Genetic diversity and structure of Sideritis raeseri Boiss. & Heldr. (Lamiaceae) wild populations from Balkan Peninsula

Journal Pre-proof Genetic diversity and structure of Sideritis raeseri Boiss. and Heldr. (Lamiaceae) wild populations from Balkan Peninsula Efstathia Patelou, Paschalina Chatzopoulou, Alexios N. Polidoros, Photini V. Mylona

PII:

S2214-7861(20)30002-4

DOI:

https://doi.org/10.1016/j.jarmap.2020.100241

Reference:

JARMAP 100241

To appear in:

Journal of Applied Research on Medicinal and Aromatic Plants

Received Date:

16 April 2019

Revised Date:

13 January 2020

Accepted Date:

14 January 2020

Please cite this article as: Patelou E, Chatzopoulou P, Polidoros AN, Mylona PV, Genetic diversity and structure of Sideritis raeseri Boiss. and Heldr. (Lamiaceae) wild populations from Balkan Peninsula, Journal of Applied Research on Medicinal and Aromatic Plants (2020), doi: https://doi.org/10.1016/j.jarmap.2020.100241

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Genetic diversity and structure of Sideritis raeseri Boiss. & Heldr. (Lamiaceae) wild populations from Balkan Peninsula

Efstathia Pateloua, Paschalina Chatzopouloub, Alexios N. Polidorosa,* and Photini V. Mylonab,*

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Department of Genetics and Plant Breeding, School of Agriculture, Aristotle

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University of Thessaloniki, 54124 Thessaloniki, Greece Institute of Plant Breeding & Genetic Resources, HAO-DEMETER, 570 01 Thermi,

Efstathia Patelou [email protected]

* Corresponding authors

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Paschalina Chatzopoulou [email protected]

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Greece

Alexios N. Polidoros [email protected]

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ORCiD: https://orcid.org/0000-0002-7575-7724

Photini V. Mylona [email protected]

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ORCiD: https://orcid.org/0000-0002-8985-1199

Graphical abstract

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Sideritis raeseri wild populations are genetically different and distinct from each

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Highlights

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Higher inter- than intra-population diversity indicates isolation and differentiation

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There is a low positive correlation of geographic and genetic distance

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Landscape morphology and high altitude prevent gene flow among nearby

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populations 

Different wild populations represent unique genetic resources and their

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conservation is important

Genetic diversity and structure of Sideritis raeseri Boiss. & Heldr. (Lamiaceae) wild populations from Balkan Peninsula

Abstract

Sideritis raeseri Boiss. & Heldr is a subalpine/alpine plant species endemic to the southern part of the Balkan Peninsula. Mountain tea (S. raeseri) is a traditional medical remedy as a decoction of high value and a source of essential oils beneficial to human health. Recent findings of Sideritis active constituents in preventive therapy of osteoporosis, cancer and neurodegenerative diseases have increased public interest with pronounced impact on natural populations and species diversity. Morphological descriptors and chemotypes are commonly used for species description and diversity

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evaluation; however, these are affected by environmental perturbations. The present study assessed the genetic diversity and relationships of nine wild S. raeseri

populations, using 12 URP molecular markers. In total, 283 distinct and reliable bands

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in all populations were amplified, including 86-122 polymorphic bands within each

population. AMOVA analysis revealed a percentage of 66.30 % inter-population genetic

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diversity. Genetic structure analysis of the individual samples indicated that nine

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relatively independent gene pools were represented in the data and each individual sample was correctly assigned to its population of origin. Geographic distance was not correlated to diversity among populations. Conservation and breeding strategies are

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suggested as appropriate management measures for protection of germplasm resources against genetic erosion.

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Keywords: mountain tea, molecular markers, Balkan flora, genetic diversity, conservation, breeding

1. Introduction Sideritis is one of the three largest genera of Lamiaceae family. It is represented by over 150 annual and perennial species, distributed in temperate and tropical regions

of the North hemisphere, mostly in the Mediterranean basin. The continental distribution of Sideritis species extends from the eastern, including the Mideast, to the western Mediterranean regions of southern Europe and northern Africa (Barber et al., 2002), comprising a diversity of Sideritis taxa. Many of them are endemic to specific regions (Gonzalez-Burgos et al., 2011) with Sideritis raeseri Boiss. & Heldr. and Sideritis scardica Griseb. being endemic to the Balkan Peninsula (Romanucci et al., 2017).

