Caryologia. International Journal of Cytology, Cytosystematics and Cytogenetics 74(3): 77-89, 2021 Firenze University Press www.fupress.com/caryologia ISSN 0008-7114 (print) | ISSN 2165-5391 (online) | DOI: 10.36253/caryologia-886 Caryologia International Journal of Cytology, Cytosystematics and Cytogenetics Citation: Songpo Liu, Yuxuan Wang, Yuwei Song, Majid Khayatnezhad, Amir Abbas Minaeifar (2021) Genetic variations and interspecific relationships in Sal- via (Lamiaceae) using SCoT molecular markers. Caryologia 74(3): 77-89. doi: 10.36253/caryologia-886 Received: March 23, 2020 Accepted: September 24, 2021 Published: December 21, 2021 Copyright: © 2021 Songpo Liu, Yuxuan Wang, Yuwei Song, Majid Khayat- nezhad, Amir Abbas Minaeifar. 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 Commons Attribution License, which permits unrestricted use, distri- bution, and reproduction in any medi- um, 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. ORCID AAM: 0000-0002-9371-1498 Genetic variations and interspecific relationships in Salvia (Lamiaceae) using SCoT molecular markers Songpo Liu1, Yuxuan Wang1, Yuwei Song1,*, Majid Khayatnezhad2, Amir Abbas Minaeifar3 1Department of Life Science and Biotechnology, Nanyang Normal University, Nanyang, 473000, China 2Department of Environmental Sciences and Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran 3Department of Biology. Payame Noor University. P.O. Box19395-3697 Tehran, Iran *Corresponding author. E-mail: nanyangyws@126.com; aaminaeifar@gmail.com Abstract. The genus Salvia includes an enormous assemblage of nearly 1000 species dispersed around the World. Iran having 19 endemic species out of 61 is regarded as one of the important regions for Salvia diversity in Southwest Asia. Salvia species are herbaceous, rarely biennial or annual, often strongly aromatic. These species are of medicinal, commercial and horticultural value. Due to the importance of these plant species, we performed a combination of morphological and molecular data for this species. For this study, we used 145 randomly collected plants from 30 species in 18 provinces. Amplification of genomic DNA using 10 primers produced 134 bands, of which 129 were polymorphic (97.78%). The obtained high average PIC and MI val- ues revealed high capacity of SCoT primers to detect polymorphic loci among Salvia species. The genetic similarities of 30 collections were estimated from 0.61 to 0.93. According to the SCoT markers analysis, S. tebesana and S. verticillata had the low- est similarity and the species of S. eremophila and S. santolinifolia had the highest similarity. The aims of present study are: 1) can SCoT markers identify Salvia spe- cies, 2) what is the genetic structure of these taxa in Iran, and 3) to investigate the species inter-relationship? The present study revealed that SCoT markers can identify the species. Keywords: Iran, species identification, structure, Salvia, SCoT (Start Codon Targeted). INTRODUCTION Identifying the accurate boundaries of a species is critical to have a bet- ter perspective of any biological studies. Therefore, species delimitation is a subject of extensive part of studies in the framework of biology (Collard & Mackill 2009, Luo et al. 2011, Wu et al. 2013). However, defining the crite- rion which could address the boundaries of species is different and the place of debates (Jamzad 2012). Among different populations, genetic diversity is 78 Songpo Liu et al. non randomly distributed and is affected by various fac- tors such as geographic variations, breeding systems, dispersal mechanisms, life span, etc. Change in envi- ronmental conditions often leads to variation in genetic diversity levels among different populations and popu- lations with low variability are generally considered less adapted under adverse circumstances (Falk & Holsinger 1991, Olivieri et al. 2016). Most of the authors agree that genetic diversity is necessary to preserve the long-term evolutionary potential of a species (Falk & Holsinger 1991). In the last decade, experimental and field inves- tigations have demonstrated that habitat fragmentation and population decline reduce the effective population size. In the same way, most geneticists consider popula- tion size as an important factor for maintaining genetic variation (Turchetto et al. 2016). Salvia L. is known as the largest genus in Lamiace- ae (Mentheae-Salviinae) with approximately 1000 spe- cies diversified in three regions of the world: Central and South America (500 spp.), Western Asia (200 spp.) and Eastern Asia (100 species) (Walker et al. 2004). Iran having 19 endemic species out of 61 is regarded as one of the important regions for Salvia diversity in South- west Asia (Jamzad 2012). Salvia species are herbaceous, rarely biennial or annual, often strongly aromatic. These species are of medicinal, commercial and horticultural value (Safaei et al. 2016). Also, some Salvia species have pharmacological properties, including antiplatelet, anti- inflammatory and antithrombotic effects (Hosseinzadeh et al. 2003, Mayer et al. 2007; Fan et al. 2010). Some spe- cies of this genus are used in folk medicine, such as S. miltiorrhiza