1. Introduction
Microsatellite markers, also known as simple sequence repeats (SSRs), are intensively used in genetic studies for several distinctive features:, as follows: each marker is locus-specific and capable of revealing multiple alleles, they are inherited as codominant markers, they demonstrate a high level of reproducibility, and they offer broad genomic coverage [
1,
2]. Owing to the substantial variability between the number of tandem repeats present in individual loci, SSRs are recognized as one of the most polymorphic types of genetic markers [
3].
Currently, microsatellites are the markers of choice in evolutionary biology studies for the assessment of genetic diversity among species, the determination of population structures, phylogenetic reconstructions, phylogeographic patterns, genetic mapping, evolutionary analyses, and molecular breeding [
4,
5].
When genomic data for specific species are unavailable, the use of microsatellites in genetic studies encounters a major bottleneck. This challenge primarily involves considerable time, research efforts, and financial resources for the development of customized primers to amplify distinct SSR loci. Therefore, overall, the information on SSR markers for less economically important species is still limited. A different approach to address this issue involves using the cross-amplification potential of microsatellite markers. This method allows for the exploration of genetic variability in various species, utilizing the conserved DNA sequences adjacent to these SSRs [
6,
7]. The efficacy of this method relies heavily on the degree of nucleotide similarity in the SSRs’ flanking regions across various species. Consequently, it is anticipated that phylogenetically related taxa will exhibit a greater likelihood of success in achieving amplification due to their closer genetic relationships and more similar genomic structures [
8,
9]. Heterologous amplification has been extensively applied across a broad spectrum of plant species, at intergeneric (e.g., [
10,
11,
12,
13,
14]) and infrageneric levels (e.g., [
15,
16,
17]). As expected, the greatest success in terms of using cross-amplification was found when applying this approach at the infrageneric level (success rate of about 60% [
8]).
Carex L. (Cyperaceae), with ca. 2061 accepted species, is the fourth most diverse angiosperm genus [
18,
19]. This genus is present all over the world, except in Antarctica [
20,
21].
Carex exhibits greater species diversity in the Northern Hemisphere, in temperate and boreal biomes [
22]. In contrast, its diversity significantly diminishes in tropical regions, where its presence is largely confined to montane ecosystems [
23].
The tremendous diversity of the genus has rendered the classification and taxonomic treatment of
Carex species particularly challenging. Traditionally, the
Carex genus has been divided into four subgenera, as follows: subg.
Psyllophora (Degl.) Peterm.; subg.
Vignea (P. Beauv. ex T. Lestib.) Peterm.; subg.
Vigneastra (Tuck.) Kük.; and subg.
Carex [
24,
25]. However, recent molecular studies have demonstrated that this treatment does not accurately reflect the phylogenetic relationships between taxa. Furthermore, the genera
Cymophyllus Mack.,
Kobresia Willd.,
Schoenoxiphium Nees, and
Uncinia Pers. have been reevaluated and included in
Carex [
21,
26]. The most recent studies [
18,
26] divided the
Carex genus into six subgenera (subg.
Siderosticta Waterway, subg.
Psyllophorae (Degl.) Peterm., subg.
Uncinia (Pers.) Peterm., subg.
Vignea (P. Beauv. ex T. Lestib.) Heer, subg.
Euthyceras Peterm., and subg.
Carex), which are further subdivided into 62 sections and groups.
Despite its enormous species diversity, only a few microsatellite markers have been specifically developed in the
Carex genus. Initially, microsatellite markers studies targeted only few species (e.g.,
Carex scabrifolia,
C. moorcroftii,
C. helodes,
C. angustisquama,
C. kobomugi,
C. macrocephala,
C. pumila [
27,
28,
29,
30,
31,
32,
33,
34]), but more recently, 42 and 17 markers, respectively, were tested for 106
Carex accessions [
33,
35]. Almost all taxa included in these studies belong to the
Carex or
Vignea subgenera. There have been even fewer attempts to assess the transferability of microsatellite loci within the genus. Consequently, in a previous study, we developed and characterized a set of 13 polymorphic SSR markers in the alpine sedge,
Carex curvula All. (
C. c. subsp.
curvula and
C. c. subsp.
rosae Gilomen), and we tested their applicability in species phylogeography and subspecies delimitation [
36].
