Group Title: BMC Genomics
Title: Gene expression profiling in peanut using high density oligonucleotide microarrays
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Title: Gene expression profiling in peanut using high density oligonucleotide microarrays
Physical Description: Book
Language: English
Creator: Payton, Paxton
Kottapalli, Kameswara
Rowland, Diane
Faircloth, Wilson
Guo, Baozhu
Burow, Mark
Puppala, Naveen
Gallo, Maria
Publisher: BMC Genomics
Publication Date: 2009
Abstract: BACKGROUND:Transcriptome expression analysis in peanut to date has been limited to a relatively small set of genes and only recently has a significant number of ESTs been released into the public domain. Utilization of these ESTs for oligonucleotide microarrays provides a means to investigate large-scale transcript responses to a variety of developmental and environmental signals, ultimately improving our understanding of plant biology.RESULTS:We have developed a high-density oligonucleotide microarray for peanut using 49,205 publicly available ESTs and tested the utility of this array for expression profiling in a variety of peanut tissues. To identify putatively tissue-specific genes and demonstrate the utility of this array for expression profiling in a variety of peanut tissues, we compared transcript levels in pod, peg, leaf, stem, and root tissues. Results from this experiment showed 108 putatively pod-specific/abundant genes, as well as transcripts whose expression was low or undetected in pod compared to peg, leaf, stem, or root. The transcripts significantly over-represented in pod include genes responsible for seed storage proteins and desiccation (e.g., late-embryogenesis abundant proteins, aquaporins, legumin B), oil production, and cellular defense. Additionally, almost half of the pod-abundant genes represent unknown genes allowing for the possibility of associating putative function to these previously uncharacterized genes.CONCLUSION:The peanut oligonucleotide array represents the majority of publicly available peanut ESTs and can be used as a tool for expression profiling studies in diverse tissues.
General Note: Periodical Abbreviation:BMC Genomics
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General Note: M3: 10.1186/1471-2164-10-265
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Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
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BMC Genomics

BioM.- Central

Research article

Gene expression profiling in peanut using high density
oligonucleotide microarrays
Paxton Payton* Kameswara Rao Kottapallil,2, Diane Rowland3,
Wilson Faircloth3, Baozhu Guo4, Mark Burow2,5, Naveen Puppala6 and
Maria Gallo7

Address: 'United States Department of Agriculture Cropping Systems Research Laboratory, Lubbock, Texas 79415, USA, 2Texas Tech University,
Department of Plant and Soil Science, Lubbock, Texas 79409, USA, 3United States Department of Agriculture, National Peanut Research
Laboratory, Dawson, Georgia, USA, 4Crop Protection and Research Management Laboratory, Tifton, Georgia, 31793, USA, 5Texas Agrilife Research,
Lubbock, Texas 79403, USA, 6New Mexico State University Agricultural Science Center, Clovis, New Mexico 88101, USA and 7Institute of Food
and Agricultural Sciences and the Genetics Institute, University of Florida, Gainesville, Florida 32611, USA
Email: Paxton Payton*; Kameswara Rao Kottapalli;
Diane Rowland; Wilson Faircloth; Baozhu Guo;
Mark Burow; Naveen Puppala; Maria Gallo
* Corresponding author

Published: 12June 2009
BMC Genomics 2009, 10:265 doi: 10.1 186/1471-2164-10-265

Received: 29 December 2008
Accepted: 12 June 2009

This article is available from:
2009 Payton et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.ore/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Transcriptome expression analysis in peanut to date has been limited to a relatively
small set of genes and only recently has a significant number of ESTs been released into the public
domain. Utilization of these ESTs for oligonucleotide microarrays provides a means to investigate
large-scale transcript responses to a variety of developmental and environmental signals, ultimately
improving our understanding of plant biology.
Results: We have developed a high-density oligonucleotide microarray for peanut using 49,205
publicly available ESTs and tested the utility of this array for expression profiling in a variety of
peanut tissues. To identify putatively tissue-specific genes and demonstrate the utility of this array
for expression profiling in a variety of peanut tissues, we compared transcript levels in pod, peg,
leaf, stem, and root tissues. Results from this experiment showed 108 putatively pod-specific/
abundant genes, as well as transcripts whose expression was low or undetected in pod compared
to peg, leaf, stem, or root. The transcripts significantly over-represented in pod include genes
responsible for seed storage proteins and desiccation (e.g., late-embryogenesis abundant proteins,
aquaporins, legumin B), oil production, and cellular defense. Additionally, almost half of the pod-
abundant genes represent unknown genes allowing for the possibility of associating putative
function to these previously uncharacterized genes.
Conclusion: The peanut oligonucleotide array represents the majority of publicly available peanut
ESTs and can be used as a tool for expression profiling studies in diverse tissues.

