JOURNAL OF
GENETICS AND
GENOMICS
J. Genet. Genomics 37 (2010) 219−230
www.jgenetgenomics.org
A comparative genomic analysis of plant hormone related genes
in different species
Zhiqiang Jiang, Hongwei Guo*
Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, China
Received for publication 26 October 2009; revised 5 March 2010; accepted 6 March 2010
Abstract
Plant hormones are small molecules that play important roles throughout the life span of a plant, known as auxin, gibberellin, cytokinin, abscisic acid, ethylene, jasmonic acid, salicylic acid, and brassinosteroid. Genetic and molecular studies in the model organism
Arabidopsis thaliana have revealed the individual pathways of various plant hormone responses. In this study, we selected 479 genes that
were convincingly associated with various hormone actions based on genetic evidence. By using these 479 genes as queries, a
genome-wide search for their orthologues in several species (microorganisms, plants and animals) was performed. Meanwhile, a comparative analysis was conducted to evaluate their evolutionary relationship. Our analysis revealed that the metabolisms and functions of
plant hormones are generally more sophisticated and diversified in higher plant species. In particular, we found that several phytohormone receptors and key signaling components were not present in lower plants or animals. Meanwhile, as the genome complexity increases, the orthologue genes tend to have more copies and probably gain more diverse functions. Our study attempts to introduce the
classification and phylogenic analysis of phytohormone related genes, from metabolism enzymes to receptors and signaling components,
in different species.
Keywords: comparative genomics; Arabidopsis hormone related gene; plant hormone; orthologue
Introduction
Hormones are small organic molecules, synthesized by
living organisms, and function in small doze in almost
every aspect of growth and developmental processes.
During the last century, eight types of phytohormones had
been discovered using physiological, biochemical and geAbbreviations: IAA, auxin; GA, gibberellin; CK, cytokinin; ABA, abscisic acid; ET, ethylene; JA, jasmonic acid; SA, salicylic acid; BR,
brassinosteriod; UPGMA, unweighted pair group method with arithmetic
mean; MCO, most conserved orthologues; AHRG, Arabidopsis hormone
related genes.
* Corresponding author. Tel: +86-10-6276 7823; Fax: +86-10-6275 1526.
E-mail address: [email protected]
DOI: 10.1016/S1673-8527(09)60040-0
netic approaches. They are auxin, gibberellin, cytokinin,
abscisic acid, ethylene, jasmonic acid, salicylic acid, and
brassinosteroid. In the past decades, forward and reverse
genetic methods had been used to identify molecular
components functioning in each hormone’s action (Alonso
and Ecker, 2006). Usually a simple and relatively specific
phenotype was employed as readout to uncover genes involved in the actions of a given phytohormone (metabolism, transport or perception). By screening for mutants
with enhanced or reduced response to the given hormone
compared to wild type plants, a large number of mutants
that either positively or negatively modulate specific hormone response pathways had been isolated. Subsequent
molecular genetics and biochemical studies revealed more
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details on the identity and the function of the corresponding genes, and eventually a genetic pathway was thus established based on the knowledge of these molecular
components. The illustration of these hormone response
pathways had remarkably improved our understanding of
how plant hormones are synthesized, metabolized, transported, and distributed, as well as how plants sense and
respond to different types and levels of phytohormones in
myriad biological processes.
As a large amount of genome sequencing projects had
been completed or are underway, we are able to obtain
massive genomic information from a wide variety of species with genome sequence available. This information
enables us to possibly carry out a comparative study of
genes related to a specific pathway or process, and eventually to uncover the common features or divergent mechanisms of such pathway or process in an evolutionary scale.
By looking for orthologues in simple organisms, it is possible to find out when and probably how a relatively complex pathway discovered in higher reference organisms
(usually angiosperms) was evolved. For example, by analyzing the genome sequence of the moss Physcomitrella
patens, a relative comprehensive ABA signaling pathway
had been found, implying that ABA was used extensively
in this lower plant species, probably to protect moss (an
early land plant) from dehydration and water stress
(Marella et al., 2006). In addition, a functional GA perception and signaling pathway was found to present in Selaginella kraussiana (a lycophyte), but not in Physcomitrella patens, illuminating the earlier events on the establishment of GA signaling (Yasumura et al., 2007). On the
other hand, based on a recent phylogenetic study of the
action of phytohormone auxin in algae, it was found that
an auxin-dependent mechanism was originated as early as
in algal lineages, but some major signaling components
began to emerge later than microalgae (Lau et al., 2009). It
seems that each type of hormone exhibited a quite distinctive origin and adaptation during the evolutionary course
of land plants to suit plants for survival and fitness in
various ecological environments.