In the present study, we explored the transferability of these genomic SSR markers, which were specifically developed for
C. curvula, to 15 populations belonging to 14 species of
Carex. We specifically targeted species within the
Euthyceras–Psyllophorae Clade [
18]. To the best of our knowledge, only one study has been conducted on a single species from this clade, namely,
Carex onoei [
35]. This particular clade is intricate, encompassing species that were formerly placed within the obsolete genera,
Schoenoxiphium and
Kobresia.
2. Materials and Methods
Young, healthy, green leaves from two randomly chosen individuals per species/population were sampled from 15 locations across the Carpathians, Alps, Pyrenees, and Svalbard (
Table 1). After collection, the plant material was stored in tubes filled with silica gel, at room temperature, until DNA extraction.
The total genomic DNA was extracted from 13 to 15 mg of silica gel-dried leaf tissue, using TissueLyser II (Qiagen, Hilden, Germany) and the innuPREP DNA Mini Kit (Analytik Jena AG, Jena, Germany), in accordance with the manufacturer’s protocol; however, the final elution took place in 50 μL in order to increase the DNA concentration. DNA quality was estimated using a 1% agarose gel stained with ethidium bromide, and the concentration was quantified using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
The nomenclature of the species listed in
Table 1 adheres to the taxonomy provided in the
Plants of the World Online (POWO) database [
19].
The amplification of the 13 SSR markers previously developed in
C. curvula was tested on DNA extracted from 15 populations/14 species of
Carex. According to [
18], nine of the studied species are included in the
Euthyceras–Psyllophorae Clade (subgenus
Psyllophorae:
C. baldensis,
C. pulicaris; subgenus
Euthyceras:
C. simpliciuscula,
C. myosuroides,
C. microglochin,
C. nardina,
C. rupestris,
C. pauciflora,
C. pyrenaica), three species in subgenus
Vignea (
C. parallela,
C. dioica,
C. maritima), and two are included in the subgenus
Carex (
C. nigra,
C. dacica).
PCR protocol was followed in accordance with the one described by [
36], as summarized below:
(i) PCR cycling conditions. Three min at 94 °C, 35 cycles for 1 min at 94 °C, 1 min at 55 °C (except VG139 and VG100, at an annealing temperature of 57 °C) and 2 min at 72 °C, followed by 7 min at 72 °C. PCR cycling was performed in a Mastercycler ep gradient S thermal cycler (Eppendorf, Hamburg, Germany).
(ii) PCR reaction conditions in a total volume of 10 µL. Here, 0.4 U KAPA Taq DNA Polymerase (Kapa Biosystems, Wilmington, MA, USA), 1× KAPA Taq Buffer A (MgCl2 1.5 mM included), 0.2 µM of each primer (the forward primer was fluorescently labelled with 6FAM: VG139, VG152, VG100, VG168, G110, VG203; NED: VG153, VG174, VG131, VG119; or HEX: VG175, VG108, G165), 0.25 mM of each dNTP (Thermo Fisher Scientific), 0.1 mg/mL BSA (Ambion, now part of Thermo Fisher Scientific), and 1.5 µL of DNA template (diluted four times) were used.
More details about the primers (including their sequences), the purification of the PCR products, and the subsequent separation that occurred through capillary electrophoresis can be found in [
36].
GeneMapper v.4.0 software (Applied Biosystems, now part of Thermo Fisher Scientific) was used for scoring the alleles.
The reliability of the experiment was assessed via a comparison of two duplicates from different populations. Only the unambiguous and repeatable peaks between duplicates were scored.
The number of alleles, number of private alleles, percentage of polymorphic markers, observed (Ho) and expected heterozygosity (He), and departure from the Hardy–Weinberg equilibrium (HWE) were determined using GenAlEx 6.5 [
37,
38].
