An Error Occurred Setting Your User Cookie
An Error Occurred Setting Your User Cookie
This site uses cookies to improve performance. If your browser does not accept cookies, you cannot view this site.
Setting Your Browser to Accept Cookies
There are many reasons why a cookie could not be set correctly. Below are the most common reasons:
You have cookies disabled in your browser. You need to reset your browser to accept cookies or to ask you if you want to accept cookies.
Your browser asks you whether you want to accept cookies and you declined.
To accept cookies from this site, use the Back button and accept the cookie.
Your browser does not support cookies. Try a different browser if you suspect this.
The date on your computer is in the past. If your computer's clock shows a date before 1 Jan 1970,
the browser will automatically forget the cookie. To fix this, set the correct time and date on your computer.
You have installed an application that monitors or blocks cookies from being set.
You must disable the application while logging in or check with your system administrator.
Why Does this Site Require Cookies?
This site uses cookies to improve performance by remembering that you are logged in when you go from page to page. To provide access without cookies
would require the site to create a new session for every page you visit, which slows the system down to an unacceptable level.
What Gets Stored in a Cookie?
This site stores nothing other than an automatically generated session ID no other information is captured.
In general, only the information that you provide, or the choices you make while visiting a web site, can be stored in a cookie. For example, the site
cannot determine your email name unless you choose to type it. Allowing a website to create a cookie does not give that or any other site access to the
rest of your computer, and only the site that created the cookie can read it.Associated material
Related literature
Other articles by authors
Related articles/pages
Download to ...
Share this article
Email updates
Research article
Comparative analyses reveal distinct sets of lineage-specific genes within Arabidopsis thaliana
Haining Lin, Gaurav Moghe, Shu Ouyang, Amy Iezzoni, Shin-Han Shiu, Xun Gu* and C Robin Buell*
Corresponding authors:
Department of Plant Biology, Michigan State University, 166 Plant Biology Building, East Lansing, MI 48824, USA
Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA 50011, USA
J. Craig Venter Institute, 9712 Medical Center Drive, Rockville, MD 20850, USA
Department of Horticulture, Michigan State University, A342 Plant and Soil Science Building, East Lansing, MI 48824, USA
Current address: Suite 205, 1003 7th Street, Frederick, MD 21701, USA
For all author emails, please .
BMC Evolutionary Biology 2010, 10:41&
doi:10.48-10-41
The electronic version of this article is the complete one and can be found online at:
Received:8 May 2009
Accepted:12 February 2010
Published:12 February 2010
& 2010 L licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The availability of genome and transcriptome sequences for a number of species permits
the identification and characterization of conserved as well as divergent genes such
as lineage-specific genes which have no detectable sequence similarity to genes from
other lineages. While genes conserved among taxa provide insight into the core processes
among species, lineage-specific genes provide insights into evolutionary processes
and biological functions that are likely clade or species specific.
Comparative analyses using the Arabidopsis thaliana genome and sequences from 178 other species within the Plant Kingdom enabled the identification
of 24,624 A. thaliana genes (91.7%) that were termed Evolutionary Conserved (EC) as defined by sequence
similarity to a database entry as well as two sets of lineage-specific genes within
A. thaliana. One of the A. thaliana lineage-specific gene sets share sequence similarity only to sequences from species
within the Brassicaceae family and are termed Conserved Brassicaceae-Specific Genes
(914, 3.4%, CBSG). The other set of A. thaliana lineage-specific genes, the Arabidopsis Lineage-Specific Genes (1,324, 4.9%, ALSG),
lack sequence similarity to any sequence outside A. thaliana. While many CBSGs (76.7%) and ALSGs (52.9%) are transcribed, the majority of the
CBSGs (76.1%) and ALSGs (94.4%) have no annotated function. Co-expression analysis
indicated significant enrichment of the CBSGs and ALSGs in multiple functional categories
suggesting their involvement in a wide range of biological functions. Subcellular
localization prediction revealed that the CBSGs were significantly enriched in proteins
targeted to the secretory pathway (412, 45.1%). Among the 107 putatively secreted
CBSGs with known functions, 67 encode a putative pollen coat protein or cysteine-rich
protein with sequence similarity to the S-locus cysteine-rich protein that is the pollen determinant controlling allele specific
pollen rejection in self-incompatible Brassicaceae species. Overall, the ALSGs and
CBSGs were more highly methylated in floral tissue compared to the ECs. Single Nucleotide
Polymorphism (SNP) analysis showed an elevated ratio of non-synonymous to synonymous
SNPs within the ALSGs (1.99) and CBSGs (1.65) relative to the EC set (0.92), mainly
caused by an elevated number of non-synonymous SNPs, indicating that they are fast-evolving
at the protein sequence level.
Conclusions
Our analyses suggest that while a significant fraction of the A. thaliana proteome is conserved within the Plant Kingdom, evolutionarily distinct sets of genes
that may function in defining biological processes unique to these lineages have arisen
within the Brassicaceae and A. thaliana.
Background
Lineage-specific genes are defined as genes in one taxonomic group that have no detectable
sequence similarity to genes from other lineages. With the availability of complete
or near-complete genome and transcriptome sequences from a wide range of species,
lineage-specific genes have been extensively studied, especially in microbial species
[-]. Several hypotheses regarding the origin of lineage-specific genes have been proposed.
One model suggests that lateral gene transfer has an important role in generating
lineage-specific genes [,]. The second model proposes that lineage-specific genes may be generated by gene duplication
followed by rapid sequence divergence [,]. It is also suggested that an accelerated evolutionary rate may be responsible for
the emergence of lineage-specific genes such that no sequence similarity to genes
from other species can be detected []. Other models include de novo emergence from non-genic sequences which are more diverged between species [] as well as artifacts from genome annotation []. Although the origin and evolution of lineage-specific genes remains unresolved,
the identification and characterization of putative lineage-specific genes can provide
insight into species-specific functions and evolutionary processes such as speciation
(divergence) and adaptation [].
Within the Plant Kingdom, the identification and characterization of lineage-specific
genes has been performed through comparative analysis of Expressed Sequence Tags (ESTs)
and/or the finished genome sequences of Arabidopsis thaliana (Arabidopsis) and Oryza sativa (rice) [-], the model species for dicotyledonous and monocotyledonous plants, respectively.
