Ecologica Montenegrina 26: 127-136 (2019) This journal is available online at: www.biotaxa.org/em

Infraspecific genetic variation and population structure of nemorosa L. () in Iran

SEYED M. TALEBI1, REZA REZAKHANLOU2, ALEX V. MATSYURA*3,4

1 Department of Biology, Faculty of Sciences, Arak University, Arak, Iran E-mail: [email protected] 2 Department of Agriculture, Islamic Azad University, Saveh, Iran. *3 Altai State University, Barnaul, Russian Federation 4 Tomsk State University, Tomsk, Russian Federation, E-mail: [email protected]

Received 23 November 2019 │ Accepted by V. Pešić: 30 December 2019 │ Published online 27 December 2019.

Abstract Salvia nemorosa is widely distributed in different parts of Iran, while, there is no information about its population genetic structure and genetic diversity. The current information of its potential protection or conservation status in Iran is almost absent and unclear. Our investigation was the first molecular study of this medicinal species. We performed analysis of genetic variability and population structure of 11 populations of S. nemorosa in Iran using ISSR technique. We revealed intra and inter-population genetic diversity in the studied populations. Genetic parameters widely varied among the studied populations and confirmed their high genetic diversity. Moreover, AMOVA test showed significant molecular variation among and within the populations. The arrangement of populations and their individuals in NJ tree, PCA and MDS plots was in agreement with AMOVA results and individuals of five groups were overlapped. The Nm value showed low amount of gene flow among the populations. Based on STRUCTURE analysis and UPGMA tree of genetic distance, six genetic groups were identified among the studied populations, while two populations had significant differences and could be definite as ecotypes.

Key words: woodland sage, ISSR technique, molecular study, genetic parameters, genetic groups.

Introduction

Infraspecific variations provide the material regards the long and short-term evolutionary adaptation to seasonal and other rapid fluctuations in environmental conditions. Diminishing populations triggered the inbreeding, genetic drift and loss of genetic difference that make the organisms vulnerable to ecological changes (Ramel 1998). Myers (1997) has believed that populations’ loss can be more fatal than loss of species. Hughes et al. (1997) estimated that about of 1800 populations per hour get extinct in tropical forests. Several studies (Chen 2000; Ellis & Burke 2007) confirmed that population genetics provides important data on genetic variation, inbreeding, self-pollination versus out-crossing, gene flow, the partitioning of genetic variability intra and inter populations, and on effective size of population. In addition, the results of population genetics investigation are useful in conservation management of important medicinal .

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Different investigations (Sheidai et al. 2014; Noormohammadi et al. 2015; Koohdar et al. 2016) have proved that ISSR molecular markers are informative for genetic diversity and population structure studies. For example, ISSR polymorphism has been widely used all over the world to characterize genetic variation at the infraspecific level in different species such Hypericum perforatum (Mohammad et al. 2015), Ginkgo biloba (Zhiqiang et al. 2014), Elettaria cardamomum (Anjali et al. 2016), Pistacia lentiscus (Turhan–Serttaş & Özcan 2018), and Capsicum annuum (Nagy et al. 2007). Salvia with over 1000 taxa is one of the largest and important genera of Lamiaceae family (Farimani et al. 2015). These species are traditionally used in various parts of the world, from Mexico to South Africa and from China to Europe. Many species of this genus have economic importance as food, spices and flavors (Bahadori et al. 2016). Salvia nemorosa (syn. S. pseudosylvestris), is commonly known as wood sage and growing in central Europe, Caucasia, Turkey, and Iran (Skala & Wysokinska 2004; Jamzad 2012). Salvia nemorosa is a perennial, , drought resistant, native of Central and West Asia and grows in large groups. The 4-edged stem is 30-100 cm tall, erect, hairy, with sessile shoots at the axil of the leaves and branched to the tip. The lower lanceolate leaves are petiolate (1-9 cm long), hairy, pointed to the apex, with a cordate base, unequally crenate margins and reticulate venation. The lower leaves are 2.5-11 cm long, 1.5-4.5 cm wide and the middle and upper leaves are smaller. The green leaves are often rough, slightly hairy ventrally and dorsally almost glabrous, with short hairs along the veins. The species forms long inflorescence (13-40 cm), purple bracts are 7-13 cm long and 5-7 mm width. The violet lowers with a bilabiate corolla, are 8-16 cm long (Rechinger 1982; Jamzad 2012). Takeda et al. (1997) reported that leaves of this plant are traditionally used in Turkish traditional medicine for stop bleeding. Moreover, in Bulgarian native medicine, this aromatic plant is used mainly for treatment of hemorrhages, diarrhea, abdominal pain, and furuncles (Daskalova, 2004). S. nemorosa has essential oil which riches in spathulenol, phytol, caryophyllene oxide, 14-Hydroxi-9-epi-(E) caryophyllene and p-Cymene (Mahdiyeh et al. 2018). This species grows in various parts of Iran and comprises several local populations. Our investigation of genetic diversity and structure of selected local populations was performed for the first time in Iran. The results of this research can be used in conservation strategies and breeding management of this aromatic herb.