3. Results
The SSR markers developed for
C. curvula showed great success in cross transferability in the analysed species. The amplification bands were scored to reveal polymorphism based on the presence or absence of alleles, and variations in allele size, at the same locus. The size range of these reproducible bands is presented in
Table 2.
All the 13 SSR primers were successfully transferred with a mean percentage of 90.76. Of the 13 primer pairs, only five primer pairs produced amplification in all 15 populations, namely, VG174, G110, VG119, VG108, and VG153, indicating 100% transferability. Moreover, 93.33% transferability was recorded for VG139, VG168, and VG131. A lower percentage of transferability was exhibited by VG152, VG100, VG203 (86.66%), and VG175 (80%), and the lowest transferability (only 60%) was registered in G165.
One hundred percent transferability for all 13 SSR primers was recorded in two species (C. baldensis and C. rupestris). This was followed by 92.3% transferability in eight species (C. pyrenaica, C. microglochin, C. nardina, C. parallela, C. maritima, C. pulicaris, C. dacica, and C. simpliciuscula), and 84.61% in five populations (C. pauciflora, C. dioica, C. nigra, and the two populations of C. myosuroides).
A total of 183 alleles were identified at these 13 amplified loci, ranging from 10 (G165) to 19 (VG168) alleles per locus, with an average of 14.07 (
Table 2).
All the 13 SSR markers that were successfully transferred from
C. curvula were polymorphic across the 15 evaluated
Carex populations (
Table 2). However, none of the populations showed polymorphism for all the loci (100% polymorphism), and the average percent of polymorphism was 27.69% (
Table 3).
C. rupestris was noted as the most polymorphic species, with only two invariable loci (84.6%), followed by
C. dacica (69.23%) and
C. baldensis (53.85%).
C. pauciflora and
C. nardina were the only two species monomorphic for all the 13 loci (0.00% polymorphism). Nevertheless, low values of polymorphism were noted for
C. microglochin,
C. nigra, and
C. simpliciuscula (7.69%), followed by
C. myosuroides (population 1) and
C. pulicaris, with a value of 23.08% for the polymorphic loci (
Table 3).
The mean number of different alleles ranged from 0.846 in the case of
C. pauciflora and
C. dioica, to a maximum of 2.077 per locus in the case of
C. rupestris (
Table 3). The number of private alleles (defined as the number of alleles unique to a single population) ranged from 0.154 in the case of
C. myosuroides (population 1), to 1.385 for
C. rupestris and
C. dacica (
Table 3). Expected heterozygosity ranged from 0.000 (for monomorphic species
C. pauciflora and
C. nardina) to 0.404 (
C. rupestris), with an average of 0.131 across all
Carex populations (
Table 3). Observed heterozygosity ranged from 0.000 (for species
C. pauciflora,
C. microglochin, and
C. nardina) to 0.462 (
C. baldensis), with a mean of 0.174 across all
Carex populations (
Table 3). The departure from the Hardy–Weinberg equilibrium was noted as non-significant for all polymorphic loci.
4. Discussion
Our research findings once again emphasize the value of the cross-species transferability of microsatellites, highlighting its role as a cost-effective method for the generation of genetic markers across various species within the same genus. A set of 13 SSR markers, previously developed for
C. curvula, were evaluated for polymorphism in 15 different populations/14 species of
Carex. The cross-amplification experiment was successful, and a total of 183 alleles were detected, with a range of 10 to 19, and an average of 14.07 alleles per locus. The total number of alleles and mean per locus, as revealed by these SSR markers, were higher than the values exhibited by
C. curvula, 137 alleles and a mean of 10.53 alleles per locus respectively [
36]. Furthermore, our values were relatively high, even compared with previous studies amplifying other SSR loci in different
Carex species, such as the study by [
35], which found a total of 173 alleles produced by 17 pairs of primers and an average of 10.18 for each locus. Similarly, the study by [
33], reported 178 alleles in 79
Carex accessions, with an average of 4.3 alleles per microsatellite, generated by 42 SSR primer pairs.