Bunge , which is used for treatment of car- diovascular diseases (Wang et al. 2007, 2009). Salvia reu- terana Boiss. is an endemic species which grows in the highlands of central Iran (Jamzad 2012). Its common name in Persian is “Mariam Goli Esfahani”, and the aerial parts of the plant are traditionally used as sedative and anxiolytic herbal medicine. In addition, the antibac- terial, antioxidant, free radical scavenging and anti-anx- iety properties of this herb have been proved in recent studies (Erbano et al. 2015). The chemical composition of Salvia strongly indicates that the herb has potential to become an important raw material for anti-inflam- matory compounds and knowledge of the diversity of wild populations will therefore be important to inform the use and conservation of this genus (Farag et al. 1986, Li & Quiros 2001). Genetic surveys, in particular, are key measures to efficiently access the genetic resources of species of pharmacological interest. Several markers have been previously applied to survey genetic variabil- ity within the genus Salvia (Song et al. 2010, Wang et al. 2011). Specifically, there are some important publications addressing S. miltiorrhiza, most of them utilizing domi- nant markers (Wang et al. 2011). Accordingly, some researchers have tried to assess this variability by ISSR and RAPD techniques in differ- ent Salvia species (Song et al. 2010, Wang et al. 2011, Sepehry Javan et al. 2012, Zhang et al. 2013, Peng et al. 2014, Erbano et al. 2015). Sepehry Javan et al. (2012) mentioned that three major factors influencing genetic variations in Salvia are: species, geographical distribu- tion and selection. These factors along with cross-pol- lination make the taxonomy and genetic relationships of Salvia species unclear (Wang et al. 2011). Morpho- logical characteristics are easily affected by environ- ment that makes identification of species more complex (Chen et al. 2013). The conservation and suitable use of plant genetic resources require accurate monitoring of their accessions. So, genetic characterization is essen- tial to manifest the extent of plant genetic diversity, and also to discover better genotypes; especially in the geographically differentiated genus such as Salvia (Song et al. 2010, Peng et al. 2014, Patel et al. 2014, Kharazian et al. 2015). With the progress in plant molecular biolog y, numerous molecular marker techniques have been developed and used widely in evaluating genetic diver- sity, population structure and phylogenetic relationships. In recent years, advances in genomic tools provide a wide range of new marker techniques such as, functional and gene targeted markers as well as develop many novel DNA based marker systems (Esfandani-Bozchaloyi et al. 2017 a, 2017b, 2017c, 2017d). Start codon targeted (SCoT) polymorphism is one of the novel, simple and reliable gene-targeted marker systems. This molecular marker offers a simple DNA-based marker alternative and repro- ducible technique which is based on the short conserved region in the plant genes surrounding the ATG (Col- lard & Mackill 2009) translation start codon (Collard & Mackill 2009). This technique involves a polymerase chain reaction (PCR) based DNA marker with many advantages such as low-cost, high polymorphism and extensive genetic information (Collard & Mackill 2009, Luo et al. 2011, Wu et al. 2013). The present investigation has been carried out to evaluate the genetic diversity and relationships among Salvia species using new gene-targeted molecular mark- ers, i.e. SCoT. This is the first study on the use of SCoT markers in Salvia genus; Therefore, we performed molecular study of 145 specimens of 30 Salvia species. We try to answer the following questions: 1) Is there infra and interspecific genetic diversity among studied species? 2) Is genetic distance among these species cor- related with their geographical distance? 3) What is the 79Genetic variations and interspecific relationships in Salvia (Lamiaceae) using SCoT molecular markers genetic structure of populations and taxa? 4) Is there any gene exchange between Salvia species in Iran? MATERIALS AND METHODS Plant materials A total of 145 individuals were sampled represent- ing 30 geographical populations belong 30 Salvia spe- cies (sp1= Salvia aristata Aucher ex Benth; sp2= S. eremophila Boiss; sp3= S. santolinifolia Boiss; sp4= S. tebesana Bunge; sp5= S. bracteata Banks & Sol; sp 6= S. suffruticosa Montb. & Aucher; sp7= S. dracocepha- loides Boiss.; sp8= S. hydrangea DC. ex Benth.; sp9= S. multicaulis Vahl.; sp10: S. syriaca L.; sp11: S. viridis L.; sp12= S. mirzayanii Rech. f. & Esfand.; sp13= S. macro- siphon Boiss.; sp14= S. sharifii Rech. f. & Esfand.; sp15= S. reuterana Boiss.; sp16= S. palaestina Benth.; sp17= S. sclareopsis Bornm. ex Hedge; sp18= S. spinose L.; sp19= S. compressa Vent.; sp20= S. sclarea L.; sp21= S. aethio- pis L.; sp22= S. microstegia Boiss. & Bal.; sp23= S. xan- thocheila Boiss. ex Benth.; sp24= S. limbata C. A. Mey.; sp25= S. chloroleuca Rech. f. & Aell.; sp26= S. virgate Jacq.; sp27= S. nemorosa L.; sp28= S. urmiensis Bunge; sp29= S. oligphylla Aucher ex Benth.; sp30= S. verti- cillata