(2023) 23:193
Abouseada et al. BMC Plant Biology
https://doi.org/10.1186/s12870-023-04196-w
BMC Plant Biology
Open Access
RESEARCH
Genetic diversity analysis in wheat cultivars
using SCoT and ISSR markers, chloroplast DNA
barcoding and grain SEM
Heba H. Abouseada1, Al‑Safa H. Mohamed1, Samir S. Teleb2, Abdelfattah Badr3, Mohamed E. Tantawy1,
Shafik D. Ibrahim4, Faten Y. Ellmouni5* and Mohamed Ibrahim1*
Abstract
Background Wheat is a major cereal that can narrow the gap between the increasing human population and food
production. In this connection, assessing genetic diversity and conserving wheat genetic resources for future exploi‑
tation is very important for breeding new cultivars that may withstand the expected climate change. The current
study evaluates the genetic diversity in selected wheat cultivars using ISSR and SCoT markers, the rbcL and matK
chloroplast DNA barcoding, and grain surface sculpture characteristics. We anticipate that these objectives may pri‑
oritize using the selected cultivars to improve wheat production. The selected collection of cultivars may lead to the
identification of cultivars adapted to a broad spectrum of climatic environments.
Results Multivariate clustering analyses of the ISSR and SCoT DNA fingerprinting polymorphism grouped three
Egyptian cultivars with cultivar El‑Nielain from Sudan, cultivar Aguilal from Morocco, and cultivar Attila from Mexico.
In the other group, cultivar Cook from Australia and cultivar Chinese‑166 were differentiated from four other cultivars:
cultivar Cham‑10 from Syria, cultivar Seri‑82 from Mexico, cultivar Inqalab‑91 from Pakistan, and cultivar Sonalika from
India. In the PCA analysis, the Egyptian cultivars were distinct from the other studied cultivars. The rbcL and matK
sequence variation analysis indicated similarities between Egyptian cultivars and cultivar Cham‑10 from Syria and cul‑
tivar Inqalab‑91 from Pakistan, whereas cultivar Attila from Mexico was distinguished from all other cultivars. Combin‑
ing the data of ISSR and SCoT with the rbcL and matK results retained the close resemblance among the two Egyptian
cultivars EGY1: Gemmeiza‑9 and EGY3: Sakha‑93, and the Moroccan cultivar Aguilal, and the Sudanese cultivar El‑
Nielain and between Seri‑82, Inqalab‑91, and Sonalika cultivars. The analysis of all data distinguished cultivar Cham‑10
from Syria from all other cultivars, and the analysis of grain traits indicated a close resemblance between cv. Cham‑10
from and the two Egyptian cultivars Gemmeiza‑9 and Sakha‑93.
Conclusions The analysis of rbcL and matK chloroplast DNA barcoding agrees with the ISSR and the SCoT markers
in supporting the close resemblance between the Egyptian cultivars, particularly Gemmeiza‑9 and Sakha‑93. The
ISSR and SCoT data analyses significantly expressed high differentiation levels among the examined cultivars. Culti‑
vars with closer resemblance may be recommended for breeding new wheat cultivars adapted to various climatic
environments.
*Correspondence:
Faten Y. Ellmouni
fyl00@fayoum.edu.eg
Mohamed Ibrahim
m.shehata@sci.asu.edu.eg
Full list of author information is available at the end of the article
© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
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Abouseada et al. BMC Plant Biology
(2023) 23:193
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Keywords Wheat (Triticum aestivum L.), Genetic diversity, Molecular markers, Chloroplast DNA barcodes, Grain
exomorphic characteristics
Background
Wheat (Triticum L.) species have been developed
into thousands of polyploid cultivars that vary in
the number of chromosomes from the diploid original primitive types [1]. Bread wheat (Triticum aestivum L.) is a hexaploid species (2n = 6x = 42) of three
genomes (AABBDD), containing three related ancestral
genomes, each having 14 chromosomes [2, 3]. Developed cultivars were classified based on horticultural
demand, food uses, and texture. The development and
growth of thousands of common wheat cultivars have
been achieved worldwide. However, new and improved
cultivars are always required to increase wheat grain
yield and meet the food needs of the ever-expanding
human population [4, 5].
Climate changes shall impact the earth’s environment through temperature fluctuations, changing rainfall distribution, loss of soil fertility, increased salinity,
biological stresses, increased pollution, and declining biodiversity [6]. The brutality of climate change on
crop production may be maximized because more than
one factor affects plant growth and development [7, 8].
Assessing and conserving wheat genetic resources for
future exploitation is very important [9, 10]. Meanwhile, pre-breeding material and cultivars are exploited
in genomics-assisted breeding strategies to improve the
productivity of wheat cultivars [11].
DNA markers have been developed and applied to
assess genetic diversity in crop plants [12]. The inter simple sequence repeats (ISSRs) developed by Bornet and
Branchard [13] involved the amplification of genomic
segments flanked by inversely oriented and closely
spaced microsatellite sequences by a single primer or
a pair of primers based on SSRs anchored 5’ or 3’ with
1–4 purine or pyrimidine residues. The Start Codon Targeted (SCoT) sequence,, a dominant and reproducible
marker like ISSR, is based on a short conserved region
in plant genes surrounding the ATG translation start
codon. SCoT uses a single 18-mer primer in a polymerase
chain reaction (PCR) assays and requires an annealing
temperature of 50 °C [14]. The ISSR and SCoT polymorphism has been used in genetic resource differentiation,
cultivar characterization, and marker-assisted breeding
programs in many plants; examples include alfalfa and
Egyptian clover [15], Nigella sativa [16], Medicago sativa
[17], Pistacia lentiscus [18], Moringa oleifera [19], Lathyrus species [20], Crepidium acuminatum [21], maize [22],
Hordeum [23], and smooth bromegrass [24, 25].
Etminan et al. [26] evaluated the genetic variation of
a mini-core collection of breeding lines and landraces
of durum wheat germplasm using 15 ISSR and 6 SCoT
markers combined with studying agro-morphological
traits. Pour-Aboughadareh et al. [27] also used 15 SCoT
primers to assess the genetic diversity of 70 accessions
of Iranian Triticum species. The SCoT markers were
also employed by Mohamed et al. [28] to genetically
characterize 14 cultivars of wheat from North Africa.
