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Distinct Structural and Functional Characteristics of Stress-Related Genes of Different
Plants Revealed by In-silico Analysis
Enliven Archive | www.enlivenarchive.org 1 2014 | Volume 1 | Issue 2
*Corresponding author: Dr. Abul Bashar Mir Md. Khademul Islam,
Assistant Professor, Department of Genetic Engineering & Biotechnology,
University of Dhaka, Science Complex Building, Dhaka 1000, Bangladesh,
Tel: +880-2-9661900 Extn. 7825; E-mail: khademul@du.ac.bd
Citation: Bhuiya AI, Islam ABMMK (2014) Distinct Structural and
Functional Characteristics of Stress-Related Genes of Different Plants
Revealed by In-silico Analysis. Enliven: Bioinform 1(2): 004.
Copyright:@ 2014 Dr. Abul Bashar Mir Md. Khademul Islam. This is an
Open Access article published and distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution
and reproduction in any medium, provided the original author and source
are credited.
Received Date: 7th April 2014
Accepted Date: 20th August 2014
Published Date: 25th August 2014
Research Article Enliven: Bioinformatics
Ashraful Islam Bhuiya1, and Abul B.M.M.K. Islam2*
1Institute of Nuclear Medicine & Allied Sciences, Dhaka Medical College Hospital Campus, Bangladesh Atomic Energy Commission, Dhaka-1000,
Bangladesh
2Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka-1000, Bangladesh
Abstract
Plants always have to ght against various environmental stress conditions like cold, drought, salinity, submergence, etc. The prime target of recent
research in plant biology is to unveil the intricate series of events in responses and adaptation to different stress conditions. Sufcient in-silico
computational studies are yet to be done to distinguish the stress related genes from the non-stress related ones. As common mechanisms of stress
responses exist among different plants, we sought to identify the general structural and functional features that may be hidden in stress related genes
of different plant species. We assumed that these features in stress-related genes might be different from non stress related genes. One hundred and
sixty stress-responsive genes from ve different plant species were studied. Computational and bioinformatics studies were done to determine several
structural properties like length of gene, exon, intron, UTRs as well as to identify overrepresented sequence motif and enrichment of gene ontology (GO)
functions. The UTRs of stress related genes were found to be signicantly different from non-stress related genes and a “G-C” rich small sequence motif
was found to be associated signicantly with stress genes. Key biological processes like small GTPase mediated signal transduction, cellular components
like thylakoid and molecular functions like oxidoreductase activity are signicantly enriched for stress related genes. Further studies are required to
identify more stress specic features of plant stress genes which may help to establish a computational model for detecting stress related genes from
various gene lists.
Keywords
Plant stress; Bioinformatics; UTRs; Motif; Gene ontology; Enrichment
Introduction
As plant cannot migrate from one place to another, harsh environmental
conditions can be an important cause of mortality for plants. Environmental
stresses can be biotic caused by different plant pathogens or abiotic
such as cold, drought, salinity, submergence, heavy metals, radiation,
etc. These stresses have great inuence on the evolution of plant species
and also have detrimental effects on plant growth and agricultural
productivity [1,2]. Nowadays, due to the abiotic stresses the estimated
gap between the attainable and actual yields of crops is 40 – 50% [http://
www.isaaa.org/]. Thus, introduction of crop varieties with enhanced
tolerance to environmental stresses and sustainable growth rate under
suboptimal conditions are the crucial objectives in modern agriculture.
Using Arabidopsis thaliana and Oryza sativa (rice) as model systems,
various genes were over expressed in these which led to the identication of
stress tolerant genes and transcription factors [3,4]. Many other studies have
also been done with the model plant Arabisopsis thaliana to elucidate the
biochemical pathways of stress perception, signal transduction and adaptive
responses [5-8].
ISSN: 2376-9416
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Functional basis of stress tolerance should be explained on the basis of
molecular mechanism and energetic vacillation. The mechanistic viewpoint
of stress tolerance focuses on the similarities between cellular responses
to different types of stresses [9]. To perceive environmental stresses and
to response to them, plants have evolved the mechanisms of complex
signaling crosstalk such as interactive and antagonistic actions of different
phytohormones like salicylic acid (SA), jasmonic acid (JA), ethylene (ET),
abscisic acid (ABA), etc [10]. They regulate the prophylactic responses
against both biotic and abiotic stresses. Again the generation of reactive
oxygen species (ROS) has been proposed to be a common response in
different stress conditions [10].