L.) in East Azerbaijan, Lorestan, Kermanshah, Guilan, Mazandaran, Golestan, Yazd, Esfahan, Teh- ran, Arak, Hamadan, Kurdistan, Ilam, Bandar Abbas, Ghazvin, Khorasan and Ardabil Provinces of Iran dur- ing July-Agust 2017-2019. Out-group taxa are: Marru- bium anisodon K. Koch and M. cuneatum Banks & Sol. For morphometric and SCoT analysis we used 145 plant accessions (five to twelve samples from each populations) belonging to 30 different populations with different eco- geographic characteristics were sampled and stored in -20 till further use. More information about geographi- cal distribution of accessions are in Fig. 1. Morphological studies Five to twelve samples from each species were used for Morphometry (Some endemic species were collected due to the rarity of 5 to 12 numbers). In total 22 mor- phological (9 qualitative, 13 quantitative) characters were studied. Data obtained were standardized (Mean= 0, variance = 1) and used to estimate Euclidean distance for clustering and ordination analyses (Podani 2000). Morphological characters studied are: corolla shape, bract shape, seed color, seed shape, bract color, corolla latex, leaf surface, calyx shape, basal leaf shape, pedi- cel length, calyx length, bract length, filament length, anther length, corolla length, nut length, nut width, basal leaf length, basal leaf width, corolla color, stem leaf length and stem leaf width. DNA Extraction and SCoT Assay Fresh leaves were used randomly from three to twelve plants in each of the studied populations. These were dried by silica gel powder. CTAB activated charcoal protocol was used to extract genomic DNA (Esfandani-Bozchaloyi et al. 2019). The quality of extracted DNA was examined by running on 0.8% agarose gel. A total of 25 SCoT primers developed by Collard & Mackill (2009), 10 primers with clear, enlarged, and rich polymorphism bands were cho- sen (Table 1). PCR reactions were carried in a 25μl volume containing 10 mM Tris-HCl buffer at pH 8; 50 mM KCl; 1.5 mM MgCl2; 0.2 mM of each dNTP (Bioron, Germany); 0.2 μM of a single primer; 20 ng genomic DNA and 3 U of Taq DNA polymerase (Bioron, Germany). The ampli- fications, reactions were performed in Techne thermocy- cler (Germany) with the following program: 5 min initial denaturation step 94°C, followed by 40 cycles of 1 min at Figura 1. Map of Iran shows the collection sites and provinces where Salvia species were obtained for this study; sp1= Salvia aristata; sp2= S. eremophila; sp3= S. santolinifolia; sp4= S. tebesana; sp5= S. bracteata ; sp 6= S. suffruticosa; sp7= S. dracocephaloides; sp8= S. hydrangea; sp9= S. multicaulis; sp10: S. syriaca; sp11: S. vir- idis; sp12= S. mirzayanii; sp13= S. macrosiphon; sp14= S. sharifii; sp15= S. reuterana; sp16= S. palaestina; sp17= S. sclareopsis; sp18= S. spinose; sp19= S. compressa; sp20= S. sclarea; sp21= S. aethiopis; sp22= S. microstegia; sp23= S. xanthocheila; sp24= S. limbata; sp25= S. chloroleuca; sp26= S. virgate; sp27= S. nemorosa; sp28= S. urm- iensis; sp29= S. oligphylla; sp30= S. verticillata 80 Songpo Liu et al. 94°C; 1 min at 52-57°C and 2 min at 72°C. The reaction was completed by final extension step of 7-10 min at 72°C. The amplification products were observed by running on 1% agarose gel, followed by the ethidium bromide staining. The fragment size was estimated by using a 100 bp molec- ular size ladder (Fermentas, Germany). Data analyses Morphological studies Morphological characters were f irst standard- ized (Mean = 0, Variance = 1) and used to establish Euclidean distance among pairs of taxa (Podani 2000). For grouping of the plant specimens, The UPGMA (Unweighted paired group using average) ordination methods were used (Podani 2000). ANOVA (Analysis of variance) were performed to show morphological differ- ence among the populations while, PCA (Principal com- ponents analysis) biplot was used to identify the most variable morphological characters among the studied populations (Podani 2000). PAST version 2.17 (Hammer et al. 2012) was used for multivariate statistical analyses of morphological data. Molecular analyses SCoT bands obtained were coded as binary char- acters (presence = 1, absence = 0) and used for genetic diversity analysis. Discriminatory ability of the used primers was evaluated by means of two parameters, polymorphism information content (PIC) and marker index (MI) to characterize the capacity of each primer to detect polymorphic loci among the genotypes (Pow- ell et al. 1996). MI is calculated for each primer as MI = PIC × EMR, where EMR is the product of the number of polymorphic loci per primer (n) and the fraction of polymorphic fragments (β) (Heikrujam et al. 2015). The number of polymorphic bands (NPB) and the effective multiplex ratio (EMR) were calculated for each primer. Parameter like Nei’s gene diversity (H), Shannon infor- mation index (I), number of effective alleles, and per- centage of polymorphism (P% = number of polymorphic loci/number of total loci) were determined (Weising et al, 2005, Freeland et al. 2011). Shannon’s index was cal- culated by the formula: H’ = -Σpiln pi. Rp is defined per primer as: Rp = ∑ Ib, were “Ib” is the band informative- ness, that