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BMC Genomics 2009, 10:265

Cultivated peanut (Arachis hypogaea L.) is the second-most
important legume in the world, with a total global pro-
duction of 48 million tons [1]. Legumes are the second-
most important food crop following grains, representing
an important source of protein for humans and livestock
in the North and South America, Africa, and Asia. Addi-
tionally, when considering oil production for cooking and
fuels, peanut represents one of the highest value-added
crops, with an annual worth of $1 billion to farmers and
$6 billion to the overall economy in the U.S. alone.

Recent progress in functional genomics has enabled the
study of plant responses at whole-transcriptome levels,
revealing the complex nature of multi-genic responses in
plants [2-4]. While genes and proteins expressed differen-
tially under a variety of environmental perturbations and
developmental stages have been identified in model plant
systems such as Arabidopsis [2,5], studies on stress-induced
or developmentally regulated genes in crop plants have
been limited but are beginning to emerge [6-9]. While
positional cloning and candidate gene approaches have
begun to identify a number of structural genes or tran-
scription factors controlling the larger response to abiotic
and biotic stimuli [10,11], this work has been limited in
peanut due to a lack of genomic data. Identification of
such genes will have a significant effect on varietal devel-
opment by traditional breeding and genetic engineering.

Greater attention is needed for genomic development in
the Leguminosae. Despite its importance as both a cash
crop and important staple, little is known about the
genetic mechanisms in peanut that control disease resist-
ance or susceptibility, stress tolerance, or pod develop-
ment [12]. Although significant efforts have gone into
legume genomics, there is a paucity of genomic data for
peanut, bean, and chickpea compared to soybean, Medi-
cago truncatula, and Lotus japonicus [4,8]. In peanut,
marker technology is relatively young and only recently
have genetic maps been published [13-15]. Although an
initial cDNA microarray with 384 unigenes was published
[16], there are no reports of high-density oligonucleotide
microarray platforms in peanut. As part of our ongoing
effort to identify the molecular mechanisms underlying
peanut development and response to abiotic stress, we
have designed a custom oligonucleotide microarray using
all publicly available peanut ESTs. There are several
advantages to the oligonucleotide microarray approach,
including uniformity of hybridization, probe perform-
ance and specificity, and the flexibility of customization
or probe addition as more sequences enter the public
domain [ 17-20]. To test the utility of this array for expres-
sion studies in both vegetative and reproductive tissues
and identify putatively pod-specific genes, we compared
transcript abundance in pod, leaf, stem, root, and peg tis-
sues. We present here, the utility of the first large-scale


publicly available peanut microarray and establish the
foundation for investigation of molecular responses on a
transcriptome scale.

Results and discussion
Peanut microarray design
An oligonucleotide microarray containing 15,744 unique
probes was created from 49,205 peanut ESTs available in
Genebank (December 2007) as templates for probe
design (Table 1). A total of 36,766 probes were designed
using the server-based eArray platform from Agilent Tech-
nologies [21]. The remaining ESTs represented duplicates,
sequences interspersed with long repeats, or a significant
number of undetermined bases which failed to meet crite-
ria required for accurate probe design. The initial set of
15,875 high quality probes with a cross hybridization
potential of zero were used to query SWISPROT with
BLASTx. The multiple matches from this query were saved
and the best match that was better than E-10 was used to
annotate each probe. Those probes not meeting the crite-
rion for annotation were annotated as having unknown
function. Probes annotated as "unknown" were binned
into two categories: 1) probes not meeting the minimum
criteria from the BLASTx query, and 2) probes matching a
sequence (E-value < -10) annotated as unknown in
SWISSPROT, i.e., "known unknowns". A final list of
14,352 probes was selected to create the probe group
AH006 for microarray design (design id 017430) in addi-
tion to 536 Agilent controls and 856 random probes
selected from the existing list of 14,352 probes.

