Caryologia. International Journal of Cytology, Cytosystematics and Cytogenetics 75(2): 45-52, 2022 Firenze University Press www.fupress.com/caryologia ISSN 0008-7114 (print) | ISSN 2165-5391 (online) | DOI: 10.36253/caryologia-1567 Caryologia International Journal of Cytology, Cytosystematics and Cytogenetics Citation: Shiva Shahsavari, Zahra Noormohammadi, Masoud Sheidai, Farah Farahani, Mohammad Reza Vazifeshenas (2022) SCoT molecular mark- ers are efficient in genetic fingerprint- ing of pomegranate (Punica granatum L.) cultivars. Caryologia 75(2): 45-52. doi: 10.36253/caryologia-1567 Received: February 04, 2022 Accepted: July 06, 2022 Published: September 21, 2022 Copyright: © 2022 Shiva Shahsavari, Zah- ra Noormohammadi, Masoud Sheidai, Farah Farahani, Mohammad Reza Vazifeshenas. This is an open access, peer-reviewed article published by Firenze University Press (http://www. fupress.com/caryologia) and distributed under the terms of the Creative Com- mons Attribution License, which per- mits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. SCoT molecular markers are efficient in genetic fingerprinting of pomegranate (Punica granatum L.) cultivars Shiva Shahsavari1, Zahra Noormohammadi1,*, Masoud Sheidai2,*, Far- ah Farahani3, Mohammad Reza Vazifeshenas4 1 Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotech- nology, Shahid Beheshti University, Tehran, Iran. 3 Department of Microbiology, Qom Branch, Islamic Azad University, Qom, Iran. 4 Improvement plant and seed Department, Yazd Agricultural and Natural Resource Research Center, AREEO, Yazd, Iran. *Corresponding authors. Zahra Noormohammadi: E-mail; marjannm@yahoo.com, z-nouri@srbiau.ac.ir. Masoud Sheidai: E-mail: msheidai@yahoo.com Abstract. The pomegranate is an economically important fruit plant species which has been utilized since ancient time as a source of food and medicine by mankind. This plant although is cultivated in certain geographical regions, but its fruits are imported and sold throughout the world. Iran is the center of origin for pomegranate and con- tains huge number of known cultivars. However, genetic studied on these cultivars are very limited and much detailed information has to be produced for better hybridiza- tion and breeding tasks in the country. A fingerprinting study was performed on 178 Punica trees in 47 known cultivars by using SCoT molecular markers. We obtained 61 SCoT bands/ loci which were used for genetic diversity analyses and grouping of the cultivars. A low genetic variability was obtained within and among Punica cultivars, but as revealed by AMOVA, this was quiet enough to produce significant genetic dif- ference among them. DAPC analysis revealed a trace of genetic admixture among the cultivars either due to gene flow or as a result of common ancestral shared alleles. Dis- criminating SCoT loci may be used in germplasm evaluation of Punica. The genetic difference of these cultivars can be utilized for hybridization and breeding programs. Keywords: AMOVA, DAPC, genetic diversity, pomegranate, SCoT. INTRODUCTION Pomegranate (Punica granatum L., family = Lythraceae) is an ancient fruit species originated from Iran (Graham et al., 1998). This plant species is native of Iran and Mediterranean region, and is also cultivated in tropi- cal and subtropical regions (Gundogdu & Yilmaz, 2012, Fischer et al., 2010, Patil et al., 2020). One of the amazing features of pomegranate is its adapta- tion to wide climatic conditions and it can grow in light and heavy soils, but 46 Shiva Shahsavari et al. it is the only limiting factor of cold winters (Vazifesh- enas et al., 2012). Recent investigations have shown medicinal value of pomegranate plant and