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In folk medicine, the decoction and infusion of the aerial parts of Sideritis plants are used for their anti-inflammatory, antimicrobial, anticonvulsant, analgesic and

healing properties and to treat disorders of the gastrointestinal, respiratory and urinary

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tract. Studies have identified several constituents including phenolics, terpenoids, hydrocarbons and essential oil components that are used for the chemotaxonomic

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identification of Sideritis species within the genus (Gonzalez-Burgos et al., 2011). It has

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been suggested that phenolic constituents (phenylpropanoids and flavonoids) are mainly responsible for the antioxidant, antiulcer, antibacterial and anti-inflammatory activity of the extracts while terpene components from the essential oil and diterpenoids contribute

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to the antibacterial, cytotoxic, and antitumor activities (Lall, et al. 2019). Due to the flavonoid content of Sideritis species, plants could also be used as a natural source of antioxidants in food additives, even in functional food design, for the prevention of

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osteoporosis (Kassi et al., 2004; Tsaknis and Lalas, 2005). The origin, systematic classification, diversification and evolution of Lamiaceae

species are of great interest to scientists, especially when it comes to biodiversity preservation in hotspot areas, including the Balkan Peninsula. Strong hybridization and large selection pressure due to collection of wild grown Sideritis for commercial purposes, especially during flowering stage, result in inefficient evaluation and

commercial development of the available germplasm. Primary karyological, palynological and morphological data was used to assess the genetic relationships among taxa (Esra et al., 2009), often supplemented with information provided by analysis of the chemical composition of essential oils of Sideritis species (GonzalezBurgos et al., 2011). In the literature there are numerous publications on qualitative and quantitative determination of various chemical compounds of Sideritis species, including S. raeseri (Gonzalez-Burgos et al., 2011). Phytochemical studies during the

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1970s and 1980s were focused on chemotaxonomy, since diterpenoids and flavonoids are typical of the genus (Gonzalez et al., 1979, 1978; Venturella and Bellino, 1977).

Sideritis species are characterized by strong hybridization as well, even between

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species of different sections, which complicates classification by standard methods, and chemotaxonomy sometimes (Sanchez-Gras and Segura, 1997; Todorova and Christov,

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2000). However, combination of certain morphological features and chemotaxonomic

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indicators has unraveled many classification issues. Nonetheless, instability of essential oil in quantity and chemical composition even within a species, has been reported (Aligiannis et al., 2001). Apart from genetic factors, this is attributed to environmental

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factors, time of harvest, chemotype, agricultural practices, and nutritional status of the plant (Gonzalez-Burgos et al., 2011), as well as the extraction method of essential oil (Pljevljakusic et al., 2011).

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Until now medicinal species counting Sideritis species are not model plants in biology. As many of them have a genome size that is not yet estimated and since there are no well-established techniques for their molecular breeding, application of DNAbased assays seems to be challenging (Canter et al., 2005). Therefore, the capability of detecting variation, the reliability of results and the statistical robustness, along with the feasibility of the method and data analyses are some aspects that must be met while

selecting the suitable genetic markers (Agarwal et al., 2008), considering the objectives of the research, the species studied and the sampling strategy. Although molecular markers proved valuable tools for genetic analyses of Lamiaceae, especially for threatened species (Verma et al 2007, Grdisa et al 2019), providing us with valuable information, however their employment in these species is not as extensive, compared to cultivated species and crops. Regarding Sideritis genus, markers were implemented mostly on the distinction of species due to frequent

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hybridization among them and the variation of ecotypes and the degree of polymorphism (Santiago-Valentin and Francisco-Ortega, 2008) and for traceability

purposes (Kalivas, et al., 2014). However, it is worth noting that in these studies bulk

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samples were analyzed rather than samples of individual plants. Recently, a study of genetic variations in natural populations sampled in contrasting environments, of S.

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scardica using AFLP markers was reported (Grdisa et al., 2019). It was found that low

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gene diversity and significant genetic differentiation characterized 9 studied populations in the south of Balkan peninsula.

In recent years, advanced molecular techniques have emerged, either combining

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the advantages of basic methods or incorporating modifications that improve sensitivity and resolution power. New techniques utilize DNA elements such as retrotransposons and chloroplast or mitochondrial microsatellites, thus increasing genome coverage. As a

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rule of thumb, DNA markers with increased evolutionary rate are more appropriate in detecting variation at low taxonomic level, in relation to more conservative ones, such as chloroplast genes. On the other hand, Single Sequence Repeats (SSRs), although promising, are not directly applicable to some species, due to lack of suitable primers (Cinar et al., 2009). Certain groups of DNA repeats are often typical of a family, genus, species or even chromosomes. They undergo different evolutionary constraints in

relation to genes and are characterized by well conserved structure, differing only in the number of repeats. Their use in taxonomic and phylogenetic studies is based on this kind of polymorphism (Smith and Flavell, 1974). Particularly repeated sequences in non-coding regions create a unique DNA pattern, therefore determining the “genetic fingerprint” of an individual (Rao et al., 2010). Kang and Kang (2008) identified retrotransposons and transposable elements of Group II, such as CACTA transposons in wild rice genome and reported that the

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repeated pKRD sequence may be a new and active transposon of CACTA family. This sequence was previously used for the random design of Universal Rice Primers (Kang et al., 2002). Of the 40 primers originally generated, 12 were polymorphic in a variety

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of plant samples, as well as in animal and microbial DNA. Xiong et al. (2013) pointed out the difference between URPs and Directed Amplification of Minisatellite-region

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(DAMD) primers regarding their design. URPs are derived from a DNA element that is