takes the values of 1-(2x [0.5-p]), being “p” the proportion of each genotype containing the band. The percentage of polymorphic loci, the mean loci by accession and by population, UHe, H’ and PCA were calculated by GenAlEx 6.4 software (Peakall & Smouse 2006). Nei’s genetic distance among populations was used for Neighbor Joining (NJ) clustering and Neighbor- Net networking (Huson & Bryant 2006, Freeland et al. 2011). Mantel test checked the correlation between geo- graphical and genetic distances of the studied popula- tions (Podani 2000). These analyses were done by PAST ver. 2.17 (Hammer et al. 2012), DARwin ver. 5 (2012) software. AMOVA (Analysis of molecular variance) test (with 1000 permutations) as implemented in GenAlex 6.4 (Peakall & Smouse 2006) were used to show genetic difference of the populations. Gene flow was determined by (i) Calculating Nm an estimate of gene flow from Gst by PopGene ver. 1.32 (1997) as: Nm = 0.5(1 - Gst)/Gst. Table 1. SCoT primers used for this study and the extent of polymorphism. Primer name Primer sequence (5’-3’) TNB NPB PPB PIC PI EMR MI SCoT-1 CAACAATGGCTACCACCA 10 10 100.00% 0.36 4.86 9.55 3.45 SCoT-3 CAACAATGGCTACCACCG 9 8 84.99% 0.43 4.91 7.43 4.85 SCoT-6 CAACAATGGCTACCACGC 13 13 100.00% 0.44 4.34 11.55 3.44 SCoT-11 AAGCAATGGCTACCACCA 16 16 100.00% 0.37 3.88 8.56 1.65 SCoT-14 ACGACATGGCGACCACGC 20 20 100.00% 0.55 6.23 8.23 2.47 SCoT-15 ACGACATGGCGACCGCGA 15 14 93.74% 0.47 5.66 7.56 3.67 SCoT-16 CCATGGCTACCACCGGCC 13 12 92.31% 0.34 3.21 5.60 5.55 SCoT-17 CATGGCTACCACCGGCCC 12 12 100.00% 0.47 4.32 9.55 3.45 SCoT-18 ACCATGGCTACCACCGCG 11 9 82.89% 0.43 5.56 6.34 2.11 SCoT-19 GCAACAATGGCTACCACC 15 15 100.00% 0.39 3.25 10.11 1.87 Mean 13.4 12.9 97.78% 0.46 4.9 8.4 3.6 Total 134 129 Note: TNB - the number of total bands, NPB: the number of polymorphic bands, PPB (%): the percentage of polymorphic bands, PI: poly- morphism index, EMR, effective multiplex ratio; MI, marker index; PIC, polymorphism information content for each of CBDP primers 81Genetic variations and interspecific relationships in Salvia (Lamiaceae) using SCoT molecular markers This approach considers the equal amount of gene flow among all populations. RESULTS Species identification and inter-relationship Morphometry ANOVA showed significant differences (P <0.01) in quantitative morphological characters among the species studied. In order to determine the most variable char- acters among the taxa studied, PCA analysis has been performed. It revealed that the first three factors com- prised over 63% of the total variation. In the first PCA axis with 42% of total variation, such characters as seed shape, calyx shape, calyx length, bract length and basal leaf shape have shown the highest correlation (>0.7), seed color, leaf surface, corolla length, filament length, nut width, basal leaf length, were characters influenc- ing PCA axis 2 and 3 respectively. Different clustering and ordination methods produced similar results there- fore, PCA plot of morphological characters are presented here (Fig. 2). In general, plant samples of each species were grouped together and formed separate groups. This result show that both quantitative and qualitative mor- phological characters separated the studied species into distinct groups. In the studied specimens we did not encounter intermediate forms. Species Identification and Genetic Diversity Ten ISSR primers were screened to study genetic rela- tionships among Salvia species; all the primers produced reproducible polymorphic bands in all 30 Salvia species. An image of the ISSR amplification generated by SCoT- 11 primer is shown in Figure 3. A total of 129 amplified polymorphic bands were generated across 30 Salvia spe- cies. The size of the amplified fragments ranged from 100 to 2000 bp. The highest and lowest number of polymor- phic bands were 20 for SCoT-14 and 8 for SCoT-3, on an average of 12.9 polymorphic bands per primer. The PIC of the 10 SCoT primers ranged from 0.36 (SCoT-1) to 0.55 (SCoT-14) with an average of 0.46 per primer. MI of the primers ranged from 1.65 (SCoT-11) to 5.55 (SCoT- 16) with an average of 3.6 per primer. EMR of the SCoT primers ranged from 6.34 (SCoT-18) to 11.55 (SCoT-6) with an average of 8.4 per primer (Table 1). The primers Figure 2. PCA plots of morphological characters revealing species delimitation in the Salvia sp1= Salvia aristata; sp2= S. eremophila; sp3= S. santolinifolia; sp4= S. tebesana; sp5= S. bracteata ; sp 6= S. suffruticosa; sp7= S. dracocephaloides; sp8= S. hydrangea; sp9= S. multicaulis; sp10: S. syriaca; sp11: S. viridis; sp12= S. mirzayanii; sp13= S. macrosiphon; sp14= S. sharifii; sp15= S. reuterana; sp16= S. palaestina; sp17= S. sclareopsis; sp18= S. spinose; sp19= S. compressa; sp20= S. sclarea; sp21= S. aethiopis; sp22= S. microstegia; sp23= S. xanthocheila; sp24= S. limbata; sp25= S. chloroleuca; sp26= S. virgate; sp27= S. nemorosa; sp28= S. urmiensis; sp29= S. oligphylla; sp30= S. verticillata. 