The transferability rate varied from 60% to 100%, the lowest transferability being registered in G165, which did not produce amplification in six species. One hundred percent amplification for all 13 SSR primers was recorded in C. baldensis; this species is very closely related to C. curvula, and it belongs to the same clade (Curvula Clade). However, 100% amplification was also registered for C. rupestris, which is part of another clade, the Pauciflora Clade. Moreover, a high percentage of amplification (92.3%) was detected for species belonging to different clades and sections of the Carex genus, as follows: Capitata Clade, Kobresia Clade 2, Disticha Clade, Section Physoglochin, and Section Psyllophorae.
The percentage of polymorphism exhibited by the 15
Carex populations ranged from 0.00% to 84.6%, with an average value of 27.69% (
Table 3). These values might be considered to be very low in comparison with the values noted for
C. curvula (mean value = 87.97%). However, the present polymorphism results should be treated with caution, given that sample size in each population was lower than that of the
C. curvula populations (five individuals) mentioned in [
36]. This would suggest that, for an exhaustive assessment of genetic variation in natural populations, increasing the number of sampled individuals per locality would offer a better resolution of the genetic structure of these species.
Nevertheless, we tested how the sample size actually influenced the polymorphism rates in the case of these 13 SSR markers, by including additional individuals (data not shown) of C. pauciflora (up to a total of four individuals) and C. myosuroides (five for each of the two populations) in the analysis. In the case of C. pauciflora, the polymorphism value increased slightly, from 0.00% to 7.69%, whereas in C. myosuroides (both populations), it remained unchanged (15.38% and 33.85%) despite the addition of three more individuals.
Carex myosuroides was represented in the present study by two populations sampled from different massifs in the Pyrenees, specifically to test how informative these SSR markers might be in the case of phylogeographic studies on other
Carex species besides
C. curvula. Both populations showed inconsistent amplification for the same SSRs (VG152 and VG100) and displayed identical alleles for VG119, VG131, G165, VG153, and VG168 markers (
Table 2). Again, both populations were recorded as being among the poorest in terms of number of private alleles. However, the population from the Aston Massif proved to contain more unique alleles, with a value of 0.385, compared with 0.154 for the other population (from the Maladeta Massif). Their values of Ho, He, and percentage of polymorphic loci were very similar (
Table 3). The number of alleles per population ranged from 0 to 2, depending on the marker, and it reached a maximum of three alleles for both populations. These results remained unchanged when analysing five individuals per population. The number of alleles displayed by both populations of
C. myosuroides in the case of these 13 SSR markers was lower compared with several other studies reporting SSR markers in the
Carex genus. For example, an analysis of 14 microsatellite loci in six populations of
C. kobomugi revealed two to eight alleles per marker [
27], and another analysis of nine microsatellite loci in four populations of
Carex scabrifolia reported two to seven alleles [
28], whereas an analysis of 30 SSR markers in three populations of
C. pumila discovered four to twelve alleles [
34]. The low number of alleles and the high level of monomorphism were also reported by [
32], for three populations of
C. angustisquama, analysed using 20 SSR markers, resulting an average of one to five alleles per marker.
Nevertheless, the most important finding was that these SSR markers were able to successfully delineate different populations of the same species.
5. Conclusions
In this study, we have revealed the successful cross-species amplification of 13 SSR loci, specifically developed for Carex curvula, in 14 other Carex species. Moreover, to the best of our knowledge, this study represents the first report focused on the identification of SSR markers suitable for genetic variation assessments in Carex species from the Euthyceras-Psyllophorae Clade.
All 13 primers were successfully transferred, with a mean percentage of 90.76, though only five primer pairs produced amplification in all 14 species andachieved 100% transferability. In addition, all the primers generated polymorphic alleles. In conclusion, our successful cross-amplification results extend the potential usefulness of these loci to other related taxa.