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in multiple copies scattered throughout the genome, while DAMD primers originated from a DNA region with tandem repeats. Cinar et al. (2009) first used the URP-PCR technique in 8 species of Sideritis, all members of the section Empedoclea. URP

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primers confirmed their universality and amplified highly reproducible bands within and between PCR reactions, regardless of the method used for the DNA isolation. Their high degree of polymorphism was expressed in Polymorphism Information Content

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(PIC) values. Results also confirmed classification according to morphological features, thus the URP-PCR technique can be safely used for discrimination and identification of Sideritis species and individuals. S. raeseri is one of the various native Sideritis species that are bulk collected from wild populations to produce dry material, although it is cultivated nowadays in Greece (Kloukina et al., 2019). Nurseries that provide plantlets to the farmers are

established mainly asexually from genetic material collected from indigenous wild populations and currently the origin and properties of this material is uncharacterized. Due to the increased interest of nurseries and farmers for S. raeseri propagation material along with the extensive harvesting during flowering stage the species distribution is severely threatened. The lack of information on the genetic diversity and structure of local wild populations and the impact of human activities on the species diversity inspired this study. We used molecular markers to investigate the genetic variation of

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nine (9) wild populations collected from the mountainous regions of Greece and the Republic of North Macedonia. Results provide for the first time, useful information on populations’ genetic structure and genetic identity, to advance management protection

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actions to prevent genetic erosion and loss of species genetic variation. Moreover, the

study provides valuable knowledge to enable breeding strategies for species cultivation

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genetic identity and habitats.

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and propagation as a measure to support conservation of wild populations’ diversity,

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2. Materials and methods 2.1. Plant material

Nine populations of wild grown S. raeseri were sampled during expeditions to the mountain areas of Greece and the Republic of North Macedonia. The geographical

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locations of the collected wild populations are depicted in Supplementary Figure 1. The collection sites represent the main areas of species distribution. Young leaves were collected separately from individual plants having a distance from each other more than 20 m apart, to avoid collecting multiple samples from the same parent. Collected plant material was immediately stored in silica gel and brought to the laboratory for further DNA analyses.

2.2. DNA extraction and URP amplification conditions Isolation of DNA was performed from each sample independently. Leaf tissue of 50 mg was ground to fine powder using liquid nitrogen and used for DNA extraction by the Qiagen DNeasy plant mini kit, according to the manufacturer's protocol (Qiagen, Germany) with minor modifications. The DNA concentration was estimated by standard spectrophotometric methods at 260 nm and 280 nm by Nanodrop ND 1000

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Spectrophotometer (Peqlab, Erlangen, Germany). The quality of DNA samples was also visualized in agarose gels. Samples were then diluted to 50 ng/μl working

concentration. After all, a total of 156 individual plants (18 plants per population,

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except P7 from which 12 plants were analyzed) were used in URP analysis.

A PCR-based approach involving twelve URP primers (Kang et al., 2002) was

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used to analyze the genetic diversity in 156 S. raeseri plant samples. The URP-PCR

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protocol was followed according to Cinar et al. (2009) with some modifications. Concentrations of DNA template, primer and deoxynucleotide triphosphates (dNTPs) and the optimum annealing temperature were standardized for each primer in

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preliminary trials to obtain DNA fingerprint profiles. Analysis indicated that the PCR profile and the optimized chemical concentrations resulted in reproducible and reliable DNA amplification. PCR reactions were conducted in a 25 μl final volume using 150 ng

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of total DNA, 0.2 mM of each dNTP (NEW ENGLAND Biolabs Inc.), 2.4 μM primer (Syntezza Bioscience Ltd.), 3.0 mM MgCl2 and 1.2 U of KAPA Taq DNA polymerase (KAPA BIOSYSTEMS). PCR reactions were performed in Veriti 96Well thermal cycler (Applied Biosystems) with the following touchdown program: 3 min at 94 oC followed by a pre-PCR of 10 cycles including 30 sec at 94 oC for denaturation, 45 sec at 56 oC for annealing and 3 min at 72 oC for primer extension reaction. Annealing

temperature was reduced by 1 degree per cycle and the PCR amplification was then continued for 30 more cycles at a constant annealing temperature of 47 oC keeping the rest of the pre- PCR parameters. A final extension step at 72 oC for 11 min was also performed. The PCR products were separated on 1% agarose gels in TBE buffer, except for those amplificated by URP9F where 2% agarose gels were used due to smaller size fragments, in a MultiSub gel electrophoresis apparatus (UVItech Ltd.). Electrophoresis

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was performed at 40V for 2.5-3 hours and band profiles were photographed using a Gene Genius Gel Documentation System (Syngene Inc, Cambridge, UK).

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2.3. Data analysis

After collecting and grouping all pictures, each band size was estimated using the

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GelAnalyzer 2010 software (http://www.gelanalyzer.com). PCR fragments are

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considered genomic DNA and bands that migrate the same distance, homologous loci. URP markers segregate as dominant alleles; thus, amplification bands were analyzed as such. Presence or absence of a band in a PCR profile was encoded with 1 or 0

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respectively, producing a binary matrix. Scoring was performed regardless of the brightness of the bands.

