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\begin{document}

\title{Physiological and Molecular Characterization of Bread Wheat (Triticum
aestivum L.) For Drought Resistance}



\author[1]{Ahmad Zada}%
\author[2]{Ahmed Ali}%
\author[3]{Azhar Shah}%
\author[4]{Shahid Gill}%
\author[5]{Irtiza Hussain}%
\author[2]{Zahid Ullah}%
\author[2]{Hassan Sher}%
\affil[1]{Yangzhou University}%
\affil[2]{University of Swat}%
\affil[3]{Hazara University Mansehra}%
\affil[4]{PARC}%
\affil[5]{Arid Agriculture University of Rawalpindi}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
The present study evaluated three wheat genotypes (SD-28, SD-32 and
Chirya-1) were evaluated for physiological attributes like Relative
Water Content, Proline Content, Membrane Stability Index and Chlorophyll
Content where Opata was used as a control check under three different
levels of drought stress (100\% FC, 80\% FC and 60\% FC). Results
revealed that chlorophyll content was significantly affected under
stressed conditions in all the studied genotypes and genotypes.
Molecular diagnosis of the selected wheat genotypes and genotypes was
carried out with RT-PCR using expression profile of 06 genes (TaLhca1,
TaLhca2, TaLhca3, TaLhcb1, TaLhcb4 and TaLhcb6) that encodes for LHCI
and LHCII proteins. RT--PCR indicated variable expression of the
selected genes in response to different level of drought stress. The
results obtained clearly showed the relation between genotypes and
severity of drought stress condition. Among the studied genotypes
Chirya-1 and SD-28 performed well with higher level of gene expression
under drought stress condition; and may be considered drought tolerant
genotypes with potential to enrich the genetic background of locally
adapted wheat lines against drought stress.%
\end{abstract}%



\sloppy


\textbf{ABSTRACT}

The present study evaluated three wheat genotypes (SD-28, SD-32 and
Chirya-1) were evaluated for physiological attributes like Relative
Water Content, Proline Content, Membrane Stability Index and Chlorophyll
Content where Opata was used as a control check under three different
levels of drought stress (100\% FC, 80\% FC and 60\% FC). Results
revealed that chlorophyll content was significantly affected under
stressed conditions in all the studied genotypes and genotypes.
Molecular diagnosis of the selected wheat genotypes and genotypes was
carried out with RT-PCR using expression profile of 06 genes
(\emph{TaLhca1, TaLhca2, TaLhca3, TaLhcb1, TaLhcb4} and \emph{TaLhcb6} )
that encodes for LHCI and LHCII proteins. RT--PCR indicated variable
expression of the selected genes in response to different level of
drought stress. The results obtained clearly showed the relation between
genotypes and severity of drought stress condition. Among the studied
genotypes Chirya-1 and SD-28 performed well with higher level of gene
expression under drought stress condition; and may be considered drought
tolerant genotypes with potential to enrich the genetic background of
locally adapted wheat lines against drought stress.

Key Words: Wheat; Drought Stress; Stay-Green; Gene Expression;
Physiological attributes.

\textbf{INTRODUCTION}

Importance of wheat as staple food is well known as being life line of
35\% of the world population.~But in past two-three decades,
unpredictable climatic conditions have resulted in stagnation in wheat
production. Drought is a major environmental stress, which can effect
growth of cereals crops and has decreased the production and performance
of the plant (Shao \emph{et al.} , 2009), (Rad \emph{et al.} , 2012).
The significant rise in drought stress condition has enforced us to
develop climate resilient high yielding genotypes; hence, there is a
need to develop better understanding of the traits which respond to
drought by exploiting key traits (Halford and Hey, 2009) It has been
suggested that due to climatic changes, the shortage of water may be
increased which will affect the cereals crops in many areas of the
world.

Leaf senescence is an intricate process where various cellular processes
occurs consistently, parallel or sequentially (Lim \emph{et al.} ,
2007). It normally starts with change in genes expression and genes are
expressed by environmental stimuli during various developmental stages.
If senescence starts at mature stage then the changes will not appear at
earlier stages of senescence until the stress is applied at initial
stages. These changes include the chlorophyll pigment breakdown normally
followed by break down of mitochondria, plastids, nuclei and vacuoles,
which leading to death of cell (Buchanan-Wollaston and Ainsworth, 1997).
To know about the whole mechanisms of leaf senescence it is important to
dissect the leaf senescence process one such approach is to evaluate
transgenic plants which show different leaf senescence phenotype which
we called stay green (Kusaba \emph{et al.} , 2013).