Also, SCoT was used to discriminate eight wheat Asian
cultivars [29]. Genetic diversity and population structure of 80 Triticum urartu accessions were investigated
using SCoT and CBDP markers [30]. Molecular variability and relationships within the set of 91 samples
of Triticum aestivum, Aegilops cylindrica, and Aegilops
crassa species were estimated by using CBDP and SCoT
markers [31]. In the same context, the genotypic and
phenotypic diversity of 96 durum wheat genotypes
were assessed using CBDP and ISSR markers [32].
DNA barcoding for identifying plant species was
introduced by Hebert et al. [33] and was further developed, as reported by Hollingsworth et al. [34]. The
chloroplast genes’ large subunit of the ribulose-bisphosphate carboxylase (rbcL) and maturase kinase
(matK) are indispensable barcodes for plant species.
Additionally, the chloroplast spacer, the psbA and the
ribosomal internal transcripted spacer (ITS) sequences
are commonly employed barcodes at the species level
[35]. Moreover, DNA barcoding provides insights into
genetic diversity and comparative investigations in
studied populations [36]. Recently, DNA barcoding
has become a valuable technique for assessing biodiversity in phylogenetic reconstruction and plant evolution [37–39]. Combined nuclear and chloroplast DNA
sequences were used to barcode the major forage plants
[15, 18, 40]. Knowledge about the genetic diversity of
12 bread wheat cultivars is limited, not only among
Northern African varieties but also in cultivars in different countries. The importance of selecting the prementioned wheat cultivars lies in their capability for
adaptation to the extreme climatic conditions in the
countries where these cultivars were developed [28,
29]. In the same context, several pilot and preliminary
measurements, including morphological characteristics
(ca. grain length, width, color, etc.), physical analyses
(ca. moisture %, falling number, ash content, wet gluten, dry gluten, etc.), and grain weight (ca. the weight
of one and 20 grains) (data not shown) were performed
Abouseada et al. BMC Plant Biology
(2023) 23:193
on previosuly published wheat cultivars [28, 29] and
revealed focusing on the 12 selected studied cultivars.
Also, selected genotypes harbor abiotic resistant genes
beside other genes encode transcription factors play a
pivotal role in plant growth and development [28, 29].
The grains of cereals have diagnostic features that help
in the separation and characterization of cultivars and
their contribution to the quality and yield for consumers, farmers, and plant breeders [41–43]. A pronounced
degree of similarity among the four genotypes of cereal
bowls was achieved by SEM screening of the grain surface [44].
The current study aims to estimate the genetic diversity in 12 selected wheat cultivars of different origins by
applications of molecular markers and classical morphological markers. Several marker types are employed; ISSR
and SCoT markers, the rbcL and matK chloroplast DNA
barcoding, and grain surface sculpture characteristics
were addressed in evaluating the genetic diversity. We
anticipate that these objectives may prioritize using the
selected cultivars to improve wheat production. Ongoing
advances in cultivars’ differentiation significantly accelerate the breeding of more productive cultivars to enhance
wheat production to achieve the goal of doubling the
yield by 2050 [11]. Using wheat cultivars of different origins may lead to successfully breeding cultivars adapted
to a broad spectrum of climatic environments. Moreover,
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the integration and complementation of executed molecular and morphological analyses trigger a more comprehensive evaluation of the genetic variability in the studied
cultivars.
Results
ISSR and SCoT fingerprinting
Photographs of some representative agarose gel electrophoresis of PCR fingerprinting in the 12 wheat
cultivars are shown in Fig. 1A. Photographs of ISSR
primers PCR fingerprinting are given as Supplementary Fig. S1. The polymorphism metrics (TNAs, MAs,
PAs, %P, PIC, RP, and MI) of the 12 ISSR primers
are summarized in Table 2A. All primers generated
150 amplicons, including 76 polymorphic amplicons
(50.67%). The number of generated amplicons per
primer varied from 8 for ISSR primer-12 to 23 for
ISSR primer-13. The number of polymorphic markers also ranged from 1 for primer ISSR-12 to 12 and
15 for ISSR primer-9 and ISSR primer-13, respectively. The average number of polymorphic amplicons per-ISSR primer was 6.33. The PIC values for the
ISSR primers ranged from 0.11 (the lowest value) for
ISSR primer-12 to 0.36 for ISSR primer-13 (the highest value). The ISSR primer-9 revealed pronounced
discrimination of 80% polymorphism. On the other
Fig. 1 Photographs of some representative agarose gel electrophoresis of PCR amplicons of A) ISSR and B) SCoT primers. M = DNA size marker in
bps (Cat. No. SB_07‑11‑0000S, MEDIBENA Life Science & Diagnostic Solutions, Vienna, Austria). Numbers from 1 to 12 refer to the sampling numbers
of the studied cultivars. The primer codes are listed in Table 2. Fully uncropped versions of the whole DNA fingerprinteing patterns either for ISSR
and SCoT molecular makrers are accompanied the manuscript as Supplementary Figs. 1 and 2, respectively
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(2023) 23:193
hand, ISSR primer-12 recorded the lowest polymorphism of 13%. The ISSR primer-13 recorded the highest PIC and RP values of 0.36 and 9.56, respectively
(Table 2A).
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The SCoT fingerprinting profiles revealed by the 11
primers are illustrated in Fig. 1B, and the ScoT fingerprinting produced by the 11 primers is provided
as Supplementary Fig. S2. The polymorphism metrics
Fig. 2 (A) Euclidean distance cluster tree and (B) PCA scatter diagram illustrating the genetic diversity between the 12 wheat cultivars based on the
analysis of ISSR and SCoT markers polymorphism using the "pvclust" R package in R software
Abouseada et al. BMC Plant Biology
(2023) 23:193
(TNAs, MAs, PAs, %P, PIC, RP, and MI) of the 11 ScoT
primers are summarized in Table 2B. A total of 153
amplicons were produced, including 85 polymorphic
(55.56%) markers. The total number of amplified PCR
amplicons varied between 10 for SCoT primer-10 and
24 for SCoT primer-5, and the number of polymorphic
bands also ranged from 1 for SCoT primer-28 to 21 for
SCoT primer-5. The average number of polymorphic
bands was 7.7 per primer. The PIC values range from
0.17 for SCoT primer-28 to 0.37 for SCoT primer-5
and primer-20. Also, the SCoT primer-5 revealed a
pronounced polymorphism of 88% and recorded the
highest RP of 10.67.