particularly, its antioxidant activity and polyphenol content (Li et al., 2006). Inter- estingly enough, different parts of pomegranate tree, such as fruit peel, seeds, fruits, leaves, and flower have different and special healing (Li et al., 2006). The pome- granate juices, seeds and extracts are used for treating cardiovascular disease, diabetes and prostate cancer (Patel et al., 2008). Based on morphological features as well as agro- nomical properties, several pomegranate cultivars are known in Iran (Beghè et al., 2019, Khadivi et al., 2020, Shahsavari et al., 2021a), moreover, there are two main pomegranate germplasm collection centers located in Yazd, and Saveh cities, which contain about 700-1000 labelled pomegranate cultivars. In spite of this huge number of pomegranate cultivars and accessions, we have very little information on their genetic structure and fingerprinting. Germplasm evaluation requires a suitable system to identify parental lines, genotypes, wild relatives and released varieties. Although the variability in morpho- logical and biochemical characters are useful for the task due to easy scoring and their economical nature, they have some weakness like low variability, environmental influence, epistasis, and complex inheritance pattern. With the advent of molecular markers, DNA fingerprint- ing became a more objective sensible, and less error- prone method of identifying plant varieties than tradi- tional methods (Poets et al., 2020). Several factors together may determine the type and number of molecular markers for plant accessions’ geno- typing. The number of loci required to discriminate ver- ity depends on the diversity of the crop and its genome size. Moreover, the polyploidy level as well as breeding system or pollination mechanism of the plant can affect the genetic variability of the target plant species. The estimated genome size of Punica granatum is about 320. Mb (Luo et al., 2020), which is almost a small genome size and shows open pollination as we can obtain hybrids among different pomegranate plant trees. Recent studies which are concerned with molecu- lar assessment of different plant cultivars and accessions produce data not only on genetic finger printing of tar- get plants but also some information may be obtained on the cultivars’ relationship, degree of gene flow among the studied samples, identify discriminating molecular loci or bands, and even identify the loci with potential adap- tive value (see for example (Saboori et al., 2020, 2021, Sepahian et al. 2021). Till present time, different DNA markers, have been utilized to investigate genetic diversity within pome- granate cultivars and illustrate their relationship as well as genetic fingerprinting. These markers are Ran- dom Amplified Polymorphic DNA (RAPD, (Sheidai et al., 2007, Hasnaoui et al., 2010, Noormohammadi et al., 2012)), Amplified Fragments Length Polymorphism (AFLP, (Jbir et al., 2008, Moslemi et al., 2010)), Inter- Simple sequence Repeats (ISSR, (Narzary et al., 2010, Ahmed, 2018)), simple sequence repeats (SSR, (Noormo- hammadi et al., 2012, Zarei et al., 2018, Patil et al., 2020, 2021, Shahsavari et al., 2021a, 2021b)) and Start Codon Targeted polymorphism marker (SCoT, (Ahmed, 2018)). SCoT technique has been successful in identify- ing cultivars and analyzing genetic diversity within and between plant species, in many different plant spe- cies including crop plants such as wheat (Abdel-Lateif & Hewedy, 2008, Collard & Mackill, 2009), barley (Dora et al., 2017) and potato (Gorji et al., 2011) and also fruit trees such as mango (Luo et al., 2010), grapes (Guo et al., 2012) and date palm (Saboori et al., 2020). Recently breeding program on pomegranate culti- vars in Iran has been focused on producing hybrids with the aim to obtain elite genotypes in Iran. In this regard, attempts are made to evaluate the standing genetic vari- ability of the parental genotypes and their hybrids. The present study is a part of such investigations with the following tasks: 1- Produce data on genetic structure of the parental genotypes and their hybrids. 