Plants produce energetic resources to activate the mechanisms of stress
tolerance and survival. These metabolic shifts of energy reallocation
represents a common response occurred under different adverse conditions
[1,11,12]. Recently it was shown that modulation of cellular energy
homeostasis and increased pool of NAD+ and NADH may play role to
improve the yield of crop in environmental stress conditions [13,14].
Stress resistance traits that are functionally correlated with different stress
mechanisms have been identied by quantitative genetic studies [11,15,16].
The complex mechanisms of stress perception, signal transduction
and intonation of gene expression in stress environment have partially
been uncovered by functional genomics study [17]. It was found that
in transgenic rice (Oryza sativa), stress-responsive transcription factor
SNAC1 over expression enhance drought resistance signicantly [4].
Stress response mechanisms bring great changes in global gene expression,
manner of protein modication and compositions of different metabolites
[18]. Recently non-coding RNA has been found to be involved in stress
response mechanisms of plants [19]. In last decade, it was revealed that
the expression of different but overlapping gene suits are regulated by both
biotic and abiotic stresses [20]. Some heat-shock proteins are generally
stimulated as a common response to various stress environments [21,22].
Again, DREB transcription factors and phytochrome abscisic acid (ABA)
have been identied as shared components in drought, salinity and unusual
temperature responsive pathways in Arabidopsis model system [23,24].
The existence of some genes associated with general stress responsive
mechanisms has been discovered by extensive study from the viewpoint of
the cell physiology [25,26], evolutionary biology [1,12] and most importantly
biotechnology [27-29]. The elucidation of the complex biochemical networks
and structural properties of these stress responsive genes may provide targets
that lead to the production of engineered stress resistant plant species.
Sufcient computational studies have not yet been done to identify signicant
characteristics of stress responsive genes that can differentiate them from
non-stress related genes. Only a few studies were carried out to discover the
stress responsive DNA regulatory motifs in Arabidopsis thaliana [30,31].
Due to the insufcient data on general structural properties of plant stress
related genes, no computational method could be devised to predict stress
related genes. For these reasons, laborious and cumbersome wet lab analyses
have to be done to identify even a single stress related gene. In this study
bioinformatics and computational analyses were performed with stress
and non-stress related genes from ve different plant species (Arabidopsis
thaliana, Oryza sativa, Zea mays, Solanum lycopersicum and Glycine max)
to identify generalized structural properties (gene length, exon length, motifs,
length of 5’ and 3’ untranslated regions) of stress responsive gene that will
distinguish them from non-stress related genes. This effort may be helpful
to develop tools to identify stress related genes in silico. We have examined
the Gene Ontology (GO) annotations in the group of stress related genes of
these plant species to delineate the trends in the biology of stress responses.
Materials and Methods
Datasets
List of stress related genes were obtained from Plant Stress Gene Database
(http://ccbb.jnu.ac.in/stressgenes/frontpage.html) [32]. A total of 160 stress
related gene sequences from ve plant species (Arabidopsis thaliana, Glycine
max, Oryza sativa, Solanum lycopersicum and Zea mays) (out of available
259 stress related genes from 11 plant species), were used in this study.
The gene sequences, both stress and non-stress, were downloaded through
the Biomart portal of Ensembl Plants, release – 19 (http://plants.ensembl.
org/biomart/martview). The datasets used in this study are Arabidopsis_
thaliana (TAIR10 GCA_000001735.1 2010-09-TAIR), Glycine_max (V1.0
GCA_000004515.1 2012-07-JGI), Oryza_sativa (MSU6 GCA_000005425.2
2009-01-MSU), Solanum_lycopersicum (SL2.40 GCA_000188115.1
2011-04-ITAG), and Zea_mays (AGPv3 2010-01-MaizeSequence). The
stress related genes were subtracted from entire genome dataset and the
remaining data were used as negative dataset. There are 160 stress related
genes (for detail list see supplementary Table S1) are included in positive
dataset of which 33 from Arabidopsis thaliana, 55 from Glycine max, 9
from Oryza sativa, 26 from Solanum lycopersicum and 37 from Zea mays;
whereas 256672 genes were included in negative dataset, of which 34259
from Arabidopdid thaliana, 56709 from Glycine max, 65518 from Oryza
sativa, 34689 from Solanum lycopersicum and 65497 from Zea mays.