Genetic relationships of wheat cultivars as revealed by ISSR
and SCoT markers
The ISSR and SCoT markers binary data, scored 1 for
presence and 0 for absence, for the 12 wheat cultivars, were used to construct an Euclidean distance tree
(Fig. 2A). The tree shows the differentiation of the 12
cultivars into two main groups. In group I, the three
Egyptian cultivars EGY2: Giza-168, EGY1: Gemmeiza-9,
and EGY3: Sakha-93 are grouped as a cluster, with the
three cultivars MEX: Attila, MAR: Aguilal, and SDN: ElNielain as the second cluster. In group II, the two cultivars: AUS: Cook and CHN: Chinese-166, are recognized
as a cluster from another cluster, comprising the cultivars
MEX: Seri-82, IND: Sonalika, SYR: Cham-10, and PAK:
Inqalab-91. The differentiation of the examined wheat
cultivars described in Fig. 2A is supported by two additional cluster trees that were constructed based on the
recorded data of ISSR and SCoT markers, as two independent analyses, using the Dice’s coefficient application
of PAST software (Supplementary Materials, Fig. S3A,
B). In both trees, the 12 wheat cultivars were generally
differentiated as in the tree based on the ISSR, and SCoT
markers combined analysis shown in Fig. 2A.
The PCA scatter diagram of 12 wheat cultivars based
on the ISSR and SCoT markers combined by plotting
Dim1 (20%) and Dim2 (14%) (Fig. 2B), which agrees with
the clustering analysis (Fig. 2A). The three Egyptian cultivars EGY1: Gemmeiza-9, EGY2: Giza-168, and EGY3:
Sakha-93 are grouped closely together as in the cluster tree shown in Fig. 2A but are distinguished from the
three cultivars MEX: Attila, MAR: Aguilal, and SDN: ElNielain, as grouped in the cluster tree of Fig. 2A. On the
other hand, the two cultivars AUS: Cook and CHN: Chinese-166 are plotted close to the cultivars MEX: Seri-82
and IND: Sonalika and the cultivars SYR: Cham-10 and
PAK: Inqalab-91, which formed two clusters of group II,
as illustrated in Fig. 2A.
Page 5 of 15
Relationship of wheat cultivars based on rbcL and matK
barcoding
The amplified DNA fragments of the rbcL and matK
genes recorded 600 bps and 800 bps, respectively, as
shown in Supplementary Materials, Fig. S4 and Table 3.
To affirm that the rbcL and matK sequences presented
in this study belonged to T. aestivum, a BLASTN nucleotide to nucleotide function analysis indicated that
all the sequences matched rbcL and matK sequences
belonging to T. aestivum accessions in the NCBI GenBank. Additional information concerning the estimates
of sequence(s) variation of rbcL and matK barcoding
loci, particularly summarized PCR amplification results,
sequencing success, variability, the aligned length, variable sites and their proportion, and statistical simulation
of BLAST Sequence homology of wheat cultivars for barcoding the rbcL and matK genes is given in Supplementary Table S1. Pairwise distances were analyzed from the
conserved rbcL and matK sequences.
The rbcL and matK sequence variations were combined to construct a cluster tree illustrating genetic
relatedness among the studied wheat cultivars using
two NCBI-extracted rbcL sequences of T. aestivum
accession [KR092108.1] and Triticum monococcum
accession [KX282834.1]. In this tree (Fig. 3A), T. monococcum accession [KX282834.1] and the cv. MEX: Attila,
a North American cultivar, were differentiated into two
branches, and the other cultivars were distinguished into
two groups. In group I, the Egyptian cultivars; EGY1:
Gemmeiza-9 and EGY3: Sakha-93 were grouped as one
branch, and the cultivar EGY2: Giza-168, cultivar PAK:
Inqalab-91, and cultivar SYR: Cham-10 cultivars were
grouped into a second branch. In group II, the reference
T. aestivum accession [KR092108.1] and the AUS: Cook
was differentiated from the other five cultivars into two
separate branches, one for MAR: Aguilal and the SDN:
El-Nielain cultivars, while the other one is for CHN:
Chinese-166, MEX: Seri-82, and IND: Sonalika cultivars
(Fig. 3A). Additional independent trees based on rbcL
and matK sequence variation analysis using T. aestivum accession and T. monococcum accession as outgroups are provided (Supplementary Materials, Fig. S5A,
B). The differentiation of the examined wheat cultivars
in that table is comparable to their differentiation as presented in the tree shown in Fig. 3A.
Relationships between wheat cultivars based on genomic
DNA fingerprinting and barcoding outcomes
An Euclidean distance tree illustrates the genetic diversity among the studied wheat cultivars, as revealed by
analysis of ISSR and SCoT fingerprinting combined with
the rbcL and matK sequences (Fig. 3B), divided the 12
wheat cultivars into two major groups. Group I includes
Abouseada et al. BMC Plant Biology
(2023) 23:193
two clusters; one consists of the two Egyptian cultivars
EGY1: Gemmeiza-9, EGY3: Sakha-93, MAR: Aguilal, and
SDN: El-Nielain, while the other includes EGY2: Giza168, MEX: Attila, and PAK: Inqalab-91. Group II consists
of the SYR: Cham-10, as a distinct branch, and the cultivars MEX: Seri-82, CHN: Chinese-166, IND: Sonalika,
and AUS: Cook.
On the other hand, in a tree based on rbcL sequence
variation (Supplementary Materials, Fig. S5A), T. monococcum accession [KX282834.1] was isolated from all
other cultivars, while MAR: Aguilal and MEX: Attila
cultivars, as well as the two Egyptian ones; EGY1: Gemmeiza-9 and EGY3: Sakha-93, are clustered as two distinct branches of the tree. The remaining nine cultivars
represented a major group, but each cultivar represented
a single line (Fig. S5A).