To obtain a systematic and comprehensive view of
genes participating in plant hormonal regulation, as well as
morphological phenotypes controlled by plant hormones,
we have previously developed an Arabidopsis Hormone
Database (AHD) (http://ahd.cbi.pku.edu.cn) (Peng et al.,
2009). In the database, we have collected 1,043 genes related to Arabidopsis hormone response pathways, includ-
ing hormone biosynthesis and metabolism, hormone
transports, hormone perception and signal transduction
(Peng et al., 2009). In this study, we selected 479 genes
that were convincingly associated with various hormone
actions based on genetic evidence. Of them 423 genes
were supported by mutant analysis while 56 genes were
derived from transgenic studies (Supplemental Table 1).
By using these 479 genes as queries, a genome-wide
search for their orthologues in several species was performed. Meanwhile, a comparative analysis was conducted
to evaluate their evolutionary relationship. Our analysis
revealed that the metabolisms and functions of plant hormones are generally more sophisticated and diversified in
higher plant species, as there are more phytohormone related genes in higher plants than in lower plants or animals.
In particular, we found that several phytohormone perception systems were not present in lower plants or animals.
Meanwhile, as the genome complexity increases, the
orthologue genes tend to have more copies and probably
more diverse functions.
Material and methods
Searching for phytohormone related gene orthologues
in different species
Phytohormone related genes were derived from Arabidopsis Hormone Database, and the whole genome
orthologue clusters were downloaded from INPARANOID
7.0 current version (http://inparanoid.sbc.su.se/download/
7.0_current/). For a meaningful comparison, whole genome
orthologue cluster for some animals and A. thaliana were
also downloaded. Using phytohormone related genes as
queries, orthologue genes were derived from the dataset of
each species. The algorithm was as described in the website, and a cut-off pairwise similarity score of 50 (the
BLAST program report asymmetric scores between sequence pairs) was set and the matching segment must
cover at least 50% of its total protein length.
Most analyses were based on deriving common
orthologue gene set between each two species. The group
of orthologue genes identified in all selected species was
named MCO (Most Conserved Orthologues). And comparison of gene orthologues numbers revealed gene duplication events in different species. By calculating
orthologue number variance between two species, we were
Zhiqiang Jiang et al. / Journal of Genetics and Genomics 37 (2010) 219−230
able to elucidate tandem duplication events.
Phylogenetic analysis
In order to compare an evolutionary relationship of
hormone related genes in different species, we compared
component similarity in hormone action, with analogy to
comparison of amino acid similarity in protein sequence,
and constructed phylogenetic trees using PHYLIP software.
The scoring matrix was substituted from sequence similarity to orthologue gene similarity between each two species.
Neighbour-Joining method was chosen for generating
orthologue phylogenetic tree.
In order to determine relationships between orthologue
phylogenetic tree and sequence phylogenetic tree, we
generated two protein sequence phylogenetic trees as
well. The genes selected for generating protein phylogenetic tree were randomly chosen from MCO. One was
AT3G28860, encoding an ATP binding cassette gene and
the other one was AT3G57530, encoding a calcium dependent protein kinase family gene. Orthologues were
also downloaded from INPARANOID webset. Protein
sequence phylogenetic trees were generated using MEGA
version 4.0 and UPGMA method with a bootstrap of
10,000 replicates.
Orthologue comparison in different species
We classified orthologue clusters according to their functions (metabolism related, transport, signaling related) in A.
thaliana. A histogram was constructed to visualize their
function distribution in different species. The percentage of
orthologue genes in AHRG (Arabidopsis Hormone Related
Genes) of all the selected species was shown in histogram.