Two main types of chlorophyll are present in higher plants,
Chlorophyll\emph{a} and Chlorophyll \emph{b} . Chlorophyll \emph{a} is a
part of all chlorophyll--protein complexes, whereas Chlorophyll \emph{b}
is confined only in PSI-associated light-harvesting complex I (LHCI) and
PSII-associated LHCII. Light-harvesting complex I and light-harvesting
complex II are present in thylakoid membranes and its function is energy
production and transfer. LHCII is mostly present in grana, and due to
intermolecular forces its main function is formation and maintenance of
grana stacks (Allen and Forsberg, 2001). The \emph{Lhca} and
\emph{Lhcb}gene families encoded the apoproteins of light-harvesting
complex I and light-harvesting complex II respectively.
\emph{Lhca1--Lhca4} genes formed the protein of LHCI which are
associated with PSI. \emph{Lhcb1} ,\emph{Lhcb2} , and \emph{Lhcb3} genes
code the polypeptides of trimeric LHCII. Lhcb4, Lhcb5, and Lhcb6
proteins (also known as CP29, CP26, and CP24,) are proposed to be
monomeric proteins which are found one set per PSII unit. The Lhca and
Lhcb genes expression and LHCI and LHCII stability are very important to
keep the photosynthetic process at high level (Standfuss \emph{et al.} ,
2005).

One of the most important goals of researcher to increase grain yield
and it can be achieved by improving the rate of photosynthesis or by
carbon assimilation (Zhu \emph{et al.} , 2010) the approach to achieve
this goal would be to delay senescence alongside the photosynthetic
activity of plant for lengthier period of time (Dohleman and Long,
2009). To achieve these objectives in wheat plant, it is necessary to
understand the leaf senescence mechanisms at molecular level.

In the present study, the selection and photosynthetic characteristics
of `stay green' mutants of wheat are described and to characterize wheat
genotypes in their response to drought stress. The characterization was
focused on both physiological and molecular aspects of wheat genotypes
to select the wheat genotype with desirable traits

\textbf{2. Materials and Methods}

\textbf{2.1 Laboratory Experiment}

A pot experiment were conducted under laboratory condition with
temperature 10 to 15 C and 13 to 14 hours light duration, to assess the
effect of three drought level Control 100\% Field Capacity, Stress 80\%
Field Capacity, stress 60\% Field Capacity. Field soil, sand and peat
compost were mixed in equal proportion and filled in 36 pots at the rate
of 1835g in each pot. Two conventional wheat genotypes Opata and
chirya-1, and two Synthetic genotypes i.e. SD-28 and SD-32 were studied.
Nine pots were used for each wheat genotype to test each genotype at
three drought levels with three replications. The pots were tapped on
the floor enough to bring the soil mixture to a uniform height to get
soil bulk density (SBD) and corresponding total soil porosity (TSP)

The height of soil mixture in each pot required to get the desired SBD
were calculated by following formula.

VSM (cm3)

Height (cm) = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

\selectlanguage{greek}π \selectlanguage{ngerman}× (\emph{Diameter of pot (cm) 2}

2

Where VSM (volume of soil mixture) were calculated by formula

Mass of soil mixture taken in each pot (g)

VSM (cm3) =
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Desired SBD (g cm -3)

The pots were irrigated with a uniform volume of tap water to saturate
all the pore space of soil mixture and kept aside for drying. The volume
of water (VW) required saturating the soil mixture in the pots were
calculated by the formula.

VW (cm3) = TSP (cm3 cm-3) × VSM (cm3)

Where TSP were calculated assuming soil particles density (SPD) of 2.65
(g cm3) using the formula

SBD (g cm3)

TSP (cm3 cm-3) = 1- \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

SPD (g cm3)

Volumetric soil water content (VSWC) were monitored daily from each pot
with Domain Reflectometer (TDR) model TRIME --FM ( IMKO
Micromodultechnik GmbH, Germany) the seed were sown in pots when VSWC
was at FC (assuming FC as 50\% of the saturation and taking saturation
=TSP). VSWC will be calculated by formula.

TSP (cm3ncm-3 ×50

VSWC at FC (cm3 cm-3) = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

100

Similarly, VSWC at other drought levels was calculate with their
respective formulae.

TSP (cm3 cm3) ×40

VSWC at 80\% FC (cm3 cm-3) = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

100

TSP (cm3 cm3) ×30

VSWC at 80\% FC (cm3 cm-3) = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

100

After germination VSWC were maintained in each pot at FC for three
weeks, Monitoring VSWC and fulfilling the irrigation requirements daily.
The VW require maintaining each pot at FC was calculated by the formula:

VW (cm3) = (VSWC at FC (cm3 cm-3) -- VSWC from TDR (cm3 cm-3)) × VSM
(cm3)

After three weeks the irrigation supply was cut off from the pots to
which drought treatment were applied and VSWC was monitored daily until
the respective drought level will achieved. Afterwards, VW required
3maintaining different drought levels were applied daily to respective
pots calculating by their respective formulas.

VW (cm3) = (VSWC at 80\% FC (cm3 cm-3) -- VSWC from TDR (cm3 cm-3)) ×
VSM (cm3)

VW (cm3) = (VSWC at 60\% FC (cm3 cm-3) -- VSWC from TDR (cm3 cm-3)) ×
VSM (cm3)

The drought stress will be maintained for 30 days and after that samples
were collected for physiological and molecular study.

\textbf{2.2. Physiological evaluation of wheat genotypes:}

After applying drought stress the data for various physiological
characters as relative water content (RWC), proline, chlorophyll content
and membrane stability index (MSI) were recorded.