2- Estimating the standing genetic diversity within our germplasm collection. 3- Illustrate genetic affinity of the studied samples. 4- Compare discriminating power of SCoT and SSR molecular markers. MATERIALS AND METHODS Plant materials This study was performed on 187 Punica tress col- lected randomly from 47 genotypes, which were grown in Agricultural and Natural Resources Research and Training Center, Yazd, Iran. Among the studied sam- ples, we had also 7 hybrid genotypes, each having four replicates. These were collected from the trees cultivat- ed in Mazandaran province, Iran (Sahebi Pomegranate Cooperative Company -Sari). Details of some of these cultivars are provided in Table 1. DNA extraction and SCoT PCR amplification Total genomic DNA was extracted from fresh leaves by CTAB with some modification based on Krizman et 47SCoT molecular markers are efficient in genetic fingerprinting of pomegranate (Punica granatum L.) cultivars Table 1. Genetic diversity parameters determined in Punica genotypes studied. No Cultivar name Geographical location Accession code Na Ne I He %P 1 Rabab Poostghermez Fars 68-119-1 0.525 1.063 0.061 0.039 13.11% 2 Vahshi Poost ghermez Roodbar 67-210-2 0.393 1.038 0.034 0.023 6.56% 3 Goojagh Shahpar Vramin Vramin 69-181-1 0.508 1.082 0.067 0.046 11.48% 4 Makhmal shar Reza Esfahan 69-143-1 0.492 1.049 0.034 0.025 4.92% 5 Marmar Ramhormoz Ramhormoz 69-161-2 0.295 1 0 0 0.00% 6 Ardestani torsh Semnan Semnan 69-179-4 0.475 1.075 0.058 0.04 9.84% 7 Golnar Farsi Shahdad Kerman 68-541-3 0.525 1.069 0.06 0.04 11.48% 8 Poostsiyah Abrand Abad Yazd 67-233-1 0.262 1 0 0 0.00% 9 Zaghe Yazdi Yazd 68-602-1 0.475 1.092 0.07 0.049 11.48% 10 Shirin Shahvar Yazdi Yazd 70-680-1 0.475 1.043 0.036 0.024 6.56% 11 Goroch Shahvar Yazdi Yazd 68-546-1 0.656 1.106 0.097 0.064 18.03% 12 Vashik malas Sistan 67-614-1 0.426 1 0 0 0.00% 13 Bihaste khafri Jahrom 67-215-1 0.623 1.143 0.111 0.077 18.03% 14 Savehie torsh Esfahan 67-209-1 0.295 1 0 0 0.00% 15 Togh Gardan Yazdi Yazd 67-203-1 0.508 1.071 0.057 0.039 9.84% 16 Malas Dane Ghermez Yazdi Yazd 67-191-1 0.525 1.061 0.054 0.036 9.84% 17 Faroogh Ij Estahban Fars 67-204-1 0.541 1.066 0.05 0.035 8.20% 18 Malas Pishva Vramin Vramin 69-173-1 0.41 1 0 0 0.00% 19 Sefid Pooste Dezfooli Dezfool 69-131-1 0.426 1.016 0.011 0.008 1.64% 20 Shirin Poost ghermez Ramsar Gilan 69-168-1 0.443 1 0 0 0.00% 21 Tabolarze Aban Mahi Yazd 69-144-1 0.541 1.103 0.075 0.053 11.48% 22 Vahshi Jangali Sisangan Sisangan 69-138-1 0.344 1 0 0 0.00% 23 Siyah Dane Shahvar Kan Tehran 69-132-1 0.361 1 0 0 0.00% 24 Dane Siyah Ramhormoz Khoozestan 69-120-1 0.541 1.058 0.05 0.034 8.20% 25 Barge Moordi Charmahl Bakhtiyari 69-108-1 0.426 1.058 0.05 0.034 8.20% 26 Zaghe Droshte Hrabarjan Yazd 69-151-1 0.475 1.021 0.018 0.012 3.28% 27 Bagh Malek Ize Khorasan 69-113-1 0.344 1 0 0 0.00% 28 Shirin Poost nazok Darjezin Semnan 69-174-1 0.656 1.114 0.098 0.065 18.03% 29 Vahshi Shirin Behbahan Behbahan 69-171-1 0.295 1 0 0 0.00% 30 Golabi haste nazok Sangan Sangan 67-226-1 0.361 1 0 0 0.00% 31 Fereshte ghermez Sari 95-3-1 0.393 1 0 0 0.00% 32 Ghandehar Afghanistan 95-1-1 0.344 1 0 0 0.00% 33 Totsh Miankale Sari 95-6-47 0.508 1.092 0.07 0.049 11.48% 34 Narm Haste Andarab Afghanistan 95-7-1 0.344 1 0 0 0.00% 35 Molar Spain 95-4-1 0.59 1.105 0.077 0.055 11.48% 36 