Material and methods

Eleven natural populations were selected for this species from different parts of Iran (Table 1). Plant samples were identified according to Flora of Iran (Jamzad 2012).

Table 1. Locality address and herbarium numbers of the studied populations of S. nemorosa.

No Name Locality address Herbarium Numbers 1 Amir kabir Markazi province, Arak, Amir Kabir new town,1320 m. AUH 50601 2 Mahneshan Zanjan province, Mahneshan, 1675 m. AUH 50602 3 Sangak Markazi province, Saveh, Sangak village, 1950 m. AUH 50603 4 Varcheh Markazi province, Khomein, Varcheh, 1400 m. AUH 50604 5 Rabor Kerman province, Rabor, 1250 m. AUH 50605 6 Polor Mazandaran province, Amol, Polor, 2730 m. AUH 50606 7 Jajroud Tehran province, Jajroud, 1420 m. AUH 50607 8 Shahzand Markazi province, Shahzand, 1700 m. AUH 50608 9 Khalkhal Ardabil, Asalem to Khalkhal road, 1670m. AUH 50609 10 Mashhad Khorasan Razavi province, Mashhad,1700 m. AUH 50610 11 Azna Lorestan province, Azna, 2000 m. AUH 50611

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Figure 1. AMOVA test of the studied S. nemorosa populations based on ISSR data.

Fresh leaves were used randomly from five plants in each population. We used Cetrimonium bromide (CTAB, Sigma-Aldrich) activated charcoal protocol to extract genomic DNA; in addition, the quality of extracted DNA was examined by running on 0.8% agarose gel (Sigma-Aldrich). We tested 15 ISSR primers for PCR reactions: UBC807, UBC810, UBC811, UBC823, UBC834, UBC849 (University of British Columbia, Canada) and (GA)9A, (GA)9T, (CA)9GT, (AGC)5GG, (AGC)5GT, (CA)7AT, (CA)7GT, (GT)7CA and (GA)9C, while only six of them ( (AGC)5GG, (AGC)5GT, (CA)7AT, (CA)7GT, (GT)7CA and (GA)9C) produced scorable bands. We used a 25 μl volume containing 10 mM Tris-HCl buffer (Sigma- Aldrich) at pH 8, 50 mM KCl( Merck), 0.2 μM of a single primer, 1.5 mM MgCl2 ( Merck), 0.2 mM of each dNTP(Kawsar Biotech Company), 3 U of Taq DNA polymerase (Bioron, Germany) and 20 ng genomic DNA for PCR reactions. The used program for amplifications reactions were: 5 min for initial denaturation step at 94°C, 40 cycles of 1 min at 94°C; 1 min at 52—57°C, 2 min at 72°C and the final extension step of 7–10 min at 72°C. The PCR results were observed by running on 1% agarose gel, and staining with ethidium bromide. We used a 100 bp molecular size ladder in order to estimate fragment size.

Molecular Analyses The obtained ISSR bands were coded as binary traits (absence = 0, presence = 1) and applied for the analyses of genetic diversity. We also calculated the number of effective alleles, Shannon information index (I), polymorphism percentage, and Nei’s gene diversity (H) (Freeland et al. 2011). We used Nei’s genetic distance among the studied populations and Neighbor-Net networking for UPGMA tree clustering (Freeland et al. 2011; Huson & Bryant 2006). Analysis of molecular variance (AMOVA) test with 1000 permutations and Nei’s Gst analysis were performed in GenAlex 6.4 and Geno- Dive ver.2, respectively (Peakall & Smouse 2006; Meirmans & Van Tienderen 2004). In addition, we investigated the population’s genetic variations by G’ST (standardized measure of genetic differentiation and D_est (Jost measure of differentiation) (Hedrick 2005; Jost 2008). We also analyzed populations’ genetic structure by Bayesian based model STRUCTURE (Pritchard et al., 2000). The data were scored as dominant markers for STRUCTURE analysis (Falush et al. 2007). The Evanno test was done on STRUCTURE results to determine correct number of K by using delta K value (Evanno et al. 2005). In K-Means clustering, two summary statistics, pseudo-F, and Bayesian Information Criterion, provide the best fit for k (Meirmans 2012).