Heat map was used to present the number of receptor
orthologue genes and the map was generated using Gene
Cluster 3.0. The data was clustered by complete hierarchical method and an uncentered Pearson correlation algorithm was used. The result was visualized as heat map
generated by TreeView-1.1.3.
Result
Phylogenetic comparison by gene similarity and protein sequence similarity
In order to compare phylogeny difference of hormone
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related genes and find out if the evolution of hormone related gene is correlated with species evolution, we chose a
wide array of species, covering from microorganisms to
lower and higher plants, as well as a number of animal
species. We selected six types of plants whose whole genome had been sequenced: A. thaliana, O. sativa, S. bicolor, P. trichocarpa, P. patens, C. reinhardtii. Several
microorganisms were selected including two plant fungal
pathogens: M. grisea, R. oryzae, a yeast S. pombe and an
archeabacterium T. thermophila. To make a comprehensive
comparison, we also chose seven animal species: C. elegans, C. japonica, X. tropicalis, D. melanogaster, B. taurus, M. domestica, and H. sapiens. Because the method in
identifying orthologues has not been standardized, we decided to take the most commonly used method supported
by INPARANOID program (O’Brien et al., 2005). In the
INPARANOID algorithm, BLASTN program is used for
all-versus-all pairwise alignment between two species. A
minimal similarity score of 50 bit and matching segments
exceeding 50% of the total length were set as our cutoff
between and within species all-versus-all BLAST search.
In general, this program is commonly used in orthologue
identification due to its efficacy and measures to avoid
false-positive results. Thus, we used the previously defined
479 AHRGs as queries, collected orthologue information
from INPARANOID orthologues searching result in different species respectively.
In constructing phylogenetic trees, similarity percentage
of amino acids in protein sequences was used to calculate
distances. As an attempt, similarity percentage of orthologue
genes was simply defined as the percentage of orthologue
genes in non-Arabidopsis species versus those in Arabidopsis. And thus the similarity could be used to represent
the relative distance between the non-Arabidopsis species
and Arabidopsis. Calculation of the similarity percentage
of the gene list between each two species (Supplemental
Table 2) allowed us to construct a matrix similar to constructing sequence phylogenetic trees. Using this matrix
we generated a phylogenetic Neighbour-Joining tree with
all orthologue genes (Fig. 1A). For comparison, a phylogenetic tree of above-mentioned species was provided
(Fig. 1B). In order to compare the accuracy of the gene
phylogenetic tree we constructed, protein sequence phylogenetic tree of the gene AT3G28860 and AT3G57530
orthologues was also generated (Fig. 1, C and D). These
two genes were randomly chosen from a set of orthologue
genes identified in all selected species (MCOs), and we
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found that both of the two genes are commonly identified
in various biological processes and in various species.
Generally these trees were of the same style that plant,
microoganism and animal genes are clustered separately.
However, there were several differences between the
orthologue phylogenetic tree and the other three trees. First,
different from species phylogenetic tree, plant genes were
not clustered into one clade. It can be explained that gene
variance of different plants is greater than the sequence
variance. Different from plant orthologue clusters, animal
orthologue clusters showed little variation. This could be
due to that AHRG orthologues in animals are limited,
which curtails the phylogenetic distance among animals in
orthologue gene phylogenetic tree. It was a little surprising
to observe that A. thaliana is located between P. patens
and C. reinhardtii, but not closer to higher plant in
orthologue gene phylogenetic tree. One possible explanation is that all orthologues were obtained from similarity
comparison with the genes in A. thaliana, so that the
orthologue clusters would have higher similarity compared with A. thaliana, which breaks the “Triangle Rule”
topologically. Just as mentioned above, the distance of
the orthologue phylogenetic tree is a relative distance as
compared to Arabidopsis orthologue genes, which is a
disadvantage and cause the problem. The sequence phylogenetic trees, on the other hand, seemed to fit well with
Fig. 1. Phylogenetic tree generated by orthologue similarity and protein sequence similarity. A: NJ-phylogenetic tree generated by orthologue genes, using
orthologue gene similarity as compared to A. thaliana hormone related genes to show the relative distance to A. thaliana. The number in brackets shows the
quantity of AHRGs in the particular organism. B: species systematic phylogenetic tree provided by INPARANOID. C and D: protein sequence phylogenetic
trees of two random selected genes AT3G28860 and AT3G57530, respectively.