Through a comparative analysis between the Arabidopsis and rice predicted proteomes,
116 protein clusters comprised of at least two Arabidopsis sequences but lacking a
rice protein were identified, suggesting they were encoded by Arabidopsis-specific
genes [,]. In a comparative analysis of legume with non-legume unigene datasets, GenBank's
nonredundant and EST databases, and the genome sequences of Arabidopsis and rice,
approximately 6% of the legume unigene sets were identified as legume-specific []. In a more recent analysis, a set of 861 rice genes termed "Conserved Poaceae Specific
Genes" that are evolutionarily conserved within the Poaceae family yet lack significant
sequence similarity to non-Poaceae species was identified by searching the finished
rice genome sequence against the genomic sequences from Arabidopsis, Medicago, poplar, and EST clusters from 184 plant species []. This set of conserved Poaceae-specific genes provides a starting point for further
research experiments to better understand the unique morphology, physiological and
developmental characteristics of Poaceae species. With the recent availability of
additional plant genome sequences, a recent study identified 165, 638, and 109 lineage-specific
genes in Arabidopsis, rice, and poplar, respectively, by searching genes with expression
evidence against EST assemblies, a non-redundant protein database, and plant genome
sequences []. In addition to lineage-specific genes, comparisons of multiple plant genomes can
provide information on lineage-specific gene expansion of gene families [].
In this study, we identified and characterized Conserved Brassicaceae-Specific Genes
(CBSGs) and Arabidopsis Lineage-Specific Genes (ALSGs) using the completed and well-annotated
A. thaliana genome, the genomes of Medicago truncatula (Medicago), Populus trichocarpa (poplar), Vitis vinifera (grapevine), Carica papaya (papaya), Sorghum bicolor (sorghum), Chlamydomonas reinhardtii (green alga), Physcomitrella patens (moss), and O. sativa (rice) [-], as well as EST clusters from 178 plant species. An earlier study on Arabidopsis
lineage-specific genes [] restricted their analysis to only genes with expression evidence and employed a relaxed
criterion to define sequence conservation. As a consequence, only 165 lineage-specific
genes were identified in Arabidopsis. In our study, we elected to limit false negatives
and identify more potential lineage-specific genes in A. thaliana by using the entire A. thaliana predicted protein-coding gene complement in our analyses and by using more stringent
searching criteria. Furthermore, we identified two types of lineage-specific genes,
those restricted to A. thaliana and those restricted to the Brassicaceae. By our definition, CBSGs are A. thaliana genes that have significant sequence similarity only to sequences from species within
the Brassicaceae family while ALSGs are A. thaliana genes that are unique to A. thaliana. As a large portion of the CBSGs and ALSGs have no known function, co-expression
and subcellular localization analyses were performed to infer possible biological
function. DNA methylation analysis was performed to investigate the epigenetic modification
and effects. To assess evolutionary pressures within these two sets of lineage-specific
genes, Single Nucleotide Polymorphisms (SNPs) within the coding regions were analyzed.
Identification of CBSGs and ALSGs
Using TBLASTN [], 26,862 A. thaliana protein-coding genes were searched against the genomic sequences of papaya, poplar,
Medicago, grapevine, rice, sorghum, moss, Chlamydomonas, and the PlantGDB-assembled Unique Transcripts (PUTs) [] from 168 species outside the Brassicaceae family. A total of 24,571 A. thaliana genes with significant sequence similarity (E-value & 1e-5) to either a genomic or
PUT sequence from a species outside the Brassicaceae family were defined as the Evolutionarily
Conserved (EC) set (Fig. ). The remaining 2,291 A. thaliana genes with no significant similarity to any sequence (genomic or PUT) outside the
Brassicaceae family were further searched against PUT sequences from ten Brassicaceae
species/subspecies including: Brassica napus, B. oleracea, B. oleracea var. alboglabra, B. rapa, Raphanus raphanistrum subsp landra, R. raphanistrum ssp. maritimus, R. raphanistrum ssp. raphanistrum, R. sativus, R. sativus var. oleiformis, and Thellungiella halophila. This resulted in two datasets: 912 CBSGs with no significant sequence similarity
to sequences from the Plant Kingdom except those from the Brassicaceae, and 1,379
ALSGs that had no significant sequence similarity to any sequences within the Plant
Kingdom (Fig. ). To further eliminate false positives due to incompleteness of the genome and transcriptome
sequence sets, the CBSGs and ALSGs were used to search against the UniProt Knowledgebase
(UniProtKB) using BLASTP []. Manual inspection of the alignments (E-value & 1e-5) identified 53 A. thaliana genes (33 CBSGs and 20 ALSGs) with sequence similarity to non-Brassicaceae UniProt
entries that were transferred from the CBSG and ALSG sets to the EC set. A total of
35 ALSGs with sequence similarity to Brassicaceae UniProt entries were also removed
from the ALSG set to the CBSG set. Thus, the final sets of CBSGs, ALSGs, and ECs contain
914, 1,324, and 24,624 A. thaliana genes, respectively (Fig. , Additional file , Additional file ).
Additional file 1. List of CBSG genes. The gene accession, number of exons (calculated from the representative gene model),
and putative function from the TAIR8 release of all the CBSG genes are provided.
Format: TXT
Size: 88KB
Additional file 2. List of ALSG genes. The gene accession, number of exons (calculated from the representative gene model),
and putative function from the TAIR8 release of all the ALSG genes are provided.
Format: TXT
Size: 152KB
Identification of lineage specific genes in A. thaliana. The solid boxes reflect non-Arabidopsis sequences used in the searches while the
hashed boxes show the Arabidopsis genes.
Characterization of the CBSGs and ALSGs
To discern whether there are significant differences between the two lineage-specific
gene sets (CBSGs, ALSGs) and the ECs and to exclude the possibility that the CBSGs
and ALSGs are Transposable Element (TE)-related genes, genic features of the CBSGs
and ALSGs were characterized and compared to those of the EC and TE gene sets (Table
). The average exon numbers per gene for the CBSGs and ALSGs were similar to that
of the TE set, but significantly smaller than that of the EC gene set (t-tests, P & 1e-5), consistent with previous findings of shorter gene size of lineage-specific
genes in rice [,]. The average exon length of the CBSGs and ALSGs was comparable to that of the EC
set, but was one fifth of that of the TE set (Table ). The average intron length of the CBSGs and ALSGs were slightly longer than that
of the EC and TE sets (t-tests, P & 1e-5). A total of 322 (35.2%) CBSGs, 861 (65.0%) ALSGs, and 4,719 (19.2%)
ECs were single-exon genes, of which, 256 (79.5%) CBSGs, 310 (36.0%) ALSGs, and 4,388
(93.0%) ECs were expressed. The CBSGs had a lower GC content for the whole gene compared
to the EC and TE genes (t-tests, P & 1e-5). The GC content for the whole gene of the ALSGs was higher than
that of the ECs yet lower than that of the TE genes (t-tests, P & 1e-5). Both the CBSGs and ALSGs had a significantly lower average GC content
for the coding sequence compared to the EC set (t-tests, P & 1e-5), with the CBSGs having the lowest GC content. The lower GC content
observed for the CBSGs and ALSGs was consistent with the previous report on lower
GC content of lineage-specific genes in Drosophila [] but contrasted with the elevated GC content within the coding sequences of the Poaceae-specific
genes in rice []. One explanation for the difference in GC content between the lineage-specific genes
and EC genes in Arabidopsis versus rice is that neither the broader GC content distribution
observed in rice exons nor the GC content gradient within rice coding sequences is
present in A. thaliana []. Overall, genic metrics for the CBSGs and ALSGs indicate they are distinct gene sets
from EC and TE-related gene sets.