82 Songpo Liu et al. with the high EMR values were considered to be more informative in distinguishing the genotypes. The genetic parameters were calculated for all the 30 Salvia species amplified with SCoT primers (Table 2). Unbiased expected heterozygosity (H) ranged from 0.11 (S. syriaca) to 0.29 (S. virgata), with a mean of 0.19. A similar pattern was observed for Shannon’s informa- tion index (I), with the highest value of 0.45 observed in S. virgata and the lowest value of 0.12 observed in S. syriaca with a mean of 0.26. The observed number of alleles (Na) ranged from 0.214 in S. eremophila to 0.89 in S. aristata. The effective number of alleles (Ne) ranged from 0.98 (S. multicaulis) to 1.440 (S. virgata). AMOVA test showed significant genetic difference (P = 0.01) among studied species. It revealed that 66% of total variation was among species and 34% was within species (Table 3) Moreover, genetic differentiation of these species was demonstrated by significant Nei’s GST (0.21, P = 0.01) and D_est values (0.177, P = 0.01). These results revealed a higher distribution of genetic diver- sity among Salvia species compared to within species. Marrubium anisodon and M. cuneatum (out-groups) were separated from the other species. Two major clus- ters were formed in WARD tree (Fig. 4). The first major cluster (A) contained two sub-clusters: S. sharifii and S. macrosiphon are separated from the other studied species and join the others with a great distance and comprised the first sub-cluster. The second sub-cluster was formed by S. xanthocheila, S. limbata, S. aethiopis, S. sclarea and S. virgate. The second major cluster also contained two sub-clusters: eight species of S. multicau- lis; S. syriaca; S. viridis, S. reuterana; S. palaestina; S. sclareopsis; S. spinose and S. oligphylla were placed close to each other, while close genetic affinity between other species. In general, relationships obtained from SCoT data agrees well with species relationship obtained from morphological. This is in agreement with AMOVA and genetic diversity parameters presented before. The spe- cies are genetically well differentiated from each other. These results indicate that SCoT molecular markers can be used in Salvia species taxonomy. The Nm analysis by Popgene software also produced mean Nm= 0.167, that is considered very low value of gene flow among the stud- ied species. Mantel test with 5000 permutations showed a sig- nificant correlation (r = 0.13, p=0.0002) between genetic distance and geographical distance, so isolation by dis- tance (IBD) occurred among the Salvia species studied. Nei’s genetic identity and the genetic distance deter- mined among the studied species (Table 4). The results showed that the highest degree of genetic similarity (0.93) occurred between S. eremophila and S. santolini- folia. The lowest degree of genetic similarity occurred between S. tebesana and S. verticillata (0.66). The low Nm value (0.167) indicates limited gene flow or ances- trally shared alleles between the species studied and indicating high genetic differentiation among and within Salvia species. DISCUSSION Genetic diversity is a basic component of biodiver- sity and its conservation is essential for long term sur- vival of any species in changing environments (Mills & Schwartz 2005, Tomasello et al. 2015, Miao et al. 2019; Fig. 3. Electrophoresis gel of studied ecotypes from DNA fragments produced by SCoT-16. 1= Salvia aristata; 2= S. eremophila; 3= S. san- tolinifolia; 4= S. tebesana; 5= S. bracteata ; 6= S. suffruticosa; 7= S. dracocephaloides; 8= S. hydrangea; 9= S. multicaulis; 10: S. syriaca; 11: S. viridis; 12= S. mirzayanii; 13= S. macrosiphon; 14= S. sharifii; 15= S. reuterana; 16= S. palaestina; 17= S. sclareopsis; 18= S. spinose; 19= S. compressa; 20= S. sclarea; 21= S. aethiopis; 22= S. microstegia; 23= S. xanthocheila; 24= S. limbata; 25= S. chloroleuca; 26= S. virgate; 27= S. nemorosa; 28= S. urmiensis; 29= S. oligphylla; 30= S. verticillata;L = Ladder 100 bp, Arrows are representative of polymorphic bands. 83Genetic variations and interspecific relationships in Salvia (Lamiaceae) using SCoT molecular markers Xu et al. 2021, Zou et al. 2019, Wang et al. 2020). This is very important in fragmented populations because are more vulnerable due to the loss of allelic richness and increased population differentiation by genetic drift (decreases heterozygosity and eventual fixation of alleles) and inbreeding depression (increases homozygosity within populations; Frankham 2005). Therefore, knowl- edge of the genetic variability and diversity within and among different populations is crucial for their conser- vation and management (e.g. Esfandani-Bozchaloyi et al. Table 2. Genetic diversity parameters in the studied Salvia species. SP N Na Ne I He UHe %P S. aristata 6.000 0.892 1.138 0.221 0.141 0.165 38.63% S. eremophila 6.000 0.244 1.032 0.26 0.23 0.18 55.53% S. santolinifolia 4.000 0.314 1.044 0.16 0.18 0.23 43.38% S. tebesana 8.000 0.201 1.00 0.33 0.17 0.12 42.23% S. bracteata 5.000 0.341 1.058 0.24 0.27 0.20 53.75% S. suffruticosa 3.000 0.567 1.062 0.24 0.224 0.113 44.73% S. dracocephaloides 5.000 0.336 1.034 0.23 0.25 0.19 51.83% S. hydrangea 4.000 0.344 1.042 0.20 0.23 0.20 57.53% S. multicaulis 5.000 0.369 1.011 0.15 0.18 0.12 42.15% S. syriaca 8.000 0.566 1.014 0.45 0.10 0.11 32.58% S. viridis 9.000 0.432 1.049 0.18 0.22 