklin, 2008). The
goal of this work is to test if the relative enrichment of green light also affects the
development of shade symptoms. Previous reports have shown a role for green light in
leaf position changes (Mullen et al., 2006). In the present work green light was added to
a constant background of red and blue light. The red and blue treatment alone was
sufficient to maintain plants presenting little to no leaf inclination, that is, with leaves
growing approximately parallel to the soil surface and perpendicular to incident
illumination.
The results presented in Figure 2-1 show that addition of green wavebands to a
constant background of red and blue light causes leaves to lift toward the light source.
Petioles become increasingly longer and leaves become pale: a suite of morphological
changes consistent with shade avoidance syndrome. Based on the conventional
understanding, increasing visible light should not induce a shade response. The test
was performed in the absence of far-red light, as the red LED light source produces
negligible output above 700 nm. Blue light levels were kept constant, as decreasing
blue light also can induce shade avoidance symptoms (Pierik et al., 2004; Keuskamp et
al., 2011).
The result is consistent with a growing body of evidence that green light signals
oppose responses generated by activation of blue and red photosensory pathways. The
opposition of a normal light response by green light has been observed in other
contexts. Green light delivered coincidently with blue light eliminates stomatal opening
48
(Frechilla et al., 2000). Addition of green light to a red and blue background decreases
seedling dry mass (Went, 1957). Green light also increases stem growth rate in the
developing seedling (Folta, 2004), whereas all other wavebands (including far-red)
promote growth inhibition (Parks et al., 2001; Shinkle et al., 2004). Addition of green
wavebands has been shown to reverse blue light-induced effects on hypocotyl
elongation and anthocyanin accumulation in seedlings (Bouly et al., 2007) as well as
affect flowering (Banerjee et al., 2007). The results herein represent another example of
how addition of green light opposes responses induced by other visible wavelengths.
Some of the effects of green light have been attributed to green-induced reversal
of blue light effects on the cryptochrome photoreceptors. Green light has been shown to
attenuate cryptochrome response by affecting the properties of the chromophore,
switching it from an active semiquinone state to the fully reduced form of FADH-
(Banerjee et al., 2007; Bouly et al., 2007). To test if the cryptochrome receptors are
mediating the responses observed in these experiments, cryptochrome mutants were
examined (Figure 2-3). The mutants exhibited changes in morphology that were
comparable with those observed in wild-type plants, indicating that the response
observed is not the effect of cryptochromes inactivation. Similarly, phyAphyB mutants
were tested for some of the responses to green and maintained changes consistent with
shade response (Figure 2-4). The phyAphyBcry1 triple mutant also showed the
response to enriched green light. It should be noted that all mutants exhibited a basal
exaggeration of petiole length due to the lack of light input through these
photomorphogenic systems. Even with a predisposition for elongate growth, the results
presented in Figure 2-4 show that the green light effect is additive to the influence of the
49
mutation, consistent with an interpretation that a separate system is mediating the
response. The data in this report do not rule out the possibility that phytochromes C-E
transduce the green response, yet it remains unlikely because phytochrome activation
would suppress shade symptoms under visible light.
An assessment of gene expression changes that accompany shade symptoms in
an enriched green environment is also informative. The gene expression profiles
elicited during far-red-induced shade responses have been well described, and provide
a means to examine the mechanism responsible for the green-induced effects. Several
transcripts pivotal to the far-red response were examined. HAT4 and PIL1 are strongly
induced during shade avoidance responses to far-red light (Devlin et al., 2003). HAT4 is
a member of the HDZip family of transcription factors, binding DNA via the 9-bp
sequence CAATNATTG (Henriksson et al., 2005; Ciarbelli et al., 2008). Analysis of
multiple phytochrome-deficient mutants revealed HAT4 expression is redundantly
suppressed by PHYB and PHYE (Franklin et al., 2003). PIL1 encodes a bHLH
transcription factor and is a member of PIF transcription factor family. It has been
described to play an important negative role in long-term shade avoidance syndrome in
a phyB background, aside from its effect on shade stimulation (Roig-Villanova et al.,
2006). The transcript levels of HAT4 and PIL1 are well-described molecular signatures
of the shade response. Together they are excellent candidates to compare and
contrast the effects of far-red and green light that have similar effects on morphology.
The accumulation patterns of HAT4 and PIL1 transcripts in an enriched green light
environment were the opposite of those induced by far-red light (Figure 2-5). With
supplemental green light, steady-state levels of HAT4 transcripts actually decreased to
50
approximately 50% of the levels observed under red and blue light alone. On the other
hand, PHYB transcripts did accumulate with the addition of green light, consistent with
the increases observed by Devlin et al. (2003) in response to far-red light. The HY5
transcript has been well described in photomorphogenic responses, yet is not required
for transducing shade-triggered signals (Roig-Villanova et al., 2006). Here, this
transcript serves as an additional non-shade-associated message that is sensitive to
changing light environments, and its levels also increase. An eIFα reference remained
constant. The results indicate that although green light induced morphological changes
reminiscent of far-red induced alterations, the signature gene expression events that
accompany these changes were not observed. These findings support an interpretation
that green light signals adjust plant form through a mechanism that is distinct from that
which imparts far-red effects. This finding is consistent with the report from Mullen et al.
(2006) indicating another signaling pathway involved in control of leaf position aside
from phytochromes. Mullen et al. demonstrate that leaf inclination in phyB as well as the
triple mutants phyAphyBphyD and phyAphyBphyE is lower than that in darkness. They
also observed that monochromatic green light induces changes in leaf position, a
finding consistent with the results herein.
Examination of HAT4 and PIL1 gene expression changes in the cry1cry2
background implicates the cryptochromes in this shade response, but only at the level
of gating shade-associated gene expression. Figure 2-3 shows that the green-induced
shade symptoms are present in the cry1cry2 mutant. It would be expected that HAT4
and PIL1 transcript levels would likely follow the patterns observed in wild-type plants if
the green signal followed the far-red mechanism. However, while HAT4 and PIL1
51
mRNA levels decrease after addition of supplemental green light in wild-type seedlings,
their accumulation in the cry1cry2 double mutant approximates the trends seen during
far-red induced shade avoidance (Figure 2-6A). The same trends were observed in
cry1, and cry2 single mutants (Figures 2-6B and 6C). These findings indicate that the
cryptochromes actively block the development of shade-driven gene expression profiles
in the absence of far-red light. In this case the wild-type seedling adopts the
morphology of a shaded seedling, but the conspicuous alterations in architecture are
uncoupled from the usual changes in gene expression by a cryptochrome-dependent
mechanism.
These results are exciting because they illustrate a role for the green absorbing
form of cryptochrome to actively drive a change in physiology. While green light
responses mediated through cryptochromes have been described to reverse blue light
responses, in this case the green light absorbing form of both of the cryptochrome
receptors is active in blocking the accumulation of two shade-inducible transcripts. This
finding is another unique facet of this study. If green light was simply reversing the blue
response, then the same phenotype should be observed in cry mutants in the absence
of G (such as in RB conditions). However, this is not observed. The induction of HAT4
and PIL1 in the absence of far-red signals requires green light and the absence of
cryptochrome receptors. These data indicate that the green light triggers cryptochrome
to actively gate at least facets of the transcriptome response normally induced by far-
red light. The attenuated shade responses of hat4 and pil1 mutants grown in green-
enriched conditions indicate that the green signaling pathway merges with far-red
signaling pathway upstream of, or at HAT4 and PIL1 (Figure 2-10, 11).
52
These data may be synthesized into a cogent model (Figure 2-12). Blue and red
light activate cryptochromes and phytochromes to present normal prone leaf position.
The addition of green light induces upward orientation of leaves and elongation of
petioles. These responses appear to occur independently of phytochromes and
cryptochromes for two reasons. First, the responses persist in the mutants tested and
second, visible light transduced through these systems should not generate a shaded
morphology. One possible exception is if green light is negating the effect of
cryptochromes, but again, analysis of cry mutant plants does not support this
interpretation (Figure 2-3). The change in inclination and/or petiole elongation in the cry
and phy mutant backgrounds indicates that green signals are due to an unknown role of
another phytochrome or perhaps a novel light sensor.
The findings of this study show that the addition of green wavebands to a
background of blue and red induces the familiar shaded plant architecture. These
results are significant in that symptoms develop with increasing fluence rate, a finding
that is in opposition to what is known about generation of shade phenotypes by low-light
environments. Gene expression changes distinguish the green response from the far-
red response, and implicate the green absorbing form of cryptochromes to connect
green control of shade-induced transcripts that are normally induced by low R:FR.
While surprising at first, the results show that plants maintain additional means to adapt
to a changing light environment, and remind us that plants are sensitive to a broad
series of inputs to shape plant form and function.