The binary matrix was used by GenAlEx v6.5 (http://biology-

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assets.anu.edu.au/GenAlEx/), (Peakall and Smouse, 2012) to generate a pairwise individual-by-individual Euclidean genetic distance matrix. Raw data was set binary haploid, since alleles are equal to molecular phenotype, according to Zhivotovsky (1999). The genetic distance matrix was used for AMOVA, which requires a Euclidean metric. AMOVA (Excoffier et al., 1992) was applied to estimate variance components for the URP phenotypes partitioning intra- and inter-population variation. The variance

components were tested statistically by randomization tests with 999 permutations. An analogue of FST, ΦPT, which is a measure of population genetic differentiation for binary data, was also calculated, along with intra- and inter-population genetic statistics. Mantel test was used to determine any correlation between the genetic and geographic distances of populations. Principal coordinate analysis (PCoA) is a multivariate analysis method for exploration and visualization of dissimilarities of data. As a result, in GenAlEx program, two axes are displayed, each accounting for a

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decreasing amount of data variation. Based on the Euclidean genetic distance matrix as well, an unweighted pair-group method using arithmetic average (UPGMA)

dendrogram was constructed with MEGA v5.05 http://www.megasoftware.net, (Tamura

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et al., 2011), indicating the clustering relationships of sampled populations. A Bayesian grouping analysis was conducted with STRUCTURE v2.3.4 (Falush et al., 2007, 2003;

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Pritchard et al., 2000) and STRUCTURE HARVESTER (Earl and Vonholdt, 2012) to

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infer population structure and assign individuals to groups. The pre-determined number of genetic groups K was set from 3 to 11 and analysis was performed for each value three times, assuming an admixture model of ancestry and correlated allele frequencies.

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MCMC algorithm was run for 50.000 iterations with an additional burn-in of 5.000. Finally, Shannon’s indices (I) and polymorphism information content (PIC) (Nagy et al., 2012) were calculated for URP markers in order to evaluate their ability to detect

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genetic diversity.

3. Results

The 12 URPs amplified a total of 283 distinct and reproducible bands across 156 individuals of nine S. raeseri populations by PCR application (representative results are shown in Supplementary Figure 2b). The number of bands per URP primer, along with

information on private and common bands across the populations is presented in Table 1. Each primer amplified 15 (9F) to 29 (32F) bands with an average of 23.58 and fragments ranged in size from 32 to 2241 bp. On the other hand, population profiles include 7 to 21 bands per primer, with an average of 13.55. Polymorphic diversity was further explored assessing Shannon’s information Index (I), Nei’s gene diversity (h), an analogue to expected heterozygosity (He) in codominant markers and informativeness of URPs, as reported in Table 1. Shannon’s

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Index and Nei’s gene diversity were calculated for each locus per population using GenAlex and an average for each marker was then generated. PIC values range between 0,358 and 0,375 indicated that all URP markers were quite similar in detecting

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polymorphism among the different populations. URP 4R had the highest score in all diversity indices and was considered as the most informative marker.

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In populations, polymorphic bands ranged from 86 (30.39%, P7) to 122

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(43.11%, P9), as given in Table 2. Shannon’s Index is considered a within- population phenotypic diversity measure (Ho=-Σpilog2pi, where pi is the frequency of presence or absence of the band), according to Lewontin (1972). Ho values per population, as well

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as their weighted average values are listed in Table 3. As might be expected from the percentage of polymorphic bands (PPB) mentioned above, population P7 had the lowest value (Ho = 0.160) and population P9 had the highest (Ho = 0.229). Average Nei’s gene

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diversity resulted in a similar ranking of populations. Average diversity within all populations, as calculated previously, is named

Hpop. Shannon’s Index based on the frequencies at each locus over all individuals considered together was also generated, with Hsp being the total diversity among all individuals within the species. The difference between Hpop and Hsp is the added diversity that arises from considering the collection of all individuals within the same

unit. The proportion of diversity within populations was estimated as Hpop/Hsp, and the proportion of diversity among populations as (Hsp – Hpop)/Hsp for each URP marker. In order to estimate partition ratios for total genetic diversity, weighted average values were used. An intra- population diversity component of 35.3% and a percentage of 64.7% attributed to inter-population diversity were determined (Table 3). Partitioning of variation among and within populations was also estimated by analysis of molecular variance (AMOVA), based on a pairwise Euclidean genetic

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distance matrix (Table 4). This also yielded pairwise PhiPT (ΦPT) values as well as the sum of squares within population values (SSWP) for each population. Dividing the

latter value through the number of individuals reduced by one, provided the sample

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size-independent measure of variation SSWP/ (n − 1). A percentage of 66.3 % was

found among all populations and 33.70% was detected within populations (Table 5),

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leading in a broad agreement with the results of Shannon partitioning. Geographic

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distance explained only a 3.77% of the total genetic diversity, when included in the analysis. Each population was considered as originating from a distinct geographic region, except for P6 and P9 which had a distance less than 10 km, thus they were

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considered as originating from the same geographic region in the AMOVA. Different geographical groupings of the populations resulted in variations of similar magnitude due to region (data not shown). AMOVA-derived diversity SSWP/ (n − 1) values

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ranged from 16.49% (P7) to 22.94% (P9), which are in accordance with corresponding Ho values. The overall ΦPT was 0.633 and a matrix of pairwise ΦPT is presented in Table 5. ΦPT values ranged from 0.618 to 0.699 and they were significant at <.001 level. This result indicates that all populations differentiate almost evenly from each other, with the P1 and P5 populations being the most different and P6-P8 and P2-P5 population pairs being the most similar.