Length Analysis
Genomic location information of features like genes, exons and UTRs
for both positive and negative dataset was obtained from the Ensembl
Plants databases using Biomart portal. Their lengths were calculated
using in house Perl script. For statistical signicance, 1000 random
sets for each feature, consisting of 160 members, were produced from
negative dataset. The Z score and p-value of signicance were calculated
from these datasets using R statistical programming [33,34]. Distribution
of both 5’ and 3’ UTR length are represented in Box-Whisker plot.
Signicance of difference of UTR length between stress related genes
and non-stress related genes was calculated using Wilcoxon test.
Motif Analysis
MEME (Multiple Em for Motif Elicitation) [35,36] package (version 4.9.1)
was used to identify the signicantly over-represented motif in stress related
genes (positive dataset). Thousands of random datasets consisting of 160
gene sequences each were produced from negative dataset and searched
for similar motif using the position weight matrix (PWM) of MEME and
STORM program [37] specifying the p-value cut off 0.00001. Z-score and
p-value of signicance were calculated using the random dataset (expected”)
and the positive dataset (“observed”) with R statistical programming [33,34].
Functional Enrichment Analysis
Functional annotation of plant stress-related genes is based on Gene
Ontology (GO) (Consortium, 2000; http://www.geneontology.org) [38]
extracted from Ensembl Plant (release - 19). Accordingly, all genes are
classied into three ontology categories (i) biological process (BP), (ii)
cellular component, (CC) and (iii) molecular function (MF) and pathways
when possible. We considered only those GO pathway categories that
have at least 9 genes annotated. We used Gitools [39] for enrichment
analysis using non-stress related genes as background, and for heatmap
generation. Resulting p-values were adjusted for multiple testing using
the Benjamin and Hochberg’s method of False Discovery Rate (FDR).
Enliven Archive | www.enlivenarchive.org 2 2014 | Volume 1 | Issue 2
Results and Discussion
Results
The length of UTRs of stress related genes differ signicantly from
non-stress related genes in different plant species
Genes encode proteins which are the key functional components in different
cellular mechanisms. Upstream and downstream sequences of genes play
role in the regulation of gene expression. We calculated the length of stress
related genes, their exons, 5’ and 3’ UTRs, and compared those to non-
stress related genes to determine if any signicant difference exists. For
gene length and exon length, no statistically signicant difference was
obtained between stress and non-stress related genes. But z-score and
p-value conrmed the signicant difference in 5’ and 3’ UTR lengths when
Stress related genes contain specic and signicantly over-
represented small motif
At this stage we sought to know if there is any common short sequence motif
that signicantly prevails in stress-related genes. We used MEME package
[35,36] to search for overrepresented motifs in 160 stress-related genes. One
signicantly overrepresented G-C rich motifs was obtained with the e-value
1.1e-007. This motif was selected for further analysis (Figure 2). This is
a 12 nucleotides long motif with pattern GGC[GT]GC[TG]GC[GTA]GC.
In non-stress related genes the frequency of occurrence of this motif was
searched by STORM program [37]. From non-stress related gene dataset
1000 sets, each consisting of randomly selected 160 genes, were constructed
to use as control for this purpose. To nd the signicant difference in
the frequency of occurrence of this motif, z-score was calculated. The
occurrence of this motif in non-stress related genes was not signicant,
which implies for its highly specic association with stress related genes.