53
Materials and Methods
Plant Materials and Growth Conditions
The genotypes used in this study were Arabidopsis thaliana (Col-0), cry1cry2
mutant (cry1-304 crossed to cry2-1), phyAphyB mutant (phyB-5 crossed into a
homozygous phyA SALK_121744 background with phenotypic and molecular
verification of double mutation), hat4 (SALK_106790) and pil1 (SALK_043937)
homozygous T-DNA mutants (ordered from ABRC). Plants were grown in plastic trays
in soilless media (ProMix BX). Seeds were distributed evenly to receive equal light
distribution and stratified at 4 oC for 72 hours. Seedlings were grown under white
fluorescent light (~100 µmol m-2 s-1) until the seedlings presented four pairs of true
leaves (typically 21-28 d). At this point the plants were transferred to LED chambers
featuring the experimental light conditions for 3-5 days. The temperature in LED
chambers keep constant at room temperature (22 ± 1.5 oC). Plants were watered
approximately three times a week under white fluorescent light and every other day
under LED arrays with 0.1X Hoagland’s solution. Plants were grown under constant
illumination.
Light Sources and Treatments
Light treatments were generated using LED light. The peak wavelengths of red,
blue and green light are 630nm, 470nm and 525nm, respectively. The emission
spectrum of all light sources is viewable online at
www.arabidopsisthaliana.com/lightsources. Four different combinatorial light treatments
were established for these experiments. The first treatment consisted of 50 μmol m-2 s-2
red LED light and 40 μmol m-2 s-2 blue LED light. The second and third treatments
consisted of the same red-blue treatment supplemented with 10 and 40 μmol m-2 s-2
54
green LED light, respectively. Treatment four was of comparable fluence rate with the
first treatment (20 μmol m-2 s-2 red light, 40 μmol m-2 s-2 blue light and 40 μmol m-2 s-2
green light).
Morphological Measurements
To observe the effect of green light on rosette architecture, several morphological
parameters were measured, including leaf angle, petiole length, leaf length, and leaf
blade area. Whole plants were carefully removed from the growing medium, cleaned of
particulate matter, and then flattened on the adhesive side of black electrical tape.
Samples were imaged at 600 dpi resolution on a standard flatbed scanner and
measured using UTHSCSA Image Tool (Version 3.0 for Windows) with comparisons to
adjacent size standard. For experimental repeat, at least two sets of 8-10 plants were
measured for each treatment.
Anthocyanin Accumulation Assay
Anthocyanin was extracted independently overnight in acidic (1% HCl) methanol in
a dark chamber. Further the extract was purified with chloroform. The absorbance of
aqueous phase was determined at 530 nm and 657 nm (Teng et al., 2005).
RNA Preparation and Real-time qPCR
The whole plants were harvested into liquid nitrogen and stored at –80 °C prior to
RNA isolation. Total RNA was isolated for using the CTAB-based method (Chang S,
1993). The reverse transcription was performed using TaqMan transcriptase kit (Applied
Biosystems, USA). Quantitative real-time PCR was performed by the StepOne Plus
system (Applied Biosystems, USA). TaqMan primers and probes were designed by
Primer Express 2.x software (Applied Biosystems). The sequences of primers and
probes are listed in Table 1. PCR reaction mixtures were in the following thermal profile:
55
2 min at 50ºC; 10 min at 95ºC; 40 cycles (15 s at 95ºC; 1 min at 60ºC). Actin2 was used
as the internal control. The relative mRNA levels were calculated using the 2-ΔΔCT
comparative method (Livak and Schmittgen, 2001; Sehringer et al., 2005).
56
Table 2-1. TaqMan primer and probe sequences used in real-time qPCR
Gene TaqMan primer/probe sequences (5′→3′)
HAT4 CACATGAGCCCACCCACTACT GGGACCGACACGTGTTCAC TGACCATGTGCCCTTC
Forward Reverse Probe
PIL1 TGCCTTCGTGTGTTTCTCAGA AGGCGGACGCAGACTTTG TCAGGCTACTTCTTTTACTCA
Forward Reverse Probe
PHYB GCGATTGGTGGCCAAGATA AAACTTCCCATTGCGGTCAA ATAAGTTCCCTTTCCCATTC
Forward Reverse Probe
HY5 CAAGCAGCGAGAGGTCATCA ATCGCTTTCAATTCCTTCTTTGA CTCTGCTCCACATTTG
Forward Reverse Probe
EF Alpha ACGGTTACGCCCCAGTTCT CGCCTGTCAATCTTGGTCAA TGCCACACCTCTCACATTGCAGTCAA
Forward Reverse Probe
Actin2 TCGGTGGTTCCATTCTTGCT Forward GCTTTTTAAGCCTTTGATCTTGAGAG
AGCACATTCCAGCAGATGTGGATCTCCAA Reverse Probe
57
Figure 2-1. Supplemental green light induces a shade response in wild-type
Arabidopsis Col-0. Wild-type (Col-0) Arabidopsis plants were grown under white light for approximately three weeks and then transferred to one of four light treatments: 50 µmol m-2 s-1 red and 40 µmol m-2 s-1 blue LED light (RB); 50 µmol m-2 s-1, 40 µmol m-2 s-1 blue and 10 µmol m-2 s-1 green light (RBg); 50 µmol m-2 s-1, 40 µmol m-2 s-1 blue and 40 µmol m-2 s-1 green light (RBG); 20 µmol m-2 s-1 red, 40 µmol m-2 s-1 blue and 40 µmol m-2 s-1 green light (rBG) for 3-5 d. Individual plant rosettes were dissected and conspicuous leaf attributes were quantified. A) Single representative plants harvested from the different light treatments. B) The mean leaf angle of plants grown in the four light conditions. Leaf angle means the number of degrees between the third pair of leaves. C) The mean petiole length as a percent of total leaf length of different light treated plants. The measurements in Panels B and C were derived from the third true leaves from 8-10 individual plants. Error bars represent standard error of the mean. Letters represent statistically different means (P < 0.05).
58
Figure 2-2. Decreasing the red light fluence rate in an RB background does not affect
rosette architecture. Wild-type (Col-0) Arabidopsis plants were grown under white light for approximately three weeks and then transferred to one of three light treatments: 20 µmol m-2 s-1 red and 40 µmol m-2 s-1 blue LED light (rB); 50 µmol m-2 s-1 and 40 µmol m-2 s-1 light (RB); 50 µmol m-2 s-1 red, 40 µmol m-
2 s-1 blue light and 40 µmol m-2 s-1 green light (RBG) for 3-5 d. Individual plant rosettes were dissected and conspicuous leaf attributes were quantified. A) The mean leaf angle of plants grown in the three light conditions. Leaf angle is the angle between the third pair of leaves. B) The mean petiole length as a percent of total leaf length of different light treated plants. The measurements were derived from the third true leaves from 8 individual plants. Error bars represent standard error of the mean. Asterisks represents significant difference compared to the first treatment (p<0.05).
59
Figure 2-3. Supplemental green light induces a shade response in Arabidopsis
cry1cry2 mutant. Arabidopsis cry1cry2 plants were grown and treated in the same conditions used in figure 2-1. A) The representative mutant plants in different light treatments. B) The mean leaf angle of cry1cry2 plants grown in the four light conditions. Leaf angle represents the number of degrees between the third pair of leaves. C) The mean petiole length as a percent of total leaf length of different light treated plants. The measurements in Panels B and C were derived from the third true leaves from 8-10 individual plants. Error bars represent standard error of the mean. Letters represent statistically different means (P < 0.05).
60
Figure 2-4. Supplemental green light effects are maintained in photoreceptor mutants.
The effect of green light was tested in photoreceptor mutant backgrounds and compared to wild type plants. The phyAphyB, cry1cry2, and phyAphyBcry1 (phyABcry1) mutants were grown under RB and RGB, their rosettes were dissected and leaf attributes were quantified. This figure presents the ratio of the average petiole length compared to total leaf length. All measurements were obtained from the second true leaves of at least 19 individual plants. Error bars represent standard error of the mean. All differences between RB and RGB were significant within each genotype (p<0.05).
61
Figure 2-5. Shade-avoidance related genes expression levels in wild-type (Col-0)
plants grown in various amounts of green light. Plants were grown and treated in the same methods described in Figure 2-1. The gene expression levels were quantified using real-time qPCR. Relative transcript values were normalized to RB conditions. Actin2 was used as a reference transcript. Letters represent statistically different means (P < 0.05).
62
63
Figure 2-6. Shade-avoidance related genes expression levels in A) cry1cry2, B) cry1,
and C) cry2 mutants grown in different light treatments. Plants were grown and treated in the same conditions used in Figure 2-1. Gene expression levels were quantified using real-time qPCR. Transcript levels were normalized to the RB condition. Actin2 was used as a reference transcript. Letters represent statistically different means (P < 0.05).
64
Figure 2-7. Shade-avoidance related genes expression levels in wild-type (Col-0),
cry1cry2, cry1, cry2 plants grown in RB light condition. Plants were grown and treated in RB (50 µmol m-2 s-1 red and 40 µmol m-2 s-1 blue light) condition described in Figure 1. The gene expression levels were quantified using real-time qPCR. Relative transcript values were normalized to WT sample. Actin2 was used as a reference gene. Lower-case letters represent statistically different means (P < 0.05).
65
Figure 2-8. Supplemental green light decreases anthocyanin accumulation in wild-type
Arabidopsis (Col-0). Wild-type (Col-0) Arabidopsis plants were grown and treated in the same conditions used in Figure 2-1. Anthocyanin was extracted and measured. Error bars represent standard error of the mean of three independent biological replicates. Asterisks represents statistically significant difference comparing to the treatment RB (p<0.05).