Zhiqiang Jiang et al. / Journal of Genetics and Genomics 37 (2010) 219−230
the systematic phylogenetic tree of species. All of the three
trees showed several common patterns: first, P. trichocarpa and A. thaliana were clustered into one subclade.
Second, those animal species were clustered into the same
clade. Third, the microorganism clade was the most distant
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to other clades. By selecting the common set of
orthologues identified in all selected species, we were able
to collect a list of the MCOs of AHRG in all examined
species (Table 1). The majority of the MCOs were related
to protein kinases or protein degradation, which were
Table 1
List of MCOs in all searched species
Locus identifier
AT3G16650 (PRL2)
AT5G45620(RPN9)
Hormone
ABA
IAA, BR
Gene description
PP1/PP2A phosphatases pleiotropic regulator 2 (PRL2)
26S proteasome regulatory subunit, putative (RPN9)
AT3G28860 (PGP19, MDR11,
MDR1, ATPGP19, ABCB19)
IAA
Belongs to the family of ATP-binding cassette (ABC) transporters. Also known as AtMDR11 and
PGP19. Possibly regulates auxin-dependent responses by influencing basipetal auxin transport in the
root. Acts upstream of phyA in regulating hypocotyl elongation and gravitropic response. Exerts nonredundant, partially overlapping functions with the ABC transporter encoded by AtPGP1.
AT3G51260 (PAD1)
JA
20S proteosomal alpha subunits. Interacts with SnRK, SKP1/ASK1 during proteasomal binding of an
SCF ubiquitin ligase.
AT4G01370 (MPK4)
ET, JA
Encodes a nuclear and cytoplasmically localized MAP kinase involved in mediating responses to
pathogens. Its substrates include MKS1.
AT2G47000 (MDR4)
CK, IAA
Multidrug resistance P-glycoprotein (MDR/PGP) subfamily of ABC transporters. Functions in the
basipetal redirection of auxin from the root tip. Exhibits apolar plasma membrane localization in the
root cap and polar localization in tissues above.
AT1G05180 (AXR1)
IAA, JA
Encodes a subunit of the RUB1 activating enzyme that regulates the protein degradation activity of
Skp1-Cullin-Fbox complexes, primarily, but not exclusively, affecting auxin responses. Acts alongside
AS1 to exclude BP expression from leaves.
AT2G32410 (AXL)
IAA
AXR1-LIKE (AXL)
AT5G04870 (PAL, CPK1)
ABA
A calcium-dependent protein kinase that can phosphorylate phenylalanine ammonia lyase (PAL), a key
enzyme in pathogen defense.
AT1G18890 (CPK10)
ABA
Encodes a calcium-dependent protein kinase whose gene expression is induced by dehydration and
high salt. Kinase activity could not be detected in vitro.
AT1G74740 (CPK30)
ABA
Member of calcium-dependent protein kinase
AT3G57530 (CPK32)
ABA
Calcium-dependent protein kinase. ABA signaling component that regulates the ABA-responsive gene
expression via ABF4. AtCPK32 has autophosphorylation activity and can phosphorylate ABF4 in vitro.
AT3G48750 (CDC2, CDKA1)
ABA
A-type cyclin-dependent kinase. Together with its specific inhibitor, the Kip-related protein KRP2, both
of them regulate the mitosis-to-endocycle transition during leaf development. Dominant negative mutations abolish cell division. Loss of function phenotype has reduced fertility with failure to transmit via
pollen. Pollen development is arrested at the second mitotic division. Expression is regulated by environmental and chemical signals. Part of the promoter is responsible for expression in trichomes. Functions as a positive regulator of cell proliferation during development of the male gametophyte, embryo
and endosperm. Phosphorylation of threonine 161 is required for activation of its associated kinase.
AT1G75820 (CLV1)
CK
Putative receptor kinase with an extracellular leucine-rich domain. Controls shoot and floral meristem
size, and contributes to establish and maintain floral meristem identity. Negatively regulated by KAPP
(kinase-associated protein phosphatase). CLV3 peptide binds directly CLV1 ectodomain.