Results

Parameters of S. nemorosa population’s genetic diversity determined in 55 plant individuals from 11 geographical populations are presented in Table 2. The highest value of polymorphism percentage (70.97%) was observed in Khalkhal (population no.9); moreover, Varcheh population (population no. 4) showed high value for gene diversity (0.26) and Shannon information index (0.23). Azna population (population no. 11)

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GENETIC VARIATION AND POPULATION STRUCTURE OF SALVIA NEMOROSA IN IRAN had the lowest value for percentage of polymorphism (22.58%) and the lowest value for Shannon information index (0.07) and He (0.06).

Table 2. Genetic diversity parameters in the studied populations (Na = Number 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, UHe = Unbiased Expected Heterozygosity = (2N / (2N-1)) * He, P (%) = percentage of polymorphism, populations).

Population Na Ne I He UHe %P Amir kabir 0.839 1.134 0.128 0.083 0.092 25.81% Mahneshan 1.258 1.279 0.248 0.164 0.182 48.39% Sangak 1.097 1.220 0.214 0.137 0.153 45.16% Varcheh 1.516 1.406 0.355 0.236 0.263 67.74% Rabor 1.323 1.230 0.220 0.143 0.158 45.16% Polor 1.516 1.411 0.353 0.238 0.264 64.52% Jajroud 1.194 1.248 0.254 0.161 0.179 54.84% Shahzand 1.226 1.258 0.257 0.162 0.180 58.06% Khalkhal 1.452 1.318 0.320 0.203 0.226 70.97% Mashhad 0.968 1.085 0.115 0.066 0.073 32.26% Azna 0.645 1.092 0.101 0.063 0.070 22.58%

AMOVA (PhiPT = 0.47, P = 0.010) showed significant variations among the studied populations of S. nemorosa. It also proved that, 47% of total genetic variability could be explained by genetic differentiation and 53% by intra-population diversity (fig. 1). We obtained similar outputs from MDS and PCoA (figs. 2 and 3) plots and Neighbor Joining (NJ) tree; therefore, Neighbor Joining tree was presented and discussed here (fig.4).

Figure 2. MDS plot of the studied populations based on ISSR data (population numbers are according to Table 1).

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Figure 3. PCoA plot of the studied populations based on ISSR data (population numbers are according to Table 1).

NJ tree revealed that the individuals of populations 1, 2, 3, 5, 10, and 11 were clustered in separated groups, but members of other groups were overlapped. This tree had two branches: the smaller contained all individuals of population 1 as a group and some individuals of populations 4 and 7 as two separated groups. The larger branch was divided into two sub-branches. In first sub-branch we founded all members of population 3 and the rest populations were placed in second sub-branch with two large groups. In one group, the members of populations 2, 5, 10, and 11 were placed separately, while the members of populations 6 and 9 were sparse inter-population. In other groups, the individuals of populations 4, 7, and 8 were spotted and mixed together. The arrangement of populations and their individuals was in agreement with AMOVA result.

Figure 4. NJ tree of S. nemorosa populations based on ISSR results (population numbers are according to Table 1).

Evanno test was performed on STRUCTURE analysis and outputted the best number of k = 7 (fig. 5). This genetic grouping coincided with the results of UPGMA tree of Nei's genetic distance (fig.6) where six groups were clustered. We obtained three groups: populations 7, 8 and 9: populations 5 and 6: populations 10 and 11. Populations 1, 2, 3, and 4 formed four groups, respectively.

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Figure 5. STRUCTURE plot of the studied populations of S. nemorosa based on k = 7 of ISSR results.

We found Nm = 0.32 for all the obtained ISSR loci, that indicated low amount of gene flow among the studied populations and supported genetic stratification as was showed by UPGMA tree and STRUCTURE analysis.

Figure 6. UPGMA tree of the studied populations based on Nei's genetic distance.