Genic features of the CBSGs, ALSGs, ECs, and TE-related genes
With respect to function, both the CBSG and ALSG gene sets are enriched in genes of
unknown function with 696 CBSGs (76.1%) and 1,250 ALSGs (94.4%) encoding proteins
with no known function (Table ). However, a large portion of both the CBSG and ALSG gene sets have transcript support
from ESTs, cDNAs, or microarray data, providing increased confidence in their annotations
(Table ). A total of 68 CBSGs (7.4%) encode low-molecular-weight, cysteine-rich (LCR) proteins
or S locus cysteine-rich like (SCRL) proteins, which have sequence similarity to members
of the pollen coat protein (PCP) gene family or the S locus cysteine-rich protein
(SCR), respectively []. SCR (also designated SP11) [-] is the pollen determinant of allele specific pollen rejection in self-incompatible
Brassicaceae species. Interaction of SCR, which is localized primarily in the pollen
coat, with its cognate stigma determinant, an S-locus receptor kinase (SRK) localized in the stigma epidermal cells [], triggers a signal transduction cascade that results in inhibition of pollen hydration,
germination, and tube growth []. At least one member of the PCP gene family in Brassica is also believed to interact with a secreted glycoprotein that is expressed specifically
in the stigmatic papilla cells [].
Functional annotation of CBSGs, ALSGs, and ECs
Neither the CBSGs nor ALSGs were distributed randomly within the A. thaliana genome (See Additional file ). Large numbers of CBSGs, ALSGs, and ECs were located within segmentally duplicated
blocks consistent with the substantial segmental duplication that occurred in A. thaliana []. However, the CBSGs and ALSGs were located more frequently in non-segmentally duplicated
regions compared to the ECs. A total of 23.7% and 27.3% of the CBSGs and ALSGs, respectively,
were located within non-segmentally duplicated regions, compared to 13.8% EC genes
(χ2 test, P & 1e-5). This could be due to differential gene loss of lineage-specific genes
(ALSGs, CBSGs) in segmentally duplicated versus non-segmentally duplicated regions
or alternatively that the ALSGs and CBSGs are located in segmentally duplicated blocks
which have rapidly evolved and thus are not detected using similarity based segmental
duplication methods.
Additional file 3. Distribution of the CBSGs, ALSGs, and ECs within the A. thaliana genome. The five A. thaliana chromosomes are shown with the CBSGs, ALSGs, and ECs plotted in purple, red, and blue
from top to bottom, respectively. Segmentally duplicated blocks are indicated in green
and the estimated centromeric regions are denoted by a yellow box.
Format: PNG
Size: 12KB
We utilized a computational pipeline in which Pfam and novel BLASTP-based protein
domains (see Methods) were used to classify paralogous families. As our pipeline involves
identification of novel BLASTP-based domains, proteins without a Pfam domain can also
be classified into paralogous families thereby removing any bias associated with lack
of a characterized protein domain. At the whole genome level, 17,911 A. thaliana genes were classified into 3,051 paralogous families (66.7%). For the lineage-specific
gene sets, 389 CBSGs (42.6%) and 65 ALSGs (4.9%) were classified into paralogous families,
substantially lower than that of the EC set (70.9%). This is consistent with what
was reported for lineage-specific genes within the Poaceae [] and consistent with previous analyses in A. thaliana which demonstrated paralogous families were enriched in genes with known function
yet the single-copy gene complement was enriched in genes with no known function [].
CBSGs are enriched with proteins targeted to the secretory pathway
To provide additional levels of functional annotation of the CBSGs and ALSGs, we used
the TargetP [] program to predict the subcellular localization of the predicted A. thaliana proteome. TargetP determines the putative subcellular localization based on the presence
of chloroplast transit peptide, mitochondrial targeting peptide, or secretory pathway
signal peptide. Consistent with previous reports [], 14.9%, 11.7%, and 20.2% of the total proteome was predicted to be targeted to the
chloroplast, mitochondrion, and secretory pathway, respectively. A dramatic enrichment
of proteins targeted to the secretory pathway was observed in the CBSG set (45.1%),
among which, only 107 (26.0%) have a putative function (Table ). Based on the TAIR8 assigned functions of these 107 CBSGs, 84 are likely targeted
to the secretory pathway including 67 proteins similar to PCP/SCR, eight defensin-like
family proteins, five putative ligands, and four Rapid Alkalinization Factor (RALF)-like
proteins. As proteins involved in the secretory pathway (e.g., receptor-ligand signaling
proteins, transporters, and extracellular signaling proteins) play fundamental roles
in various aspects of plant functions, the finding that the majority of the secreted
CBSGs have no known function suggests that Brassicaceae species possess one or more
biological processes that are either specific to the Brassicaceae family or have diverged
significantly from species outside the Brassicaceae family. No bias was seen in the
ALSG set for proteins targeted to the secretory pathway.
Subcellular localization of the CBSGs, ALSGs, ECs, and TAIR8 non-TE protein-coding
Although mitochondria and chloroplasts have conserved functions throughout the Plant
Kingdom and conserved sets of nuclear-encoded proteins across taxa have been documented
(for example, [-]), both computationally predicted and empirically determined proteomes of mitochondria
and chloroplasts have shown the presence of lineage-specific proteins [,]. For example, experimental analysis of the rice mitochondrial proteome suggested
that approximately 20% of the rice mitochondrial proteome may be lineage-specific
as no clear homolog was detected in the Arabidopsis mitochondrial proteome []. Consistent with these reports, we observed a significant difference in the percentage
of genes that encode proteins targeted to the mitochondrion between the CBSG, ALSG,
and EC sets. The CBSGs had a significantly lower than expected percentage of genes
encoding proteins targeted to the mitochondrion and the ALSGs had a significantly
higher than expected percentage (χ2 test, P & 1e-5). With respect to targeting to the chloroplast, CBSGs and ALSGs were
detected although at a significantly lower percentage compared to the EC set (Table
, χ2 test, P & 1e-5). In sharp contrast to the EC set, the majority of these putative mitochondrial
and chloroplast targeted ALSGs and CBSGs have no known function (Table ), suggesting these lineage-specific genes may encode novel functions within these
two organelles.