\textbf{2.3. Molecular Characterization of Selected Wheat Genotypes}

The genes related to pigment-binding proteins were selected to examine
the different expression in four genotypes of wheat which was grown in
normal condition 100\% field capacity and drought stress condition 80\%
field capacity and 60\% field capacity. Total 6 genes were studied and
specific primers for genes encoding LHCI and LHCII were designed based
on published expressed sequence tags. The primers were: \emph{TaLhca1} -
F 5' CAACCTGCCGACCATCCTG-3' and \emph{TaLhca1-} R
5'CAGCCGCCCGTTCTTGAT-3' and \emph{TaLhca2} - F 5'CCCCAACCGCAAGAACC-3'
and\emph{TaLhca2-} R 5'CCGACGAAGGCGAGCAT-3', and \emph{TaLhca3} - F
5'CCTCACCAGCCTCAAGTTCC-3', and \emph{TaLhca3-} R 5'CGCACGCTCACGTTTCC-3',
and \emph{TaLhcb1} - F 5'GGAGAACACACAATACACC-3' and \emph{TaLhcb1-} R
5'CCCATTATGTGTGCAGTTC-3' and \emph{TaLhcb4} - F 5'AAAGGCCGAGGAGGACAA-3'
and \emph{TaLhcb4-} R 5' CCACCGACCACTTAAGAGG-3' and \emph{TaLhcb6} - F
TCAGCGACCTCACCGTCA-3' and \emph{TaLhcb6-} R 5'CCCCAAAGAAGTCACGGACA-3',
and amplification of the \emph{T. aestivum tubulin} gene using
primer\emph{Tubulin- F} 5'ACCGCCAGCTCTTCCACCCT- 3' and \emph{Tubulin- R}
5 TCACTGGGGCATAGGAGGAA- 3' exhibiting constitutive expression was used
as a positive control, from which a linear relationship between the
amount of RNA used for amplification and the amount of cDNA fragment
amplified, as well as the quality of both extracted RNA and RT--PCRs
were determined.

\textbf{2.4. RNA Extraction by using Gene JET Plant RNA Purification
Minikit}

Leaf samples were collected from each replication and immediately
transferred to liquid nitrogen. Put 100 mg of plant tissue into liquid
nitrogen and grind thoroughly with a mortar and pestle. after grinding
the tissue powder were quickly transferred into 1.5 ml micro centrifuge
tube which Containing 500 \selectlanguage{greek}μ\selectlanguage{english}L of Plant RNA Lysis Solution and Vortex all
the mixture for 15-20 seconds to mix thoroughly and after that Incubate
the samples for 3 min at 56\selectlanguage{ngerman}°C and then Centrifuge for 5 min at \selectlanguage{english}[?]20,000 x
g (\selectlanguage{english}[?]14,000 rpm). Collect the supernatant 550 \selectlanguage{greek}μ\selectlanguage{english}L and transfer to the
clean micro centrifuge tube and then Add 250 \selectlanguage{greek}μ\selectlanguage{english}L of 96\% ethanol and Mix
by pipetting and then transferred the mixture into purification column
inserted in a collection tube. Centrifuge the column for 1 min at 12,000
\selectlanguage{ngerman}× g (\textasciitilde{}11,000 rpm). Discard the flow through solution and
reassemble column and collection tube. Then we Add 700 \selectlanguage{greek}μ\selectlanguage{english}L of Wash Buffer
WB 1 to the purification column (ethanol has been added to Wash Buffer
WB 1). Centrifuge for 1 min at 12,000 \selectlanguage{ngerman}× g (\textasciitilde{}11,000 rpm).
Discard the flow-through and collection tube. Place the purification
column into a clean 2 mL collection tube. Then Add 500 \selectlanguage{greek}μ\selectlanguage{english}L of Wash Buffer
2 to the purification column (ethanol has been added to Wash Buffer 2).
Centrifuge for 1 min at 12,000 \selectlanguage{ngerman}× g (\textasciitilde{}11,000 rpm).
Discard the flow-through solution and reassemble column and collection
tube after that we repeat this step again. To elute the RNA, add 50 \selectlanguage{greek}μ\selectlanguage{english}L
of nuclease-free water to the centre of the purification column membrane
and centrifuge for 1 min at 12,000 \selectlanguage{ngerman}× g (\textasciitilde{}11,000 rpm).
Discard the purification column and extracted RNA immediately stored at
-20°C for further analysis.

\textbf{2.5. Synthesis of cDNA}

The complementary DNA was synthesized from RNA by using a kit (Revert
Aid Reverse Transcriptase, Thermo Scientific, Catalog \# EP0442).
Briefly 11 uL template RNA were mixed with 1.0 \selectlanguage{greek}µ\selectlanguage{english}l of Oligo (dt) 18
primers (\#S0132) and incubate for 5 mints at 65\selectlanguage{ngerman}°C. Then 4.0 \selectlanguage{greek}µ\selectlanguage{english}l of RT
buffer, also add 1 uLRibolock Rnase inhibitor and 1 \selectlanguage{greek}µ\selectlanguage{english}l of reverse
transcriptase (Revert Aid) and 2 \selectlanguage{greek}µ\selectlanguage{english}l of dNTPs mix (\#R0181) were added to
making a total volume of 20 \selectlanguage{greek}µ\selectlanguage{english}l. now the mixture was centrifuged briefly
and incubated at 42\selectlanguage{ngerman}°C for 80 minutes. Then the reaction was Terminate
the by heating at 70 °C for 5 minutes cDNA were stored at minus 20 for
RT-PCR analysis.