Important biological processes, cellular components and molecular
functions are enriched for stress related genes
Functional enrichment analysis is performed to assign biological meaning
to genes. It is performed to assess if a gene or a group of genes show
any signicant over-representation of any biological characteristics. In
this study Gitools [39] was used for enrichment analysis using Gene
Ontology (GO) [38] database. GO database centralizes and disseminates
the prior knowledge of known gene which allows researchers to assign
attributes to their experimentally identied genes. Stress related genes of
ve plants were analyzed in a background of non-stress related genes, to
determine the enrichment of particular biological processes (BP), molecular
functions (MF) and cellular components (CC) as termed by GO. Detail
compared between species. It was further conrmed by Wilcoxon test
(Figure 1). In case of 3’ UTRs, signicant difference was obtained for
Glycine max and Solanum lycopersicum with p-value 1.078e-05 and 3.145e-
06, respectively. In case of 5’ UTRs signicant difference was obtained for
Arabidopsis thaliana, Glycine max, Oryza sativa and Solanum lycopersicum
with p-value of 6.54e-09, 0.0001262, 6.084e-06 and 6.315e-06,
respectively. These distinct UTRs of stress related genes may play important
role in more stringent regulation of gene expression in stress conditions.
statistical results are presented in supplementary tables (S2 to S12). The
stress related genes of Arabidopsis thaliana were enriched for removal
of superoxide redicals, generation of precursor metabolites and energy,
thylakoid, metal ion binding, copper ion binding, chromatin binding, etc
(Figure 3 and Table 1). Glycine max stress related genes were enriched for
transferase activity, oxidoreductase activity, etc (Figure 4 and Table 2). In
Oryza sativa these were enriched for plasma membrane localized proteins
(Figure 5 and Table 3). The stress related genes of Solanum lycopersicum
were enriched for oxidoreductase activity, organelle, etc (Figure 6 and
Table 4). In Zea mays they were enriched for small GTPase mediated signal
transduction, oxidation–reduction process, carbohydrate metabolic process,
biosynthetic process, plasma membrane, cytoplasm, organelle, chloroplast,
GTP binding, oxidoreductase activity, nucleotide binding, etc. (Figure
7 and Table 5). In this analysis oxidoreductase activity was found to be a
common mechanism to stress response in almost all plants. Some ndings
from this analysis are seemed to be specically signicant like thylakoid and
chromatin binding of A. thaliana and small GTPase activity of Zea mays.
From literature mining enough supporting data regarding the signicant
association of these components with stress were obtained. In Arabidopsis,
ascorbate peroxidase bound to thylakoid contributes in scavenging reactive
oxygen species produced in different stress conditions [40]. Arabidopsis
TAAC (Thylakoid ATP/ADP Carrier) gene is highly up-regulated in leaves
under different stress environments [41]. Gene activation in dehydration
stress responses depends on a specic pattern of histone modication and
chromatin structure [42]. H3K4me3 (H3 Lys4 trimethylation) has a function
as epigenetic marker of stressed memory [43]. Epigenetic regulations
mediated by the modication of histone proteins are conserved in plant [44].
Modications on the sites of H3K4 and H3K9 are correlated with the activities
Figure 1: Box-and-whisker plots show distribution of UTR length of stress genes and non-stress genes. Each pair (V1-V2;
V3-V4 ….......) represents UTR from one plant species. Left side box-plot in each pair shows UTR lengths of stress genes and
right side box of that pair represents UTR length of non-stress genes. In this, star mark indicates that the difference of UTR
length between stress genes and non-stress genes is signicant. (a) Box-plot results of 3’ UTRs (b) Box-plot results of 5’ UTRs.
Enliven Archive | www.enlivenarchive.org 3 2014 | Volume 1 | Issue 2
Figure 3: Signicantly enriched Gene Ontology terms for the stress related genes of Arabidopsis thaliana. The enrichment of
the terms with signicant p-value is denoted by red color coding. The deep red color indicates to the highly signicant p-val-
ue. The lighter the red color the lesser the signicance. Gray color indicates the insignicant p-value. (a) Enriched GO terms
of Biological Process. (b) Enriched GO terms of Cellular Component. (c) Enriched GO terms of Molecular Function.
Figure 2: Selected motif obtained from MEME analysis. This is represented by position-specic probability matrices that specify
the probability of each possible letter appearing at each possible position in an occurrence of the motif. There are stacks of letters at
each position in the motif. The total height of the stack is the “information content” of that position in the motif in bits. The height of
the individual letters in a stack is the probability of the letter at that position multiplied by the total information content of the stack.
Enliven Archive | www.enlivenarchive.org 4 2014 | Volume 1 | Issue 2
of abiotic stress responsive genes in Arabisopsis [45]. In Arabidopsis, Rop
GTPase signalling inuences the mechanisms of alcohol dehydrogenase
activity at low O2 condition [46]. Monomeric RopGTPases regulate the
production of H2O2, responses to hormones, programmed cell death, etc
[47]. As small GTPase activity was signicantly enriched in Zea mays, it can
be deciphered that they may play similar role in this plant as in Arabidopsis.