AT1G56070 (LOS1)
ABA
Encodes a translation elongation factor 2-like protein that is involved in cold-induced translation. Mutations in this gene specifically blocks low temperature-induced transcription of cold-responsive genes
but induce the expression of CBF genes and mutants carrying the recessive mutations fail to acclimate
to cold and is freezing sensitive.
AT4G15900 (PRL1)
ABA, CK,
ET, IAA
Mutations confer hypersensitivity to glucose and sucrose and augments sensitivity to cytokinin, ethylene, ABA and auxin. Encodes a nuclear WD40 protein that is imported into the nucleus. Essential for
plant innate immunity. Interacts with MOS4 and AtCDC5. It is also predicted to have two DWD motifs.
It can bind to DDB1a in Y2H assays, and DDB1b in co-IP assays, and may be involved in the formation of a CUL4-based E3 ubiquitin ligase, and may affect the stability of AKIN10.
AT1G64520 (RPN12a)
IAA, CK
Regulatory particle non-ATPase 12a (RPN12a)
AT1G02500 (SAM1, MAT1)
ET
Encodes an S-adenosylmethionine synthetase. SAM1 is regulated by protein S-nitrosylation. The covalent binding of nitric oxide (NO) to the Cys114 residue inhibits the enzyme activity.
AT1G71830 (SERK1)
BR
Plasma membrane LRR receptor-like serine threonine kinase expressed during embryogenesis in locules until stage 6 anthers, with higher expression in the tapetal cell layer. SERK1 and SERK2 receptor
kinases function redundantly as an important control point for sporophytic development controlling
male gametophyte production.
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commonly found in various signal transduction pathways.
Additionally, some genes may have altered functions in
different species. For examples, SAM1 was known to be a
key enzyme in ethylene biosynthesis in A. thaliana, but in
animals it is widely used as one-carbon unit carrier. Intriguingly, the number of ABA related genes was
over-represented, indicating that certain aspects of ABA
response pathways were shared by a wide range of species,
which is coincided with the finding that some human diseases are influenced by ABA (Nagamune et al., 2008;
Bodrato et al., 2009). Besides ABA-related genes, the
number of IAA-relate genes was also over-represented,
consistent with the hypothesis that some auxin-dependent
mechanisms emerged before land plants occurred (Lau et al.,
2009). However, only a small portion of IAA-relate genes
showed ancient orthologues, suggesting that an intact auxin
response pathway was not evolved prior to land plants.
Comparison of Arabidopsis hormone related components in different species
To categorize the function of orthologues in different
hormone pathways, the percentage of the function distribution of AHRG orthologue in all selected species was
shown in histogram (Fig. 2 and Supplemental Fig. 1). Different colors represent distinct function category (metabolism, transport, signaling). In general, the overall distribution of the orthologues in different species was largely
similar. However, the histogram revealed that the numbers
of hormone related components were increased with the
evolution of plants, but remained almost the same in different animals. The expanded orthologues were mainly
related to signal transduction, especially transcription factors. The increasing repertoire of transcription factors indicate more sophisticated and multi-leveled regulations
evolved in the higher plants. Compared with higher plants,
animals have much limited orthologues, suggesting that
much of the functions and processes operated by the
AHRG orthologues in plants were not present in animals.
The hormone receptors represent the first perception
site and probably the most critical step in plant hormone
signaling pathways. In A. thaliana, several receptors have
been identified in 7 hormones except for SA. Orthologues
of these receptors have been searched in all selected species, and the number of receptor orthologues of each hormone was clustered by species in complete hierarchical
method and in uncentered Pearson algorithm. The result
was visualized in a heat map (Fig. 3). By clustering species,
we found that all selected plants have most of hormone
receptors genes (except BR receptors in P. patens), supporting the conserved feature of plant hormone responses
over the evolution course. Not surprisingly, two microorganisms M. grisea and T. thermophila have numerous cytokinin receptors-like orthologues (a type of histidine
kinases) that are common in prokaryotic two-component
systems but absent in animals. Whether phytohormone
Fig. 2. Distribution of orthologues in function category of hormone related genes in different species. The height of each bar showing in different color
represents the percentage of orthologue genes in AHRG of selected plants as compared to that of A. thaliana. Color in blue shows orthologues belonging to
hormone metabolism related genes; color in purple shows orthologues belonging to hormone transport genes; color in light yellow shows orthologues
belonging to genes related to signal transduction. The number in brackets shows the quantity of AHRGs in the particular organism.