\textbf{2.6. Expression analysis of LHCI and LHCII genes through RT-PCR}

2.0 \selectlanguage{greek}µ\selectlanguage{english}l of cDNA from reverse transcription was used as template and LHCI
and LHCII genes specific primers were employed to amplify the desired
fragment through RT- PCR with optimized condition as initial
denaturation at 95 \selectlanguage{ngerman}°C for 10 minutes, denaturation at 95 °C for 20
seconds, annealing at 58 °C for 40 seconds extension at 72 °C for 30
seconds by 34 cycles. The final product of RT-PCR was separated running
on 1\% agrose gel by the process of electrophoresis and visualized on
alpha in notech gel documentation system.

\textbf{2.7. Statistical analysis:}

Data obtained were subjected to descriptive statistics, analysis of
variance and Correlation test. The analysis over treatments will also
performed by using STATISTIX 10 and means will be compared by using
Fisher's least significant difference (P\textless{}0.01 and 0.05)

\textbf{3. RESULTS}

\textbf{3.1. Physiological analyses}

\textbf{3.1.1. Membrane Stability Index (MSI)}

The characterization of wheat genotypes contrasting in their response to
drought stress was performed by checking membrane stability index. The
results showed that the mean value for membrane stability 55.227 was
recorded in control 100\% field capacity. Similarly in drought condition
80\% field capacity the mean value for membrane stability was 60.69 was
recorded. Likewise in severe drought condition 60\% field capacity
condition the mean value 54.106 was recorded. (Table. 1, Fig. 1)
Analysis of variance (ANOVA) regarding MSI revealed that the effect of
drought stress is highly significant while genotypes and their
interaction with different level of drought stress are non-significant
as shown in (Table 2). The least significant difference (LSD) showed
that in normal condition 100\% field capacity the membrane stability not
different significantly and stay-green chirya-1 genotype showed maximum
58.415 membrane stability index followed by SD-32. In drought condition
80\% field capacity and 60\% field capacity the means are not
significantly different from each other and highest value for membrane
stability 62.685 and 55.966 were recorded for SD-28 wheat genotype
respectively (Table 3).

\textbf{3.1.2. Relative Water Content (RWC)}

The relative water content in all the studied genotypes showed
significant difference in both control and drought stress condition. The
mean value under normal condition 100\% field capacity 94.580\% was
recorded while in drought stress condition 80\% field capacity the mean
value for relative water content 81.887\% was recorded for all the
studied genotypes. In drought stress 60\% field capacity the mean value
for relative water content 73.659\% was recorded (Table 1, Fig. 2). In
general, RWCs declined during water stress in all the studied wheat
genotypes. The Analysis of variance (ANOVA) result showed that in
drought condition the interaction between genotypes and different levels
of drought stress are not positively significant (Table 2). The least
significant difference (LSD) indicated that in 100\% field capacity the
relative water content were not affected and maximum relative water
content 96.477\% were recorded for SD-32 genotype followed by chirya-1
genotype. Similarly in drought stress 80\% and 60\% field capacity the
maximum relative water content was recorded for chirya-1 genotype which
was 82.8\% and 76\% respectively (Table 3)

\textbf{3.1.3. Proline Contents}

The results regarding proline content revealed that mean proline content
under 100\% FC 0.828 were observed. Similarly under drought stress
condition 80\% FC the mean value for proline content 1.433 was recorded.
Likewise in drought stress 60\% the mean value for proline content 2.509
was recorded shown in (Table1 and Fig 3.). The results indicate that
proline content was increased under drought stress condition as compared
to control condition. As drought condition were increased the proline
content were also increased. Analysis of variance (ANOVA) regarding
proline content revealed that the effect of drought stress and response
the genotypes were highly significant and the interaction of genotypes
and different level of drought stress were also highly significant as
shown in (Table 2). The least significant difference (LSD) regarding
proline content showed that in 100\% FC the proline content are not
significantly different and maximum proline content 0.872 were recorded
for genotype SD-28 followed by SD-32 wheat genotype. Similarly under
drought stress 80\% FC the maximum proline content 1.667 was recorded
for stay green chirya-1 genotype followed by SD-32. Similarly in drought
stress 60\% FC the maximum proline content 3.091 was recorded for Opata
genotype followed by stay green chirya-1 genotype as shown in (Table 3).
The results clearly demonstrated that drought stress could improve
proline content and different genotypes have different response to
various drought levels.