On the other hand, in a tree based on the analysis of
matK sequences, the T. aestivum accession [AF164405.1],
T. monococcum [HM540031.1], and the Australian cultivar AUS: Cook was isolated as a separate cluster from
two major clusters representing the remaining wheat cultivars; the first cluster comprised the Egyptian cultivars
together with MEX: Attila and PAK: Inqalab-91, while
the second one includes the remaining wheat cultivars
(Supplementary Materials, Fig. S5B). A PCA scatter diagram of the 12 wheat cultivars (Supplementary Materials,
Fig. S6) reflected the differentiation among the examined
cultivars, as illustrated in the combined tree shown in
Fig. 3B.
Relationships between wheat cultivars based on the grain
surface sculpture
The grain surface sculpture traits analysis revealed a
somewhat different grouping of the examined cultivars
but indicated a close similarity between cv. Cham-10
from Syria and the two Egyptian cultivars Gemmeiza-9
and Sakha-93. The grains sculpture revealed a wide range
of variation in the ventral and dorsal sides of the grains
of the examined cultivars (Supplementary Table S2). A
total of 38 exomorphic character states of 10 main characters were scored in the studied cultivars (Supplementary Table S2). These states were then analyzed for their
correlation with each other (Fig. 4). High correlations
were recorded between AWSDT, AWSVT, and SPVSRF
that might play a prominent role in distinguishing the
two Egyptian cultivars, EGY1: Gemmeiza-9 and EGY3:
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Sakha-93 from EGY2: Giza-168. Also, AWSDM and
AWSVM were correlated and negatively correlated to
AWSDT and AWSVT.
A distance cluster tree, using the UPGMA algorithm, illustrating the classification of wheat cultivars
based on the grain sculpture data is shown in Fig. 5.
This tree divided the cultivars into two major groups.
Group, I include the Egyptian cultivar EGY2: Giza168, cultivars MEX: Seri-82, SDN: El-Nielain, and the
IND: Sonalika. Group II, a larger group, includes the
two Egyptian cultivars, EGY1: Gemmeiza-9 and EGY3:
Sakha-93, grouped with cv. SYR: Cham-10, as a cluster,
while the rest of the studied cultivars (cv. MEX: Attila
and cv. CHN: Chinese-166, cv. MAR: Aguilal, cv. PAK:
Inqalab-91 and cv. AUS: Cook. were grouped in another
cluster (Fig. 5).
Discussion
Genetic diversity was assessed using 303 polymorphic
markers, including 151 ISSR markers produced by 12
ISSR and 155 SCoT markers produced by 11 SCoT primers, with a low average of 48.25% polymorphism and a
high average of 13.3 bands per primer. It was also noticed
that the number of MAs (monomorphic amplicons) was
higher for ISSR markers than for SCoT markers. On the
other hand, SCoT markers recorded a higher proportion
of polymorphic amplicons (PAs) than ISSRs. Furthermore, SCoT markers also recorded higher PIC, RP, and
MI mean values than ISSR markers. However, the highest
RP value per primer was recorded for the ISSR-13 primer.
Some Northern African wheat cultivars reported low levels of polymorphism ranging from 8 to 57% by the SCoT
primers [28]. The low polymorphism percentage might
be attributed to low genetic diversity and high conservation among the examined wheat cultivars. Genetic diversity and relationships among eight cultivars of Egyptian
wheat, including cv. Sakha-93 and Giza-168 using six
ISSR primers and eight SCoT primers, were investigated
by Abdel-Lateif and Hewedy [45]. The ISSR primers produced 34 bands, including 23 (68%) polymorphic markers, with a mean of 4.6 per primer. These numbers are
much lower than those recorded in the current study of
151 ISSR amplicons, including 76 (50.67%) polymorphic
markers with an average of 6.33 per primer. Abdel-Lateif
and Hewedy [45] also reported a lower number of SCoT
markers (32 bands), including 19 (59%) PAs with a mean
(See figure on next page.)
Fig. 3 (A) Cluster tree constructed using R software illustrating genetic relatedness among the 12 wheat cultivars as revealed by sequence variation
of combined rbcL (T. monococcum accession [KX282834.1] and T. aestivum accession [KR092108.1]) and matK (T. aestivum accession [AF164405.1],
T. monococcum [HM540031.1]) which were used as reference sequences. (B) Euclidean distance tree, constructed using the "pvclust" R package in
R‑Software, illustrating the genetic diversity among the wheat cultivars, as revealed by the analysis of the ISSR and SCoT markers polymorphism and
DNA barcoding of rbcL and matK sequence variation
Abouseada et al. BMC Plant Biology
Fig. 3 (See legend on previous page.)
(2023) 23:193
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Abouseada et al. BMC Plant Biology
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Fig. 4 Correlogram was based on the correlation coefficients of 38 macro‑morphological features in 12 wheat cultivars based on grain surface
sculpture. Abbreviations of the macro‑morphological features are listed in the Supplementary Table S2
of 3.16, whereas 155 bands were amplified in the current
study, including 84 ( 54.19%) PAs with an average of 7.63
per primer.
Multivariate clustering and PCA scatter plot of ISSR,
and SCoT markers grouped the three Egyptian cultivars
EGY1: Gemmeiza-9, EGY2: Giza-168, and EGY3: Sakha93 together with a cv. El-Nielain from Sudan, cv. Aguilal
from Morocco, and cv. Attila from Mexico. In a second
group, ISSR and SCoT data analysis showed a close relationship between cv. Cook from Australia and the Chinese-166 cultivar differentiated from the other four
cultivars; cv. Cham-10 from Syria, cv. Seri-82 from Mexico, cv. Inqalab-91 from Pakistan, and cv. Sonalika from
India. The abundance of the ISSR and SCoT markers
polymorphism and significant sequence variation support the use of these molecular markers extensively for
DNA fingerprinting as a useful tool and straight-forward
techniques in genetic diversity studies [22, 29, 46] and
confirm that the characterization based on DNA polymorphism, i.e., molecular markers basis is more efficient,
accurate, and justifying their wide use in genetic diversity
assessment in the last two decades.