66
Figure 2-9. Green light reverses blue-induced anthocyanin accumulation in lettuce. Lettuce were grown under white light for approximately one month and then transferred to different light treatments. Anthocyanin was extracted and measured. A) The anthocyanin levels in lettuce treated with 90 µmol m-2 s-1 white florescent light (WL) and blue LED light (B) at the amount of 20, 40 and 80 µmol m-2 s-1 for 7 days, respectively. B) The anthocyanin accumulation in lettuce treated with 90 µmol m-2 s-1 florescent light (WL), 50 µmol m-2 s-1 blue (B), 50 µmol m-2 s-1 blue LED light plus 40 µmol m-2 s-1 green (BG) and 40 µmol m-2 s-1 green (G) LED light, respectively. Error bars represent standard error of the mean of three independent biological replicates
67
Figure 2-10. Supplemental green light does not induce a shade response in
Arabidopsis hat4 mutant. Arabidopsis hat4 and wild-type (Col-0) plants were grown and treated under the same conditions as in Figure 2-1. A) The representative hat4 mutant plants in different light treatments. B) The mean leaf angle of wild-type (Col-0) and hat4 plants grown in the four light conditions. Leaf angle means the degree between the third pair of leaves. C) The mean petiole length as a part of total leaf length from different light conditions. The measurements in Panels B and C were derived from the third true leaves from at least eight individual plants. Error bars represent standard error of the mean.
68
Figure 2-11. Supplemental green light does not induce a shade response in
Arabidopsis pil1 mutant. Arabidopsis pil1 plants were grown and treated in the same conditions used in Figure 2-1. A) The representative pil1 mutant plants responding to different light treatments. B) The mean leaf angle of wild-type (Col-0) and pil1 plants grown in the four light conditions. Leaf angle means the degree between the third pair of leaves. C) The mean petiole length as a fraction of total leaf length under the different light conditions. The measurements in Panels B and C were derived from the third true leaves from at least eight individual plants. Error bars represent standard error of the mean.
69
Figure 2-12. A model depicting green-light influence in far-red independent shade
avoidance responses. Green light signals induce a shaded plant morphology that is independent of cryptochromes and phytochromes A and B. Simultaneously green signals induce gene expression patterns that resemble those induced by far-red light, except that cryptochrome receptors appear to block the changes in gene expression in the presence of green light and the absence of far-red. The model shows that multiple light receptors coordinate adaptation to a light environment based on input from several portions of the light spectrum.
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CHAPTER 3 GREEN LIGHT INTERACTIONS WITH FAR-RED LIGHT IN SHADE RESPONSE
Introduction
To adapt to their ambient environment, plants have evolved sophisticated
signaling networks to receive, transduce and respond to various light conditions (Cole et
al., 2011). Plants compete with other neighboring vegetation for photosynthetically
active energy by adjusting morphology, including stem elongation, leaf hyponasty,
reduced leaf expansion, and early flowering with reduced yields—a phenomenon
termed “shade avoidance syndrome” (Vandenbussche et al., 2005; Ruberti et al., 2011).
The shade avoidance response is a typical adaptive strategy of plants, that is
conspicuous in low R/FR, attenuated blue, as well as green enriched environments
(Stamm and Kumar, 2010; Keuskamp et al., 2011; Zhang et al., 2011). Thus, it provides
researchers an opportunity to study the interactions of multiple light signaling pathways
as they converge on a common shade response.
The role of far-red light in the control of plant form has been extensively studied. At
high canopy density red and blue light are efficiently absorbed by photosynthetic
pigments. Far-red is relatively abundant in the understory, resulting in a low R/FR ratio
(Franklin, 2008). Plants adjust their plant architecture and gene expression profiles to
accommodate growth in shade. Within a hour exposure to low R/FR substantial
changes in the transcriptome are observed (Cole et al., 2011). The far-red-induced
shade phenotype is primarily mediated by phyB, while phyD and phyE act redundantly
on its suppression (Stamm and Kumar, 2010). However, phyA attenuates elongation
growth caused by low R/FR, antagonizing the function of phyB, phyD, and phyE (Devlin
et al., 2003).
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The mechanisms for integration of the phytochrome-mediated shade responses
are well understood from studies in Arabidopsis thaliana. Multiple genes pivotal to far-
red responses are controlled by phytochromes. The HAT4 gene, encoding a HDZip
transcription factor, was the first gene reported to be reversibly induced by low R/FR in
Arabidopsis seedlings. (Ruberti et al., 1991; Henriksson et al., 2005; Ciarbelli et al.,
2008; Ruberti et al., 2011). Analysis of multiple phytochrome-deficient mutants revealed
that HAT4 expression is redundantly suppressed by phyB and phyE (Franklin et al.,
2003). The genes PIL1 and HFR1, both encoding bHLH transcription factors, are
negative regulators of shade response, and are rapidly and strongly induced by shade
(Salter et al., 2003; Sessa et al., 2005).
Two other bHLH transcription factors, PIF4 and PIF5 directly promote shade
avoidance, and also represent a central integration point of multiple signals (Huq and
Quail, 2002; Lorrain et al., 2008; Keller et al., 2011). PIF4 and PIF5 physically interact
with light-activated Pfr form of phyB, and are degraded via 26S proteasome in high
R/FR conditions. In dense vegetation, the phytochrome-mediated degradation of PIF4
and PIF5 is reduced, resulting in shade response (Lorrain et al., 2008). ChIP analysis
indicates that PIF5 binds in vivo to the G-box-containing regions of the promoters of the
shade marker genes PIL1, HFR1, and upstream of HAT4 (Hornitschek et al., 2009;
Leivar and Quail, 2010; Kunihiro et al., 2011). PIF4 and PIF5 are required for both far-
red- and low-blue-induced shade avoidance (Keller et al., 2011). In response to
enriched green environments HAT4 and PIF1 transcripts accumulated in ways that were
the opposite to the patterns seen in response to far-red or low-blue environments
(Zhang et al., 2011). Together these observations suggest that these multiple signaling
72
pathways may independently contribute to the response by adjusting a common set of
regulators.
In this report, we test how far-red and green light interact with each other in the
induction of shade symptoms. Far-red- and green-light-induced shade responses were
examined alone and together, monitoring both plant morphology and shade-responsive
gene expression. The results indicate that far-red and green have additive effects on the
development of shade avoidance responses. We propose that PIF4 and PIF5 are the
convergence point for far-red- and green-induced shade sensing
Results
Green Light and Far-Red, Alone, or Together Induces Shade Response in Wild-Type Arabidopsis Col-0
A narrow-bandwidth LED-based light platform was used to test the interactions
between green light and far-red in shade avoidance. Arabidopsis seeds were planted on
soil, stratified for 48 h, and then germinated and grown under white light for three
weeks. Plants were then transferred to experimental conditions. Four LED light
treatments were established. In all of the treatments, blue light fluence rates were kept
constant at 40 µmol m-2 s-1, and different red/far-red and green light combinations were
added. The baseline treatment for comparison is 40 µmol m-2 s-1 blue, 18 µmol m-2 s-1
red, and 18 µmol m-2 s-1 far-red light (red/far-red≈1, the treatment is noted as “BRFR”).
In the second treatment green light was added to the BRFR background at 40 µmol m-2
s-1 (BRFRG) to test if green light induces shade avoidance syndrome in the presence of
far-red light. In the third treatment, red/far-red ratio was reduced to ≈0.1 at the same
blue light background to induce shade appearances (BrFR). In order to determine the
interactions between green light and far-red, the fourth treatment was conducted by
73
adding 40 µmol m-2 s-1 green light to the third treatment (as in BrFRG). Examples of
representative wild-type Arabidopsis (Col-0) plants grown under the different light
treatments are presented in Figure 3-1A. The shade avoidance phenotype induced by
an added green light environment and/or reduced red/far-red were conspicuous in
BRFRG, BrFR and BrFRG conditions within three days of transfer.
Morphological parameters, including leaf angle, leaf length, leaf blade length,
petiole length, were measured in the third pair of true leaves. Eight to ten plants were
measured in at least two independent biological replicates, with similar results observed
over many independent trials in different growth chambers. Leaf angle is used to
quantify the degree of leaf hyponasty. It is reported as the absolute angle between the
third pair of true leaves. Therefore, increasing hyponasty results in a lower value.
Petiole length as a function of total leaf length describes the magnitude of petiole
elongation, as the higher ratio represents more elongation. Because it is a relative
value, it eliminates the error of using absolute value caused by individual differences
between plants.
Addition of green light to the background of BRFR induced 25.3% (31.2 degrees)
decrease of leaf angle, while reduction of red/far-red from 1 to 0.1 resulted in 21.0%
(25.9 degrees) decrease in leaf angle. Both are statistically significant (P < 0.05). The
leaf angle of plants grown in BrFRG condition that combines both green light and low
red/far, decreased 39.8% (49.0 degrees) compared to that of plants grown in BRFR.
The decrease of this parameter is also statistically significant (P < 0.05) compared to
that of BrFR plants (Figure 3-1B). The ratio of petiole length to total leaf length did not
change significantly among the different light treatments (Figure 3-1C).