\textbf{3.1.4 Chlorophyll Contents}

Data concerning Chlorophyll ``a'' presented in Table 1 demonstrated that
there were significant differences in chlorophyll ``a'' after different
level of drought stress. The mean value for chlorophyll ``a'' 1.186 was
recorded and under drought stress 80\% FC the mean value for chlorophyll
``a'' 1.041was recorded. Likewise under drought stress 60\% FC the mean
value for chlorophyll ``a'' 0.542 was recorded as shown in Fig 1. The
results clearly indicate that different drought stress level effect the
chlorophyll content in all studied wheat genotypes. The least
significant difference (LSD) regarding chlorophyll ``a'' presented in
Table 3 showed that in 100\% FC the chlorophyll ``a' content are not
significantly different and maximum chlorophyll ``a'' content 1.230 were
recorded for stay green chirya-1 genotype followed by SD-28 wheat
genotype. Similarly under drought stress 80\% FC the mean maximum
chlorophyll a content 1.099 was recorded for SD-32 followed by Opata
wheat genotype. Similarly in drought stress 60\% FC the maximum
chlorophyll ``a' content 0.576 was recorded SD 28 followed by stay green
chirya-1 genotype.

Data regarding chlorophyll ``b'' presented in Table 1 demonstrated that
drought stress can affect the chlorophyll ``b'' content. In control
condition 100\% FC the mean value 1.108 was recorded and under drought
stress 80\% Fc the mean value 0.926 was recorded. Likewise in drought
stress 60\% FC the mean value 0.559 was recorded as shown in (Table 1
and Fig 4). The least significant difference (LSD) regarding chlorophyll
``b'' presented in Table 3 revealed that in control condition 100\% FC
the chlorophyll ``b'' content are not significantly different and
maximum chlorophyll ``b'' content 1.247 were recorded for stay green
chirya-1 genotype followed by Opata wheat genotype. Similarly under
drought stress 80\% FC the maximum chlorophyll ``b'' content 1.159 was
recorded for stay green chirya-1 genotype followed by Opata wheat
genotype. Similarly in drought stress 60\% FC the maximum chlorophyll
``b'' content 0.576 was recorded SD 28 followed by stay green chirya-1
genotype.

The data regarding total chlorophyll presented in (Table 1) revealed
that drought stress affect the total chlorophyll content. The mean total
chlorophyll value under control condition 100\% FC 2.295 and drought
stress 80\% FC 1.968 mean value were recorded for total chlorophyll. In
drought stress 60\% FC the mean value 1.101with CV 4.846 was recorded
shown in Fig 1. Analysis of variance (ANOVA) regarding Chlorophyll ``a''
chlorophyll ``b'' and total chlorophyll presented in (Table 3.) showed
that drought stresses are highly significant and the response of
genotypes was also highly significant. The interaction of drought stress
and wheat genotypes was observed highly significant. The least
significant difference (LSD) regarding total chlorophyll presented in
Table 6 revealed that in control condition 100\% FC the total
chlorophyll content are not significantly different and maximum total
content 2.477 were recorded for stay green chirya-1 genotype followed by
Opata wheat genotype. Similarly under drought stress 80\% FC the maximum
total chlorophyll content 2.477 was recorded for Opata wheat genotype
followed by SD-28. Similarly in drought stress 60\% FC the maximum total
chlorophyll content 1.143 was recorded for stay green chirya-1 genotype
followed by SD 28 wheat genotype. The Overall results regarding
chlorophyll contend suggested that different level of drought stress
highly affect the chlorophyll ``a'' ``b'' and total chlorophyll
contents. The results clearly indicate that stay-green chirya-1 genotype
perform well under drought stress condition regarding chlorophyll
content.

\textbf{3.2. Expression levels of genes involved in LHCI and LHCII in
response to drought stress.}

Six genes which are responsible for coding pigment binding proteins were
studied to find out different transcriptional responses to different
level of drought Stress in two synthetic genotypes (SD-28 and SD-32) and
two conventional wheat genotypes (Chirya-1 and Opata). As shown by
semi-quantitative RT--PCR, that the expression pattern was different
among the studied genes in response to different level of drought
stress, and different level of genes expression was observed. However it
is observed that expression of each genes were related to drought stress
condition and the genotypes.

The expression level of genes which are encoding proteins for LHCI,
namely \emph{TaLhca1, TaLhca2} and \emph{TaLhca3, showed} different
level of expression in SD-28 genotype as shown in (Fig 5).
The\emph{TaLhca1} gene show down regulation in drought stress condition
while \emph{TaLhca2} gene show up regulation, similarly
\emph{TaLhca3}are down regulated in 80\% FC but in severe drought stress
condition show high level of expression. Similarly the genes which are
involved in LHCII namely, \emph{TaLhcb1, TaLhcb4} and \emph{TaLhcb6}
also show distinct level of expression as shown in (Fig 6). The
\emph{TaLhcb1}show down regulation under drought stress condition and
expression level was decreased as the drought level was increased.
\emph{The TaLhcb4}show up regulation in both drought stress level 80\%
FC and 60\% capacity while the expression of \emph{TaLhcb6} show down
regulation under 80 FC but the expression was induced in severe drought
stress condition 60\% FC.

The expression level of genes involved in LHCI, in genotype SD-32 were
not affected by drought stress condition as shown in (Fig 7).
The\emph{TaLhca1} genes slightly affected by drought stress and show low
level of expression as compare to control condition, while the
Expression level of \emph{TaLhca2} showed up regulation under 80\% and
60\% FC\emph{.} Similarly \emph{TaLhca3} gene was also not affected by
drought stress and show up regulation. The expression level of genes
which are responsible for LHCII show similar expression pattern with
those involved in LHCI as shown in (Fig 8). The expression level
of\emph{TaLhcb1, TaLhcb4,} and \emph{TaLhcb6} was not affected by
drought stress and showed up expression.