0.25 55.05% S. mirzayanii 8.000 0.313 1.026 0.144 0.13 0.26 49.23% S. macrosiphon 12.000 1.244 1.322 0.28 0.284 0.192 50.91% S. sharifii 5.000 0.358 1.117 0.28 0.15 0.12 44.30% S. reuterana 6.000 0.458 1.039 0.28 0.18 0.23 49.38% S. palaestina 5.000 0.455 1.077 0.377 0.24 0.22 55.05% S. sclareopsis 8.000 0.499 1.067 0.14 0.101 0.14 49.26% S. spinose 9.000 0.261 1.014 0.142 0.33 0.23 43.15% S. compressa 6.000 0.555 1.021 0.39 0.25 0.28 43.53% S. sclarea 10.000 0.431 1.088 0.33 0.22 0.13 57.53% S. aethiopis 3.000 0.255 1.021 0.15 0.18 0.12 42.15% S. microstegia 3.000 0.288 1.024 0.23 0.15 0.17 64.30% S. xanthocheila 9.000 0.352 1.083 0.23 0.22 0.14 45.05% S. limbata 8.000 0.333 1.016 0.122 0.12 0.22 48.23% S. chloroleuca 12.000 1.247 1.199 0.271 0.184 0.192 55.91% S. virgata 5.000 0.358 1.440 0.114 0.30 0.29 66.50% S. nemorosa 6.000 0.299 1.029 0.231 0.18 0.23 44.38% S. urmiensis 5.000 0.462 1.095 0.288 0.25 0.22 62.05% S. oligphylla 8.000 0.399 1.167 0.259 0.234 0.133 32.88% S. verticillata 8.000 0.477 1.167 0.356 0.233 0.148 31.26% Abbreviations: N = number of samples, Na= number of different alleles; Ne = number of effective alleles, I= Shannon’s information index, He = gene diversity, UHe = unbiased gene diversity, P%= percentage of polymorphism, populations. Table 3. Analysis of molecular variance (AMOVA) of the studied species. Source df SS MS Est. Var. % ΦPT Among Pops 28 1801.364 75.789 12.154 66% Within Pops 129 334.443 3.905 2.888 34% 66% Total 144 1955.807 14.060 100% df: degree of freedom; SS: sum of squared observations; MS: mean of squared observations; EV: estimated variance; ΦPT: proportion of the total genetic variance among individuals within an accession, (P < 0.001). 84 Songpo Liu et al. 2018a, 2018b, 2018c, 2018d; Salari et al. 2013; 2020; Jaha- ni et al. 2019). In the present study we used morphological and molecular (SCoT) data to evaluate species relationship in Salvia. Morphological analyses of the studied Salvia spe- cies showed that they are well differentiated from each other both in quantitative measures (the ANOVA test result) and qualitative characters (The PCA plot result). In addition, PCA analysis suggests that characters like bract length, stipule length, bract shape, calyx shape, petal shape, length and width of stem-leaf, length and width of petal could be used in species groups delimita- tion. This morphological difference was due to quantita- tive and qualitative characters. Genetic structure and gene flow PIC and MI characteristics of a primer help in deter- mining its effectiveness in genetic diversity analysis. Sivaprakash et al. (2004) suggested that the ability of a marker technique to resolve genetic diversity may be more directly related to the degree of polymorphism. Generally, PIC value between zero to 0.25 imply a very low genetic diversity among genotypes, between 0.25 to 0.50 shows a mid-level of genetic diversity and value ≥0.50 suggests a high level of genetic diversity (Tams et al. 2005). In this research, the SCoT primers’ PIC values ranged from 0.36 to 0.55, with a mean value of 0.46, which indicated a mid-ability of SCoT primers in determining genetic diversity among the Salvia species. Comparable but low PIC values have been reported with other markers like RAPD and AFLP in African plan- tain (Ude et al. 2003), ISSR and RAPD in Salvia species (Yousefiazar-Khanian et al. 2016), AFLP in wheat (Bohn et al. 1999) and SCoT markers (Etminan et al. 2018, Pour-Aboughadareh et al. 2017, 2018). In Heikrujam et al. (2015), CBDP markers were found to be more effec- tive than SCoT markers with regard to the average PIC which was higher. In our study, the SCoT markers were found to be effective in the estimation of different Sal- via species genetic diversity with regard to average per- centage polymorphism (97.78%), average PIC value of SCoT markers (0.46), average MI (3.6) and average EMR of SCoT markers (8.4), which were higher than other reported markers on Salvia (Wang et al. 2009, Song et al. 2010, Yousefiazar-Khanian et al. 2016, Etminan et al. 2018, Gholamin and Khayatnezhad 2020 a, b, c, d). However, various marker techniques were found to have different resolution of the genome regions and the num- ber of loci that cover the whole genome for estimating of genetic diversity (Souframanien & Gopalakrishna 2004). A diverse level of polymorphism in Salvia species using ISSR, CoRAP, SRAP, SCoT and RAPD markers had been reported earlier by Wang & Zhang (2009), Song et al. (2010), Yousefiazar-Khanian et al. (2016) and Etminan et al. (2018). Gene flow is inversely correlated with the gene differentiation but is very important for population evo- lution, and takes place by pollen and seeds between pop- ulations (Song et al. 2010). In the current study, detected gene flow (Nm) among Salvia species was 0.167, showed low genetic differentiation among Salvia species. As a general rule, insects are the pollinators of Sal- via in Old World (Claßen-Bockhoff et al. 2004, Khayat- nezhad and Gholamin, 2012a, b). At the lower elevations, bees and at the higher altitudes insects like flies are the dominate pollinators among bilabiate flowers such as Salvia (Pellissier et al. 2010). According to Moein et al. (2019) genetic structure of SRAP marker showed that despite the presence of a limited gene flow, two distinct ecotypes were formed which may be the consequences of reproductive isolation Figure 4. WARD tree of SCoT data revealing species delimitation in the Salvia. 