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Page 9 of 15
Fig. 5 Distance UPGMA distance tree illustrating the genetic distance, based on the analysis of 38 grain surface sculpture traits of the twelve T.
aestivum cultivars using R Software
The ISSRs involve amplifying genomic segments
flanked by inversely oriented sequences closely spaced by
microsatellites [13], while the SCoT sequence is a short,
conserved sequence surrounding the start codon ATG
[14]. However, using the PAST software, both markers
produced two similar classifications of the studied wheat
cultivars expressed in constructing two similar trees.
SCoT polymorphism analysis was performed to differentiate 14 wheat cultivars from North Africa (five of these
cultivars were used in the current study). However, low
levels of polymorphism ranging from 8 to 57%, with an
average of 34.5%, were recorded in the 14 Northern African cultivars [28]. In the same context, Ibrahim et al. [29]
reported polymorphism percentages ranging from 0 to
67%, with an average of 38.4% using 30 SCoT primers in
8 wheat cultivars from Asia. However, these levels were
considered sufficient to indicate the cultivars’ genetic
differentiation and generate genomic loci that encode
functional mRNA. In the current study, a higher level
of polymorphism ranging between 11 and 88%, with an
average of 51.4%, was recorded using 12 primers in the
global 12 examined wheat cultivars. In this connection,
about 923 ISSR and sequence-related amplified polymorphism (SRAP) molecular markers, besides numerous
phenotypic traits were employed to monitor triggered
improvement of orchadgrass polycross populations subjected to water deficit conditions [24, 25].
SCoTs markers and the gene/trait defining them to
be directly employed in breeding programs than SSRs,
ISSRs, and RAPDs in fingerprinting newly synthesized
tritordeums and their respective parents [47]. In Triticum urartu, 72 accessions from Iran were grouped into
two main clusters using two sets of markers. However,
the grouping patterns were not obeyed by the geographic origins of the accessions [30]. The latter study
also showed that Iranian T. urartu, especially the Kerend-e-Gharb and Sisakht-Pataveh populations, could
greatly affect wheat improvement. Taheri et al. [48] also
used IRAP and REMAP markers to assess the genetic
divergence and relatedness among T. urartu and T. boeoticum populations in Iran. Cluster and PCA analyses
using REMAP data grouped the populations based on the
species and geographical origin. Although the grouping
based on IRAP could not separate the two species, considerable diversity was observed among and within the
studied populations based on both marker systems. In
the same context, durum wheat genotypes were differentiated into five groups. The clustering of these genotypes
based on the SCoT markers polymorphism supported
the best clustering pattern and was more efficient, as
reported by Etminan et al. [26]. Moreover, Ghobadi
et al. [31] analyzed the genetic diversity and population
structure in T. aestivum, Aegilops cylindrica and Aegilops
crassa using CBDP and SCoT markers. They showed
that both molecular markers grouped all samples based
on their genomic constitutions and concluded that both
techniques effectively evaluate the genetic diversity in
wild relatives of wheat.
In the current study, the tree based on the rbcL
and matK sequence analysis differs mainly from the tree
based on the SCoT and ISSR data, particularly the close
relation of cv. EGY2: Giza-168, cv. PAK: Inqalab-91, and
cv. SYR: Cham-10 in clade 1 and between cv. MAR: Aguilal and cv. SDN: El-Nielain with cultivars of cv. CHN:
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Chinese-166, cv. MEX: Seri-82, and cv. IND: Sonalika.
In the tree based on the rbcL sequence variations, the
cultivars were isolated as three groups with some resemblance to the trees based on the ISSR and SCoT fingerprinting polymorphism. In particular, the grouping of cv.
MAR: Aguilal and cv. MEX: Attila and the two Egyptian
cultivars EGY1: Gemmeiza-9 and EGY3: Sakha-93. On
the other hand, the tree based on the analysis of matK
sequences alone clearly isolated T. aestivum accession
[AF164405.1] and the Australian cv. AUS: Cook as a clade
from another two major clades representing the remaining wheat cultivars; one clade of cv. EGY2: Giza-168 and
cv. MEX: Attila together with a cv. EGY1: Gemmeiza-9,
cv. EGY3: Sakha-93, and cv. PAK: Inqalab-91, while the
other cultivars were grouped into another clade.
The matK and rbcL DNA barcoding loci were used in
the distinction between different Egyptian landraces of T.
aestivum and T. turgidum subsp durum using eleven different landraces, and seven local varieties were examined
for their ability to distinguish between other Poaceae
crops and herbs, including Avena fatua, Hordeum vulgare, and Hordeum apertum [49]. The results showed
that matK and rbcL had a limited capability in differentiating between the questionable Triticum accessions. In
the same study, the conducted in silico analysis emphasized the differentiation potential of using combinations
of chloroplast intergenic regions more than coding genes
in the ten Triticum species and sub-species [49].
In the presented study, the differences in the wheat cultivars genotypes based on SCoT and ISSR fingerprinting
had great potential in differentiating T. asetivum cultivars other than rbcL and matK sequence analysis. This
may be attributed to the rbcL and matK genes, similar
to other chloroplast genes, which elucidate diversity at
higher taxonomic levels [50, 51]. On the other hand, the
ISSR markers, which amplify genomic segments flanked
by inversely oriented sequences closely spaced by microsatellites [13] and the SCoT sequence, which amplifies
the conserved sequence surrounding the start codon of
functional genes [14], reveal sufficient polymorphism for
stable and reproducible differentiation below the species
level as reported here between the cultivars of wheat. Feltaous [52] reported that genetic diversity among Egyptian
wheat cultivars showed a narrow morphological variation compared to the SSR markers polymorphism. The
same study concluded that SSR markers were more accurate and informative than morphological characters. This
result supports the use of DNA fingerprinting in estimating the genetic diversity of wheat cultivars, as molecular
markers are abundant, easy to handle, and independent
of environmental factors. Abdel-Lateif and Hewedy [45]
and Badr et al. [53] presented results supporting our conclusion that SCoT and ISSR markers produced higher
Page 10 of 15
polymorphism than SCoT markers and can be employed
in wheat breeding programs to evaluate genetic diversity that may be used in producing new cultivars of more
resilient to the environmental changes in the future.
The grains surface sculpture using SEM screening
revealed 38 exomorphic character states and offered various variations among the studied cultivars. The analysis
of these traits indicated a close resemblance between cv.