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The Green Light Interactions with Far-Red Persist/Exaggerate in cry Mutants
Green light responses can be categorized as cryptochrome (cry)-dependent or
cry-independent. To test if the shade responses observed are mediated by cry
photoreceptors, the experiments in Figure 3-1 were repeated, this time using cry1cry2
double mutants. The cry1cry2 mutant plants exhibited similar shade avoidance
response compared to wild-type plants (Figure 3-2A). As shown in figure 3-2, the leaf
angle in BRFR plants significantly decreased 26.3% (26.0 degrees) and 15.4% (15.3
degrees) due to additional green and low red/far-red, respectively (P < 0.05). The
interactions of green and far-red caused 27.2% (27.0 degrees) reduction of leaf angle
compared to that of BRFR plants.
Similarly, plants in BRFR condition exhibited petioles that measured 53.9% of their
total leaf length, while under BRFRG and BrFR conditions the percentages of petiole to
total leaf length increased to 57.2% and 60.8%, respectively (Figure 3-2C). The
combination of green and far-red also increased this parameter to 60.4%, which is
statistically significant compared to that in BRFR plants (P < 0.05).
Green Light Does Not Induce Excessive Shade Avoidance Syndrome in hfr1 Mutant
The green light and far-red light systems utilize separate light sensors to generate
what appears to be a common response (Zhang et al., 2011). It is of interest to
determine if the two are imparting their effect(s) through completely separate systems or
if there is a point of convergence. The pathway inducing far-red-induced shade
response has been well described. Here we conduct additional experiments to identify
regulatory components, besides HAT4 and PIL1, that are shared with the green light
signaling pathway.
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Arabidopsis plants were grown in the same conditions as in Figures 2-1. Three-
week-old, white light grown plants were transferred to one of four light treatments. In the
first three treatments, red and blue light fluence rates were kept constant at 50 µmol m-2
s-1 red and 40 µmol m-2 s-1 blue light (RB), the baseline treatment for comparison. In the
second and third treatments, 10 µmol m-2 s-1 (RBg) and 40 µmol m-2 s-1 (RBG) of green
light were added. Because our previous results have demonstrated that lowering red
light does not affect rosette architecture (Zhang et al., 2011), a fourth treatment was
conducted at 40 µmol m-2 s-1 green light (as in RBG) while decreasing red light (rBG) to
keep photosynthetically active radiation (PAR) identical to other treatments.
The gene HFR1, encodes a bHLH transcription factor, is a negative regulator in
shade avoidance syndrome, and prevents excessive responses to shade in phyA
signaling pathway (Sessa et al., 2005). We first tested whether HFR1 is involved in
green-induced shade response. Wild-type plants were used as positive control of green-
responses, because they exhibited shaded appearance in green enrichment
environment. The hfr1 mutants showed similar green response to wild-type plants
(Figure 3-3).
Green-Induced Shade Avoidance Response is Attenuated in pif4 and pif5 Mutants
The PIF4 and PIF5 genes encode bHLH transcription factors that play crucial roles
in phytochrome-mediated shade avoidance response. Therefore, we examined their
participation in the green light induced shade response. The pif4 and pif5 mutants and
wild-type Col-0 plants were grown and treated the same way to hfr1 mutants. Wild-type
Arabidopsis responded to the supplemental green light as expected, as a control for
inductive conditions. Neither the pif4 nor the pif5 mutant displayed typical shade
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response in the green enriched environment, suggesting that PIF4 and PIF5 are
required components in green light signaling or response (Figures 3-4 and 3-5).
Neither Green Light and Far-Red, Alone, or Together Induces Typical Shade Response in pif4 and pif5 Mutants
Because HAT4 and PIL1 are downstream targets of PIF4 and PIF5 in shade
response (Kunihiro et al., 2011), we further addressed that whether far-red and green
light signals converge at PIF4 and PIF5. The pif4 and pif5 mutants were tested in
additional green, low red/far-red, and green+low red/far-red conditions as in Figure 3-1.
Neither mutant showed significant petiole elongation or reduced leaf angle to these light
environment (Figures 3-6 and 3-7).
Comparative Gene Expression
To further explore the molecular mechanism of green-induced shade avoidance
and also delineate the interaction nodes between green and far-red shade responses,
we quantified the expression of shade-associated genes affected by far-red and/or
green light using real-time qPCR. The transcripts HAT4 and PIL1 are strongly induced
by phytochrome under low R:FR conditions (Devlin et al., 2003), and can also be
induced by green light when added to a background of red and blue light (Zhang et al.,
2011). PIF4, PIF5 and IAA19, regulating plant elongation, are also elevated in low
red/far-red environment. Plants were treated in the light conditions described in Figure
3-1 and then total RNA was prepared and analyzed. At least two independent biological
replicates were tested and consistent gene expression patterns were observed.
In wild-type plants, HAT4 and PIL1 transcripts were highly induced by low R/FR,
consistent with published results (Devlin et al., 2003). When green light was added to
the same low R/FR condition, the expression of HAT4 and PIL1 were elevated to a
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higher level than with R/FR alone. The similar trend of increase in abundance along the
four light treatments was also observed with the PHYB transcript. The expression of
PIF4 and PIF5 were induced by both green light and low R/FR, alone and together
(Figure 3-8). Because cryptochromes influence green light-induced shade response,
gene expression patterns were also assessed in the cry1cry2 mutant background. In
cry1cry2 mutants, the similar expression patterns were observed as those in Col-0
plants. The interaction of green and far-red resulted in further increases in PIF4
expression level beyond either single treatment alone (Figure 3-9).
Discussion
The shade environment is dominated by a marked shift in the relative amounts of
red and far-red light. Under the shade of leaves, red and blue light are efficiently filtered
to produce an environment rich in far-red light that leads to dramatic changes in plant
morphology, guided by coordinated changes in gene expression. The same shade
environment also presents a shift in the ratio of blue and red to green wavebands.
Previous work from our laboratory has shown that this change in red-blue and green
ratio can also drive adaptive shade responses (Zhang et al., 2011; Zhang and Folta,
2012). While these responses result in morphological changes that are parallel to far-
red responses, coincident changes in gene expression are quite different. In an
enriched green light environment gene expression changes indicative of far-red shade
response act in a manner contrary to expectations, opening the hypothesis that the
basic mechanisms for far-red and green induced shade are discrete.
The goal of the present study was to examine interactions between far-red and
green light sensing pathways as they contribute to production of morphological change
and coincident alterations of gene expression. Two different spectral inputs result in a
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common output, so examination of their independent and simultaneous contributions to
observed changes allows dissection of the sensory and response pathways, revealing
common nodes of interaction. The interactions were examined through manipulation of
light conditions and analysis of genetic mutants.
Figure 3-1 presents evidence that the green and far-red systems work in an
additive manner to adjust plant morphology. In the Arabidopsis rosette, a shift to low
R/FR ratio results in a conspicuous change in leaf inclination. The same response is
observed when green light is added to a background of red and blue. The conditions
and wild-type responses from Figure 1 are a comparator for all subsequent results.
Here co-irradiation with blue, red, far-red and green light show the effect of the green
light response. It is important to note that the R/FR ratio is not conducive to promoting
shade symptoms, yet they are visible nonetheless. This result indicates that the addition
of far-red light does not limit the green light effect at these fluence rates.
When the R/FR ratio is decreased in the absence of green, the typical shade
response is observed. When green light is added, an augmentation of key shade
response symptoms is observed. For instance, no differences in petiole elongation were
observed under any condition. This may be due to the fact that petiole elongation was
maximally induced by far-red, and green light could not add to the response. These
results demonstrate that low R/FR and green responses can occur simultaneously and
both can contribute additively to some classical shade symptoms that affect plant
morphology.
Green light has been proposed to oppose blue responses, such as stem
elongation and flowering, through the neutral semiquinone flavin of the receptor’s
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chromophore (Banerjee et al., 2007; Bouly et al., 2007) or autophosphorylation of
cryptochromes caused by a photolyase-like cyclic electron shuttle (Liu et al., 2010). The
green-light-absorbing form of cry receptors has also been shown to block the induction
of shade-associated gene expression caused by green light (Zhang et al., 2011). Here
we also tested the response of cry mutants to the combination of green and far-red light.
The results in Figure 3-2 showed that cry mutants have comparable, or even excessive,
shade response to supplemental green and/or far-red light. It indicated that the shade
response is not attributed to inactivation of cryptochromes. Furthermore, the enhanced
shade response in cry mutants suggests that cry receptors do negatively affect gene
expression and morphology changes induced by green light, consistent with previous
results (Zhang et al., 2011).
The molecular mechanisms of far-red-induce shade avoidance have been
extensively studied. Shade indicator genes participating in the far-red signaling pathway
were used to test the integration node of green- and far-red sensing pathways. The
HFR1, a negative regulator in shade avoidance response in phyA background, was
tested under light conditions where supplemental green light was added to the mixture
of red and blue (Figure 3-3). It has been known that hfr1 deficient mutant plants show
more exaggerated shade avoidance response than wild-type Arabidopsis in low R/FR
condition (Sessa et al., 2005). The result showed that hfr1 mutant plants exhibited
comparable shade responses to Col-0 plants in green or far-red conditions. However,
the characteristic enhanced petiole elongation and leaf hyponasty were not observed,
indicating that HFR1 does not have a measurable morphological effect on green-
induced shade response.