Populations P1 and P4 seem to be the most different from the others in general, whereas P9 exhibit lower ΦPT values. A Mantel test was performed in GenAlEx for the linearized ΦPT values (ΦPT ⁄ (1 – ΦPT) and the logarithm of geographic distances, which revealed a low positive correlation between genetic and physical distances (r= 0.278, Supplementary Figure 3). However, a permutation test indicated that the correlation was not significant at the 0.01 level (P≥ 0.165). Euclidean distance matrix was also used in the UPGMA cluster analysis to

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illustrate the relationships among the individuals sampled (Figure 1). Individuals from a given population tend to cluster together and are therefore more genetically similar than individuals from different populations. Population P4, followed by P9, clearly

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differentiates from the rest, whereas P6 and P8 are closely related, and so are P2 and P5, and P1 and P7 (as indicated earlier by ΦPT values, in most cases).

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Principle Coordinates Analysis using the genetic distance matrix as input also

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shows the grouping of individuals according their populations of origin and clear separation between them (Figure 2). The first three axes of the PCoA explain 56.23% of the total variation (PC1: 20.54%, PC2: 18.14% and PC3: 17.55%).

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Populations are clearly separated along PC1 and PC2 axes, while separation is also retained between PC1 and PC3, while it is lower between PC2 and PC3 (supplementary Figure 4). We also performed Principal Component analysis (PCA) on

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the original marker data to plot the projection of the variables (marker loci) along with populations in the graph. The results indicated that variables (loci) are forming a cloud around the cross of the axes and no grouping according the corresponding marker was observed (supplementary Figure 5). In STRUCTURE HARVESTER program, Evanno’s Delta K takes consideration of the variance of LnP(K) among repeated runs to indicate the best K value, so

according to Table 6 and the diagram in Figure 3 (a), the maximum likelihood was obtained, when individuals are grouped into K=9 clusters. Genetic structure analysis of the individual samples using STRUCTURE shows that nine relatively independent gene pools are represented in the data and each individual is correctly assigned to its population of origin (Figure 3 b).

4. Discussion

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Genetic variation is positively correlated to fitness and sustainability, although the way one is related to another remains unclear (Woodruff, 2001). Thus, evolutionary potential of a population is determined by its gene pool, the size of which determines

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whether the action of natural selection and genetic drift could operate. Genetic data,

once acquired, are used by conservation geneticists to quantify within- and between-

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populations variability. Variability within populations is used to establish pedigree

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relations, mating system, sex ratio and effective population size. By estimating the latter, one can assess the effects of different population management strategies in conservation biology. Inter-population comparisons reveal spatial structuring and

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historical patterns of gene flow (Woodruff, 2001). Population structure contributes in understanding past events in the history of a species, in recognizing current speciation events and in testing hypotheses concerning evolutionary processes. It also helps in

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subspecies classification and the determination of intraspecific units where genetic variability is maintained (Rosenberg et al., 2001). Geographical, ecological and anthropogenic barriers among other factors are partly responsible for population fragmentation to smaller units. Depending on the degree of their subsequent differentiation, which resulted both from random events, such as genetic drift and founder effect, and adaptation to different

environmental conditions (Falk et al., 2001), subpopulations can diverge into subspecies that may gradually become reproductively isolated. As a result of evolutionary processes, high variability in introns and the presence of repeated sequences in the noncoding "junk DNA" characterize most natural populations of animals and plants (Woodruff, 2001). This is the reasoning behind choosing the particular type of molecular markers in our study to analyze S. raeseri populations, as URPs derived from a DNA element that is in multiple copies scattered throughout the genome. Among the

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283 distinct and reproducible URP bands across 156 individuals, 8 exclusive (private) zones were found. Four were only present in population P1. Absence of bands from a

population was also descriptive. Specifically, a total of 30 zones were detected in 8 out

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of 9 populations, 3 of which indeed were monomorphic. Therefore, the mere presence or absence of a band at a rate of approximately 13% was enough to characterize

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populations and may be relevant to their differentiation.

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All S. raeseri populations analyzed proved quite distinct and differentiated from each other, as clustering analysis, principal coordinate analysis (PCoA), AMOVA and Shannon’s indices demonstrated. Inter-population diversity was significantly higher

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than intra-population, but this does not apply to exclusively cross-pollinated species (Nybom, 2004; Schoen and Brown, 1991). Sideritis is partially vegetatively propagated, but there is insufficient data available about its mating system.