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Fig. 3. Heat map to visualize the complete hierarchy clustering of the species based on the number of hormone receptor orthologue genes. Uncentered
Pearson algorithm is used. Different color represents the numbers of orthologues each species possesses. Species names are listed on the right side of the
map; hormones names are listed above the map.
cytokinin can influence the physiology of these two microorganisms remains unknown yet. Although GA, ABA
and BR receptor orthologues were found to exist in animals, their authentic functions might differ dramatically
from their counterparts in plants. In general, the clustering
clades of species in receptor clustering heat map also show
an evolutionary pattern. Together with previous function
distribution of AHRG orthologues, the results revealed that
the identification of receptor orthologue genes is accompanied by identification of more downstream component
orthologues. It indicates that the expansion of signaling
components may depend on the evolution of perception
systems.
Comparison of orthologues in plants
Further study was performed to compare the difference
between higher and lower plants, as well as monocot and
dicot plant (Fig. 4 and Supplemental Table 3). The difference between higher and lower plants was pronounced,
because several hormone response pathways were absence
in lower plants. For instance, two lower plant species (C.
reinhardtii and P. patens) did not contain a key component
in Arabidopsis ethylene signaling pathway namely EIN3
(Chao et al., 1997; Alonso et al., 1999). By contrast, the
difference between monocot and dicot plant was marginal.
Most of divergent genes between monocot and dicot plants
are from gene family, with more family members found in
dicot plants. Some of the new-evolved members in dicot
plants might carry on new gene functions. The increase of
complexity in dicots may integrating more hormone responses and gene regulation in biological processes, which
may as well enhance the robustness of hormone response
network, making the network fine-tunable and flexible
enough for numerous intrinsic or environmental disturbances.
The orthologues shared by all selected plants were limited to certain signaling related genes, including protein
kinases, and some hormone metabolism related genes.
However, the number of orthologues shared by higher
plants was increased (Supplemental Table 4). The number
of orthologues in a specific function category (in the mean
of percentage relative to the number in A. thaliana) was
presented in broken line chart (Fig. 5). From the chart we
can see that higher plants have more orthologues than
lower plants. Besides, orthologues in higher plants are
more conserved than lower plants. One possible explanation is that genome complexity is increasing from lower to
higher plants, and higher plants have evolved more
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Fig. 4. Venn graph of AHRG orthologues uniquely and commonly identified in higher plants. A: orthologue genes between monocot and dicot plants.
Color in red represents the orthologue genes uniquely identified in dicot plants, color in yellow represents the orthologue genes uniquely identified in S.
bicolor, and color in blue represents orthologue genes uniquely identified in rice. The overlapping part represents the common orthologue genes shared by
S. bicolor and/or rice together with dicot plants. B: a detailed analysis of 278 higher plant commonly shared orthologues (gray part in A) as compared to
lower plants. Color in red represents orthologues uniquely identified in higher plants, color in yellow represents orthologue genes uniquely identified in
algae, and color in blue represents orthologue genes uniquely identified in moss. The overlapping part represents commonly shared orthologues.
Fig. 5. Breaking line chart representing component changes in different species. Each color represents particular function category of hormone related
component. The height of each point represents the percentage derived from the numbers of AHRG orthologues in a given non-Arabidopsis species versus
the number of AHRGs.
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complicated and diverse regulation thus possess more
genes participating in hormone responses. In general, the
number of genes in each function category increases from
lower to higher plants. An exception was that orthologues
involved in protein degradation remains quite constant in
number, suggesting the importance of this regulatory
mechanism during the evolution of hormone actions.
Comparison of higher plant orthologue tandem duplications
We next sought to study the variation of the copy numbers of hormone related orthologue genes among species.