Author Contributions
Conceptualization, D.Ș. and M.P.; methodology, D.Ș. and M.P.; software, D.Ș.; validation, D.Ș. and M.P.; formal analysis, D.Ș., I.B. and Z.R.B.; investigation, D.Ș., I.B. and Z.R.B.; resources, D.Ș. and M.P.; data curation, D.Ș.; writing—original draft preparation, D.Ș.; writing—review and editing, D.Ș., M.P. and P.C.; visualization, M.P. and P.C.; supervision, M.P. and P.C.; project administration, M.P. and P.C; funding acquisition, M.P and P.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by ODYSSEE (ANR-13-ISV7-0004, PN-II-ID-JRP-RO-FR-2012) and BioDivMount projects (BRANCUSI No. 32660WB and PN-II-CT-ROFR-2014-2-0011), funded by ANR France and UEFISCDI Romania (coordinators P.C. and M.P.). D.Ș. and I.B. received funding from the Core Project BIORESGREEN, subproject BioClimpact no. 7N/03.01.2023, code 23020401.
Institutional Review Board Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to M.P., upon reasonable request.
Acknowledgments
We thank Inger G. Alsos, Pernille Bronken Eidesen, Patrik Mráz, Tudor Ursu and Daniel Dítě for help with field sampling. We also extend our gratitude to three anonymous reviewers for their insightful feedback on earlier versions of this manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Baraket, G.; Chatti, K.; Saddoud, O.; Abdelkarim, A.B.; Mars, M.; Trifi, M.; Hannachi, A.S. Comparative assessment of SSR and AFLP markers for evaluation of genetic diversity and conservation of fig, Ficus carica L. , genetic resources in Tunisia. Plant Mol. Biol. Rep. 2011, 29, 171–184. [Google Scholar] [CrossRef]
- Miah, G.; Rafii, M.Y.; Ismail, M.R.; Puteh, A.B.; Rahim, H.A.; Islam, K.N.; Latif, M.A. A review of microsatellite markers and their applications in rice breeding programs to improve blast disease resistance. Int. J. Mol. Sci. 2013, 14, 22499–22528. [Google Scholar] [CrossRef]
- Ben-Ari, G.; Lavi, U. Marker-assisted selection in plant breeding. In Plant Biotechnology and Agriculture; Altman, A., Hasegawa, P.M., Eds.; Academic Press: San Diego, CA, USA, 2012; pp. 163–184. [Google Scholar]
- Kpatènon, M.J.; Salako, K.V.; Santoni, S.; Zekraoui, L.; Latreille, M.; Tollon-Cordet, C.; Mariac, C.; Jaligot, E.; Beulé, T.; Adéoti, K. Transferability, development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin. BMC Genet. 2020, 21, 145. [Google Scholar] [CrossRef]
- Pinto-Carrasco, D.; Košnar, J.; López-González, N.; Koutecký, P.; Těšitel, J.; Rico, E.; Martínez-Ortega, M.M. Development of 14 microsatellite markers in Odontites vernus s.l (Orobanchaceae) and cross-amplification in related taxa. Appl. Plant Sci. 2016, 4, 1500111. [Google Scholar] [CrossRef]
- Pan, L.; Huang, T.; Yang, Z.; Tang, L.; Cheng, Y.; Wang, J.; Ma, X.; Zhang, X. EST-SSR marker characterization based on RNA-sequencing of Lolium multiflorum and cross transferability to related species. Mol. Breed. 2018, 38, 80. [Google Scholar] [CrossRef]
- Aiello, D.; Ferradini, N.; Torelli, L.; Volpi, C.; Lambalk, J.; Russi, L.; Albertini, E. Evaluation of cross-species transferability of SSR markers in Foeniculum vulgare. Plants 2020, 9, 175. [Google Scholar] [CrossRef]