Functional inference by co-expression analyses
Given the lack of functional assignment for a large percentage of the ALSG and CBSG
sets, we performed co-expression analyses to associate these lineage-specific genes
with genes with annotated functions in Gene Ontology (GO) functional categories []. To prevent ascertainment bias, GO annotation solely based on expression evidence
was excluded. Using Arabidopsis ATH1 microarray expression data, we computed Pearson's
Correlation Coefficients (PCC) for the ALSGs and CBSGs in comparison to all other
genes on the microarray. Probes for 345 (26%) ALSGs and 314 (34%) CBSGs are present
on the ATH1 array. Based on a simulation study, we found that 99% of randomly selected
A. thaliana gene pairs have a PCC & 0.6. Using a 0.6 PCC value as the cutoff, 260 ALSGs (75%)
and 250 CBSGs (80%) with microarray probes were regarded as co-expressed with ≥ 1
gene(s) with GO annotations.
With the co-expression and GO annotation information, we then determined if any GO
categories were over-represented among genes co-expressed with the ALSGs or CBSGs.
We found that 75 out of the 260 ALSGs (29%) and 138 out of the 250 CBSGs (55%) had
≥ 1 significantly enriched GO categories (See Methods and Additional files
and ). Upon closer inspection, 35 out of 138 (25%) CBSGs were associated with GO categories
related to pollen, meiosis and sexual reproduction while 34 CBSGs (25%) were associated
with GO categories related to intra-cellular transport and secretory pathways (See
Additional files ,
and ). Analysis using the Fisher Exact Test revealed a highly significant over-representation
of genes in these categories in CBSG versus the non-CBSG datasets. These observations,
despite being obtained from a subset of the lineage-specific genes, are consistent
with the TargetP prediction of an enrichment of CBSGs targeted to the secretory pathway.
Additional file 4. GO category assignments for 75 ALSGs. Gene name, GO-ID, GO-Term, Fisher Exact Test table, p-values and Q-values for each
of the 75 ALSGs with their over-represented GO categories.
Format: XLS
Size: 81KB This file can be viewed with:
Additional file 5. GO category assignments for 138 CBSGs. Gene name, GO-ID, GO-Term, Fisher Exact Test table, p-values and Q-values for each
of the 138 CBSGs with their over-represented GO categories.
Format: XLS
Size: 194KB This file can be viewed with:
Additional file 6. Grouping of GO categories into five groups. The five defined GO category groups and their content GO IDs and GO terms.
Format: XLS
Size: 24KB This file can be viewed with:
Additional file 7. Results of Fisher Exact Test of ALSGs classified into groups. Group name, Fisher Exact Test table, p-values and Q-values for each of the five
GO category-groups for ALSGs.
Format: XLS
Size: 14KB This file can be viewed with:
Additional file 8. Results of Fisher Exact Test of CBSGs classified into groups. Group name, Fisher Exact Test table, p-values and Q-values for each of the five
GO category-groups for CBSGs.
Format: XLS
Size: 14KB This file can be viewed with:
CBSGs and ALSGs have a higher density of cytosine methylation
DNA methylation is considered one of the most important epigenetic modifications in
eukaryotes. To better understand the regulation of expression of the CBSGs and ALSGs,
methyl-cytosine data from floral tissue [] was used to measure the degree of DNA methylation. More than half of DNA cytosine
methylation occurs in a CG context while the remainder occurs in a CHG or CHH context
(where H = A, C, T) []. We calculated the density of cytosine methylation for three regions of each A. thaliana gene: 500 bp upstream, the coding region, and 500 bp downstream. In general, the average
density of methylation in the coding regions is significantly higher than that in
the 500 bp upstream or downstream regions (t-tests, P & 1e-3). We found that the ALSGs and CBSGs had significantly more cytosine
methylation than the ECs in all three regions: 500 bp upstream, coding, and 500 bp
downstream regions (Fig. , t-tests, P & 1e-5). DNA methylation in the promoter region and/or coding region of
genes can repress gene expression [], which is consistent with the fact that 95.9% of ECs compared to 52.9% of ALSGs and
76.7% of CBSGs have transcript support (Table ). Analysis of the proteins predicted to be targeted to the secretory pathway showed
decreased methylation density in all three regions compared to the full ALSG, CBSG,
and EC sets (Fig. , t-tests, P & 1e-5). For the CBSGs targeted to the secretory pathway, significantly
less methylation was detected in the 500 bp upstream region compared to the full CBSG
set (t-test, P & 1e-5), with the average methylation density (0.16 per 100 bp per gene)
similar to that of the ECs (0.15 per 100 bp per gene). This suggests less suppression
of expression of the secretory pathway targeted CBSGs in floral tissue.
Density of cytosine methylation in the 500 bp upstream, coding, and 500 bp downstream
regions. A) ALSGs, CBSGs, and ECs, and B) ALSGs, CBSGs, and ECs predicted to be targeted
to the secretory pathway.
CBSGs and ALSGs have a higher ratio of non-synonymous to synonymous SNPs
A total of 249,344 non-redundant SNPs were used to assess the genetic variation of
the CBSGs and ALSGs among 20 A. thaliana ecotypes []. SNPs (243,963, 97.8%) with only a single variant base, i.e., biallelic, were used
to calculate the SNP frequency within the coding regions as well as the frequencies
of synonymous and non-synonymous SNPs. Taking the length of the coding sequence into
account, both the ALSGs (0.41) and CBSGs (0.40) had significantly more SNPs per 100
bp per gene than the EC genes (0.35, t-tests, P & 1e-2). We further investigated two types of SNPs: synonymous and non-synonymous
SNPs. Synonymous SNPs result in the same amino acid as the gene model from the reference
genome while non-synonymous SNPs result in a different amino acid from the reference
gene model. The number of non-synonymous SNPs per 100 bp per gene is significantly
higher in the ALSGs (0.27) and CBSGs (0.25) compared to the ECs (0.17, t-tests, P & 1e-5) while the number of synonymous SNPs per 100 bp per gene is similar
among the ALSG (0.14), CBSG (0.15), and EC (0.18) sets. A total of 414 (31.3%), 326
(35.7%), and 8,670 (35.3%) genes from the ALSG, CBSG, and EC sets, respectively, had
more non-synonymous SNPs than synonymous SNPs. The ALSGs (1.99) and CBSGs (1.65) had
greatly elevated ratios of non-synonymous to synonymous SNPs, compared to the EC set
(0.92, Fig. ). The elevated ratios of non-synonymous to synonymous SNPs in the ALSG and CBSG sets
are mainly due to the elevated non-synonymous SNP density rather than the synonymous
SNP density observed in the ALSG and CBSG sets (Fig. ), indicating that a number of the ALSGs and CBSGs evolve substantially faster than
the ECs at the protein sequence level. With respect to genes encoding proteins targeted
to the secretory pathway, SNP density was comparable to that observed in the full
ALSG, CBSG, and EC sets (Fig. ), consistent with our hypothesis that some of these genes may be involved in biological
functions such as self-recognition which experience diversifying selection.