From this enrichment analysis, it can be concluded that components enriched
in different plants are highly co-related with different stress conditions
and can be considered as distinct features of plant stress related genes.
GO term for Biological Process Total studied
Gene
Observed Expected mean Corrected
right-p-value
Removal of superoxide redicals 33 7 0.0117853635 5.81E-16
Response to cadmium ion 33 8 0.4586470641 3.66E-6
Generation of precursor metabolites and energy 33 6 0.5823933811 0.0015
Response to auxin stimulus 33 5 0.3594535877 0.0015
Oxidation reduction process 33 8 1.3287997381 0.0023
Positive regulation of transcription, DNA dependent 33 5 0.4537364959 0.0033
Regulation of transcription, DNA dependent 33 9 1.8404809381 0.0035
Carbohydrate metabolic process 33 8 1.909228892 0.0157
Secondary metabolic process 33 5 0.7081039255 0.0155
GO term for Cellular Component
Thylakoid 33 6 0.4910568137 0.0019
GO term for Molecular Function
Metal ion binding 33 7 0.2632064522 4.77E-6
Copper ion binding 33 5 0.2386536115 6.31E-4
Chromatin binding 33 5 0.3280259516 0.0021
DNA binding 33 10 2.0182435047 0.0036
Sequence specic DNA binding transcription factor 33 8 1.6509330079 0.0168
Nucleic acid binding transcription factor activity 33 8 1.6519151216 0.0147
Oxidoreductase activity 33 8 1.6764679623 0.0144
Table 1: Enriched Gene Ontology terms for stress related genes of Arabidopsis thaliana
Enliven Archive | www.enlivenarchive.org 5 2014 | Volume 1 | Issue 2
Enriched GO term
P-value
transferase activity, transferring alkyl or aryl (other than methyl) groups
oxidoreductase activity
ion binding
Glycine max
Figure 4: Signicantly enriched GO terms for the stress related genes of Glycine max.
The red color coding indicates the signicance of the p-value as described in Figure 3.
GO term Total
Studied
Genes
Observed expected-mean Corrected
right-p-value
Transferase activity, transferring
alkyl or aryl (other than methyl)
groups
55 22 0.12182966 9.28E-41
Oxidoreductase activity 55 15 2.9300033226 1.89E-5
Ion binding 55 14 5.437663824 0.025
Table 2: Enriched Gene Ontology terms for stress related genes of Glycine max
Figure 5
P-value
Enriched GO term
Oryza sativa
plasma membrane
Figure 5: Signicantly enriched GO terms for the stress related genes of Oryza sativa.
The red color coding indicates the signicance of the p-value as described in Figure 3.
GO term Total Studied genes Observed Expected-mean Corrected right-p-value
Plasma membrane 10 4 0.3035335430 0.0385
Table 3: Enriched Gene Ontology term for stress related genes of Oryza sativa
Enliven Archive | www.enlivenarchive.org 6 2014 | Volume 1 | Issue 2
P-value Enriched GO term
oxidoreductase activity
organelle
cytoplasm
intracellular
ion binding
Solanum lycopersicum
Figure 6: Significantly enriched GO terms for the stress related genes of Solanum lycopersi-
cum. The red color coding indicates the significance of the p-value as described in Figure 3.
GO term Total
Studied
Genes
Observed Expected-mean Corrected
right-p-value
Oxidoreductase activity 26 8 1.2154928765 0.0021359861
Organelle 26 8 1.2589836765 0.0018170424
Cytoplasm 26 6 0.9020591798 0.0061442772
Intracellular 26 9 2.1580434908 0.0062167642
Ion binding 26 7 2.0980561804 0.0478418769
Table 4: Enriched Gene Ontology terms for stress related genes of Solanum lycopersicum
Enliven Archive | www.enlivenarchive.org 7 2014 | Volume 1 | Issue 2
Enriched GO term
Zea mays (GOEIP)
Zea mays (GOCC)
Zea mays (GOMF)
small GTPase mediated signal transduction
signal transduction
oxidation,eduction process
carbohydrate metabolic process
biosynthetic process
P-value
(a)
(b)
P-va lue Enriched GO term
intracellular
membrane
cytoplasm
plasma membrane
organelle
chloroplast
plastid
GTP binding
oxidoreductase activity
nucleotide binding
metal ion binding
ion binding
P-value Enriched GO term
(c)
Figure 7: Signicantly enriched GO terms for the stress related genes of Zea mays. (a) Enriched GO terms
of Biological Process. (b) Enriched GO terms of Cellular Components. (c) Enriched GO terms of Mo-
lecular Function. The red color coding indicates the signicance of the p-value as described in Figure 3.