Cham-10 from Syria and the two Egyptian cultivars Gemmeiza-9 and Sakha-93. However, the grouping pattern
obtained from the analysis of the grain surface sculpture
traits revealed wasn’t in accord with that obtained from
the molecular studies of the examined wheat cultivars.
The value of the features of the grain in the classification
of grasses at the species level, mainly using grain surface
scanning, is well documented [41, 43, 44].
Conclusions
In conclusion, the differentiation and characterization
of wheat cultivars using the ISSR and the SCoT markers using clustering and PCA analyses grouped the three
Egyptian cultivars; Gemmeiza-9, Giza-168, and Sakha93, together with cv. El-Nielain from Sudan, cv. Aguilal
from Morocco, and cv. Attila from Mexico in one group,
and the other cultivars in a second group. The close relationship between cv. Cook from Australia and cv. Chinese-166 differentiated from the other four cultivars from
Syria, Mexico, Pakistan, and Bangladesh. The analysis of
the chloroplast DNA agrees with the ISSR and the SCoT
markers for some cultivars but differs for others, such as
the close resemblance between cv. Cham-10 from Syria
and the two Egyptian cultivars Gemmeiza-9 and Sakha93. The ISSR and SCoT analyses significantly expressed
the genetic diversity among the studied wheat cultivars
with higher differentiation levels than the rbcL and matK
genes. The differentiation of the cultivars using the rbcL
and matK sequences variation and the SEM screening
of the grain surface sculpture may add helpful insights
into the cultivars’ genetic diversity and provide essential knowledge for their selection as genetic resources in
breeding new cultivars.
Methods
Plant material
The present study dealt with twelve selected cultivars
of bread wheat (Triticum aestivum L.). The grains were
received with a full identification file from the Libyan
Agricultural Research Center, the eastern region subcenter in Elbeida/Benghazi, Libya, based on a joint collaboration with the Food and Agriculture Organization
of the United Nations (FAO-Libya) and International
Center for Agricultural Research in Dry Land (ICARDA),
Aleppo, Syria. Affirmed needed permissions were
Abouseada et al. BMC Plant Biology
(2023) 23:193
Page 11 of 15
Table 1 Origin, codes, names, and pedigree of the wheat cultivars as recorded by ICARDA and the GenBank deposited accession
numbers for the rbcL and matK genes for the 12 cultivars of bread wheat
Serial No
Origin
Codea
Cultivar name and pedigree
Genebank
Accession
rbcL
Genebank
Accession
matK
1
Egypt
EGY1
Gemmeiza‑9 ARC (Ald”S”/Huac”S”//CMH74A.630/5 × CGM4583‑5GM‑1GM‑0GM)
MT797209
MW620988
2
Egypt
EGY2
Giza‑168 ARC (MIL/BUC//Seri CM93046‑8 M‑0Y‑0 M‑2Y‑0B)
MT797200
MZ207916
3
Egypt
EGY3
Sakha‑93 ARC (Sakha 92/TR 810328 S 8871‑1S‑2S‑1S‑0S)
MT797205
MW598256
4
Morocco
MAR
Aguilal (Saïs*2/1/KS‑85–14‑2)
MT797208
MW598252
5
Sudan
SDN
El‑Nielain (S948.A1/7*SANTA ELENA, CMH 72A.390‑OSDN)
MT797202
MW620987
6
India
IND
Sonalika (II53.388/AN//YT54/N10B/3/LR/ 4/B4946.A.4.18.2.1Y/Y53// 3*Y50)
MT797204
MW598251
7
China
CHN
Chinese 166 (S‑Chinese 165(= Intro. from CHN); Chinese Land Variety [JIC])
MT797207
MW598250
8
Pakistan
PAK
Inqalab‑91 (WL‑711/Crow)
MT797201
MW598254
9
Syria
SYR
Cham‑10 (Kauz//Kauz/Star)
MT797203
MW598255
10
Australia
AUS
Cook (Timgalen/ Condor’s’//Condor)
MT797211
MW598257
11
Mexico
MEX
Seri‑82 (Kavkaz/Buho sib//Kalyansona/Bluebird)
MT797206
MZ207917
12
Mexico
MEX
Attila (ND/VG9144//KAL/BB/3/YACO/ 3/VEERY #5)
MT797210
MW598253
a
Three-digit codes used in this study are according to official ISO country codes listed in (http://www.nationsonline.org/oneworld/country_code_list.htm and http://
www.worldatlas.com/aatlas/ctycodes.htm). All the listed cultivars were used for ISSR, SCoT molecular markers, and DNA barcoding of rbcL and matK genes. The origin
of the studied cultivars were listed according to United States Department of Agriculture (USDA) (https://www.usda.gov/)
obtained and complied with the International Union for
Conservation of Nature (IUCN) approved by the 27th
meeting of the IUCN council, GLAND SWITZERLAND
(1989). Dr. Mohamed Tantawy, professor of plant taxonomy and flora, Department of Botany, Faculty of Science,
Ain Shams University, Cairo, Egypt, double checked
these cultivars and the voucher specimens were kept
in the Herbarium of Department of Botany, Ain Shams
University (CAIA; http://sweetgum.nybg.org/science/
ih/herbarium-details/?irn=123925). The country of origin, country code, and pedigree of the cultivars used are
listed in Table 1. They include three cultivars from Egypt
and one from nine other countries, each in Africa, Asia,
Europe, Middle America, and Australia.
Extraction of genomic DNA
DNA extraction was performed from 50 mg freezedried powder of ground grains of the wheat cultivars
using the DNeasy Plant Mini Kit (QIAGEN, Hilden,
Germany). Extracted DNA was quantified as described
by the Molecular Cloning Laboratory Manual [54]. The
purity was measured using an ND-1000 spectrophotometer (Nano-Drop Technologies, Thermo Fisher Scientific
Inc.).
PCR technique for ISSR and SCoT was carried out, as
described in Badr et al. [17], in a 25 μl reaction volume
containing 1X PCR buffer, 1.5 mM MgCl2, 0.15 mM
dNTPs, 25 µM primer, 25 ng wheat DNA, and 1 unit
of Phusion® High-Fidelity DNA Polymerase (Espoo,
Finland). PCR was performed using a PerkinElmer
GeneAmp® PCR System 9700 (PE Applied Biosystems,
Bedford, MA, United States). The ISSR and SCoT primers; name, sequence, GC%, TM°C, as well as the information on the amplicons per primer, in the 12 wheat
cultivars, are given in Table 2A and B, respectively.