Ratio of non-synonymous to synonymous SNPs substitutions and the number of SNPs, non-synonymous
SNPs, and synonymous SNPs per 100 bp per gene within coding regions of A) ALSGs, CBSGs,
and ECs, and B) ALSGs, CBSGs, and ECs predicted to be targeted to the secretory pathway. Ratio of non-synonymous to synonymous substitutions SNPs is plotted in the solid
line. Number of SNPs, non-synonymous SNPs, and synonymous SNPs per 100 bp per gene
within coding regions is plotted in the dotted line. Lines are drawn between the 3
classses of genes to facilitate interpretation.
Discussion
The 914 CBSGs and 1,324 ALSGs identified in this study are attractive targets for
experimental discovery as they are lineage-specific and the majority (76.1% CBSGs
and 94.4% ALSGs) encode functions yet to be determined. Both the CBSGs and ALSGs had
shorter genes compared to the ECs, primarily due to fewer numbers of exons per gene
and a higher percentage of single-exon genes. A total of 68.6% of the 26,862 A. thaliana genes used in our analyses are high confidence genes as the gene structure (including
splice junctions) of at least one or more isoforms has been confirmed with a single
cDNA or multiple overlapping cDNAs [,]. The percentages of high confidence genes within the ALSG, CBSG, and EC sets are
18.5%, 38.1%, and 72.4%, respectively. However, 54.0% and 83.9% of ALSGs and CBSGs,
respectively, have transcript evidence from full length-cDNA, ESTs or microarray data,
or have a putative function assigned, which provides strong support that they are
likely to be bona fide genes rather than false positive gene predictions from the
ab initio gene prediction programs utilized in genome annotation processes.
One question regarding these lineage-specific genes is their origin(s). One possibility
is horizontal gene transfer. In the final step of our pipeline (Fig. ), we utilized UniProtKB to filter any remaining conserved genes and identified 53
A. thaliana proteins with sequence similarity to genes from bacteria, viruses, nematodes, fungi,
animals, or other plant species not available in our other large-scale plant genome
and transcriptome sequence datasets. This limited number, which includes matches to
other plant species, suggests that lateral gene transfer is a not a major source of
lineage-specific genes in A. thaliana. Another potential source of lineage-specific genes is gene duplication followed
by rapid evolution. Consistent with this, we found that a large number of ALSGs (73%)
and CBSGs (76%) were located within segmentally duplicated regions suggesting they
may have been generated by segmental duplication followed by rapid sequence divergence
due to relaxed selective pressure on duplicated genes.
We have identified more lineage-specific genes (1,324 ALSGs) than the 165 Arabidopsis
species-specific genes (ASS) identified by Yang et al. []. Comparison of the ASS with our ALSGs and CBSGs revealed that 10 of the ASS were
transposable element genes or miRNAs and thus were not included in our analysis. Of
the remaining 155 ASS described in Yang et al., 129 were identified as either a ALSG or a CBSG. There are two major reasons for
this difference. First, different BLAST E value cutoff were utilized in the lineage-specific
gene identification pipelines. Our pipeline used a BLAST E-value cutoff of & 1e-5
while Yang et al. [] used a BLAST E-value cutoff of & 0.1. Because the cutoff in earlier study is relaxed,
it likely has a higher false negative rate in identifying truly lineage-specific genes
compared to this study while our lineage-specific gene sets will likely have higher
proportion of false positives. Secondly, Yang et al. [] restricted their analysis to genes with expression evidence from ESTs or full length-cDNAs.
As a consequence, lineage-specific genes that were expressed under limited conditions
or whose expression level was too low to be detected by traditional transcript profiling
were excluded. This practice also leads to false negatives because there is ample
evidence that genes annotated as hypothetical are bona-fide genes []. In contrast, we examined the entire A. thaliana predicted protein-coding gene complement and our approach likely has a significantly
lower number of false negatives.
Both the ALSGs and CBSGs have more genetic variation among the 20 re-sequenced A. thaliana ecotypes than the EC genes, with the ALSGs and ECs having the most (0.41) and least
(0.35) SNPs per 100 bp per gene, respectively. This was inversely correlated with
the degree of evolutionary conservation of the ALSG, CBSG, and EC sets within the
Plant Kingdom. However, the three sets have similar synonymous SNP density. As a consequence,
ALSGs and CBSGs have higher ratios of non-synonymous to synonymous SNPs compared to
the EC genes, indicating they are fast-evolving at the protein level.
The dramatic enrichment of secretory proteins in the CBSGs indicates there may be
specific or highly evolved secretion processes within the Brassicaceae family as no
significant sequence similarity could be detected in other dicot genomes including
poplar, Medicago, papaya, and grapevine for which genome sequences are available. A majority of Brassicaceae
species share a highly specialized self-incompatibility system that restricts self-pollination
and promotes out-crossing [,] through the ability to recognize and reject self-pollen or pollen from closely related
plants. In our analysis, the majority of the Brassicaceae species used are self-incompatible
(B. rapa, B. oleracea, B. oleracea var. alboglabra, R. raphanistrum, and R. sativus) while three are self-compatible (T. halophila, B. napus, and A. thaliana). Within each self-incompatible Brassica species, specificity of the self-incompatibility response is genetically determined
by the alleles at the S (self-incompatibility) locus and involves the arrest of pollen development upon self
pollination []. SCR is the male determinant of the self-incompatibility response which is expressed
specifically in the anther tapetum and microspores [] and is predicted to interact with the female determinant S locus receptor kinase gene expressed in the papillar cells of the stigma []. Out-crossing is thought to be the ancestral mode of mating in the Brassicaceae [] and the S-locus specificity genes are predicted to be derived from common ancestors [].