Wonderful zoodras USA 95-5-1 0.557 1.087 0.063 0.045 9.84% 37 Wonderful dirras USA 95-9-1 0.377 1.005 0.006 0.004 1.64% 38 Malas Saveh Saveh 92-29-1 0.361 1 0 0 0.00% 39 Malas Yazdi Yazd 67-299-1 0.377 1 0 0 0.00% 40 Sefid Pooste Rabi Ardel Boroojen 69-137-1 0.393 1 0 0 0.00% 41 Code 6 Sari 95-11-1 0.328 1 0 0 0.00% 42 Code 16 Sari 95-23-1 0.311 1 0 0 0.00% 43 Code 17 Sari 95-22-1 0.328 1 0 0 0.00% 44 Code 18 Sari 95-24-1 0.279 1 0 0 0.00% 45 Code 33 Sari 95-16-1 0.295 1 0 0 0.00% 46 Code 40 Sari 95-18-1 0.328 1 0 0 0.00% 47 Code 48 Sari 95-19-1 0.311 1 0 0 0.00% Total 0.427 1.034 0.028 0.019 4.78% Na = No. of Different Alleles, Ne = No. of Effective Alleles = 1 / (p^2 + q^2, I = Shannon’s Information Index = -1* (p * Ln (p) + q * Ln(q), He = Expected Heterozygosity = 2 * p * q, %P = Percentage of Polymorphic Loci. 48 Shiva Shahsavari et al. al. (2006) (32). We used activating charcoal and poly- vinyl pyrrolidone (PVP) for binding of polyphenolics during extraction. The genomic DNA was examined for quality and quantity by using 0.8% agarose electrophore- sis and Nanodrop spetrophotometer respectively Five primers (SCOT5, SCOT6, SCOT7, SCOT8, SCOT8) were selected based on high polymorphic genet- ic indices (Collard & Mackill, 2009). For SCoT amplif ication, 20 ng genomic DNA and 3 U of Taq DNA polymerase (Parstous, Iran); 2 X PCR buffer, 1.5 mM MgCl2; 0.2 mM of each dNTP (Parstous, Iran) with 0.2 µM of each primer, was imple- mented for 20µL polymerase chain reaction (PCR). The reactions were amplified in Technethermocycler (Bio-Rad, USA) using the following procedure 5 min at 95°C, 40 cycles of 1 min and 15 sec at 94°C, 1 min and 30 sec at 46.9-52.9°C (SCoT-5 46.9°C, SCoT-6 49.7 °C, SCoT-7 50 °C, SCoT-8 52.6 °C, SCoT-9 52.9°C) and 1 min at 72°C and a final cycle of 5 min at 72°C. All PCR products were visualized on 2 % agarose gel fol- lowed by the SYBR Green staining. For fragment size, we used 100-base pair (bp) molecular size ladder (Fer- mentas, Germany). Data analysis In total 61 SCoT bands/ loci were obtained in this study. These bands were coded as binary data (pres- ence = 1, absence = 0), for further analyses. The genetic diversity parameters like, number of alleles (Na), effec- tive number of alleles (Ne), Shannon index (I), Nei’s genetic diversity (He), unbiased He (UHe), and percent- age of polymorphism (P%)) were determined for the studied cultivars by using GeneAlex ver. 6.4 (Peakall & Smouse, 2006). Genet ic d if ferent iat ion of t he stud ied geno- types was examined by Analysis of Molecular Vari- ance (AMOVA) as implemented in GeneAlex ver. 6.5 (Peakall & Smouse, 2006). The genetic distinctness of the genotypes and their replicates was determined by TCS- networking as implemented in POPART ver. 3 (Hammer et al., 2001). We used principal coordinate analysis (PCoA), of PAST ver.3 to differentiate the genetic groups, and dis- criminant analysis of principal components (Hammer et al., 2001). (DAPC), to identify discriminating SCoT loci among Punica genotypes (Jombart et al., 2010). The assignment test of the same program was used to reveal genetic admixture in Punica genotypes. These analyses were performed by adegenet package of R (Jombart, 2008). RESULTS In total we obtained 61 SCoT bands/ loci in this study. The genetic diversity parameters determined in Punica. genotypes based on SCoT markers are provided in Table 1. The mean number of effective alleles (Ne) was almost alike in all Punica genotypes studied. However, the mean Shanon index and gene diversity differed in these genotypes. The same holds true for genetic poly- morphism as it. varied from 0.0. (complete genetic uni- formity within a cultivar), to about. 