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The PIF4 and PIF5 genes were identified as a hub for phyB- and cry1-mediated
shade avoidance (Keller et al., 2011). Therefore, it was important to investigate their
possible functions in green signaling pathway. The limited response in pif4 and pif5
mutants to additional green light indicates that PIF4 and PIF5 are necessary
components in green-regulated shade avoidance (Figures 3-4 and 3-5). As important to
both far-red- and green-light-induced shade symptoms, PIF4 and PIF5 were further
tested for response in green and far-red combination environment. No shade responses
were observed in either mutant regardless of R/FR and green light conditions, alone or
together (Figures 3-6 and 3-7). These results suggest that green and far-red shade
sensing and response systems likely converge at PIF4 and PIF5.
The genetic studies provide us a genetic mechanism of green and far-red shade
interactions. We further examined shade-related gene expression patterns of plants
grown under the same green and far-red combined treatments (Figure 3-8). In wild-type
Arabidopsis, the transcription factors HAT4 and PIL1 are induced by low R/FR,
consistent with previous data (Devlin et al., 2003). The same transcript levels remain
constant with addition of green light, again consistent with previous data (Devlin et al.,
2003; Zhang et al., 2011). In green and low R/FR conditions, the expression level of
PIL1 significantly increased compared to plants grown in either supplemental green or
low R/FR alone, while the expression of HAT4 was also higher than that of plants
treated with additional green light alone. Similarly, phyB, identified as the primary
photoreceptor regulating shade phenotypes, was also induced by the interaction of
green and far-red. These results demonstrate the additive effect of green and far-red
responses.
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The HY5 transcript, an important component in photomorphogenic responses, has
been shown to be down-regulated in shade (Devlin et al., 2003), which is consistent
with the result herein. Low R/FR caused reduction of HY5 expression to about 50%,
while the addition of green light to high or low R/FR did not change its level significantly.
This result is interesting in that green and far-red do not act additively to reduce HY5
transcript levels, indicating intricate interactions between light sensing systems.
The expression of PIF4 and PIF5 has the similar trend to HAT4 and PIL1. The
PIF4 transcripts increase with addition of green or far-red, but the combination is not
additive. On the other hand, PIF5 transcripts increase with either light treatment and
significantly even higher when co-irradiated. It also well explained the phenotype we
observed. Elongation Factor1α (elF1α) was used as an additional reference gene, and
exhibited some fluctuations under certain conditions. These gene expression data
further confirm the synergistic effect of green and far-red signaling on the induction of
shade avoidance syndrome, and together with genetic analyses further implicate PIF4
and PIF5 as an interaction node of green and far-red signals.
Because the cry1cry2 mutants displayed similar, or even enhanced, shade
avoidance syndrome to that of wild-type Arabidopsis (Figure 3-2), we assessed these
shade-responsive genes expression in the cry1cry2 mutants to investigate the role of
cry receptors in the green- and far-red-induced shade response. As shown in Figure 3-
9, the expression patterns of HAT4, PIL1 and PIF5 are similar to those of wild-type
plants. The induction of PIF4 was further elevated of plants treated with a combination
green and far-red light. These results indicate that cryprochromes limit the green-
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induced shade avoidance response, consistent with our previous results (Zhang et al.,
2011).
In conclusion, photophysiological and genetic interactions indicate that traditional
far-red induced shade responses may be augmented by addition of green light to a far-
red enriched environment. Green light induces shade avoidance syndrome through an
independent pathway from far-red sensing system. These two independent signaling
pathways act additively and flow through PIF4 and PIF5 to induce the common
phenotype. The results also indicate that PIF4 and PIF5 are not limiting, as the two
systems together can induce stronger changes in gene expression and morphology
than either system alone.
Materials and Methods
Plant Materials and Growth Conditions
The genotypes used were Arabidopsis thaliana (Col-0), cry1cry2 mutant (cry1-304
crossed to cry2-1), hat4 (SALK_106790), pil1 (SALK_043937), pif4 (SALK_140393),
pif5 (SALK_087012) and hfr1 (SALK_037727) homozygous T-DNA mutants (ordered
from ABRC). Plants were grown in plastic trays in soilless media (ProMix BX). Seeds
were distributed evenly to receive equal light distribution and stratified at 4 oC for 72
hours. Seedlings were grown under white fluorescent light (~100 µmol m-2 s-1) until the
seedlings presented four pairs of true leaves (typically 21-28 d). At this point the plants
were transferred to LED chambers featuring the experimental light conditions for 3-5
days. The temperature in LED chambers keep constant at room temperature (22 ± 1.5
oC). Plants were watered approximately three times a week under white fluorescent light
and every other day under LED arrays with 0.1X Hoagland’s solution. Plants were
grown under constant illumination.
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Light Sources and Treatments
Light treatments were generated using LED light. The peak wavelengths of far-red,
red, green and blue light are 760nm, 670nm, 543nm and 460nm, respectively. The
emission spectrum of all light sources is viewable online at
www.arabidopsisthaliana.com/lightsources. Four different combinatorial light treatments
were established for these experiments. The first treatment consisted of 40 μmol m-2 s-2
blue LED light, 18 μmol m-2 s-2 red and far-red light, in which the red to far-red ratio is 1.
The second treatment consisted of the same blue, red and far-red light supplemented
with 40 μmol m-2 s-2 green light. The third treatment consisted of the same blue and far-
red light, but the red light was attenuated with neutral density filters (Gamproducts, Inc.
CA). The red/far-red ratio was approximately 0.1. Treatment four consisted of the same
blue, red and far-red light combination as treatment three, supplemented with 40 μmol
m-2 s-2 green light.
Morphological Measurements
To observe the effects of green and far-red light on rosette architecture, several
morphological parameters were measured, including leaf angle, petiole length, leaf
blade length. Whole plants were carefully removed from the growing medium, cleaned
of particulate matter, and then flattened on the adhesive side of black electrical tape.
Samples were imaged at 600 dpi resolution on a standard flatbed scanner and
measured using UTHSCSA Image Tool (Version 3.0 for Windows) with comparisons to
an adjacent size standard. For experimental replication, at least two independent sets of
8-10 plants were measured for each treatment.
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RNA Preparation and Real-time qPCR
Whole aerial tissues were harvested into liquid nitrogen and stored at –80 °C prior
to RNA isolation. Total RNA was isolated using a modification of the CTAB-based
method (Chang S, 1993). The reverse transcription was performed using TaqMan
transcriptase kit (Applied Biosystems, USA). Quantitative real-time PCR was performed
the StepOne Plus system (Applied Biosystems, USA). TaqMan primers and probes
were designed by Primer Express 2.x software (Applied Biosystems). The sequences of
primers and probes for PIF4, PIF5 and IAA19 are listed in Table 2. All others are the
same as those used in Zhang and Folta, 2011 (Table 1). PCR reaction mixtures were in
the following thermal profile: 2 min at 50ºC; 10 min at 95ºC; 40 cycles (15 s at 95ºC; 1
min at 60ºC). Actin2 was used as the internal control. The relative mRNA levels were
calculated using the 2-ΔΔCT comparative method (Livak and Schmittgen, 2001; Sehringer
et al., 2005).
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Table 3-1. TaqMan primer and probe sequences used in real-time qPCR
Gene TaqMan primer/probe sequences (5′→3′)
PIF4 GCAGCCGATGGAGATGTTG GACGACGGTTGTTGACTTTGC ATTTAGTTCACCGGCGGGA
Forward Reverse Probe
PIF5 GCCTAACTACGCTGCTCTAGATGAT TGACGTCATCCGGAGGGTAT ACCGTCTCCTGGATAC
Forward Reverse Probe
IAA19 GAGCATGGATGGTGTGCCTTAT TTCGCAGTTGTCACCATCTTTC ATAAGCTCTTCGGTTTCCGTGGCATCG
Forward Reverse Probe
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Figure 3-1. Green light and far-red additively induce shade response in wild-type
Arabidopsis Col-0. Wild-type (Col-0) Arabidopsis plants were grown under white light for approximately 3 weeks and then transferred to one of four light treatments: 40 µmol m-2 s-1 blue, 18 µmol m-2 s-1 red, and 18 µmol m-2 s-1 far-red light (red/far-red≈1, BRFR); 40 µmol m-2 s-1 blue, red/far-red≈1, 40 µmol m-2 s-1 green light (BRFRG); 40 µmol m-2 s-1 blue, red/far-red≈0.1(BrFR); 40 µmol m-2 s-1 blue, red/far-red≈0.1, 40 µmol m-2 s-1 green light (BrFRG) for 3 to 5 d. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, Single representative plants harvested from the different light treatments. B, Mean leaf angle of plants grown in the four light conditions. Leaf angle represents the number of degrees between the third pair of leaves. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from 8 to 10 individual plants. Error bars represent SE. Different letters represent statistically different means (P<0.05).