Table 5: Enriched Gene Ontology terms for stress related genes of Zea mays
GO term for Biological Process Total Observed
Genes
Observed Expected-mean Corrected
right-p-value
Small GTPase mediated signal transduction 41 8 0.0506382897 8.78E-14
Signal transduction 41 9 0.2282392476 2.19E-10
Oxidation – reduction process 41 9 0.6502249947 8.91E-7
Carbohydrate metabolic process 41 7 0.4109774233 4.93E-6
Biosynthetic process 41 10 1.2270611638 9.41E-6
GO term for Cellular Component
Intracellular 41 22 2.8695030804 3.05E-11
Membrane 41 13 0.9826763746 1.15E-9
Cytoplasm 41 16 1.8493984048 2.85E-9
Plasma membrane 41 10 0.5753683926 9.67E-9
Organelle 41 13 2.3095463412 1.04E-5
Chloroplast 41 7 0.5438111976 1.59E-5
Plastid 41 7 0.6186677997 3.36E-5
GO term for Molecular Function
GTP binding 41 8 0.1343015508 4.51E-10
Oxidoreductase activity 41 9 0.7192104907 5.41E-6
Nucleotide binding 41 9 0.7705826686 7.23E-6
Metal ion binding 41 7 0.5739006161 6.78E-5
Ion binding 41 8 1.3518221673 0.0016
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Discussion
Environmental stresses are limiting factors for plant growth. Extensive
studies are being done on Arabidopsis and some other plants regarding
their responses to different environmental stresses. But not enough data are
available on mechanisms of stress responses in most of the plants. Even
all the stress responsive genes are not identied yet and the mechanisms
of stress responses are not completely known. Some bioinformatics
studies have been done to identify specic features of regulatory regions
of stress related genes [30,31] but not in the region of genes themselves.
In this study bioinformatics and computational analyses were performed
with plant stress related genes listed from plant stress gene database [32].
Each gene is anked by short 5’ and 3’ untranslated regions (UTRs) followed
by gene start site and gene end site [48]. Computational analyses identied
signicant differences in the length of 5’ and 3’ UTRs between stress
related and non-stress related genes (Figure 1). The signicant differences
calculated as z-score and p-value by Perl scripts, were further conrmed by
the Wilcoxon test. It is well established that the regulation of gene expression
become highly stringent in different stress conditions [49-52]. UTRs play
vital roles in the regulation of gene expression [53-59]. Therefore, distinct
UTRs of stress related genes may play pivotal roles to ensure tight regulation
of gene expression in stress environments. Further analysis should be
propounded to discover the exact role of distinct UTRs in stress conditions.
Distinct motifs were discovered in the regulatory regions of plant stress
related genes in previous studies [30,31]. But in this study, by MEME
analysis with the clause that the motif should exist in 50% or more genes,
a small G-C rich motif (Figure 2) was obtained in the regions of genes.
The p-value justied that the motif was signicantly overrepresented
in stress related genes. The frequency of occurrence of these motifs in
non-stress related genes calculated by STORM program with PWM was
accountably low. This result corroborates the possibility of occurrence
of such distinct motifs in stress related genes. Occurrence of such motif
insinuates that it may have important role in up-regulation, down-
regulation or epigenetic regulation of genes in stress condition. Further
extensive studies including more plant species and newly discovered
genes are necessary to discover more such motifs and their plausible roles.
In past decade, each gene product was studied individually to assign its role in
biological process but now tools exist to make this process automated. Gitools
is such a tool used in this study. Primarily a group of genes are clustered based
on some common properties. Enrichment analysis is performed to assess if
a group of genes shows any signicant over-representation of any biological
characteristics. In this study, after detecting the over-represented biological
characteristics of stress related genes, data mining was performed manually
to explain their possible role in stress responsive mechanisms of plants.