The expression level of genes which are responsible encoding proteins
for LHCI in drought sensitive wheat genotype Opata showed distinct level
of expression as shown in (Fig 9). The \emph{TaLhca1} genes were down
regulated and expressions level decreased slowly as drought condition
were increased. Similarly TaLhca2 gene expression was not significantly
affected by drought stress condition, while\emph{TaLhca3} did not
decrease significantly and still remained high at severe drought stress
condition under 60\% FC. The expression level of genes which are
responsible for LHCII also showed different level of expression as shown
in (Fig 10). \emph{TaLhcb1} genes show down regulation under drought
stress condition while \emph{Talhcb4} genes were highly sensitive to
severe drought stress condition but not affected under 80\% FC.
The\emph{TaLhcb6} gene also shows up regulation and not affected by
drought stress condition. The results indicate that Opata genotype is
drought sensitive as compare to synthetic derivatives SD-28.

The expression level of genes which encode proteins for pigment binding
molecule LHCI showed highly significant results in Stay-Green Chirya-1
genotype as shown in (Fig 11). All the genes which involved in LHCI,
namely The TaLhca1, TaLhca2 and TaLhca3 show higher level of expression
and their expression is not effected by drought stress condition.
Similarly the genes which involve in LHCII, namely TaLhcb1, TaLhcb4 and
TaLhcb6 also show similar results like those which involved in LHCI as
shown in (Fig 12). The expression levels of these genes were not
significantly affected by drought stress condition which is and evident
that Stay-Green Chirya-1 genotype is drought resistant as compared to
Opata and Synthetic derivatives genotypes. The studied morphological
parameters are also evident that Chirya-1 Varity performs well under
drought stress condition.

3. DISCUSSION

The four selected wheat genotypes under different drought level showed
significant difference regarding physiological parameters. Chlorophyll
``a'' and chlorophyll ``b'' is one of the events which are used for
showing of water stress. The genotypes and stress level show different
results regarding chlorophyll content. Data concerning Chlorophyll ``a''
and ``b'' presented in Table 1 demonstrated that there were significant
differences in chlorophyll ``a'' and ``b'' after different level of
drought stress. The data regarding total chlorophyll presented in (Table
1) revealed that drought stress affect the total chlorophyll content.
Similar results under different drought level were observed by
(Kumar\emph{et al.} , 2013) that chlorophyll content of leaf was
demolished under drought stress treatment and also stops it from making.
A number of investigators have observed that harm to chlorophyll content
of leave as a result of drought stress (Arjenaki \emph{et al.} , 2012;
Nilsen and Orcutt, 1996). The cause for decrease in chlorophyll content
of leaves as affected by water scarcity is that due to drought treatment
reactive oxygen species (ROS) such as O2- and H2O2, was produced in
which an escort to lipid peroxidation and as a result, chlorophyll
demolition (Saeidi \emph{et al.} , 2015; Foyer \emph{et al.} , 1994;
Schlemmer\emph{et al.} , 2005) also reported that drought stress highly
affect chlorophyll content in which changing the green color of leaf
into yellow color, the reflectance of the event radiation is enlarged.

Relative water content is the relation between fully turgid water
content and actual water content of plant tissues when they are
subjected to drought stress condition. Therefore leaf relative water
content indicates the ability of plants to keep their water status
adequate enough to sustain water stress. In the current research the
RWCs declined during water stress in all the studied wheat genotypes.
Similar results were reported by (Siddique \emph{et al.} , 2001) that
drought stress condition considerably reduced the leaf potential and
relative water content and transpiration rate with an associated raised
in leaf temperature. This current result was also supported by the
statement of (Almeselmani \emph{et al.} , 2011) that drought regime lead
to decrease status of water during the growth of crop, soil moisture
potential and plant osmotic potential for water and nutrient uptake
which finally moderate leaf turgor pressure as results metabolic
activities of crop was disturbed.

Membrane stability and integrity is one of the significant selection
measurements of non-irrigation stress charitable genotypes
(Tripathy\emph{et al.} , 2000). Because under water deficit environment
membrane stability and integrity sure water scarcity resistance (Bewley,
1979). Analysis of variance (ANOVA) Water stress caused water loss from
plant tissues which seriously impair both membrane structure and
function (Cave \emph{et al.} , 1981). Our results in agreement with
(Vasquez-Tello\emph{et al.} , 1990) that electrolyte leakage was
correlated with drought tolerance. Our results also support the finding
of (Sayar\emph{et al.} , 2008) that they reported drought stress highly
effect the membrane stability of the plant.

The results indicate that proline content was increased under drought
stress condition as compared to control condition. As drought condition
were increased the proline content were also increased. Our current
experimental work was also supported the finding of (Parida \emph{et
al.} , 2007) that they reported that proline are highly accumulate in
drought stress condition.