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Figure 3-2. Green light and far-red additively induce shade response in the Arabidopsis
cry1cry2 mutant. Arabidopsis cry1cry2 plants were grown and treated in the same conditions used in Figure 3-1. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, representative mutant plants in different light treatments. B, Mean leaf angle of cry1cry2 plants grown in the four light conditions. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from 8 to 10 individual plants. Error bars represent SE. Different letters represent statistically different means (P<0.05).
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Figure 3-3. Green-light-induced shade avoidance symptom is not enhanced in the
Arabidopsis hfr1 mutant. Arabidopsis hfr1 mutants and wild-type Col-0 plants were grown under white light for approximately 3 weeks and then transferred to one of four light treatments: 50 µmol m-2 s-1 red and 40 µmol m-2 s-1 blue LED light (RB); 50 µmol m-2 s-1 red, 40 µmol m-2 s-1 blue, and 10 µmol m-2 s-1 green light (RBg); 50 µmol m-2 s-1 red, 40 µmol m-2 s-1 blue, and 40 µmol m-2 s-1 green light (RBG); or 20 µmol m-2 s-1 red, 40 µmol m-2 s-1 blue, and 40 µmol m-2 s-1 green light (rBG) for 3 to 5 d. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, Representative hfr1 plants harvested from the different light treatments. B, Mean leaf angle of Col-0 and hfr1 plants grown in the four light conditions. C, Mean petiole length as a fraction of total leaf length under the different light conditions. The measurements in B and C were derived from the third true leaves from 10 individual plants. Error bars represent SE. Different letters represent statistically different means (P<0.05).
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Figure 3-4. Green-light-induced shade response is limited in the Arabidopsis pif4
mutant. Arabidopsis pif4 plants and wild-type Col-0 plants were grown and treated in the same conditions used in Figure 3-3. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, representative mutant plants in different light treatments. B, Mean leaf angle of wild-type Col-0 and pif4 plants grown in the four light conditions. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from 8 individual plants. Error bars represent SE. Different letters represent statistically different means (P<0.05).
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Figure 3-5. Green-light-induced shade response is limited in the Arabidopsis pif5
mutant. Arabidopsis pif5 plants and wild-type Col-0 plants were grown and treated in the same conditions used in Figure 3-3. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, representative mutant plants in different light treatments. B, Mean leaf angle of wild-type Col-0 and pif5 plants grown in the four light conditions. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from 8 individual plants. Error bars represent SE. Different letters represent statistically different means (P<0.05).
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Figure 3-6. Shade avoidance response induced by green and far-red is absent in the
Arabidopsis pif4 mutant. Arabidopsis pif4 plants were grown and treated in the same conditions used in Figure 3-1. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, representative mutant plants in different light treatments. B, Mean leaf angle of pif4 plants grown in the four light conditions. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from eight individual plants. Error bars represent standard error of the mean. Lower-case letters represent statistically different means (P<0.05).
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Figure 3-7. Shade avoidance response induced by green and far-red is absent in the
Arabidopsis pif5 mutant. Arabidopsis pif5 plants were grown and treated in the same conditions used in Figure 3-1. Individual plant rosettes were dissected, and conspicuous leaf attributes were quantified. A, representative mutant plants in different light treatments. B, Mean leaf angle of pif5 plants grown in the four light conditions. C, Mean petiole length as a percentage of total leaf length of different light-treated plants. The measurements in B and C were derived from the third true leaves from eight individual plants. Error bars represent standard error of the mean. Lower-case letters represent statistically different means (P<0.05).
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Figure 3-8. Shade-responsive gene expression levels in wild-type (Col-0) plants grown
in various green and far-red light conditions. Plants were grown and treated in the same conditions described in Figure 3-1. The gene expression levels were quantified using real-time qPCR. Relative transcript values were normalized to BRFR condition. Actin2 was used as a reference gene. Lower-case letters represent statistically different means (P<0.05).
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Figure 3-9. Shade avoidance-related gene expression levels in cry1cry2 mutants grown
in different light treatments. Plants were grown and treated in the same conditions used in Figure 1. Gene expression levels were quantified using real-time qPCR. Transcript levels were normalized to the BRFR condition. Actin2 was used as a reference transcript. Lower-case letters represent statistically different means (P<0.05).
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CHAPTER 4 A STRWBERRY (FRAGARIA SP.) RALF PEPTIDE CONTRIBUTES TO ARCHITECTURE OF THE CANOPY, THE ROOT SYSTEM, AND THE
INFLORESCENCE
Introduction
The previous chapters discussed how light, an environmental factor, provides cues
used to adjust plant architecture in model plant system Arabidopsis. In this chapter, we
explored the importance of genetic factors in plant form, using the horticultural crop—
strawberry (Fragaria spp.). Strawberry is an economically important crop with a short
growth cycle and compact growth habit. The rapidly expanded sequence resources,
efficient genetic transformation capacities, and high relevance to the Rosaceae family
make strawberry an excellent plant system for research.
Peptide signals have been well described in animals, however, few plant peptides
have been isolated and extensively studied. Recent researche has indicated that
peptides used as receptor-mediated intercellular signals regulate various environmental
responses and developmental processes. These responses include roles in defense,
root growth, pollen development, and meristem differentiation (Matsubayashi and
Sakagami, 2006; Srivastava et al., 2009; Bedinger et al., 2010). For example, systemin
was the first signaling peptide discovered in plants (Pearce et al., 1991). It was first
isolated from tomato leaves, and was demonstrated to induce the systemic defense
response in tomato (Ryan and Pearce, 2003). In 2001, Pearce et al., also isolated
tobacco systemins I and II using the same cell culture assay (Pearce et al., 2001a). The
CLAVATA3 peptide regulates meristem development in Arabidopsis (Fletcher et al.,
1999), while cysteine-rich plant defensins are involved in the innate immune system
(Lay and Anderson, 2005).
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RALFs (Rapid Alkalinization Factors) are a family of peptides ubiquitously existing
in dicots, monocots and gymnosperms (Bedinger et al., 2010). They were first purified
from tobacco leaves using the cell alkalinization assay. The RALF gene was identified
as a 49 amino acid peptide released from a 115 amino acid preproprotein. This small
polypeptide caused alkalinization of the medium, inhibition of root growth, and
intracellular MAP (Mitogen-activated protein) kinase activation (Pearce et al., 2001b).
Later, RALF genes were also isolated from tomato, alfalfa, poplar and Arabidopsis. In
Arabidopsis the genome contains 34 RALF-Like genes, distributed over all five
chromosomes. All of them consist of a single exon (Olsen et al., 2002). Most RALF
genes are strongly predicted to target the endomembrane system, where they are also
predicted to undergo proteolytic processing near a conserved RR dibasic site. The
predicted mature peptide of RALFs isabout 50 aa at the C-terminusand is highly
conserved across species. Other typical structural features of RALF peptides include a
YIXY motif at the mature N-terminus and four conserved cysteines likely involved in
disulfide bridges in the mature active protein (Germain et al., 2005; Bedinger et al.,
2010).
Recent functional studies indicated the negative role of RALFs in plant growth.
Although the capacity to alkalinize medium is often associated with defense responses
(Bolwell, 1999), RALFs differed from systemins in not causing defense response in
plants (Haruta and Constabel, 2003). Also, tobacco trypsin inhibitor was not induced in
leaves treated with the peptide (Pearce et al., 2001b). In addition, the RALFs in poplar
also could not be increased by chitosan or Phytophtora megasperma elicitors (Haruta
and Constabel, 2003). Instead, RALF peptides appear to have a role in regulating
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growth and developmental processes in plants (Bedinger et al., 2010). For example, the
addition of synthesized tomato RALF peptide inhibited root growth in tomato and
Arabidopsis seedlings (Pearce et al., 2001b). Silencing a NaRALF gene expressed in
the root of Nicotiana attenuata induced increased root growth and abnormal root hair
development (Wu et al., 2007). In addition, overexpression of either AtRALF1 or
AtRALF23 resulted in semi-dwarfism of Arabidopsis (Matos et al., 2008; Srivastava et
al., 2009). In Medicago trunculata, overexpression of a MtRALFL1 gene induced
abnormal nodule development (Combier et al., 2008). Also, exogenous tomato pollen
SlPRALF inhibits pollen tube growth in vitro during specific developmental period
(Covey et al., 2010).
In contrast to all other plants studied, strawberry contains only a single RALF
gene. The strawberry therefore provides us with an effective system to characterize the
functions of RALF. In this report, we described the isolation and characterization of
FaRALF from strawberry plants, and its expression in various strawberry tissues. We
also generated and analyzed the phenotypes of RNAi transgenic plants to further
explore the biological functions of strawberry RALF. Our work on RALF extends the
characterization of the active peptide to another important plant family, and helps define
its biological role as well as economic value in plants.
Results
FaRALF Isolation and Sequence Analysis
The partial FaRALF cDNA sequence was first isolated from a flower cDNA library
prepared from F. ×ananassa cv. Strawberry Festival. This sequence was used for a
Blast search against the genome of woodland strawberry Fragaria vesca, and only one
sequence was identified as having significant similarity. The full-length FaRALF cDNA
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and genomic DNA from diploid Fragaria vesca ‘Hawaii-4’ were cloned and sequenced.