18%, which is still a low value for within cultivar genetic variability. AMOVA produced significant genetic difference among the studied Punica cultivars. The analysis showed that about 9% of total genetic variability is due to with- in population diversity, while 91% of genetic difference. occurs due to among cultivar genetic difference. TCS network constructed based on SCoT loci obtained revealed a high degree of genetic uniformity within replicates of each genotype studied (Fig. 1). This particularly holds true for the genotypes 12, 18, 30, 8, 27, 14, 20, 41, and 42. The replicates of these genotypes were 100% alike in SCoT loci and were positioned on each other in TCS network nodes. The other genotypes which showed some level of within population genetic variability were also separat- ed from the other genotypes, and their replicates were placed closer to each other than the other genotypes. This result indicates that SCoT markers can differentiate the studied tunica genotypes from each other. PCoA plot of tunica genotypes (Fig. 2), placed them in four different genetic groups. For example, the geno- types 2, 3, 6, 7, 9, 13, 14, 20, and 29, comprised the first genetic group due to their genetic similarity. The other genotypes formed the rest of genetic groups. Figure 1. TCS network of Punica genotypes based on SCoT data showing that these markers can differentiate the replicates of tunica genotypes from each other. 49SCoT molecular markers are efficient in genetic fingerprinting of pomegranate (Punica granatum L.) cultivars To illustrate the genetic distance between these four genetic groups, we determined Dice’ genetic similar- ity (S), between representative genotypes of each group and from that, we estimated the genetic distance by reducing 1-S. For example, genetic distance between genotypes number 14 and. 8, from the genetic groups. 1 and 2, produced genetic distance D = 0.55. Similarly, D value between genotypes 14 and 42, was 0.62, and between genotypes 8 and 15 was 0.70. Finally, D value between 10 and 42, was 0.40. Therefore, genetic distance obtained between the four genetic groups ranged from 40-70%, which i a high magnitude of genetic dissimilar- ity, and we can use these genetic differences for further breeding tasks in Punica. DAPC analysis of SCoT data, revealed that the first five Linear discriminant axes (LDA), comprise the high- est percentage of discrimination factors (Fig. 3). LDA plot constructed based on the first two LA axes, grouped the studied Punica genotypes in 3-4 genetic groups (Fig. 4), which is in agreement with PCoA analysis presented before. LDA analysis identified the SCoT loci with the highest discriminating power (see for exapmle, Fig. 5). SCoT loci 7-500, and 600, as well as SCoT loci9-500, are important. loci of the first LDA axis. Similarly, SCoT loci5-950, and 1000, SCoT7-400, and. SCoT8-800, are important loci of the second LDA axis. In the third LDA axis, SCoT loci 5-200, 300, and 950, as well as SCoT7-400. Therefore, of 61 SCoT loci obtained, a combination of SCoT. 5 and 7, may be used for genetic fingerprinting of Punica genotypes. Figure 2. PCoA plot separating Punica genotypes in four main genetic groups. Figure 4. DLA plot of Punica genotypes based on SCoT data. Figure 3. LDA. analysis of. SCoT data in Punica genotypes, show- ing. the first five LDA axes as discriminating factors among them. Figure 5. LDA loading of SCoT loci showing important loci of the first LDA axis. Figure 6. Assignment plot of Punica genotypes based on SCoT markers. Similarly colored individuals have similar genetic content, while admixed colors. indicated gene flow or ancestral shared alleles. 