Functional category enrichment based on Gene Ontology
(GO) was performed for all 14,352 probes present in the
array using the Blast2GO search tool [22]. Query against
SWISPROT resulted in the annotation of 5,086 known
genes and 6,793 transcript probes with unknown func-
tion. Figure 1A shows GO functional groups for known
transcripts represented on the AH006 array and a detailed
description of the GO molecular function (MF) cluster is
displayed in Figure 1B which indicates uniform distribu-
tion of probes with binding and catalytic functions. This
represents ~24% of genic content, given that the total
number of genes in peanut is estimated to be 50,000 [23].
The peanut ESTs used in this design were from libraries
representing diverse tissues, although root, stem, and cot-
yledon are under-represented (Table 1). Therefore, these
microarray probes have a broad utility for tissue specific
transcripts expressed under a variety of conditions. Fur-
thermore, the use of the Agilent system allows for flexibil-
ity in future array versions as additional ESTs can be added
from the public domain.

Microarray quality
The quality of the microarray was evaluated using the two
comparisons: (1) two biological replicates of the same tis-
sue-type labeled with the same dye and (2) the same tis-

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BMC Genomics 2009, 10:265

Table 1: Source tissue and number of ESTs from each library
used to design the AH006 peanut microarray.



Leaf control
drought + Aspergillus
Seed/Pod control
drought + Aspergillus
Root control
Cotyledon control
Stem control

ESTs for Array Design


A full description of the array is available in the Gene Expression
Omnibus database as platform GPL6661.

sue from two biological replicates labeled with either Cy3
or Cy5 dyes. The correlation coefficients of log trans-
formed normalized ratios between the two replicates and
two different dyes (dye-swap) were calculated by Kaleida-
Graph 3.6 (Synergy Software, USA) (Figures 2A and 2B).
For pod and leaf comparisons, the correlation coefficient
between the biological replicates labeled with Cy3/Cy5
was 0.93 (Figure 2A). Reciprocal hybridizations (dye-
swaps) were utilized for all tissue comparisons to avoid
dye bias. The correlation coefficient for the same pod and
leaf tissues in a dye-swap experiment where the same tis-
sue was labeled with Cy5 in one biological replicate and
Cy3 in another biological replicate was 0.91 (Figure 2B).
For labeling, 50 ng to 1 jig total RNA is needed for Agilent
custom arrays unlike cDNA arrays which require 20 jig to
30 jig. This is very important for profiling samples con-
taining limited amounts of RNA or small structures such
as floral parts and developing pods. Further the 8 x 15 k
array design has the feature of eight independent arrays in
one slide, which is not only cost effective but also can
reduce variation among the arrays within a slide.

Tissue specific gene expression using the peanut
oligonucleotide microarray
In recent studies, leaf, root, seed coat, and cotyledon tis-
sues were utilized for global expression profiling in soy-
bean [6,8] and leaf and bud tissues were used to test a
spotted cotton oligonucleotide microarray for tissue-spe-
cific gene expression analysis [24]. Four pairs of tissue
comparisons were performed for each tissue in the present
study. These comparisons resulted in the list of statisti-
cally-significant, differentially expressed genes in each tis-
sue shown in Figure 3, Additional file 1. The entire data set
can be accessed at the Gene Expression Omnibus (GEO)
database as platform GPL6661 and series GSE11365.
Additionally, GO annotations based on biological process
(BP) are presented in Figure 4 for those transcripts show-
ing tissue-specific expression patterns. Transcripts show-
ing at least two-fold difference in abundance (expression
ratio > 2 or < 0.5) at a P-value _< 0.05 were classified as dif-


ferentially expressed and those with differential expres-
sion unique to a single tissue were considered as having
putatively tissue-specific functions [25]. In summary,
there were 4046 transcripts representing 3650 gene func-
tions that were differentially expressed in at least one tis-
sue comparison. Of these transcripts, 1204, 401, 78, and
396 showed tissue-specific differential expression patterns
in leaf, stem, root, and peg, respectively (Figure 3). Two-
hundred-eleven gene transcripts were differentially
expressed in all four comparisons, 161 pod-abundant and
50 that were significantly more abundant in leaf, stem,
root, and peg compared to pod.