The amplification of ISSR markers was performed in
40 cycles as follows: an initial denaturation cycle at 94°C
for 1 min, annealing at 50°C for 1 min, elongation at 72°C
for 2 min, and a final extension for 5 min. On the other
hand, SCoT amplification was performed in 35 cycles as
follows: 5 min at 94°C denaturation, 7 min annealing at
50°C, and elongation in the final cycle at 72°C. The PCR
products of ISSR and SCoT markers were separated on
1.5% agarose gel. Gels were stained with 100 µM/L EtBr
(100 µM/L, Sigma‒Aldrich®) in 1X TBE. The PCR products were visualized and documented using a Bio-Rad
ChemiDoc™ MP gel documentation and imaging system
(Cat. no. 1708280).
ISSR/SCoT primers and ISSR/SCoT PCR amplification
rbcL and matK chloroplast gene barcoding
The 12 ISSR and 11 SCoT primers, used for DNA fingerprinting, were synthesized by the HVD Egypt under
license from HVD Vertriebs-Ges. GmbH, Vienna, Austria, delivered, rehydrated, and stored at -20 °C. The
The forward and reverse primer sequences used for barcoding are given in Table 3. The PCR amplification of the
rbcL and matK genes was performed at an initial denaturation for 5 min at 94°C followed by 40 cycles, each
Abouseada et al. BMC Plant Biology
(2023) 23:193
Page 12 of 15
Table 2 List of the ISSR (A) and SCoT (B) primers; name, sequence, GC%, TM°C, total number of amplicons (TNAs) per primer,
monomorphic amplicons (MAs), polymorphic amplicons (PAs), percentage of polymorphism (%P), polymorphic information content
(PIC), resolving power (RP), and marker index (MI) as revealed by ISSR and SCoT profiles in the 12 wheat cultivars. The primer sequences
were synthesized by the HVD Egypt company
(A) ISSR Primer list
Ser
Primer Name
Sequence (5́ › 3́)
TNAs
MAs
PAs
%P
PIC
RP
MI
1
ISSR‑1
AGAGAGAGAGAGAGAGYC
13
7
6
46%
0.34
7.69
0.023
2
ISSR‑2
AGAGAGAGAGAGAGAGYG
11
7
4
36%
0.32
6.55
0.026
3
ISSR‑6
CGCGATAGATAGATAGATA
14
5
9
64%
0.35
7.00
0.021
4
ISSR‑7
GACGATAGATAGATAGATA
12
5
7
58%
0.33
6.67
0.025
5
ISSR‑8
AGACAGACAGACAGACGC
9
6
3
33%
0.20
3.11
0.022
6
ISSR‑9
GATAGATAGATAGATAGC
13
3
10
77%
0.32
5.54
0.022
7
ISSR‑12
ACACACACACACACACYC
8
7
1
13%
0.11
1.50
0.013
8
ISSR‑13
AGAGAGAGAGAGAGAGYT
26
9
17
65%
0.37
10.31
0.011
9
ISSR‑14
CTCCTCCTCCTCCTCTT
11
7
4
36%
0.28
5.09
0.023
10
ISSR‑15
CTC TCTCTC TCTCTC TRG
11
5
6
54%
0.26
4.54
0.023
11
ISSR‑19
HVHTCC TCC TCC TCC TCC
13
7
6
46%
0.30
5..84
0.021
12
ISSR‑20
HVHTGTGTGTGTGTGTGT
10
7
3
30%
0.21
3.40
0.118
151
75
76
-
-
-
-
6.33
46.5
0.28
5.12
0.028
Total
Mean
(B) SCoT Primer list
Ser
Primer Name
Sequence (5́ › 3́)
TNAs
MAs
PAs
%P
PIC
RP
MI
1
ScoT‑2
ACCATGGCTACCACCGGC
18
9
9
50%
0.34
7.89
0.016
2
SCoT‑3
ACGACATGGCGACCCACA
16
7
9
56%
0.33
6.75
0.018
3
SCoT‑4
ACCATGGCTACCACCGCA
12
8
4
33%
0.19
3.00
0.015
4
SCoT‑5
CAATGGCTACCACTAGCG
27
5
22
81%
0.37
10.00
0.009
5
SCoT‑11
ACAATGGCTACCACTACC
11
5
6
55%
0.32
6.55
0.026
6
SCoT‑12
CAACAATGGCTACCACCG
10
6
4
40%
0.32
6.60
0.028
7
SCoT‑13
ACCATGGCTACCACGGCA
15
6
9
60%
0.27
4.93
0.017
8
SCoT‑16
CCATGGCTACCACCGGCA
14
6
8
57%
0.32
6.71
0.021
9
SCoT‑20
CAACAATGGCTACCACGC
11
4
7
64%
0.37
9.45
0.025
10
SCoT‑22
CCATGGCTACCACCGCAC
12
7
5
42%
0.25
4.16
0.019
11
SCoT‑28
CAACAATGGCTACCACCA
9
8
1
11%
0.17
2.44
0.018
155
71
84
-
-
-
-
7.6
50
0.30
6.23
0.019
Total
Mean
Table 3 Primer codes and sequences for barcoding the rbcL and matK genes and their size in bps
Primer Code
Sequence
Product Size
Reference
600 bp
Fay et al. [55]
700–800 bp
Yu et al. [56]
rbcLa-1F
5’‑ATGTCACCACAAACAGAGACTAAAGC‑3’
rbcL724-R
5’‑TCGCATGTACCTGCAGTAGC‑3’
matK-472F
5’‑CCCRTYCATCTGGAAATC TTGGTTC‑3’
matK-1248R
5’‑GCTRTRATAATGAGAAAGATT TCTGC‑3’
comprised of denaturation at 94°C for 30 s, annealing at
45°C for 30 s, and elongation at 72°C for 30 s. The primer
extension was extended for 7 min at 72°C in the final
cycle. PCR-specific products were subsequently electrophoresed in 1.5% (W/V) agarose, stained with 100 µM/L
EtBr (Sigma-Aldrich®) in 1X TBE buffer, visualized, and
finally documented according to the Molecular Cloning
Laboratory Manual [54]. PCR-specific amplified fragments of rbcL and matK were purified from agarose gel
by QIAquik® PCR PURIFICATION KIT (Qiagen Inc.,
Cat. no. 28106). Specific purified amplicons were then
cloned, and the DNA sequencing protocol for the rbcL
Abouseada et al. BMC Plant Biology
(2023) 23:193
and matK amplified fragments was executed as previously described by Badr et al. [17].