- Barbará, T.; Palma-Silva, C.; Paggi, G.M.; Bered, F.; Fay, M.F.; Lexer, C. Cross-species transfer of nuclear microsatellite markers: Potential and limitations. Mol. Ecol. 2007, 16, 3759–3767. [Google Scholar] [CrossRef]
- Bombonato, J.R.; Bonatelli, I.A.S.; Silva, G.A.R.; Moraes, E.M.; Zappi, D.C.; Taylor, N.P.; Franco, F.F. Cross-genera SSR transferability in cacti revealed by a case study using Cereus (Cereeae, Cactaceae). Genet. Mol. Biol. 2019, 42, 87–94. [Google Scholar] [CrossRef]
- Miranda, F.D.; Gontijo, A.B.P.L.; Santiliano, F.C.; Favoreto, F.C.; Soares, T.C.B. Transferability and characterization of microsatellite markers in five Bromeliaceae species belonging to the subfamilies Pitcairnioideae and Bromelioideae. Biota Neotrop. 2012, 12, 319–323. [Google Scholar] [CrossRef]
- Mengistu, D.; Kidane, Y.; Catellani, M.; Frascaroli, E.; Fadda, C.; Pè, M.; Dell’Acqua, M. High-density molecular characterization and association mapping in Ethiopian durum wheat landraces reveals high diversity and potential for wheat breeding. J. Plant Biotechnol. 2016, 14, 1800–1812. [Google Scholar] [CrossRef] [PubMed]
- Satya, P.; Paswan, P.K.; Ghosh, S.; Majumdar, S.; Ali, N. Confamiliar transferability of simple sequence repeat (SSR) markers from cotton (Gossypium hirsutum L.) and jute (Corchorus olitorius L.) to twenty two Malvaceous species. 3 Biotech 2016, 6, 65. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C.; Jia, C.; Liu, X.; Zhao, H.; Hou, L.; Li, M.; Cui, B.; Li, Y. Genetic diversity study on geographical populations of the multipurpose species Elsholtzia stauntonii using transferable microsatellite markers. Front. Plant Sci. 2022, 13, 903674. [Google Scholar] [CrossRef] [PubMed]
- Shekhar, C.; Rawat, A.; Bhandari, M.S.; Barthwal, S.; Ginwal, H.S.; Meena, R.K. Cross-transferability-based identification and validation of simple sequence repeat (SSR) markers in oaks of western Himalayas. Silvae Genet. 2021, 70, 108–116. [Google Scholar] [CrossRef]
- Haerinasab, M.; Rahiminejad, M.; Ellison, N. Transferability of simple sequence repeat (SSR) markers developed in red clover (Trifolium pratense L.) to some Trifolium species. Iran. J. Sci. Technol. Trans. A Sci. 2016, 40, 59–62. [Google Scholar] [CrossRef]
- Jiang, Y.; Xu, S.; Wang, R.; Zhou, J.; Dou, J.; Yin, Q.; Wang, R. Characterization, validation, and cross-species transferability of EST-SSR markers developed from Lycoris aurea and their application in genetic evaluation of Lycoris species. BMC Plant Biol. 2020, 20, 522. [Google Scholar] [CrossRef]
- Singleton, J.J.; Mangat, P.K.; Shim, J.; Vavra, C.; Coldren, C.; Angeles-Shim, R.B. Cross-species transferability of Solanum spp. DNA markers and their application in assessing genetic variation in silverleaf nightshade (Solanum elaeagnifolium) populations from Texas, USA. Weed Sci. 2020, 68, 396–404. [Google Scholar] [CrossRef]
- Roalson, E.H.; Jiménez-Mejías, P.; Hipp, A.L.; Benítez-Benítez, C.; Bruederle, L.P.; Chung, K.S.; Escudero, M.; Ford, B.A.; Ford, K.; Gebauer, S.; et al. A framework infrageneric classification of Carex (Cyperaceae) and its organizing principles. J. Syst. Evol. 2021, 59, 726–762. [Google Scholar] [CrossRef]
- POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. 2023. Available online: https://powo.science.kew.org/ (accessed on 11 September 2023).