Gene expression profiles of different tissues provide infor-
mation about the biological function of the genes
expressed in those tissues [24,26]. For the pod abundant
pool, only 21 transcripts could be assigned a putative
function based on BLAST analysis. All tissues showed sim-
ilar GO BP enrichments associated with metabolic proc-
esses (I), cellular processes (J), and response to stimuli
(K). While peanut pod undisputedly is the most impor-
tant organ from an agronomic perspective and the genes
specifically up-regulated in that tissue are of interest, other
tissue-specific genes or expression patterns may reveal sig-
nificant information related to productivity, disease resist-
ance, development, and physiological response. Figure 4
shows that the functional roles of putative tissue-specific
genes are similar for leaf, stem, and peg compared to root.
While this is not surprising given the similarities of genes
highly expressed in green leaves or stems, it should be
noted that the majority of peanut EST sequences in the
public domain are from leaf and pod. However, despite
the absence of a large number of ESTs from root libraries,
there are genes whose expression appears to be root spe-

Genes and pathways identified in pods
Due to paucity of information on peanuts in global repos-
itories like NCBI, only half of the pod-abundant tran-
scripts could be meaningfully annotated (Additional file
2). Two major categories of transcripts, namely storage
proteins and desiccation-related proteins, were identified
in pods. Five transcripts related to seed storage proteins
such as globulin, conglutin and glycinin were abundant in
pod tissues. The desiccation-related transcripts over-repre-
sented included seed maturation protein, LEA, early
methionine labeled (EM), legumin, plasma membrane
intrinsic proteins (aquaporins) and desiccation related
pccl3-62 proteins. In most higher plants the later seed
maturation phase is characterized by a desiccation phase
during which number of proteins distinct from the storage
proteins are accumulated in embryos. According to their
accumulation pattern it has been suggested that these par-
ticular proteins, called Late Embryogenesis Abundant
(LEA) could be involved in seed desiccation tolerance

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j-GO IDs

Enzyrnes Linled
to Pathways -


GO Cellular


protein binding

structural constituent of
ribosorne (222) .

activity (290)

nucleic acid binding

transferase activity

ligase activity (198) _

cofactor binding (310) -

GO Molecular




ion binrdng

F signal transducer activity (202)

tetrapyrole binding (157)

Snucleotide binding (1021)

'-- transcription factor activity (138)

-isorerase activity (116)

ase activity (248)

substrate-specific transporter

hdrdlase activity (963) -
L. oxidoreductase activity (945)

Figure I
Functional classification of unique, known genes on the AH006 peanut microarray. A. Gene Ontology hits regis-
tered for the 5086 unique transcripts that could be assigned putative function based on Swiss-Prot query. B. Gene Ontology
Molecular Functions for the AH006 array. Only known genes are shown to simplify the diagram.

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BMC Genomics 2009, 10:265




go P

R2 = 0.93; p< 0.0001

R2= 0.91; p< 0.0001

-1 -0.5 0 0.5 1 1.5 2

LeafPod ratio of transcripts in
biological replicate 2 (Cy5/Cy3)

Figure 2
The correlation of normalized ratios between biological replicates and dye swaps. A. The correlation of normal-
ized ratios between leaf vs pod from two biological replicates R2 = 0.93. B. The correlation of normalized ratios between the
same leaf vs pod with dye swapped in two different biological replicates, R2 = 0.91.

[27,281. In addition to their expression during seed desic-
cation, many of the genes coding for LEAs can be highly
induced in immature seeds or activated in vegetative tis-
sues upon osmotic stress [29], indicating that they are, in
part, regulated at the transcriptional level [30]. On the
other hand EM proteins could be responsible for the
maintenance of a minimal water content allowing preser-
vation of cell content in dried seeds [30,31].