Relative expression of six genes which encode proteins for LHCI and
LHCII were studied to check the effect of drought stress at molecular
level. As shown by semi-quantitative RT--PCR, that the expression
pattern was different among the studied genes in response to different
level of drought stress, and different level of genes expression was
observed. However it is observed that expression of each genes were
related to drought stress condition and the genotypes. The results
revealed that the Chirya-1 genotype perform well under water stress
condition followed by Synthetic derivatives genotypes. The results
regarding level of gene expression were related with the observation of
(Zhao \emph{et al.} , 2007) who reported that in drought stress
condition the photosynthesis in the chloroplast is the most sensitive
region. In the previous work it was observed that expression level of
genes in Stay-Green was found to be greater than that of the wild type
under drought condition (Tian \emph{et al.} , 2012). The genes involved
in LHCII namely \emph{TaLhcb1, TaLhcb4} and \emph{TaLhcb6} show higher
level of gene expression in Stay-Green genotype and similar results was
also observed by Tian \emph{et al} . (2013) they observed higher level
of gene expression in Stay-Green Genotype as compare to wild type wheat
genotype. Our results revealed that LHCI genes \emph{TaLhca2, TaLhca3}
and LHCII genes \emph{TaLhcb4} and \emph{TaLhcb6} show up regulation in
SD-28, Opata and Chirya-1 and their expression level is not affected by
drought stress condition but \emph{TaLhca1} and\emph{TaLhcb1} are the
most sensitive genes to drought stress condition. In comparison the
expression level of genes is more affected in SD-32 followed by Opata
wheat genotype. The overall results regarding level of genes expression
is in general agreements with (Oksman-Caldentey and Saito, 2005;
Reinders and Sickmann, 2007) who reported that the levels of regulation
based on post-transcriptional and post-translational mechanisms are
involved in the abiotic stress response.

\textbf{Conclusion}

It is concluded from current research work that Different drought levels
had substantial effects on physiological traits of wheat. Photosynthetic
pigments and Relative Water Contents were decrease when increase in
drought stress. It is observed that over expression of genes
(\emph{TaLhca1, TaLhca2, TaLhca3, TaLhcb1, TaLhcb4} and \emph{TaLhcb6} )
that encodes for LHCI and LHCII proteins is directly proportional to
drought stress. Among the studied genotypes wheat variety Chirya-1 and
SD-28 performed well with higher level of gene expression under drought
stress condition; considered drought tolerant genotypes with potential
to enrich the genetic background of locally adapted wheat lines against
drought stress.

\textbf{Acknowledgement}

This work was accomplished under the Higher Education Commission NRPU
Project 4363. Special thanks to the Higher Education Commission (HEC)
Islamabad, and University of Swat for providing financial support to
accomplish this work.

Authors Contribution

\textbf{Ahmad Ali:} ~Conceptualization. \textbf{Shahid Maqsood
Gill:}Methodology, Software.~\textbf{Ahmad Zada} : Data curation,
Writing- Original draft preparation.~\textbf{Irtiza Hussain} :
Visualization, Investigation.~\textbf{Azhar Hussain Shah} \emph{:}
~Supervision:\textbf{Zahid Ullah} : Software, Validation.~\textbf{Hassan
Sher:} ~Writing- Reviewing and Editing,

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\textbf{Table 1: Descriptive Statistics of study wheat genotypes under
different field capacity}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Variables} & & \textbf{Mean} & \textbf{SD} & \textbf{Variance} &
\textbf{SE Mean} & \textbf{C. V.} & \textbf{Minimum} &
\textbf{Maximum}\tabularnewline
\midrule
\endhead
\textbf{Membrane Stability Index} & \textbf{Membrane Stability Index} &
\textbf{Membrane Stability Index} & & & & & &\tabularnewline
100\% FC & & 55.227 & 3.552 & 12.619 & 1.026 & 6.432 & 49.368 &
59.382\tabularnewline
80\% FC & & 60.691 & 1.978 & 3.912 & 0.571 & 3.259 & 57.210 &
62.833\tabularnewline
60\% FC & & 54.106 & 5.012 & 25.124 & 1.447 & 9.264 & 43.943 &
62.260\tabularnewline
\textbf{Proline Content} & & & & & & & &\tabularnewline
100\% FC & & 0.828 & 0.089 & 7.951 & 0.026 & 10.767 & 0.638 &
0.929\tabularnewline
80\% FC & & 1.433 & 0.242 & 0.059 & 0.070 & 16.875 & 1.012 &
1.684\tabularnewline
60\% FC & & 2.509 & 0.472 & 0.223 & 0.136 & 18.814 & 1.711 &
3.193\tabularnewline
\textbf{Relative Water Content} & \textbf{Relative Water Content} &
\textbf{Relative Water Content} & & & & & &\tabularnewline
100\% FC & & 94.580 & 2.506 & 6.282 & 0.724 & 2.650 & 91.249 &
98.321\tabularnewline
80\% FC & & 81.887 & 2.386 & 5.694 & 0.689 & 2.914 & 77.632 &
86.612\tabularnewline
60\% FC & & 73.659 & 2.870 & 8.238 & 0.829 & 3.897 & 70.343 &
79.339\tabularnewline
\textbf{Chlorophyll a} & & & & & & & &\tabularnewline
100\% FC & & 1.186 & 0.039 & 1.515 & 0.011 & 3.282 & 1.122 &
1.255\tabularnewline
80\% FC & & 1.041 & 0.044 & 1.909 & 0.013 & 4.196 & 0.986 &
1.139\tabularnewline
60\% FC & & 0.542 & 0.037 & 1.378 & 0.011 & 6.853 & 0.473 &
0.592\tabularnewline
\textbf{Chlorophyll b} & & & & & & & &\tabularnewline
100\% FC & & 1.108 & 0.174 & 0.030 & 0.050 & 15.721 & 0.854 &
1.344\tabularnewline
80\% FC & & 0.926 & 0.156 & 0.024 & 0.045 & 16.791 & 0.722 &
1.176\tabularnewline
60\% FC & & 0.559 & 0.035 & 1.239 & 0.010 & 6.295 & 0.517 &
0.622\tabularnewline
\textbf{Total Chlorophyll} & & & & & & & &\tabularnewline
100\% FC & & 2.295 & 0.180 & 0.032 & 0.052 & 7.841 & 2.042 &
2.567\tabularnewline
80\% FC & & 1.968 & 0.141 & 0.020 & 0.041 & 0.041 & 1.792 &
2.204\tabularnewline
60\% FC & & 1.101 & 0.053 & 2.848 & 0.015 & 4.846 & 0.999 &
1.201\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2Mean Square of studied wheat lines}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{sov} & \textbf{DF} & \textbf{MSI} & \textbf{Proline} &
\textbf{RWC} & \textbf{Chla} & \textbf{Chlb} & \textbf{Total
Chl}\tabularnewline
\midrule
\endhead
\textbf{Stress} & 2 & 148.9*** & 8.692*** & 1333*** & 1.371*** &
0.938*** & 4.563***\tabularnewline
\textbf{Genotypes} & 3 & 21.82NS & 0.244*** & 7.35NS & 0.004** &
0.120*** & 0.106***\tabularnewline
\textbf{stress* Genotypes} & 6 & 20.07NS & 0.323 *** & 7.38NS & 0.004***
& 0.033*** & 0.031***\tabularnewline
\textbf{Error} & 22 & 11.980 & 0.022 & 7.380 & 0.001 & 0.002 &
0.031\tabularnewline
\bottomrule
\end{longtable}