In addition to the self-recognition mechanisms required to avoid inbreeding within
a single species, plants have mechanisms to control mating between different species
[]. For example, B. napus pollen placed on B. oleraceae stigmas became hyd however, the pollen produced short coiled
tubes that failed to penetrate the papillar cell wall []. This phenotype suggests that discrimination between desirable and undesirable pollen
between species also involves biochemical interactions on the stigma. Therefore, it
is possible that some of the CBSG PCP and SCR-like members may play a role in inter-specific
mating by influencing critical aspects required for successful pollination. Our finding
that 67 of the 107 CBSGs with an assigned function and putatively involved in secretory
pathways are similar to SCR or PCP proteins, and that these genes are subjected to
diversifying selection, a phenomena almost always associated with genes involved in
recognition events, supports this hypothesis. For example, SLR1 (for S locus glycoprotein-like receptor 1), a stigma-specific protein, interacts with members
of the PCP []. Whether any of the other members of the PCP or SCR like genes contribute to pollination
biology in the Brassicaceae remains to be determined.
Conclusions
In summary, we have identified two sets of A. thaliana lineage-specific genes, CBSGs and ALSGs, which are specific to the Brassicaceae family
and A. thaliana, respectively. The CBSGs are especially enriched in proteins with binding function
such as receptor binding that may play a role in the self-incompatibility response.
The exact functions of a majority of these lineage-specific genes remain an enigma
at this time. Further biological experiments will be necessary to fully understand
their functions in A. thaliana and Brassicaceae species.
Data sources and preparation
The proteome of A. thaliana was obtained from the TAIR8 release
[]. Pseudogenes and TE genes were excluded from the original gene set based on the TAIR8
annotation, which resulted in 27,025 protein coding genes. Further screening against
two in-house transposon databases identified an additional 163 putative TE-related
genes, resulting in 26,862 A. thaliana genes for further analysis. The repeat-masked assembled scaffolds (v1.0) of poplar
(P. trichocarpa) were downloaded from DOE Joint Genome Institute
[]. The repeat-masked assembly of the grapevine (V. vinifera) genome was downloaded from Genoscope
[]. The release 2.0 assembly of the Medicago (M. truncatula) genome was downloaded from the Medicago Genome Sequence Consortium
[]. Release 6 pseudomolecules of rice (O. sativa ssp. japonica) were downloaded from the Rice Genome Annotation Project
[]. The repeat-masked Sbi1 assembly of the sorghum (S. bicolor) genome was downloaded from DOE Joint Genome Institute
[]. The repeat-masked assembly (v4.0) of C. reinhardtii was downloaded from DOE Joint Genome Institute
[]. The masked assembly (v1.1) of the moss (P. patens ssp. patens) genome was downloaded from DOE Joint Genome Institute
[]. The papaya (C. papaya) genome was downloaded from NCBI
[]. The PUTs from 178 plant species (excluding A. thaliana in this analysis) were downloaded from PlantGDB on August 11, 2009
. UniProtKB (Release 14.6) was downloaded from UniProt
Genic features
The TE set was comprised of 3,900 TE genes from the TAIR8 release and 163 putative
TE genes identified by screening against two in-house transposon databases. For the
TE set, only three sequence files were created: gene, exon, and intron as they lack
CDS or protein sequences. For each of the CBSG, ALSG, and EC set, the sequences of
gene, exon, CDS, intron, and protein were either downloaded directly from the TAIR8
release or extracted from the chromosome sequences according to the coordinates provided
in the GFF3 file. Perl scripts were used to calculate the exon number, length of gene,
CDS, exon, intron, and protein, GC content of CDS, gene, and three codon positions.
Construction of paralogous protein families
A total of 26,862 non-TE A. thaliana proteins from the TAIR8 release were used to construct paralogous protein families
in the A. thaliana proteome using a computational pipeline that utilized Pfam [] and novel BLASTP-based novel domains described previously []. In brief, Pfam domains were identified using HMMER2 [] with scores above the trusted cutoff value. Peptide regions that were not covered
by Pfam domains were clustered based on homology (&45% identity over 75 amino acids,
E-value & 1e-3) derived from an all versus all BLASTP search (WU-BLASTP 2.0 MP-WashU
[22-Mar-2006]) []. Clustered peptides were then aligned using CLUSTALW [,] to develop BLASTP-based domains. Paralogous protein families were then classified
based on the domain composition of each protein.
Identification of segmental duplication
A total of 26,862 non-TE A. thaliana proteins from the TAIR8 release were used to identify segmental duplication in the
A. thaliana genome using a method described previously []. In brief, similar protein pairs were identified by all versus all BLASTP search
(WU-BLASTP 2.0 MP-WashU [22-Mar-2006], parameters "V = 5 B = 5 E = 1e-10") [], which were then used to defined segmental duplication using DAGChainer [] with parameters "-s -I -D 100000".
Co-expression Analyses
The ATH1 microarray compendium of 3,037 experiments (hereafter called "supercluster")
was downloaded from the NASCArrays website
. Only the genes having probes on the ATH1 array, 345 of the 1,324 ALSGs and 314 of
the 914 CBSGs, were used for further analysis. Pairwise Pearson's Correlation Coefficient
was computed between all lineage-specific genes (ALSGs and CBSGs) with array data
and all genes in the supercluster. The threshold value (r = 0.6) was defined as the
99 percentile of all pairwise correlation coefficients obtained during the above computation.
Using this threshold, we obtained a set of co-expressed genes for each ALSG and CBSG
gene tested. 260 of the 345 ALSGs and 250 of the 314 CBSGs had & = 1 unique gene with
a significantly correlated expression profile. To define the potential functions of
the lineage specific genes, GO annotation of A. thaliana genes co-expressed with ALSGs or CBSGs were used. The A. thaliana GO annotation was downloaded from the TAIR website [] excluding annotations with the evidence codes IEP, IEA and RCA. For each ALSG/CBSG,
we identified the enriched GO categories among the genes significantly co-expressed.