To test orthologue copy-number variation, we determined
the copy numbers of each orthologue gene in O. sativa, S.
bicolor, P. trichocarpa, and compared them with that of A.
thaliana. The result presenting in bar chart showed the
distribution of each kind of copy-number variation by percentage (Fig. 6). We concluded that in monocot plants,
single-copy orthologues are the majority, whereas in P.
trichocarpa (a dicot), nearly half of genes have two
orthologues. This can be explained by the fact that P.
trichocarpa is a hybrid species whose genome contains
allele polymorphism that is absent in A. thaliana. It was
reported that at the genome level, each A. thaliana gene
has an average of 1.4–1.6 orthologues in P. trichocarpa
(yet no such study reported in rice or S. bicolor), except
for certain type of genes such as F-Box family genes. In a
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previous study, 4DTV test was conducted to evaluate the
genome duplication of P. trichocarpa as compared to A.
thaliana (Tuskan et al., 2006; Jansson and Douglas, 2007).
The result showed that no genome duplication occurred
between P. trichocarpa and A. thaliana. At the same time,
the relative frequency of domains represented in the protein database (Prints, Prosite, Pfam, ProDom and SMART)
in these two genomes is similar. In conclusion, tandem
duplication predominately contributes to the observation of
coding gene expansion (Tuskan et al., 2006; Jansson and
Douglas, 2007). Our study revealed that each hormone
related gene in A. thaliana had an average of 2.67 orthologues in P. trichocarpa, which is a little higher than the
average of global genes. This finding is consistent with the
previous study in P. trichocarpa that some hormone related gene family are greatly expanded, mainly due to independent expansion of these family genes (Tuskan et al.,
2006). As a woody plant with a much longer life time, P.
trichocarpa has to face a significantly more environmental
disturbance throughout its life span. A higher level of allele
polymorphism may provide genetic basis to boost the
complexity of the regulatory networks through the process
of gene divergent evolution or gene redundant regulation,
which is also consists with previous finding. Interestingly,
we found only one of the two key F-Box genes (EBF1/2)
in ethylene signaling pathway in P. trichocarpa genome,
suggesting that not every gene family was expanded in this
woody species (Guo and Ecker, 2003).
Fig. 6. Comparison of the copy numbers of AHRG orthologues in two cereals (rice and S. bicolor), P. trichocarpa, and A. thaliana. Different colors represent ratios that are calculated by the number of orthologue genes in selected species versus the number of AHRG in A. thaliana.
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Discussion
Plant hormones play important roles during a plant life
span. Biosynthesized hormones are perceived by hormone
receptors and the signal passes downstream through protein kinases, transcription factors together with other signaling components. Meanwhile, F-boxes, ubiquitins and
other protein/nucleic acid degradation related genes might
provide negatively-regulating brakes to the pathways,
making responses in a controllable manner. Recently,
study of the responses to phytohormones in lower plants
or even animals became attractive (Marella et al., 2006;
Yasumura et al., 2007; Nagamune et al., 2008; Bodrato et
al., 2009; Lau et al., 2009). The release of massive genome information enables us to identify orthologues of
hormone related genes within species beyond A. thaliana.
Comparison of orthologues in different species would no
doubt provide us with a more comprehensive understanding on the action of phytohormones from an evolutionary view.
In the present study, we used 479 Arabidopsis hormone
related genes as queries, searching for orthologues in several species across from animals to plants, monocot plants
to dicot plants, lower plants to higher plants. In general,
species phylogenetically close to each other would have a
high similarity of their orthologue clusters. It indicated that
an evolutionary process had been initiated in AHRG evolution. Meanwhile, MCOs of all selected species were collected (Table 1). These orthologue genes in Arabidopsis
are mostly involved in auxin and ABA signaling pathways,
consistent with previous studies that auxin-dependent
mechanism was early evolved, and that ABA might have a
biological role in both plants and animals. Most of the
MCOs are protein kinases, indicating an important role of
protein kinases in hormone perception. Histograms of
orthologue function distribution in different species were
also generated to visualize the difference between different
species (Fig. 2 and Supplemental Fig. 1). The number of
orthologue genes in lower plants was limited than higher
plants. However, there are even fewer hormone related
gene orthologues identified in H. sapiens than that of C.
reinhardtii. It did not indicate a simple regulation used by
H. sapiens. These orthologues, on the other hand, may
interact with other genes in human genome to carry out
different functions or regulations. Different from animals,
failure to find key components orthologues in lower plants
implied that some signaling pathways might be not actu-
ally functional in those organisms.