The comparison of the cDNA with genomic sequence indicated that it does not contain
any introns. The FaRALF gene encodes a predicted protein of 108 aa, which is
predicted to be processed into a mature peptide of 49 amino acid residues. FaRALF
has all typical features of RALF peptides, including a conserved RR dibasic site, an
YIXY motif, and four conserved cysteines in the mature protein (Figure 4-1A).
According to the phylogenetic analysis, the FaRALF peptide is most closely
related to Ricinus communis RcRALFL 33, sharing 61% overall identity at the amino
acid-level, and 88% identify in the highly conserved C-terminal domain. It is also related
to previously described tobacco RALF peptide (Pearce et al., 2001b; Wu et al., 2007),
Arabidopsis AtRALFs(Olsen et al., 2002), and tomato SlRALF2 (Germain et al.,
2005)(Figure 4-1B).
There is Limited FaRALF Sequence Variability across Diploid and Octoploid Strawberries
Because there is only one FaRALF gene in strawberry, and we isolated it from
only one of the species, we were interested in testing its sequence polymorphisms
among different strawberry species and levels of ploidy. The FaRALF sequence was
cloned from a representative set of diploid strawberries, including Fragaria vesca
semperflorens ‘Hawaii-4’ (the sequenced genome), Fragaria iinumae, Fragaria vesca
bracteata, and commercial octoploid variety Fragaria ×ananassa ‘Strawberry Festival’.
The first two were chosen because they are thought to share a common ancestor with
cultivated strawberry, therefore providing some level of assessment of diversity over
time. All sequences were cloned into P-GEMT Easy vectors, and sequenced. More than
10 colonies were sequenced from Fragaria ×ananassa ‘ Strawberry Festival’ to gain
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greater confidence that allelic variants were detected. The alignment of amino acid
sequences with the original FaRALF indicated that the FaRALF protein is almost
identical across accessions tested, with only two amino acid differences predicted
among all proteins (Figure 4-2A). All of the colonies sequenced from F. x ananassa are
predicted to encode peptide with high similarity (Figure 4-2B).
Expression Pattern of FaRALF Transcripts
To understand the accumulation pattern of FaRALF transcripts, its accumulation
was measured by Dr. Mithu Chatterjee in various tissues of Fragaria vesca using real-
time qPCR. The results indicated that FaRALF mRNA were expressed in all tissues
including leaf, flower, fruit, runner tip, achene, crown, stolon, petiole, and root. The
expression of FaRALF transcripts was most abundant in fruit, stolon, petiole, achene
and flower (Figure 4-3). Compared to the flower reference sample, the accumulation of
FaRALF mRNA in fruit was about two-fold greater. The FaRALF transcript abundance
was considerably less in mature leaves and root.
The FaRALF Gene Contributes to Architecture of Canopy and inflorescence in Mature Strawberry Plants
Because there is only one RALF gene in strawberry, loss- and gain-of-function
assays are especially helpful to characterize its function. The first loss of function lines
were generated by Dr. Mithu Chatterjee. In the RNAi suppression construct, two copies
of the FaRALF cDNA fragment were induced by the CaMV (cauliflower mosaic virus)
35S promoter in a head-to-head configuration. This construct was shuttled into the
diploid strawberry F. vesca ‘Hawaii-4’ using the modified protocols based on (Oosumi et
al., 2006). Positive T0 transgenic lines were selected and seed was harvested. The
seeds from more than 10 independent T0 RNAi lines were replanted, and the
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phenotypes of the T1 plants were carefully analyzed. Because FaRALF is the only
RALF family gene, it is not necessary to test whether the interruption of FaRALF
expression affected other RALF genes, or whether the phenotypes of RNAi plants were
completely attributed to the suppression of the specific FaRALF transcript. The
generation of transgenic lines from multiple co-cultivation dates likely ensures
independent transformation events.
The morphological phenotypes described were present in plants grown in the
greenhouse and represent preliminary, first-pass observations only in T1 plants (derived
from seeds from foundational transgenic plants). The plants are currently being
vegetatively propagated for formal quantitative experiments scheduled for Fall of 2012.
Compared to wild-type plants, the RNAi plants exhibited an open canopy
architecture, characterized by non-erect petioles. The plants produced longer petioles
that lay prone to the soil. Near the leaves, the petioles tend to curve upward, thus
orienting leaf blade perpendicular to gravity (Figure 4-4). The FaRALF-deficient
transgenic plants also produced clear differences in flowers. Floral pedicle length
increased, and branches of the floral truss were elongated and thin. Flowers themselves
in most RNAi lines failed to fully open, resulting in a cup shape. Flowers appeared to be
female, yet close inspection showed stamens that were small and undeveloped.
Occasionally, one or two stamens would mature in these plants compared to the 20-24
typical of wild-type strawberry flowers (Figure 4-5).
The FaRALF Gene Affects Root Development and Acidification of Media Around Roots in Strawberry Seedlings
Overexpression of AtRALF23 in Arabidopsis results in slow root growth and
reduced capacity for root acidification (Srivastava et al., 2009). To test whether the
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FaRALF gene in strawberry mediates a similar effect, we planted wild-type and RNAi
strawberry seeds on plates, and transferred 2-week-old FaRALF RNAi seedlings to
media plates containing the pH indicator bromocresol purple, with an original pH of 6.3.
Compared to the wild-type seedlings, the transgenic seedlings showed greater root
development, as indicated by longer root lengths and more lateral roots (Figure 4-6A).
In addition, the media surrounding FaRALF RNAi seedlings turned yellow after 24
hours, indicating that the RNAi seedlings acidified the medium around their roots
quickly. However, the comparable wild-type seedlings did not show this change (Figure
4-6B). These results demonstrated that FaRALF has the predicted capacity to alkalinize
media adjacent to roots, consistent with the function of RALF family members in other
species. It also was observed that the seedlings grew faster and produced larger aerial
tissues over the same time frame.
Discussion
The RALF family of peptides were initially discovered in a search for peptides that
induce media-alkalinization in tobacco culture cells (Pearce et al., 2001a). Due to the
similar properties as systemins, RALF was first associated with plant defense response.
However, additional experiments have shown that RALFs are involved in developmental
and physiological processes rather than defense systems in plants (Haruta and
Constabel, 2003; Ryan and Pearce, 2003).
The sequence used in the present study was chosen for analysis because it bore
little similarity to known genes at the time of library characterization and sequencing in
2006. The nucleotide sequence appeared to be completely novel and without a
significant open reading frame. The identification of this sequence as a RALF only came
after comparison of the sequence to predicted gene models in the strawberry genome.
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The sequence was not originally studied because it was a RALF, but rather, because it
appeared to be novel. RNAi lines preceded analysis of the sequence, so phenotypes
were obtained long before other characterization was attempted. This study represents
a “phenotype-first” analysis, where a clear phenotype in the plant prompted additional
study of causal sequence.
The FaRALF shares high sequence similarity with the mature RALF peptides of
several species such as Arabidopsis, tobacco and tomato. It contains the classic
features associated with RALF amino acid sequences (Figure 4-1). The sequence of
FaRALF is virtually identical among various varieties of dipoloid and octoploid
strawberry (Figure 4-2), suggesting that if the protein contributes to phentotypic
variation, it would be due to expression and not likely structural differences.
To assess expression of FaRAFL, the accumulation of FaRALF transcripts was
measured in several strawberry tissue types. The transcript is detected in leaf, flower,
fruit, runner tip, achene, crown, stolon, petiole, and root samples (Figure 4-3). These
findings show that RALF expression is virtually ubiquitous, as in other species.
Transgenic studies in both tobacco and Arabidopsis have shown that the RALFs
affect the rhizosphere acidification (Wu et al., 2007). Wu et al. showed that
downregulation of a RALF gene in Nicotiana attenuata (NaRALF) did not elevate
acidification of medium adjacent to roots. This was contrary to expectations since RALF
induced alkalinization of the medium in tobacco cell suspension cultures (Pearce et al.,
2001b). Recent studies indicated that the overexpression of AtRALF23 also prevented
the media-acidification surrounding the Arabidopsis roots. Our results showed that
silencing FaRALF resulted in rapid acidification of medium around roots, which is
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contrary to Wu et al., but consistent with the common observation that RALF induces
alkalinizaiton of medium (Pearce et al., 2001b; Srivastava et al., 2009)(Figure 4-6B).
Phenotypic analysis of RNAi transgenic plants revealed that FaRALF functions in
multiple important physiological aspects of strawberry growth and development (Figures
4-4 and 4-5). Seedlings grown on media were larger and grew longer roots. These
findings are consistent with earlier reports that RALFs inhibit growth. One of the most
conspicuous roles for FaRALF was that it regulated canopy architecture, which was
evident as a more open canopy of FaRALF-suppressed plants. This phenotype is
desirable to breeders because an open canopy may improve the efficiency of
photosynthesis. More importantly, an open canopy facilitates drying after rains, better
penetration of fungicides, and easier harvesting. The importance of an open canopy,
and the RALF role in this process may lead to development of markers to select plants
with a higher likelihood of maintaining this trait.