- Naczi, R.F.C.; Ford, B.A. Sedges: Uses, diversity, and systematics of the Cyperaceae. In Monographs in Systematic Botany from the Missouri Botanical Garden; Naczi, R.F.C., Ford, B.A., Eds.; Missouri Botanic Garden Press: St. Louis, MO, USA, 2008; Volume 108, pp. xi + 298. [Google Scholar]
- Waterway, M.J.; Bruhl, J.J.; Wilson, K.L.; Ford, B.A.; Starr, J.R.; Jin, X.F.; Zhang, S.R.; Gebauer, S.; Hoffmann, M.H.; Gehrke, B.; et al. Making Carex monophyletic (Cyperaceae, tribe Cariceae): A new broader circumscription. Bot. J. Linn. Soc. 2015, 179, 1–42. [Google Scholar] [CrossRef]
- Hoffmann, M.H.; Gebauer, S.; von Rozycki, T. Assembly of the Arctic flora: Highly parallel and recurrent patterns in sedges (Carex). Am. J. Bot. 2017, 104, 1334–1343. [Google Scholar] [CrossRef]
- Benítez-Benítez, C.; Martín-Bravo, S.; Bjora, C.; Gebauer, S.; Hipp, A.; Hoffmann, M.; Garcés, M.; Pedersen, T.; Reznicek, A.; Roalson, E.; et al. Geographical vs. ecological diversification patterns in Carex section Phacocystis (Cyperaceae): Patterns hidden behind a twisted taxonomy. J. Syst. Evol. 2021, 59, 642–667. [Google Scholar] [CrossRef]
- Kükenthal, G. Cyperaceae-Caricoideae; Wilhelm Engelmann: Leipzig, Germany, 1909. [Google Scholar]
- Jiménez-Mejías, P.; Hahn, M.; Lueders, K.; Starr, J.R.; Brown, B.H.; Chouinard, B.N.; Chung, K.S.; Escudero, M.; Ford, B.A.; Ford, K.A.; et al. Megaphylogenetic specimen-level approaches to the Carex (Cyperaceae) phylogeny using ITS, ETS, and matK Sequences: Implications for Classification. Syst. Bot. 2016, 41, 500–518. [Google Scholar]
- Villaverde, T.; Jiménez-Mejías, P.; Luceño, M.; Waterway, M.J.; Kim, S.; Lee, B.; Rincón-Barrado, M.; Hahn, M.; Maguilla, E.; Roalson, E.H.; et al. A new classification of Carex (Cyperaceae) subgenera supported by a HybSeq backbone phylogenetic tree. Bot. J. Linn. Soc. 2020, 194, 141–163. [Google Scholar] [CrossRef]
- Ohsako, T.; Yamane, K. Isolation and characterization of polymorphic microsatellite loci in Asiatic sand sedge, Carex kobomugi Ohwi (Cyperaceae). Mol. Ecol. Notes 2007, 7, 1023–1025. [Google Scholar] [CrossRef]
- Hodoki, Y.; Ohbayashi, K.; Kunii, H. Genetic analysis of saltmarsh sedge Carex scabrifolia Steud. populations using newly developed microsatellite markers. Conserv. Genet. 2009, 10, 1361–1364. [Google Scholar] [CrossRef]
- King, M.G.; Roalson, E.H. Isolation and characterization of 11 microsatellite loci from Carex macrocephala (Cyperaceae). Conserv. Genet. 2009, 10, 531–533. [Google Scholar] [CrossRef]
- Liu, W.; Zhou, Y.; Liao, H.; Zhao, Y.; Song, Z. Microsatellite primers in Carex moorcroftii (Cyperaceae), a dominant species of the steppe on the Qinghai-Tibetan Plateau. Am. J. Bot. 2011, 98, e382–e384. [Google Scholar] [CrossRef]
- Arroyo, J.M.; Escudero, M.; Jordano, P. Isolation of 91 polymorphic microsatellite loci in the western Mediterranean endemic Carex helodes (Cyperaceae). Appl. Plant Sci. 2016, 4, 1500085. [Google Scholar] [CrossRef]
- Nagasawa, K.; Setoguchi, H.; Maki, M.; Goto, H.; Fukushima, K.; Isagi, Y.; Sakaguchi, S. Development and characterization of EST-SSR markers for Carex angustisquama (Cyperaceae), an extremophyte in solfatara fields. Appl. Plant Sci. 2018, 6, e1185. [Google Scholar] [CrossRef]