Utilizing the blast2GO tool, twelve transcripts with an
Enzyme Commission (EC) number were mapped to
twenty five different Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathways. Of these, 18 pathways rele-
vant to pods were presented in Additional File 3. As
expected, five major pathways leading to the production
of sugars and starch involving the enzymes UDP-glucose
pyrophosphorylase (EC: and dTDP-glucose 4-6-

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- ------------
" " ill f


II- -- - I -- -- I- -- - I -- --

2 -1.5 -1 -0.5 0 0.5 1 1.5 2

LeafPod ratio of transcripts in biological replicate 3

6 -- - ------

------ ------ -- --- ---....... I --.- -.. ... .... -........
i e

-2 -1.5

BMC Genomics 2009, 10:265

------- -------- -----

BMC Genomics 2009, 10:265

Figure 3
Venn diagram showing number of differentially
expressed tissue specific transcripts.

dehydratase (EC: were identified. Peanut being
an oilseed crop, the pathways leading to lipid metabolism
(2 pathways) and sulfur containing amino acid metabo-
lism (5 pathways) were predominant in pods. Peroxidase
enzyme (EC: found abundant in pods have mul-
tiple roles in plants. Apart from its reactive oxygen scav-
enging and water-stress signaling activity [32], the
peroxidase enzyme also catalyses phenylpropanoid bio-
synthesis and phenylalanine metabolism resulting in
defense compounds which may protect the developing
peanut pods in the soil. Two enzymes involved in pyru-
vate metabolism phosphoenolpyruvate carboxylase
(EC: and hydroxyacylglutathione hydrolase
(EC. were also found to be more abundant in
pods. Pyruvate thus generated may be involved in biosyn-
thesis of secondary metabolites like terpenoids by the
action of 1-deoxy-d-xylulose 5 phosphate synthase
(EC: Together the pathway analyses suggests that
in pod tissues apart form basic starch and lipid metabo-
lism, secondary metabolites such as phenylpropanoids
and terpenoids are also synthesized and may impart
defense for developing pod tissues in soil.

Validation of array data with quantitative real-time PCR
Quantitative real time PCR has become a gold standard
for the gene expression and generally used for validation
of microarray results [33]. To validate the microarray data
from our study, quantitative real time PCR (qRT-PCR)
analyses were performed on the same mRNA samples
used for the microarray experiments. Eight differentially-
expressed transcripts, 7 pod-enriched and 1 pod-deficient,
were selected for qRT-PCR analysis (Table 2). The relative
expression pattern of all eight selected genes resembled
respective microarray expression patterns (Table 3) and


suggested that microarray analyses utilizing the current
array were highly reliable and accurate.

Peanut, being an under represented crop in terms of
genome sequencing and physical mapping, needs a com-
prehensive tool for dissecting complex mechanisms of
development and tolerance to biotic and abiotic stresses.
To attain this broad objective, we have designed and char-
acterized a high density oligonucleotide microarray suita-
ble for transcript profiling of various peanut tissues.
Analysis of pod abundant transcripts suggested the pres-
ence of distinct pathways involved in generation of sec-
ondary metabolites apart from the accumulation of
transcripts for storage and desiccation-related protein.
These peanut microarrays are publicly available and can
be upgraded with additional oligonucleotides designed
from subsequent sequencing efforts from the peanut
research community. The expression profiles generated by
these peanut microarrays will provide starting points for
in-depth studies on candidate genes that can be utilized in
reverse genetics to assign gene functions.

Plant tissue
Field grown plants of peanut cultivar FlavRunner 458
were used for tissue collection. The harvested tissue from
leaves, pegs, stem, root and pods were immediately frozen
in liquid nitrogen and stored at -80 C until further analy-

RNA extraction
Total RNA from different tissue was isolated using the
RNeasy Plant Minikit (Qiagen, Valencia, CA). Pooled fro-
zen tissue from five plants were ground to a fine powder
in liquid nitrogen and approximately 100 mg of homoge-
nized tissue was used for total RNA isolation according to
manufacturer's protocol, except the homogenized seed
tissue was initially extracted in 600 gl of RLT buffer and
during purification, samples were incubated in buffer
RW1 for 5 min during the column washing step. RNA
samples were treated with Turbo DNAfree (Ambion, Inc.,
Austin, TX) prior to cDNA synthesis.

cRNA synthesis
An aliquot of 450 ng of total RNA was used for cDNA syn-
thesis utilizing the Low RNA Input Fluorescence Linear
Amplification Kit (Agilent Technologies). Resulting cDNA
was transcribed into cRNA and labeled with either cya-
nine 3 or cyanine 5-labeled nucleotides (Perkin Elmer,
Wellesley, MA) using T7 RNA polymerase (Agilent Tech-
nologies). Labeled cRNA was purified with RNeasy Mini
columns (Qiagen, Valencia, CA). The cRNA quality and
quantity were determined spectrophotometrically using a
NanoDrop ND-1000 spectrophotometer.