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In previous works on Sideritis species, higher intra-population diversity is reported, although comparison was held among populations either distributed in different subspecies (Las Heras Vazquez et al., 1999) or geographically isolated (Batista et al., 2004). In a similar study regarding S scardica populations using AFLP markers, results demonstrated relatively low within population genetic diversity and significant population differentiation (Grdisa et al 2019). Within population diversity in terms of

polymorphic markers was 36.21% and even the within population variation component in the AMOVA was higher, φst value for the among-populations component was significant, indicating the existence of population differentiation. It seems that Sideritis species in the southern Balkan mountainous area maintain well differentiated populations that exhibit particular genetic structure due to their pollination system or other, yet unidentified reasons. In literature, higher variation among populations is reported for species

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exhibiting mixed pollination systems (Gormally et al., 2011; Zheng et al., 2008) and this may be true for the studied Sideritis populations. Selfing is expected to reduce

polymorphism and increase linkage disequilibrium compared with outcrossing. On the

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other hand, increased isolation between populations which results directly from selfing or indirectly from evolutionary changes, such as small flowers and low pollen output,

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leads to greater differentiation of molecular markers than under outcrossing (Knight et

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al., 2005; García-Camacho and Totland, 2009; Glemin et al., 2006). Habitat fragmentation is considered to shift mating patterns towards increased selfing (Aguilar et al., 2008).

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Populations of S. raeseri develop in distant mountainous regions and since Mantel test and results of clustering analysis showed no evidence of isolation by distance, as well as the evenly distributed variation among populations in principal

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coordinates analysis, the increased inter-population diversity demonstrated in our study could be attributed to an adaptation of the species breeding system. Another relevant aspect to consider is that in higher altitude where populations were collected, bees which is the most common pollinator of Lamiaceae species decline, and other insects predominate, thus cross-pollination even between individuals within a population could be impaired (Warren et al.,1988).

Intensive collection of Sideritis flowering shoots as well as many environmental factors has led to a decrease in census and effective population size, and under extreme conditions, to extinction of neighboring populations. This reduction in number and size also induces population differentiation and isolation (Ellstrand and Elam, 1993). Genetic drift and inbreeding exhibit allelic loss and increased homozygosity in populations that have undergone bottlenecks or size reduction in general (Wright, 1951). Landscape morphology in cases like S. raeseri prevents gene flow between near

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populations which also leads to further population isolation and differentiation, since gene flow cannot counterbalance for allele loss that may have preceded (Wright, 1951). For the study of gene flow, it is important to adjust the variability of the marker

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to the spatial scale of investigation and the questions asked. At spatial scales of within

or between populations, the presence or absence of structure in the distribution of highly

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variable markers would give us insight into the amount of gene flow. Less variable

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markers like slowly evolving cpDNA or mtDNA markers are more suited in larger spatial scales where differentiation is determined almost completely by genetic drift and mutational divergence (Ouborg et al., 1999). Maybe a complementary study of our

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populations using more conservative markers and comparing with closely related species would give us a broader view of the genetic relationships established, or even track initiating allopatric speciation events.

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Seed dispersal is very important for the viability of species in fragmented habitats (Ouborg et al., 1999). Spatial dynamics of populations is determined mostly by seed dispersal, the range of which determines whether colonization of new habitats or extinction of certain populations will occur. Collection of flowering parts of mountain tea leads to smaller seed production and as Sideritis lacks competitive ability, due to the marginal environment in which it grows, even if there is some seed dispersal to

proximal sites, it is uncertain whether establishment will succeed. Genetic erosion is observed in fragmented populations which are susceptible to inbreeding depression, thus their viability is threatened (Wright, 1951). The critical early stages of the process in nature have gone undocumented, because the changes are rapid and difficult to monitor (Woodruff, 2001). Luikart et al. (1998) pointed out that management programs for threatened species or ecologically and commercially important populations should include genetic monitoring, as population bottlenecks can

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occur without being detected by traditional demographic monitoring approaches. In addition, Sideritis spp. natural populations can be threatened by cultivation in neighboring fields with cultivars, originated from unrelated resources. Close proximity

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of natural populations and such cultivars could result in cross fertilization and loss of genetic identity for the natural population. Uncertain origin of propagation material

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could alter the genetic makeup of wild local populations as gene flow from the cultivar

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cannot be avoided. Results of this study pointed to the unique and well-defined genetic structure of different populations and measures to prevent modification of the natural populations’ gene pool should be envisaged. Collectively the species genetic diversity is

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vulnerable as wild populations are unique and different from each other, thus measures for in situ conservation and protection in the natural habitats are of pressing importance. Furthermore as S. raeseri natural populations are highly exploited for various traditional

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medicinal preparations and pharmaceutical applications, these germplasms could be used in propagation and breeding programs for the development of propagative material for cultivation and to protect the wild populations and habitats from continuous collection and destruction.