The enrichment analysis is based on a Fisher Exact Test at a False Discovery Rate
of 5% as defined by the Q-value program []. After associating GO categories with each gene, we investigated whether there is
an enrichment of genes in categories related to 1) Pollen, meiosis and sexual reproduction,
2) Intra-cellular transport and secretion, 3) Photosynthesis, 4) Defense responses,
and 5) Development, cell cycle and differentiation. We manually assigned the GO categories
to these five groups and determined whether these groups were over-represented among
the lineage specific genes versus the non-lineage specific genes using a False Discovery
Rate of 1% (See Additional files ,
Determination of subcellular localization
The subcellular localization of 32,419 protein sequences from the 26,862 A. thaliana protein-coding genes was identified by TargetP program [] using plant networks and default parameters. Subcellular localization prediction
with the best (lowest) Reliability Class was used to represent the subcellular localization
of the deduced protein if multiple different locations were predicted for isoforms
predicted for the gene. If none of the isoforms had a prediction of 'Chloroplast',
'Mitochondrion', or 'Secretory pathway', then the subcellular localization of the
gene was assigned 'Other'. If multiple subcellular localizations with equal Reliability
Class were predicted for the isoforms of a gene, the subcellular localization of that
gene was assigned 'Uncertain'.
Analyses of DNA methylation
The cytosine methylomic sequence data from floral tissues of wild-type A. thaliana were generated by sequencing-by-synthesis technology and mapped to the A. thaliana genome as reported previously []. The methylation data for the coding region, 500 bp upstream, and 500 bp downstream
of all the 26,862 A. thaliana protein-coding genes were kindly provided by the Ecker group. The density of methylation
of cytosines was defined as the number of 5-methylcytosines per 100 bp per gene. Only
representative gene models were used in our analyses.
SNP analyses
The SNP data from re-sequencing of 20 diverse A. thaliana accessions using high-density oligonucleotide arrays [] was downloaded from the TAIR8 release
. The polymorphism GFF3 file that includes the mapping information of the SNP markers
was also downloaded from the TAIR8 release
. PERL scripts were used to parse the data and calculate synonymous and non-synonymous
SNPs within protein coding regions. A total of 249,344 SNPs were downloaded. Only
base calls from the MBML2 dataset [] were used in our analyses. Base calls of 'N' were ignored. A total of 5,381 SNPs
with more than two variations within all 20 accessions were excluded from our analyses.
Representative models were used whenever alternative-splicing isoforms existed. SNPs
that produce same amino acid as the reference codon (Columbia-0 ecotype) was counted
as synonymous SNPs while SNPs that produce a different amino acid than the reference
codon was counted as non-synonymous SNPs.
Authors' contributions
HL designed the study, conducted the majority of the computational analyses, and drafted
the paper. GM and SS carried out the co-expression analysis. SO generated the additional
data file . AI assisted in the analyses. XG supervised the analyses of SNPs and the study. CRB
designed the study, supervised the study, and drafted the paper. All authors read
and approved the final manuscript.
Acknowledgements
We are grateful to Dr. Kevin Childs for critical review of the manuscript and helpful
comments. This work was supported by funds to CRB from Michigan State University.
References
Andersson SG:
Birth and death of orphan genes in Rickettsia. Mol Biol Evol 2003,
Renesto-Audiffren P,
Fournier PE,
Cossart P,
Weissenbach J,
Claverie JM, et al.:
Mechanisms of evolution in Rickettsia conorii and R. prowazekii. Science 2001,
293(5537):.
Fischer D:
Analysis of singleton ORFans in fully sequenced microbial genomes. Proteins 2003,
53(2):241-251.
Domazet-Loso T,
An evolutionary analysis of orphan genes in Drosophila. Genome Res 2003,
Perriere G:
The source of laterally transferred genes in bacterial genomes. Genome Biol 2003,
Striepen B,
Pruijssers AJ,
Gubbels MJ,
Umejiego NN,
Hedstrom L,
Kissinger JC:
Gene transfer in the evolution of parasite nucleotide biosynthesis. Proc Natl Acad Sci USA 2004,
Castresana J:
Inverse relationship between evolutionary rate and age of mammalian genes. Mol Biol Evol 2005,
22(3):598-606.
Accelerated evolutionary rate may be responsible for the emergence of lineage-specific
genes in ascomycota. J Mol Evol 2006,
63(1):1-11.
Levine MT,
Lindfors HA,
Novel genes derived from noncoding DNA in Drosophila melanogaster are frequently X-linked and exhibit testis-biased expression. Proc Natl Acad Sci USA 2006,
Schmid KJ,
Aquadro CF:
The evolutionary analysis of "orphans" from the Drosophila genome identifies rapidly diverging and incorrectly annotated genes. Genetics 2001,
159(2):589-598.
Rensink WA,
Comparative analyses of six solanaceous transcriptomes reveal a high degree of sequence
conservation and species-specific transcripts. BMC Genomics 2005,
Assaying gene content in Arabidopsis. Proc Natl Acad Sci USA 2002,
Graham MA,
Silverstein KA,
Cannon SB,
Computational identification and characterization of novel genes from legumes. Plant Physiol 2004,
Gaillard S,
Phylogenomics of plant genomes: a methodology for genome-wide searches for orthologs
in plants. BMC Genomics 2008,
Gaillard S,
GreenPhylDB: a database for plant comparative genomics. Nucleic Acids Res 2008,
(36 Database):D991-998.
Campbell MA,
Childs KL,
Hamilton JP,
Identification and characterization of lineage-specific genes within the Poaceae. Plant Physiol 2007,
Tschaplinski TJ,
Tuskan GA:
Genome-wide identification of lineage-specific genes in Arabidopsis, Oryza and Populus. Genomics 2009,
93(5):473-480.
Lehti-Shiu MD,
Shinozaki K,
Importance of lineage-specific expansion of plant tandem duplicates in the adaptive
response to environmental stimuli. Plant Physiol 2008,
148(2):993-1003.
Rensing SA,
Zimmer AD,
Salamov A,
Shapiro H,
Nishiyama T,
Perroud PF,
Lindquist EA,
Kamisugi Y, et al.:
The Physcomitrella genome reveals evolutionary insights into the conquest of land by plants. Science 2008,
319(5859):64-69.
Merchant SS,
Prochnik SE,
Harris EH,
Karpowicz SJ,
Witman GB,
Salamov A,
Fritz-Laylin LK,
Marechal-Drouard L, et al.:
The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 2007,
318(5848):245-250.
Paterson AH,
Bowers JE,
Bruggmann R,
Dubchak I,
Grimwood J,
Gundlach H,
Haberer G,
Hellsten U,
Poliakov A, et al.:
The Sorghum bicolor genome and the diversification of grasses. Nature 2009,
457(7229):551-556.
Dionne-Laporte A,
Lewis KL, et al.:
The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus). Nature 2008,
452(7190):991-996.
Tuskan GA,
Difazio S,
Jansson S,
Bohlmann J,
Grigoriev I,
Hellsten U,
Rombauts S,
Salamov A, et al.:
The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 2006,
313(5793):.