Oxidative stress in plants is a common scenario in different stress conditions
like cold, submergence, drought, salinity etc. [60-64]. In our enrichment
analysis oxidoreductase activity was signicantly enriched in stress related
genes of almost all plants included in the study (Figure 3, 4, 6, 7 & Table
1, 2, 4, 5), which is coherent with this scenario. Therefore oxidoreductase
activity can be considered as a specic feature of plant stress related genes.
Small GTPase mediated signal transduction was signicantly enriched
in Zea mays. The G proteins have important role in signal transduction.
They mediate the signal transduction to downstream effectors [65]. In
rice, a small GTPase, Rac1, regulate the death of hypersensitive cells in
innate immune response while heterotrimeric G protein regulates the Rac1
[66,67]. Low O2 regulates the ADH (alcohol dehydrogrnase) activity that
depends on RopGTPase signaling in Arabidopsis [68]. Chromatin binding
was signicantly enriched as molecular function in Arabidopsis thaliana
which insinuates toward the epigenetic correlation with stress conditions.
Though it is not well understood whether chromatin mediated regulation has
positive effects on stress tolerance, it is obvious that there are correlations
between epigenetic modications and plant stress responses [43]. It was
observed that linker histones and HMGB (High Mobility Group) proteins
play role in abiotic stress responses [69]. Promoter specic histone
modication H3K4me3 plays an important role in dehydration and ABA
stress responses [70]. In drought response, some lysine modication states
on histone H3 N tail are altered which revealed that upon gene activation in
stress responses histone modication states changes [42]. From these data
it can lucidly be told that small GTPase mediated signal transduction and
chromatin binding are the specic phenomena in different stress conditions.
Again, in Arabidopsis, only the cellular component, thylakoid was
enriched. This result indicates toward the unique role of thylakoid in stress
responses. It has been discovered that stresses have signicant effects on the
different components of thylakoid [40]. The transcript level of OsCYP20-2
gene in thylakoid lumen of rice is highly regulated under abiotic stress
conditions and CYP20-2 gene is also found to be well conserved in some
photosynthetic plants [71]. TLP18.3 gene is up regulated in dehydration
stress and thylakoid protease Deg2 consorts in stress related degradation
of Lhc6, light harvesting protein of photosystem II, in Arabisopsis thaliana
[72,73]. Therefore, thylakoid is a very important cellular component that
may have more crucial role in stress responses than other organelles in
Arabidopsis. Some other molecular functions, biological processes and
cellular components (shown in Figure 3 - 7 and Table 1 – 5) were signicantly
enriched in stress related genes. All these ndings from enrichment analysis
can be considered as signicant and specic features of stress related genes.
Conclusions
In this study, structural and functional analyses have been done with
plant stress related genes with a view to identify hidden features that can
discriminate them from non-stress related genes. Extensive computational
and bioinformatics analysis were performed and differential outcomes
gave an overall idea that discriminating features between stress related and
non-stress related genes exist at every level of biological hierarchy. The
different UTRs length, existence of distinct G-C rich motifs and selectively
enriched some biological phenomena and constituents like small GTPase
mediated signal transduction, chromatin binding, oxidoreductase activity
and thylakoid identied as stress specic features prove this decipherment.
Enliven Archive | www.enlivenarchive.org 9 2014 | Volume 1 | Issue 2
This analysis proved that there are specic features hidden in stress related
genes which are different from non -stress related genes. It can be suggested
that further studies should be done by including updated and classied data
of plant stress to identify more common and specic features. If enough
features can be identied which are highly specic for stress related genes and
also discriminating from non-stress related genes, a computational model can
be devised that can discern stress related genes from the stockpile of genes.
Acknowledgments
We acknowledge Professor Dr. Haseena Khan, Chairperson, Department of
Biochemistry and Molecular Biology, University of Dhaka, Dhaka-1000,
Bangladesh for her support at the beginning of the project. We also acknowledge
Dr. Abu A Sajib, Assistant Professor, Department of Genetic Engineering &
Biotechnology, University of Dhaka for critical reading of the manuscript.
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