5. Conclusions

Natural wild populations of S. raeseri are distinct and different from each other. Higher inter- than intra-population variation in collection sites of Balkan peninsula may be indicative of population fragmentation. In addition, the mountain landscape morphology and altitude as well as the species mating system are parameters that possibly enhance population isolation. Thus, collection of wild populations may further impact loss of variation, therefore necessitating urgently conservation measures and ecosystem protection.

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The URP molecular markers employed in this study could provide a useful simple system for monitoring and studying the population genetic makeup in mountain

tea. The URP markers can also help to screen natural variation in diverse populations of

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closely related species and can consequently be applied in conservation actions as well

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as in genetic and breeding programs.

Acknowledgments

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Conflicts of Interest: The authors declare no conflict of interest.

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This work was partly supported by the SEE-ERA.NET Plus Program (ERA 135/01). We are grateful to Prof G. Stefkov, University Ss Cyril and Methodius, Faculty of Pharmacy, Skopje, Republic of North Macedonia for providing Sideritis raeseri plant

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samples.

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Figures

Fig. 1. UPGMA dendrogram of 156 S. raeseri individual plants based on Euclidean genetic distances, indicating 9 clusters, which are the corresponding populations of

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origin.

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Fig. 2. Principal coordinate analysis (PCoA) of 9 S. raeseri populations computed from

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the genetic distance matrix based on URP marker data, plotted along the first two

principal axes. Samples from each population are depicted with dots of different color

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and the population is indicated on the plot.

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Fig. 3. (a) ΔΚ values for Κ=1 to 9, determining the optimal K number of segments that

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represents the individual’s estimated membership fractions, according to Evanno et al.,

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(2005). (b) Barplot of Bayesian cluster analysis runs for K=9.

Table 1. Characteristics of the URP primers used: band size range of amplified band, number (total, average, per population), common and private bands, Shannon’s Index (I), Nei’s gene diversity (h), unbiased Nei’s gene diversity (uh) and PIC values for each primer are given.

URP Band size Primer range (bp)

Total Number Number Average number Number of number of of of of bands/ private bands bands/po common population bands observed pulation bands

Ι

h

uh

PIC

0.36 6 0.36 7

122-1939

20

8-15

11.11

1

0

0.198

0.130

0.139

2F

39-2057

22

9-16

12.00

1

1

0.208

0.141

0.150

2R

194-1697

17

7-12

9.89

0

0

0.160

0.106

0.113

0.37 2

4R

40-2241

27

12-20

18.00

4

0

0.245

0.163

0.173

6R

54-1948

26

11-17

14.22

0

1

0.152

0.099

0.105

9F

35-1375

15

7-10

8.56

1

0

0.136

0.091

0.096

0.37 5 0.37 3 0.37 3

13R

32-1994

25

11-17

14.22

17R

34-2131

25

12-18

14.33

25F

44-1975

24

11-19

14.11

30F

52-2241

26

13-18

15.33

32F

39-1970

29

12-21

38F

84-2228

27

13-19

23.58

283

-p 0

0.236

0.157

0.167

0.36 4

0

1

0.205

0.137

0.146

0.37 1

1

0

0.202

0.135

0.143

2

1

0.199

0.131

0.139

15.33

1

1

0.218

0.146

0.155

0.37 2 0.37 3 0.35 8

15.44

1

3

0.216

re

0

lP

ur na

Jo

Averag e Total

ro of

1F

13.55 8

0.143

0.152

0.37 0

Table 2: Analysis of polymorphic loci identified with the URP molecular markers. Number of polymorphic bands amplified by each primer per population, their average and percentage (PPB), Nei’s gene diversity (h) and unbiased diversity (uh) values are presented.

P1

P2

P3

P4

P5

P6

P7

P8

P9

Average

1F

6

8

7

4

10

7

8

7

15

8

2F

5

7

6

10

10

13

6

8

10

8

2R

3

4

8

5

7

5

5

5

5

5

4R

14

13

11

14

16

15

6

13

14

13

6R

7

11

10

10

10

9

5

8

5

8

9F

3

7

5

4

3

4

3

4

13R

9

15

13

11

10

6

10

13

17R

6

10

7

9

12

11

10

9

25F

13

11

12

11

6

7

5

8

30F

10

11

10

10

9

14

8

13

32F

10

11

16

16

10

10

12

14

38F

13

10

9

16

14

13

8

Total

99

118

114

120

117

114

PPB

34.98

41.70

40.28

42.40

41.34

40.28

h

0.120

0.142

0.132

0.142

0.134

uh

0.128

0.151

0.139

0.151

0.142

4

14

11

13

10

12

9

10

11

10

12

8

12

11

86

110

122

110

30.39

38.87

43.11

0.138

0.107

0.138

0.153

0.146

0.117

0.146

0.162

re

2

lP ur na Jo

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URP

-p

Number of polymorphic bands per population

Table 3. Shannon’s indices of phenotypic diversity (Ho). Partitioning of the genetic diversity generated by 12 URPs into within- and between-population components for the 9 populations of S. raeseri.