85Genetic variations and interspecific relationships in Salvia (Lamiaceae) using SCoT molecular markers Ta bl e 4. Th e m at ri x of N ei g en et ic s im ila ri ty ( G s) e st im at es u si ng S C oT m ol ec ul ar m ar ke rs a m on g 30 S al vi a sp ec ie s. sp 1= S al vi a ar is ta ta ; s p2 = S. e re m op hi la ; s p3 = S. s an to lin ifo lia ; sp 4= S . t eb es an a; s p5 = S. b ra ct ea ta ; sp 6 = S. s uff ru tic os a; s p7 = S. d ra co ce ph al oi de s; sp 8= S . h yd ra ng ea ; s p9 = S. m ul tic au lis ; s p1 0: S . s yr ia ca ; s p1 1: S . v ir id is ; s p1 2= S . m ir za ya ni i; sp 13 = S. m ac ro si ph on ; s p1 4= S . s ha ri fii ; s p1 5= S . r eu te ra na ; s p1 6= S . p al ae st in a; s p1 7= S . s cl ar eo ps is ; s p1 8= S . s pi no se ; s p1 9= S . c om pr es sa ; s p2 0= S . s cl ar ea ; s p2 1= S . a et hi op is ; s p2 2= S . m ic ro st e- gi a; s p2 3= S . x an th oc he ila ; s p2 4= S . l im ba ta ; s p2 5= S . c hl or ol eu ca ; s p2 6= S . v ir ga te ; s p2 7= S . n em or os a; s p2 8= S . u rm ie ns is ; s p2 9= S . o lig ph yl la ; s p3 0= S . v er tic ill at a sp 1 1. 00 0 sp 1 sp 2 0. 84 2 1. 00 0 sp 2 sp 3 0. 78 6 0. 93 3 1. 00 0 sp 3 sp 4 0. 76 7 0. 83 6 0. 84 2 1. 00 0 sp 4 sp 5 0. 82 3 0. 82 3 0. 78 6 0. 75 4 1. 00 0 sp 5 sp 6 0. 78 1 0. 76 6 0. 76 7 0. 75 7 0. 79 3 1. 00 0 sp 6 sp 7 0. 74 9 0. 68 3 0. 82 3 0. 75 9 0. 83 6 0. 86 2 1. 00 0 sp 7 sp 8 0. 68 1 0. 77 6 0. 78 1 0. 66 0 0. 82 3 0. 84 6 0. 92 8 1. 00 0 sp 8 sp 9 0. 81 7 0. 66 0 0. 74 9 0. 77 1 0. 76 6 0. 80 8 0. 87 5 0. 95 1 1. 00 0 sp 9 sp 10 0. 71 5 0. 88 4 0. 81 2 0. 82 0 0. 72 1 0. 61 8 0. 70 8 0. 70 4 0. 68 0 1. 00 0 sp 10 sp 11 0. 64 5 0. 75 4 0. 70 3 0. 72 5 0. 63 5 0. 81 6 0. 88 4 0. 81 2 0. 82 0 0. 72 1 1. 00 0 sp 11 sp 12 0. 74 5 0. 75 7 0. 71 7 0. 67 2 0. 63 2 0. 75 2 0. 75 4 0. 70 3 0. 72 5 0. 63 5 0. 83 9 1. 00 0 sp 12 sp 13 0. 83 9 0. 75 9 0. 70 9 0. 68 0 0. 66 7 0. 71 2 0. 77 9 0. 79 8 0. 83 4 0. 75 0 0. 79 9 0. 64 2 1. 00 0 sp 13 sp 14 0. 75 9 0. 85 9 0. 78 5 0. 77 5 0. 66 6 0. 73 7 0. 67 5 0. 80 8 0. 76 8 0. 67 5 0. 72 7 0. 72 8 0. 68 4 1. 00 0 sp 14 sp 15 0. 64 1 0. 87 2 0. 79 2 0. 77 3 0. 64 9 0. 80 7 0. 69 1 0. 66 5 0. 72 0 0. 68 1 0. 74 6 0. 79 6 0. 67 6 0. 72 2 1. 00 0 sp 15 sp 16 0. 76 7 0. 74 0 0. 67 1 0. 65 0 0. 61 7 0. 78 2 0. 73 4 0. 79 9 0. 82 9 0. 73 3 0. 80 0 0. 70 9 0. 77 0 0. 75 4 0. 77 0 1. 00 0 sp 16 sp 17 0. 78 4 0. 80 2 0. 75 7 0. 71 6 0. 77 8 0. 70 2 0. 74 4 0. 77 8 0. 81 6 0. 74 0 0. 78 5 0. 67 6 0. 69 9 0. 75 6 0. 73 5 0. 77 8 1. 00 0 sp 17 sp 18 0. 82 7 0. 81 7 0. 78 4 0. 77 0 0. 64 1 0. 81 4 0. 73 5 0. 70 6 0. 71 9 0. 95 3 0. 74 1 0. 75 8 0. 74 6 0. 75 3 0. 79 5 0. 79 9 0. 75 6 1. 00 0 sp 18 sp 19 0. 70 1 0. 80 0 0. 75 1 0. 77 4 0. 73 2 0. 79 0 0. 75 0 0. 79 7 0. 81 2 0. 77 4 0. 99 0 0. 72 2 0. 63 5 0. 81 6 0. 88 4 0. 81 2 0. 75 0 0. 79 9 1. 00 0 sp 19 sp 20 0. 76 4 0. 72 3 0. 68 3 0. 65 9 0. 67 9 0. 75 4 0. 77 9 0. 79 8 0. 83 4 0. 75 0 0. 79 9 0. 75 5 0. 63 2 0. 75 2 0. 75 4 0. 70 3 0. 67 5 0. 72 7 0. 75 5 1. 00 0 sp 20 sp 21 0. 75 4 0. 84 4 0. 80 4 0. 79 3 0. 69 5 0. 68 1 0. 68 9 0. 82 5 0. 77 8 0. 69 1 0. 74 4 0. 63 6 0. 66 7 0. 71 2 0. 77 9 0. 79 8 0. 68 1 0. 74 6 0. 68 4 0. 71 1 1. 00 0 sp 21 sp 22 0. 63 6 0. 82 6 0. 78 6 0. 77 2 0. 68 6 0. 75 6 0. 70 1 0. 67 6 0. 71 0 0. 68 8 0. 75 7 0. 70 3 0. 66 6 0. 73 7 0. 67 5 0. 80 8 0. 73 3 0. 80 0 0. 84 8 0. 77 4 0. 71 2 1. 00 0 sp 22 sp 23 0. 77 3 0. 69 1 0. 63 2 0. 61 5 0. 60 2 0. 75 1 0. 73 4 0. 79 9 0. 82 9 0. 73 3 0. 80 0 0. 68 1 0. 64 9 0. 80 7 0. 69 1 0. 66 5 0. 74 0 0. 78 5 0. 84 6 0. 75 7 0. 70 7 0. 98 0 1. 00 0 sp 23 sp 24 0. 78 4 0. 80 2 0. 75 5 0. 73 0 0. 61 4 0. 65 1 0. 74 4 0. 77 8 0. 81 6 0. 74 0 0. 78 5 0. 62 4 0. 61 7 0. 78 2 0. 73 4 0. 79 9 0. 95 3 0. 74 1 0. 69 0 0. 65 7 0. 64 5 0. 72 6 0. 73 5 1. 00 0 sp 24 sp 25 0. 84 4 0. 81 7 0. 78 4 0. 77 0 0. 64 1 0. 80 9 0. 80 2 0. 75 5 0. 73 0 0. 61 4 0. 84 3 0. 75 9 0. 59 9 0. 70 2 0. 74 4 0. 77 8 0. 77 4 0. 99 0 0. 77 8 0. 69 1 0. 74 4 0. 63 6 0. 66 7 0. 75 7 1. 00 0 sp 25 sp 26 0. 70 1 0. 81 2 0. 76 1 0. 76 2 0. 73 6 0. 79 0 0. 81 7 0. 78 4 0. 77 0 0. 64 1 0. 82 5 0. 72 2 0. 64 1 0. 81 4 0. 73 5 0. 70 6 0. 75 0 0. 79 9 0. 71 0 0. 68 8 0. 75 7 0. 70 3 0. 66 6 0. 69 0 0. 79 7 1. 00 0 sp 26 sp 27 0. 76 4 0. 71 2 0. 67 2 0. 67 0 0. 66 9 0. 75 5 0. 81 2 0. 76 1 0. 76 2 0. 73 6 0. 86 0 0. 75 9 0. 73 2 0. 79 0 0. 75 0 0. 79 7 0. 69 1 0. 74 4 0. 82 9 0. 73 3 0. 80 0 0. 68 1 0. 64 9 0. 67 3 0. 75 5 0. 76 8 1. 00 0 sp 27 sp 28 0. 75 4 0. 84 4 0. 80 4 0. 79 3 0. 69 5 0. 66 9 0. 71 2 0. 67 2 0. 67 0 0. 66 9 0. 72 6 0. 64 7 0. 67 9 0. 75 4 0. 77 9 0. 79 8 0. 68 8 0. 75 7 0. 81 6 0. 74 0 0. 78 5 0. 62 4 0. 61 7 0. 65 6 0. 76 7 0. 69 0 0. 70 4 1. 00 0 sp 28 sp 29 0. 70 9 0. 82 6 0. 78 6 0. 77 2 0. 68 6 0. 75 6 0. 84 4 0. 80 4 0. 79 3 0. 69 5 0. 85 8 0. 70 3 0. 69 5 0. 68 1 0. 68 9 0. 82 5 0. 73 3 0. 80 0 0. 73 0 0. 61 4 0. 84 3 0. 75 0 0. 79 9 0. 71 0 0. 68 8 0. 75 7 0. 70 3 0. 