MSI Membrane Stability Index, RWC Relative Water Content, Chla
Chlorophyll a, Chlb Chlorophyll b, Total Chl Total Chlorophyll

Table 3 LSD for membrane stability, proline, relative water content,
chlorophyll a and b and total chlorophyll under control and drought
stress condition\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Treatment} & \textbf{Genotypes} & \textbf{MSI} &
\textbf{Proline} & \textbf{RWC} & \textbf{Chla} & \textbf{Chlb} &
\textbf{Total chl}\tabularnewline
\midrule
\endhead
\textbf{100\% FC} & \textbf{SD-28} & 58.176AB & 0.872 H & 93.701 A &
1.200 AB & 0.914 CD & 2.115 B\tabularnewline
\textbf{100\% FC} & \textbf{SD-32} & 50.679 C & 0.864 H & 96.477 A &
1.168 BC & 0.985 C & 2.153 B\tabularnewline
\textbf{100\% FC} & \textbf{Opata} & 53.637BC & 0.857 H & 93.010 A &
1.144 C & 1.285 A & 2.431 A\tabularnewline
\textbf{100\% FC} & \textbf{Chirya-1} & 58.415AB & 0.719 H & 95.131A &
1.230 A & 1.247 A & 2.477 A\tabularnewline
\textbf{80\% FC} & \textbf{SD-28} & 62.685 A & 1.148 G & 82.303 B &
1.017 E & 0.850 DE & 1.869 C\tabularnewline
\textbf{80\% FC} & \textbf{SD-32} & 62.047 A & 1.556 EF & 80.193 BC &
1.099 D & 0.777 E & 1.876 C\tabularnewline
\textbf{80\% FC} & \textbf{Opata} & 59.268AB & 1.358 FG & 82.175 B &
1.034 E & 0.917 CD & 1.952 C\tabularnewline
\textbf{80\% FC} & \textbf{Chirya-1} & 58.764AB & 1.667 E & 82.876 B &
1.014 E & 1.159 B & 2.173 B\tabularnewline
\textbf{60\% FC} & \textbf{SD-28} & 55.966BC & 2.272 C & 73.396 DE &
0.576 F & 0.562 F & 1.115 D\tabularnewline
\textbf{60\% FC} & \textbf{SD-32} & 53.740BC & 1.971 D & 71.46 E & 0.547
F & 0.542 F & 1.090 D\tabularnewline
\textbf{60\% FC} & \textbf{Opata} & 54.707BC & 3.091 A & 73.659 DE &
0.492 G & 0.562 F & 1.055 D\tabularnewline
\textbf{60\% FC} & \textbf{Chirya-1} & 52.009 C & 2.698 B & 76.111 CD &
0.550 F & 0.592 F & 1.143 D\tabularnewline
\bottomrule
\end{longtable}

\textbf{Hosted file}

\verb`Figures 1-12.docx`  available at \url{https://authorea.com/users/359844/articles/481615-physiological-and-molecular-characterization-of-bread-wheat-triticum-aestivum-l-for-drought-resistance}

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