50 Shiva Shahsavari et al. Assignment test of DAPC. analysis (Fig. 6), revealed genetic affinity of the studied Punica genotypes (similar- ly colored individuals). Almost four genetic groups can be identified based on genetic content (similar colors). This plot also revealed some degree of genetic admixture (mixed colors) among Punica genotypes studied. The genetic admixture may be due to cross pollination of the genotypes or due to ancestral common shared alleles DISCUSSION Genetic fingerprinting as a mean for genetic equa- tion of plants germplasm are very important and of immediate use for planning selection and hybridization programs. Data obtained from these investigations illus- trate molecular basis of cultivar differences and if such differences are also accompanied to important agro- nomic characteristics, then plant breeders have a very good source of genetic material for improvement of that target plant (Nandakumar et al., 2004, Gorji et al., 2011, Nybom et al., 2014, Saboori at al., 2021). Finding the proper molecular markers for genetic fingerprinting is an essential step in genetic evaluation. For this reason, different molecular markers are used and compared in genetic fingerprinting of economically important plant species. This also holds true for Punica plant. The molecular markers can be assayed for their util- ity in the cultivar differentiation, and also revealing genetic affinity. The present study revealed that SCoT markers are very efficient markers for both showing within cultivar/ population genetic variability, and also for differentiation Punica cultivars. A previous study on twelve pomegranate cultivars grown in Egypt (Ahmed , 2018) showed a high level of genetic variability among the cultivars by using SCoT markers. They reported that none of SCoT primers were able to identify all cultivars independently while our findings on Iranian pomegran- ate cultivars identified different alleles of SCoT loci that successfully isolated some cultivars. For instance, alleles in SCoT 5, SCoT 6, SCoT 7 and SCoT 9 loci distinguished Poostsiyah Abrand Abad cultivar from other cultivars. The magnitude of genetic diversity obtained may differ in different molecular markers. However, an interesting similar results are obtained when we com- pare SCoT and SSR markers results obtained in the same genotypes. Shahsavari et al. (2021), studied the same Punica cultivars by SSR molecular markers and reported that these cultivars have a high genetic simi- larity with genetic distance ranging from 0.005 to 0.52, which is in close agreement with our study based on SCoT markers (10). They also reported significant genet- ic difference among Punica cultivars and that 8% of total genetic variability was due to among genotype dif- ference, while 92% was due to. between cultivar genetic differences, which is almost similar to the present study results of SCoT markers. These authors (Shahsavari et al. 2021), based on SSR data, reported some degree of genetic admixture among Punica. cultivars and also identified discriminating SSR loci to differentiate Punica. cultivars. We could also identify a few SCoT loci which can discriminate Punica cultivars (10). Also barcoding and mini-barcode analyses based on trnH-psbA and matK sequences on the same cultivars were provided better resolution of pomegranate cultivars’ assignment by Shahsavari et al. 2021b (25). Therefore, in conclusion we suggest that a combina- tion of SCoT and SSR molecular markers may be used in Punica germplasm evaluation and the results obtained on the genetic grouping and genetic difference of these cultivars can be utilized for hybridization and breeding tasks of this important fruit crop. 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