Receptor is the starting point of a signaling pathway.
Comparison of hormone receptor gene orthologues in different species has several implications. First, orthologue
genes involved in downstream signal transduction tend to
expand as the receptor gene evolved. Second, higher plants
have all hormone receptors genes, supporting the conserved feature of plant hormone perception over the evolution course. Third, although some receptor-like genes were
found in animals, for instance, GA, ABA and BR receptor
orthologues, their authentic functions might differ dramatically from their counterparts in plants. Forth, microorganisms also contain plant hormone receptors-like
orthologues (e.g., histidine kinase receptors), but the
physiological function of these genes might be distinctive
from that in plants given that prokaryotes frequently use
two-component systems to sense environmental changes.
In a detailed analysis of plant orthologue clusters, in
comparison to higher plants, lower plants contain fewer
number of component orthologues. By contrast, the majority of orthologue genes were shared by both monocot
plants and dicot plants. It seemed that components related
to hormone responses tend to be convergent as species
became complex. Variance in higher plants orthologue
clusters was mainly copy number difference. As a decadeslong life span genus organism, P. trichocarpa has more
gene copies than A. thaliana and rice. The larger gene
pools might enable P. trichocarpa population to gain enhanced viability and ability to detect and respond to deleterious stresses and thus adapt to various environmental
conditions. However, tandem duplication events of hormone related genes were not observed in monocot plants
such as rice and S. bicolor, although in four higher plants
assayed gene contents are quite similar to each other. It
indicates that after monocot plants diverged from dicot
plants, the genome sequences of monocot plants had
changed in a slower evolutionary pace. The genome sequences of dicot plants, on the other hand, evolved much
faster. We found that in P. trichocarpa, which is a woody
dicot plant, the number of hormone-related genes more
than doubles that in A. thaliana, although certain classes of
genes such as F-Box genes were lost. Thus, both as a Dicotyledoneae species, P. trichocarpa and A. thaliana chose
different evolutionary routes.
Many eukaryotes duplicate their genomes and the resultant duplicated genes would either produce redundant or
new functions (Soyer and Bonhoeffer, 2006; Ma et al.,
Zhiqiang Jiang et al. / Journal of Genetics and Genomics 37 (2010) 219−230
2009). At the same time, divergent evolutionary, de novo
gene evolution events take place anywhere (Lynch and
Conery, 2003). The intrinsic tendency of increasing hormone regulation network size may result from an increasing complexity of genome. Our analyses also revealed that
when a perception system (indicated by the appearance of
receptors) is formed in a given organism, the numbers of
components downstream of the receptors will increase
dramatically and a primitive pathway would thus occur.
During the evolutionary process, the initial members of the
pathway may recruit more partners. This process gradually
increases the complexity and amount of genes participating in the hormone actions. Under this circumstance, the
hormone-related pathways will expand even more significantly along with the whole genome duplication. It is thus
conceivable that the primary causes in phytohormone related gene evolution should be the diversification of the
pathway functions as well as the increasing complexity of
the genome. The evolution of receptors should be regarded
as the first event for the most hormone signal pathways to
form. Afterward, during the evolutionary process, existing
genes may be recruited to join the pathway. Otherwise, the
existing genes may be lost or became functional divergent.
The next step of the work is to concentrate on a more detailed analysis of each hormone pathway components. A
deep comparison of eight hormones would tell us additional information about the natural force shaping the formation and evolution of all hormone related genes.
Acknowledgements
This work was supported by the National Science
Foundation of China (No. 30625003, and 30730011) and
the China Ministry of Education (No. 20060047). We
thank Professor Jingchu Luo, He Zhang, and Ming Ni at
Center of Bioinformatics of Peking University for the
helpful discussion. We thank Pengpeng Li for manuscript
revision, and Wenyang Li, Mingzhe Li for their technical
assistance.
Supplemental data
Supplemental Fig. 1 and Tables 1–4 associated with
this article can be found in the online version at
www.jgenetgenomics.org.
229
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