2R 4R 6R

Ho

9F 13R 17R 25F 30F 32F 38F

P3

P4

P5

P6

P7

P8

P9

Hpo p

Hsp

0.1 33 0.1 35 0.1 01 0.2 83 0.1 37 0.1 14 0.2 02 0.1 07 0.2 59 0.1 88 0.1 65 0.2 63

0.1 90 0.1 87 0.0 96 0.2 64 0.1 76 0.2 33 0.3 58 0.2 34 0.2 44 0.1 98 0.1 66 0.1 91

0.1 58 0.1 35 0.2 56 0.2 05 0.1 62 0.1 50 0.2 97 0.1 35 0.2 34 0.1 84 0.2 88 0.1 74

0.0 68 0.2 42 0.1 37 0.2 58 0.2 03 0.1 35 0.2 07 0.2 01 0.2 25 0.1 80 0.3 00 0.3 07

0.2 59 0.2 29 0.1 88 0.2 83 0.1 67 0.1 26 0.1 92 0.2 39 0.1 23 0.1 68 0.1 72 0.2 75

0.1 87 0.3 52 0.1 69 0.2 25 0.1 93 0.1 37 0.1 22 0.2 18 0.1 71 0.2 65 0.1 95 0.2 22

0.2 26 0.1 53 0.1 49 0.1 42 0.0 91 0.1 02 0.2 22 0.2 08 0.1 05 0.1 31 0.2 25 0.1 47

0.1 81 0.2 23 0.1 89 0.2 48 0.1 42 0.1 63 0.2 28 0.1 97 0.1 73 0.2 77 0.2 50 0.1 68

0.3 80 0.2 18 0.1 54 0.2 95 0.0 97 0.0 68 0.2 93 0.3 05 0.2 86 0.1 97 0.2 00 0.2 03

0.1 98 0.2 08 0.1 60 0.2 45 0.1 52 0.1 36 0.2 36 0.2 05 0.2 02 0.1 99 0.2 18 0.2 16

0.5 20 0.5 55 0.5 22 0.6 10 0.6 03 0.5 61 0.5 99 0.6 12 0.5 68 0.5 77 0.5 82 0.5 35

(HspHpop/H Hpop)/Hs sp p 0.381

0.619

0.375

0.625

0.307

0.693

0.402

0.598

0.252

0.748

ro of

2F

P2

-p

1F

P1

re

URP

0.242

0.758

0.394

0.606

0.335

0.665

0.356

0.644

0.345

0.655

0.375

0.625

0.404

0.596

Jo

ur na

lP

Weight ed 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.5 0.353 0.647 averag 80 14 00 14 04 07 60 06 29 02 73 e †Hpop, average diversity within all populations; Hsp, total diversity among all individuals within the species.

Table 4. Results of the molecular variance analyses (AMOVA). Source of variation Among populations

df 8

SS 5690.904

MS 711.363

Estimated variation 39.928

V% 66.30

Within populations Total

147 155

2983.500 8674.404

20.296

20.296 60.224

33.70 100.00

Total

Populations n SSWP

P1

7 1

4991.571 699.333

713.082 699.333

2.272 37.724

3.77 62.57

147 155

2983.500 8674.404

20.296

20.296 60.292

33.66 100.00

P2

18 18 306.833 362.389

P3 18 335.556

P4

P5

18 362.444

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Among regions Among populations Within populations

18 342.333

SSWP/ (n-1) (%)

P6

18 350.667

P7

12 181.333

P8

18 351.944

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ur na

lP

re

-p

18.05 21.32 19.74 21.32 20.14 20.63 16.49 20.70 †df, degrees of freedom; SS, sum of squares; MS, means squares; V, proportion of variance in %. ‡All calculations were significant at p < .001.

P9 18 390.000 22.94

Table 5. Pairwise ΦPT values (below diagonal) and geographic distances in km (above diagonal). Population

P1

P1

P2

P3

P4

P5

P6

P7

P8

P9

31.146

24.674

13.144

27.653

70.252

199.028

65.724

72.700

29.108

30.216

25.705

42.889

168.198

35.382

44.551

12.140

43.667

53.700

191.609

54.174

44.551

36.820

63.016

197.445

61.158

65.891

67.620

180.136

57.606

68.790

141.906

15.128

3.735

137.528

138.200

0.688

P3

0.690

0.662

P4

0.687

0.662

0.665

P5

0.699

0.618

0.663

0.662

P6

0.656

0.620

0.653

0.660

0.675

P7

0.676

0.671

0.685

0.694

0.665

0.667

P8

0.665

0.663

0.646

0.682

0.679

0.618

0.680

0.664

0.635

0.671

0.648

0.646

0.633

0.638

P9

Jo

ur na

lP

re

-p

†All ΦPT calculations were significant at p < .001.

13.852 0.665

ro of

P2

Table 6. Results of STRUCTURE HARVESTER for K= 3 to 11 (Evanno et al., 2005). The best DeltaK value is 211,36 for K=9. Reps

Mean LnP(K)

StDev LnP(K)

Ln’(K)

3

|Ln”(K)|

Delta K

3

-21324.300000

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4

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-13859.366667

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-82019.866667

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-11053.433333

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