Discussion

S. nemorosa is naturally grows in Iran and has different medicinal uses, while no information is available on its genetic variability and structure at population level. Our research showed interesting outputs about its genetic differences and stratification throughout Iran. This species has several populations in various parts of Iran; each population is large and covers a wide area. Genetic parameters such as Shannon’s information index and expected heterozygosity revealed high level of genetic diversity among the studied populations. There are several factors that influence the level of diversity within and among populations, such as ecological and biogeographical factors and the mating systems (Pérez de Paz & Caujape´-Castells 2013; Coppi et al. 2014). For example, Hamrick and Godt (1989) have stated that geographic distribution of each species can be correlated with its genetic variability. Taxa that show small population size and a restricted geographic range are expected to have reduced level of genetic diversity. Furthermore, natural selection or demographic phenomena are more pronounced in small size population (Gibson et al. 2008). Sheth & Angert (2014) have

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Therefore, we based on the UPGMA tree of Nei's genetic distance and STRUCTURE analysis, concluded that populations 2 and 1 were more different from others and could be definite as two ecotypes. These populations were significantly separated from the others in UPGMA tree and had different colors pattern in STRUCTURE plot. This may be related to restricted gene flow, because the low value of Nm indicated low amount of gene flow among the studied populations. If these conditions continue, these ecotypes are likely to become a new taxon. Previous studies have shown that this predication can be correct. For example, different investigations (Abbott & Comes 2007; Foster et al. 2007) have suggested that, if various barriers, such as differences in flowering time, limit gene flow among ecotypes, the divergent selection may ultimately result in the evolution of separate biological species. Several authors (Turesson 1922; Silvertown & Charlesworth 2001) have suggested that natural selection creates ecotype via evolution of genetically divergent populations bound to specific habitats in populations, which exist in conditions differed from the original habitats of parental populations. In more recent infraspecific phytochemical and anatomical study on S. nemorosa, high levels of variations were found in leaf anatomical traits and essential oil compositions among the populations, also some ecotypes were identified in this species (Mahdiyeh et al. 2018). The Gst value in S. nemorosa indicated that greater differences existed within populations than among populations. In addition, PCoA and MDS plots as well as NJ three produced similar results and individuals of populations 4,6,7,8, and 9 were mixed together. Zhao et al. (2007) suggested that several factors contribute to genetic structure in plant populations, especially reproductive biology, gene flow, seed dispersal and nature selection. It seems that reproductive biology plays a central role in the genetic structure. Similar results were reported for several species of Salvia. For example, the Gst values in S. lachnostachys and S. miltiorrhiza indicated that greater difference exists within populations than among populations (Erbano et al. 2015). Bijlsma et al. (1994) suggested that out-crossing strategy existed among Salvia taxa is responsible for the higher level of diversity within populations. This is particularly probable with high population densities where out-crossing rates are higher. In contrary, the high adaptability and wide range of distribution of this species in various parts of Iran also contribute to the low levels of genetic differentiations among some populations. Specially, similar pattern of genetic structure among some populations (example 5 and 6–11) revealed gene exchange between populations. Previous studies showed that the most important mechanism promoting gene flow among various populations of the same species is through the seeds dispersal and pollination (Li & Chen 2014). Furthermore, this species very often grows in farms as a weed and it seeds might disperse by crop seeds. Therefore, seed dispersal among populations can acts as gene flow. Heijting et al. (2009) have reported that harvest and tillage operations are a major factor in dispersal of seed in agricultural crops. Species that had seeds on the plant at the harvest time are spread further in the direction of traffic by harvest and cultivation combined than species whose seeds had been placed on the soil surface. Populations 1, 2, 3, 5, 10, and 11 have lower genetic polymorphism rather than other populations. Individuals of these populations were clustered closely, rather than populations with higher percentage of genetic polymorphism. Our results from STRUCTURE analysis confirmed that populations with lower genetic polymorphism had uniform genetic structure among the individuals. Most of uniformity was recorded in populations no 1 and 11, that had lowest percentages of polymorphism. Freeland et al. (2011) have suggested that degree of genetic variation within species is highly correlated with reproductive mode, whereas high degree of open pollination/cross breeding correlates with high level of genetic variability.

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Conclusion

We used Inter Simple Sequence Repeat (ISSR) molecular markers to investigate genetic diversity and population structure of S. nemorosa in Iran. AMOVA test revealed significant variations among the populations and as is typical for out-crossing herbs, the majority of genetic difference occurred at the intra- population level. In addition, PCoA and MDS plot along with NJ tree indicated majority of intra-population genetic variations that were observed in some populations. The results of UPGMA tree of Nei genetic distance confirmed existence of six genotypes among the population: all these groups were unique according to results of STRUCTURE analysis. We also revealed that two populations were very different from others by genetic structure and could be define as ecotypes.

References

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