72 3 1. 00 0 sp 29 sp 30 0. 72 1 0. 79 4 0. 75 4 0. 71 7 0. 79 5 0. 75 1 0. 82 6 0. 78 6 0. 77 2 0. 68 6 0. 83 6 0. 68 1 0. 68 6 0. 75 6 0. 70 1 0. 67 6 0. 74 0 0. 78 5 0. 77 0 0. 64 1 0. 82 5 0. 72 2 0. 81 6 0. 74 0 0. 78 5 0. 62 4 0. 61 7 0. 65 6 0. 76 5 1. 00 0 sp 30 sp 1 sp 2 sp 3 sp 4 sp 5 sp 6 sp 7 sp 8 sp 9 sp 10 sp 11 sp 12 sp 13 sp 14 sp 15 sp 16 sp 17 sp 18 sp 19 sp 20 sp 21 sp 22 sp 23 sp 24 sp 25 sp 26 sp 27 sp 28 sp 29 sp 30 86 Songpo Liu et al. caused by altitude gradient and different niches through parapatric speciation. The heterozygosity (H) and Shan- non index (I) reflect diversity and differentiation among and within the germplasm collections, respectively (Que et al. 2014), and the higher the indices, the greater the genetic diversity. The magnitude of variability among Na, Ne, H and I indices using studied SCoT markers demonstrated a high level of genetic diversity among and within Salvia species. The similar results reported in Salvia miltiorrhiza based on ISSRs (Zhang et al., 2013) and other Salvia spe- cies using AFLP markers (Sajadi et al., 2010) as 95% and 99% polymorphism, respectively. Also, polymorphism index (PI) in RAPD primers was higher; whereas, other indices like PIC, EMR and MI were somewhat high in ISSRs. On the other hand, RP index was approximately equal in both techniques. In general, small differences in terms of calculated indices showed that both techniques had similar efficiency to differentiate the closely related ecotypes of Salvia. Chen et al. (2013) reported PIC val- ues about 0.20 in ocimum species by ISSR and RAPD markers and also showed the RP values as 1.39 and 5.13, respectively. PIC analysis can be used to select the most appropriate markers for genetic mapping. Also, the high MI reflects the marker efficiency to simultaneously ana- lyze a large number of bands (Powell et al., 1996; Patel et al., 2014). The high average Simpson’s coefficients (about 0.80) indicate high genetic variability among studied accessions of Salvia, too. This finding was similar to the study by Manica-Cattani et al. (2009) on accessions of Lippia alba by ISSR and RAPD. In their study on Salvia lachnostachys ecotypes by ISSR primers, Erbano et al. (2015) showed a range of 0.66-0.86 for Simpson’s index. Comparison of Nei’s similarity coefficients between ISSRs and RAPDs showed that both markers had high diagnostic capability. This is consistent with the results of ISSR markers in Mint accessions by Kang et al. (2013) and Salvia miltiorrhiza germplasms studied by Zhang et al. (2013); while the genetic similarity derived from SRAPs and ISSRs represented high proximity among Salvia miltiorrhiza populations (Song et al., 2010). Cluster analysis could group all 21 ecotypes and the results showed reasonable congruency in RAPD and ISSR in terms of species topology. Zhang et al. (2013) showed five major clusters for S. miltiorrhi- za germplasms based on Nei’s similarity coefficient for ISSRs; which did not indicate any clear pattern accord- ing to their locations. Patel et al. (2014) reported that in dendrograms of ISSR and RAPD, the genotypes of each Ocimum species were grouped, separately. Similar stud- ies in populations of S. japonica and some other Salvia species (Sudarmono and Okada 2008) did not show cor- relation between morphological variations and allozyme and DNA sequences. It was concluded that S. japonica is still at the early stage of speciation process Sympatry or co-occurrence of closely related species can either result from a sympatric speciation process or from sec- ondary contact due to range expansion after speciation. Under the allopatric scenario, genetic variation tends to be uniform across the genome due to a large proportion of the genome changing through a combination of diver- gent selection, differential response to similar selective pressures and genetic drift (see for example Strasburg et al. 2012). In contrast, in the extreme case of sympa- tric speciation, gene flow between the incipient species can homogenize most of the genome, except for loci that experience strong divergent selection pressures or regions that are tightly linked with these loci (see for example, Strasburg et al. 2012, Via 2012). In conclusion, the results of this study showed that to evaluate the genetic diversity of the Salvia genus, the primers derived from SCoT were more effective than the other molecular markers. Also, Salvia ecotypes/species were clearly separated from each other in the dendro- gram and MDS, indicating the higher efficiency of SCoT technique in Salvia species identification. ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (U1404303) and Postgrad- uate Education Reform and Quality Improvement Pro- ject of Henan Province(YJS2021JD17). REFERENCES Al-Quran S. 2008. Taxonomical and pharmaco- logical sur vey of therapeutic plants in Jor- dan. Journal of Natural Products,l(1):10-26. doi: 10.1556/034.59.2017.3-4.3 Bohn M., Utz H. F. Melchinger AE. 1999. Genetic simi- larities among wheat cultivars determined on the basis of RFLPs, AFLPs and SSRs and their use for predicting progeny variance. Crop Sci. 39, 228-237. 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