The FaRALF is also a negative regulator of root growth, which was also noted by
Wu et al. More interestingly, the downregulation of FaRALF induced obvious reduction
in numbers of maturing stamens , enhanced pedicel length, and result in more fruits per
plant. The good explanation is that the female flowers have been demonstrated to have
higher fertility advantages and greater fruit set compared to hermaphrodite flowers
(Ashman, 1999). Beyond the contribution to basic scientific research, this finding has
implications for both ecology and economic importance of strawberry.
The functional characterization of RALF signaling peptides is still an emerging and
interesting research area. Although several related articles have been published in the
last decade, there are still many important questions that need exploration. Although the
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alkalization response caused by LeRALF was reportedly due to the ligand–receptor
interaction (Scheer et al., 2005), it is unknown how RALFs elicit the alkalization
response, and what the receptors are. The results in this study extend our knowledge in
the physiological function of RALF family peptides in plants, and demonstrate the utility
of the strawberry system in the study of RALFs.
Materials and Methods
Isolation of FaRALF from Different Varieties of Strawberry
The genomic sequence of FaRALF was isolated from Fragaria vesca
semperflorens ‘Hawaii-4’ , Fragaria iinumae, Fragaria vesca bracteata, and commercial
octoploid variety Fragaria ×ananassa ‘Strawberry Festival’ by PCR amplification using
primer pair 5’- ACAGAGAAGAAGATCAAGCAACCA -3’and 5’-
CATCTTCACATTCTACCTTTCCCAT -3’. The full-length FaRALF genomic sequence
were was cloned into pGEM-T EASY vectors (Promega, http://www.promega.com).
The insertions were confirmed by PCR as well as restriction digestion, and then were
sequenced. The sequences were aligned using software ClustalW.
Phylogenetic Analysis and Accession Numbers
The evolutionary history was inferred using the Neighbor-Joining method (Saitou
and Nei, 1987). The bootstrap consensus tree inferred from 1000 replicates is taken to
represent the evolutionary history of the taxa analyzed (Felsenstein,1985). Branches
corresponding to partitions reproduced in less than 50% bootstrap replicates are
collapsed. The percentage of replicate trees in which the associated taxa clustered
together in the bootstrap test (1000 replicates) are shown next to the branches
(Felsenstein,1985). The evolutionary distances were computed using the Poisson
correction method (Zuckerkandl and Pauling, 1965) and are in the units of the number
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of amino acid substitutions per site. The analysis involved 15 amino acid sequences. All
positions containing gaps and missing data were eliminated. There were a total of 90
positions in the final dataset. Evolutionary analyses were conducted in MEGA5 (Tamura
et al., 2011). Accession numbers used in the relationship analysis are provided as a
Table 1.
Generation of Transgenic Plants
The partial FaRALF cDNA fragment was isolated by random Sanger sequencing
of cDNA clones from Fragaria × ananassa flowering library. The fragment of FaRALF
cDNA was shuttled into pK7GWIWG2D gateway binary vector for RNAi construct using
standard clonase reaction conditions (Invitrogen, Carlsbad, CA). GFP with a separate
promoter and Kanamycin were used for the selection of both transgenic callus. The
insertion were confirmed by restriction digestion and sequencing. Constructs were
introduced into Agrobacterium strain GV3101 using electroporation.
For strawberry transformation and regeneration, leaves and petioles from tissue
cultured F. vesca plants (‘Hawaii-4’) were co-cultivated with Agrobacterium harboring
RNAi or overexpression constructs. The media contained MS with Gamborg vitamins,
2% sucrose, 3 mg/L 6-benzyladenine, 0.2 mg/L indole-3-butyric acid and 0.7% agar.
After 2 d of co-cultivation, explants were washed and transferred to the same media
plus 5 mg/L kanamycin and 250 mg/ml cefotaxime (pH 5.8). Explants were subcultured
every 7 days until shoots appeared, typically 60–90 d. Healthy shoots were transferred
to rooting media (0.01 mg/L of IBA, 2% glucose, 0.56MS salts, 0.7% phytoagar, pH 5.8).
The GFP-positive rooted plants were transferred to soil, acclimated to ambient
temperature and humidity, and then moved to greenhouse. More than ten RNAi lines
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were generated and selected by the presence of GFP and alterations in rosette
architecture.
Root Acidification
To test the ability of WT and RNAi strawberry plants to acidify their rhizosphere,
14-day-old seedlings were transferred onto media containing bromocresol purple pH
indicator. The media contained MS with Gamborg vitamins, 1% sucrose, 0.7% agar, 1
mM CaSO4, 0.006% bromocresol purple (pH 6.3). After 1 day,.the media with RNAi
seedlings turned yellow. This experiment was repeated once (Wu et al., 2007).
RNA Isolation and Real-time PCR
Total RNA was isolated from various tissues of strawberry using a modification of
a CTAB-based method (Chang, 1993). Tissue-specific expression analysis was
performed using real-time quantative PCR-SYBR Green method. Total RNA was
reverse transcribed into cDNA using ImProm-II reverse transcriptase (Promega,USA).
Quantitative real-time PCR was performed on the StepOne Plus system (Applied
Biosystems, USA). The primer pairs of FaRALF are 5’- GCTCAGGCCAACCCGTATAA-
3’ and 5’-CAATAATAACAACAATACACCATCAC-3’, while the primers used for internal
control M8 are 5’-TGCATATATCAAGCAACTTTACACTGA-3’ and 5’-
ATAGCTGAGATGGATCTTCCTGTGA-3’ PCR reaction mixtures were in the following
thermal profile: 2 min at 50ºC; 10 min at 95ºC; 40 cycles (15 s at 95ºC; 1 min at 60ºC).
M8 was used as the internal control. The relative mRNA levels were calculated using
the 2-ΔΔCT comparative method (Livak and Schmittgen, 2001; Sehringer et al., 2005).
107
Table 4-1. Accession numbers and their corresponding genes used for phylogenetic clustering
Species names Protein names Accession number
Arabidopsis thaliana AtRALF1 AtRALF23 AtRALF33
NP_171789 NP_566555 NP_567476
Ricinus communis RcRALFL33 RcRALFL33a
XP_002512426 XP_002531878
Populus trichocarpa x Populus deltoides
PtdRALF1 PtdRALF2
AAO27366 AAO27367
Nicotiana attenuata NaRALF AAS13437 Medicago truncatula MtRALF ABN08027 Litchi chinensis LcRALF ABS72341 Solanum chacoense ScRALF2 AAR00326 Vitis vinifera Vvupp1
Vvupp2 CBI26076 CBI39521
Glycine max GmU ACU15614
108
109
Figure 4-1. Analysis of RALF family genes. A) Alignment of FaRALF amino acid
sequence along with six close related RALFs using ClustalW. The arrow indicates the presumptive cleavage site. The red boxs indicate the classic structural features of RALFs. The line above the sequences marks the predicted mature RALF peptide. The triangles indicate conserved Cys residues. The asterisks represent identical residues and the colons and periods represent similar residues. B) The phylogenetic analysis of RALFs. The neighbor-joining method was used for phylogenic clustering which were conducted by MEGA5. Bootstrap values were based on 1000 replicates.
110
Figure 4-2. ClustalW alignment of the FaRALF isolated from different strawberry variaties. A) Alignment of the FaRALF amino acid sequences of different strawberry species. B) Alignment of FaRALF sequences in different clones of octoploid ‘Strawberry Festival’. The asterisks represent identical residues and the colons and periods represent similar residues.
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Figure 4-3. Relative expression of the FaRALF transcript in various strawberry tissues.
The flower sample was used as reference tissue and transcripts were quantified against the reference gene using real-time qPCR. Error bars represent standard error of the mean derived from three independent replicates.
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Figure 4-4. The plant architecture of FaRALF RNAi transgenic plants. A) The wild-type
Fragaria vesca Hawaii-4 plant compared to FaRALF RNAi lines. B) Wild-type strawberry canopy architecutre. C) FaRALF RNAi rosette architecture. Bars = 1 cm.
113
Figure 4-5. The flower morphology of FaRALF RNAi lines. A) A wild-type Hawaii-4 plant
compared to B) FaRALF RNAi plants. The red boxes mark the pedicels. C) Wild-type strawberry flower. D) FaRALF RNAi flowers. In panels A and B, bars = 1 cm. In panels C and D, bars=0.5 cm
114
Figure 4-6. FaRALF contributes to root development and acidification of root adjacent media in strawberry seedlings. A) The root growth of wild-type and RNAi transgenic seedlings grown 3 weeks on Gamborg B5 plates. B) Acidification of the root adjacent media of FaRALF RNAi lines. Strawberry seedlings were grown vertically on B5 plates for 2 weeks, and then transferred onto plates containing 0.006% bromocresol purple (pH 6.3). The pH indicator is generally yellow below pH 5.2, and purple above pH 6.8. Bars=0.5 cm
115
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BIOGRAPHICAL SKETCH
Tingting Zhang was born in Luoyang, China. She completed her undergraduate
study in horticulture at the Northwest Agriculture and Forest University in China. Then
she received a research assistantship to the University of Florida and began her
graduate study in horticultural science in Dr. Kevin M. Folta’s lab.