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Pod up-regulated
_ - A

Leaf up-regulated


Stem up-regulated

Peg up-regulated

S- K

Pod down-regulated


Leaf down-regulated



Stem down-regulated


Peg down-regulated

-- A

Root up-regulated

Root down-regulated

- A

- G

Figure 4
Gene Ontology terms for biological process classification for genes showing tissue-specific expression patterns
in pod, leaf, stem, peg and root abundant transcripts (also described in Table I). A. multi-cellular organismal proc-
ess; B. localization; C. multi-organism process; D. establishment of localization; E. growth; F. reproductive process; G. biological
regulation; H. developmental process; I. metabolic process; J. cellular process; K. response to stimuli.

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Table 2: List of primers for qRT-PCR analysis of tissue-abundant genes.

Accession # Primer name Primer sequence (5'-3')

Primer efficiency Amplicon size (bp)

Glycinin precursor

Late embryogenesis abundant
protein 2

Protein disulfide-isomerase

Putative GPI anchored protein

Plasmamembrane intrinsic
protein 2

Transmembrane emp24 domain-
containing protein 2 precursor

Dessication-related protein
PCC 13-62

Lipoxygenase 4

gi| 146771807

gi| 110810624

gil| 56690261

gil 110811592

gi| 149221199

gil 149222425

gil 110811067

gil 126159580


Oligonucleotide microarray hybridization
Labeled cRNA from pod tissue was hybridized in combi-
nation with different tissues (Figure 5) using the in situ
hybridization kit from Agilent Technologies. A total of 5
tissue samples were compared in three biological repli-
cates with dyes swapped in the second biological repli-
cate. Arrays were incubated at 65C for 17 h in rotating
hybridization chamber. Arrays were washed at room tem-

Table 3: Expression pattern of peanut pod abundant transcripts.


perature under constant agitation for 10 minutes in 6x
SSC with 0.005% Triton X-102 followed by a 5 minutes in
cold, 0.1x SSC, 0.005% TritonX-102.

Image scanning and data analysis
Arrays were scanned using a GenePix 4000B microarray
scanner at 5- im resolution and images were saved as
uncompressed tagged image files. For detection of signifi-

Microarray fold change Quantitative real-time PCR fold change

desiccation-related protein (DRP)
lipoxygenase 4 (LOX4A)
transmembrane emp24 domain-containing protein
protein disulfide-isomerase precursor (PDI)
late embryogenesis abundant protein 2 (LEA2)
glycinin precursor (GLY)
aquaporin PIP2-1 (PIP2)
putative GPI anchored protein (GPI-AP)

Leaf Stem Peg Pod

38 61 46 27
0.16 0.33 0.29 0.27
4 3 3 3

2467 69811
0.25 0.62
6 6

4 3
43 38
106 103
4 3
9 8

Microarray and quantitative real time PCR expression values are mentioned as fold change of transcript in pods compared to different tissues.

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Gene name

Probe Gene name


AH 19851

Leaf Stem Peg Pod



BMC Genomics 2009,10:265

BMC Genomics 2009, 10:265


Stem Pod ._ Peg


Figure 5
Diagrammatic representation of microarray experi-
mental design. The arrow represents Cy5 and the end of
the arrow represents Cy3.

cant differentially expressed genes, each slide image was
processed by Agilent Feature Extraction software (version
9.1). This software measured Cy3 and Cy5 signal intensi-
ties of whole probes. Since dye bias tends to be signal
intensity-dependent, probe sets for dye normalization
were selected by rank consistency. Normalization was
done by locally weighted linear regression (LOWESS).
Ratios were log-transformed and significance values (P-
value) were calculated based on a propagate error model
and universal error model. In this analysis, the threshold
of significant differentially expressed genes was deter-
mined with a p-value 0.05 (p-value is a measure of the
confidence that the feature is not differentially expressed).
Low-quality spot data generated due to artifacts were
eliminated prior to data analysis. Processed intensities
from feature extraction analysis were imported into the
TIGR Multiexperiment Viewer software (MEV 4.1) and sig-
nificant genes at a p-value of < 0.05 and more than two-
fold difference in expression were defined as differentially