Dissection of grain surface sculpture and grain traits used
The scanning electron microscope (SEM) grain surface
sculpture was done at the Regional Center of Mycology
and Biotechnology, Al-Azhar University, Cairo, Egypt.
The procedures of sample mounting, coating with a
gold sputter coater (SPI module), and examination by
JEOL-JSM 5600 LV SEM were performed as described
by Mohamed et al. and Ibrahim et al. [26, 27]. The grain
sculpture was characterized by examining 5–10 grains of
each cultivar following the scheme adopted by Murley
[57]. The characteristics scored were combined to assess
the genetic variability of the wide range of wheat genotypic sources. Extracted exomorphic characters and used
abbreviations of analyzed sculptures are listed in Supplementary Table S1.
Data analysis
Sharp, evident reproducible bands amplified as ISSR or
SCoT markers in the agarose gel were scored as "1" for
the presence and "0" for the absence. Differentiating
between and resolving studied genotypes, the capacity of
ISSR or SCoT primers was judged by calculating the polymorphic metrics summarized in Table 2 (TNAs, MAs,
PAs, %P, PIC, RP, and MI). The PIC value for each primer
was calculated according to Ghislain et al. [58]. The rbcL
and matK barcoding gene sequences were analyzed using
Bayesian analysis with MrBayes software ver. 3.2 [59].
The best-fit substitution model (SYM + G) was chosen
based on the Akaike information criterion inferred by
MrModel-test v.2.3 [60]. The Markov chain Monte Carlo
(MCMC) process was run for 1,000,000 generations, and
the resulting trees were sampled every 1000 generations
with 16 chains. Stationarity was accomplished when
"the average standard deviation of split frequencies"
remained < 0.01. The first 25% of runs were discarded as
a relative burning. In the R-studio interface [61] to run
R software, several packages "seqinr", "adegenet", "DECIPHER", and "ape" were used, followed by reading the
aligned data from the fasta file and creating a distance
matrix for the alignment. The complete linkage method
was used for the UPGMA dendrogram construction [62].
The phylogenetic correlation matrix corresponded to a
given phylogenetic tree generated using MrBayes software [62]. Moreover, "ggplot2" packages was used to visualize the similarity and dissimilarity within and among
the cultivars [63, 64].
For phenetic analysis, a data matrix for T. aestivum
L. cultivars based on ten characters of grain surface
sculpture for 38 characters’ states was conducted (Supplementary Table S1). The phenetic binary data matrix
Page 13 of 15
was employed for grain surface sculpture traits. Hierarchical clustering (UPGMA) was used to determine how
closely the species or varieties are related [65]. Euclidian distance has been used after the data matrix scaling and standardization [66]. Using "pvclust" R-package,
the agglomerative cluster analysis was created [67].
The Principal Component Analysis (PCA) ordination
analyses were employed to examine the repeatability of
the grouping acquired by the cluster analyses (61). The
R-packages "factoextra" and "ggplot2" were handled for
visualizing the distance matrices "fviz_pca" that provide
ggplot2- based innovative visualization of PCA [66].
Using the "Corrplot" package, the correlation coefficients for the variable’s relationship were performed and
visualized according to [68]. All previous packages were
employed to run R software [69].
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12870‑023‑04196‑w.
Additional file 1.
Additional file 2.
Additional file 3.
Acknowledgements
The authors are grateful to the postgraduate studies and research affairs,
Faculty of Science, Ain Shams University, Cairo, Egypt, for making all research
facilities and materials available to conduct the experimental part.
Authors’ contributions
MI assessed the conceptualization of studied research point. HHA, AHM, SST,
SDI, FYE and MI conceived and designed the experimental methodology.
HHA, AHM, SST, SDI, FYE and MI performed the experimental analysis. HHA,
AHM, SST, SDI, FYE and MI were responsible for data curation. HHA, AHM, FYE
and MI prepared and wrote the original manuscript draft. AB, MET and MI
wrote, reviewed and edited the approved manuscript. AB and MET supervised
all procedures. All authors have read and approved the final manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation
Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank
(EKB). All gratitude to postgraduate studies and research affairs at Faculty of
Science, Ain Shams University for supporting the experiments of this study.
Availability of data and materials
The rbcL and matK sequences investigated in this study have been deposited
in NCBI"GenBank" (https://www.ncbi.nlm.nih.gov/genbank/) with primary
accession codes [the list of deposited accession codes with links, e.g.,
"MT797209; https://www.ncbi.nlm.nih.gov/nuccore/MT797209" was fully
investigated in Table 1. Furthermore, all generated and/or analyzed datasets
(e.g., binary matrices and multistate characteristics of grain surface sculpture)
during this study were completely included within the manuscript’s main text
and its accompanied supplementary information.
Declarations
Ethics approval and consent to participate
All the experimental research besides the field studies performed on wheat
plant grains (12 studied cultivars), including the collection of plant material
(wheat grains), complied accordingly with relevant institutional, national, and
international guidelines and legislation.
Abouseada et al. BMC Plant Biology
(2023) 23:193
Page 14 of 15
Consent for publication
Not applicable.
16.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Botany, Faculty of Science, Ain Shams University, Cairo, Egypt.
2
Botany and Microbiology Department, Faculty of Science, Zagazig University,
Zagazig 44519, Sharqia, Egypt. 3 Botany and Microbiology Department, Faculty
of Science, Helwan University, Cairo, Egypt. 4 Agricultural Genetic Engineering
Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt.
5
Botany Department, Faculty of Science, Fayoum University, 63514 Fayoum,
Egypt.
17.
18.
19.
Received: 24 October 2022 Accepted: 28 March 2023
20.
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