Jaillon O,
Policriti A,
Casagrande A,
Choisne N,
Aubourg S,
Jubin C, et al.:
The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm
phyla. Nature 2007.
Cannon SB,
Sequencing the genespaces of Medicago truncatula and Lotus japonicus. Plant Physiol 2005,
Hamilton J,
Campbell M,
Thibaud-Nissen F,
Zheng L, et al.:
The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res 2007,
(35 Database):D883-887.
Swarbreck D,
Lamesch P,
Berardini TZ,
Garcia-Hernandez M,
Foerster H,
Ploetz L, et al.:
The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res 2008,
(36 Database):D.
Lawrence CJ,
Schlueter SD,
Wilkerson MD,
Lushbough C,
Brendel V:
Comparative plant genomics resources at PlantGDB. Plant Physiol 2005,
139(2):610-618.
Significant Comparative Characteristics between Orphan and Nonorphan Genes in the
Rice (Oryza sativa L.) Genome. Comp Funct Genomics .
Zhang X, et al.:
A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 2002,
296(5565):79-92.
Vanoosthuyse V,
Two large Arabidopsis thaliana gene families are homologous to the Brassica gene superfamily that encodes pollen coat proteins and the male component of the self-incompatibility
response. Plant Mol Biol 2001,
46(1):17-34.
Schopfer CR,
Nasrallah ME,
Nasrallah JB:
The male determinant of self-incompatibility in Brassica. Science 1999,
286(5445):.
Takayama S,
Shimosato H,
Watanabe M,
The pollen determinant of self-incompatibility in Brassica campestris. Proc Natl Acad Sci USA 2000,
Takayama S,
Watanabe M,
Genomic organization of the S locus: Identification and characterization of genes
in SLG/SRK region of S(9) haplotype of Brassica campestris (syn. rapa). Genetics 1999,
153(1):391-400.
Takasaki T,
Hatakeyama K,
Watanabe M,
The S receptor kinase determines self-incompatibility in Brassica stigma. Nature 2000,
403(6772):913-916.
Kachroo A,
Nasrallah ME,
Nasrallah JB:
Self-incompatibility in the Brassicaceae: receptor-ligand signaling and cell-to-cell
communication. Plant Cell 2002,
14(Suppl):S227-238.
Takayama S,
Watanabe M,
Isolation and characterization of pollen coat proteins of Brassica campestris that interact with S locus-related glycoprotein 1 involved in pollen-stigma adhesion. Proc Natl Acad Sci USA 2000,
A recent polyploidy superimposed on older large-scale duplications in the Arabidopsis genome. Genome Res 2003,
13(2):137-144.
Meyers BC,
Characterization of paralogous protein families in rice. BMC Plant Biol 2008,
Emanuelsson O,
Nielsen H,
von Heijne G:
Predicting subcellular localization of proteins based on their N-terminal amino acid
sequence. J Mol Biol 2000,
Arabidopsis Genome Initiative:
Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 2000,
408(6814):796-815.
Dangoor I,
Peled-Zehavi H,
Levitan A,
A small family of chloroplast atypical thioredoxins. Plant Physiol 2009,
Schmitz AJ,
Miyagishima SY,
Osteryoung KW:
PARC6, a novel chloroplast division factor, influences FtsZ assembly and is required
for recruitment of PDV1 during chloroplast division in Arabidopsis. Plant J 2009,
59(5):700-711.
Rensing SA,
Kiessling J,
Decker EL:
Diversification of ftsZ during early land plant evolution. J Mol Evol 2004,
58(2):154-162.
Taylor NL,
Millar AH:
Experimental analysis of the rice mitochondrial proteome, its biogenesis, and heterogeneity. Plant Physiol 2009,
149(2):719-734.
Leister D:
An improved prediction of chloroplast proteins reveals diversities and commonalities
in the chloroplast proteomes of Arabidopsis and rice. Gene 2004,
329:11-16.
Berardini TZ,
Mundodi S,
Garcia-Hernandez M,
Mueller LA,
Lander G, et al.:
Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiol 2004,
135(2):745-755.
O'Malley RC,
Tonti-Filippini J,
Gregory BD,
Millar AH,
Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 2008,
133(3):523-536.
Saluz HP: DNA Methylation: Molecular Biology and Biological Significance.
Springer Science & B
Schweikert G,
Toomajian C,
Ossowski S,
Warthmann N,
Hinds DA, et al.:
Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 2007,
317(5836):338-342.
Whitelaw CA,
Cloning and sequencing of cDNAs for hypothetical genes from chromosome 2 of Arabidopsis. Plant Physiol 2002,
Hendershot J,
Vrebalov J,
Nasrallah JB,
Nasrallah ME:
Self-incompatibility in the genus Arabidopsis: characterization of the S locus in
the outcrossing A. lyrata and its autogamous relative A. thaliana. Plant Cell 2001,
13(3):627-643.
Bateman AJ:
Self-incompatibility systems in angiosperms: III. Cruciferae. Heredity 1955,
Nasrallah JB:
Cell-cell signaling in the self-incompatibility response. Curr Opin Plant Biol 2000,
3(5):368-373.
Nasrallah ME,
Nasrallah JB:
Generation of self-incompatible Arabidopsis thaliana by transfer of two S locus genes from A. lyrata. Science 2002,
297(5579):247-249.
Fujimoto R,
Okazaki K,
Comparison of the genome structure of the self-incompatibility (S) locus in interspecific
pairs of S haplotypes. Genetics 2006,
Swanson R,
Edlund AF,
Species specificity in pollen-pistil interactions. Annu Rev Genet 2004,
38:793-818.
Zuberi MI,
Dickinson HG:
Pollen-stigma interaction in Brassica. III. Hydration of the pollen grains. J Cell Sci 1985,
76:321-336.
Bateman A,
Hollich V,
Griffiths-Jones S,
Marshall M,
Sonnhammer EL, et al.:
The Pfam protein families database. Nucleic Acids Res 2004,
(32 Database):D138-141.
Profile hidden Markov models. Bioinformatics 1998,
14(9):755-763.
Thompson JD,
Higgins DG,
Gibson TJ:
CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through
sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994,
Sugawara H,
Gibson TJ,
Higgins DG,
Thompson JD:
Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res 2003,
Intron gain and loss in segmentally duplicated genes in rice. Genome Biol 2006,
Delcher AL,
Wortman JR,
Salzberg SL:
DAGchainer: a tool for mining segmental genome duplications and synteny. Bioinformatics 2004,
Storey JD:
A direct approach to false discovery rates. Journal Of The Royal Statistical Society Series B 2002,
64(3):479-498.
Sign up to receive new article alerts from BMC Evolutionary Biology