The Gene Ontology functional annotation tool Blast2GO
[22] was utilized to assign GO ids, enzyme commission
numbers, and mapping to Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathways. The Blast2GO tool also
enabled statistical analysis related to over representation
of functional categories based on a Fisher Exact statistic

Gene expression analysis using real time-PCR
cDNA synthesis and Primer Design
Total RNA samples were treated with Turbo DNAfree
(Ambion, Inc., Austin, TX) prior to cDNA synthesis. One
microgram of total RNA was used to synthesize first strand
cDNA using SuperScript First Strand Synthesis system for


RT-PCR (Invitrogren, CA). The primers for pod abundant
genes and actin standard were designed using Integrated
DNA Technologies primer designing tools. The efficiency
of the primer pairs was determined on cDNA derived from
the pod of FlavRunner 458 cultivar using a 1:2 serial dilu-
tion series. Primer efficiency reactions were performed in
triplicate in volumes of 25 gL using SuperArray
SYBRGreen reaction mix (SuperArray Bioscience Corp.,
MD). Reactions were subjected to real-time qRT-PCR
using the Roche LightCycler 480 Real-Time PCR System
and data analyzed using the LightCycler 480 quantifica-
tion software (Roche Biochemicals, Indianapolis, IN)

Real-Time qRT-PCR Conditions
Samples were analyzed in a 25 gL volume using the Roche
LightCycler 480 (Roche Biochemicals, Indianapolis, IN).
Reactions were performed in triplicate using cDNA tem-
plates from five tissues samples for each gene. A master
mix of SYBRGreen and primers was prepared for each
primer pair. RT-PCR reactions were performed on 40 ng
total RNA with 400 nM specific primers under the follow-
ing conditions: one cycle of denaturation at 95 C for 10
min followed by 40 cycles of 95 C for 15 sec (denatura-
tion) and 600C for 15 sec (annealing and elongation).
The PCR reaction was followed by a melting curve pro-
gram (60 95 oC with a heating rate of 0.1 oC per second
and a continuous fluorescence measurement) and then a
cooling program at 400 C. Negative controls lacking
reverse transcriptase were run with all reactions. PCR
products were also run on agarose gels to confirm the for-
mation of a single product at the desired size. Crossing
points for each transcript were determined using the 2nd
derivative maximum analysis with the arithmetic baseline
adjustment. Crossing point values for each gene were nor-
malized to the respective crossing point values for the ref-
erence gene actin. Data are presented as normalized ratios
of genes along with error standard deviations estimated
using the Roche Applied Science E-method [34].

Authors' contributions
PP was responsible for the conception and design of the
experiment and final revisions of the manuscript. PP and
KRK designed the array and performed all data analysis
and interpretation. KRK carried out the tissue collection,
performed RNA extractions, array hybridizations, and
real-time PCR. DR and WF assisted in tissue collection and
participated in data interpretation and preparation of the
manuscript. BG generated cDNA libraries and contributed
to array design and preparation of the manuscript. MB
and NP provided seed and contributed to data analysis
and manuscript preparation. MG contributed to the con-
ception of the experiment and manuscript preparation.
All authors have read and approved the final manuscript.

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Additional material

We thank Joseph Quilantan and Meenakshi Mittal for technical help. This
research was supported by grants from the Ogallala Aquifer Program, a
consortium between USDA-Agricultural Research Service, Kansas State
University, Texas Agrilife Research, Texas Agrilife Extension Service, Texas
Tech University, and West Texas A&M University, USDA-ARS CRIS 6208-
21000-012-OOD, and New Mexico State University Agricultural Science
Center, Clovis.

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Additional file 1
flni t, nii.ll, expressed tissue specific genes. This table includes the list
of .......... ..1.... (p< 0.05) tii..i. ii expressed genes,includ-
ing fold changes and functional descriptions, for leaf, stem, peg, and root
tissue-specific genes.
Click here for file

Additional file 2
Pod abundant transcripts compared to all other tissues. The data pro-
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scripts. GO mapping and annotation of probe sequences was performed by
Blast2go tool (version 2.2.3).
Click here for file

Additional file 3
Pathways catalyzed by pod specific enzymes. This figure includes eight-
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