- Liu, L.; Fan, X.; Tan, P.; Wu, J.; Zhang, H.; Han, C.; Chen, C.; Xun, L.; Guo, W.; Chang, Z.; et al. The development of SSR markers based on RNA-sequencing and its validation between and within Carex L. species. BMC Plant Biol. 2021, 21, 17. [Google Scholar] [CrossRef]
- Kim, K.-R.; Yu, J.-N.; Hong, J.M.; Kim, S.-Y.; Park, S.Y. Genome assembly and microsatellite marker development using Illumina and PacBio Sequencing in the Carex pumila (Cyperaceae) from Korea. Genes 2023, 14, 2063. [Google Scholar] [CrossRef]
- Cui, R.F.; Wei, B.; Liang, F.; Li, Z.J.; Dong, A.X.; Zhou, Y.; Naizhe, J.; Liu, D.Y.; Wang, Q.; Fu, F.; et al. Development of SSR markers identification system for Carex L. based on RAD sequencing. Phytotaxa 2023, 626, 170–180. [Google Scholar] [CrossRef]
- Șuteu, D.; Pușcaș, M.; Băcilă, I.; Miclăuș, M.; Balázs, Z.R.; Choler, P. Development of SSR markers for Carex curvula (Cyperaceae) and their importance in investigating the species genetic structure. Mol. Biol. Rep. 2023, 50, 4729–4733. [Google Scholar] [CrossRef] [PubMed]
- Peakall, R.; Smouse, P.E. GenAlEx 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
- Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar] [CrossRef]
Table 1.
The Carex species used in the study (Clade/Section are in accordance with [
18]).
Table 1.
The Carex species used in the study (Clade/Section are in accordance with [
18]).
Species | Clade/ Section | Country | Range | Massif | Longitude (E) | Latitude (N) |
---|
Carex pauciflora Lightf. | Pauciflora | Romania | Carpathians | Vlădeasa | 22.69 | 46.44 |
Carex pyrenaica Wahlenb. | Pauciflora | Romania | Carpathians | Făgăraș | 24.74 | 45.6 |
Carex parallela (Laest.) Sommerf. | Physoglochin | Norway | Svalbard | Ny-Friesland, Flatøyrdalen | 15.99 | 79.28 |
Carex dioica L. | Physoglochin | Slovakia | Carpathians | Low Tatras | 19.35 | 48.94 |
Carex baldensis L. | Curvula | Italy | Alps | Garda Mountains | 10.36 | 45.61 |
Carex maritima Gunnerus | Disticha | Norway | Svalbard | Ny-Friesland, Wijdefjorden, Ringhorndalen | 15.99 | 79.28 |
Carex pulicaris L. | Psyllophorae | France | Alps | Plateau Matheysin | 5.77 | 44.99 |
Carex nardina Fr. | Capitata | Norway | Svalbard | Ny-Friesland, Flatøyrdalen | 16.02 | 79.28 |
Carex microglochin Wahlenb. | Capitata | France | Alps | Vanoise Massif | 7.1 | 45.37 |
Carex rupestris All. | Capitata | Romania | Carpathians | Bucegi | 25.46 | 45.41 |
Carex dacica Heuff. | Phacocystis | Romania | Carpathians | Făgăraș | 24.62 | 45.59 |
Carex nigra (L.) Reichard | Phacocystis | Slovakia | Carpathians | High Tatras | 20.1 | 49.16 |
Carex simpliciuscula Wahlenb. | Kobresia 2 | Romania | Carpathians | Bucegi | 25.46 | 45.41 |
Carex myosuroides Vill. (pop. 1) | Kobresia 1 | Spain | Pyrenees | Maladeta Massif | 0.68 | 42.59 |
Carex myosuroides Vill. (pop. 2) | Kobresia 1 | Andorra | Eastern Pyrenees | Aston Massif | 1.7 | 42.58 |
Table 2.
Cross-species amplification: size range of reproductible bands and number of alleles for 13 microsatellite loci developed for Carex curvula across 15 populations of Carex. IA = inconsistent amplification; NA = non-existent amplification.
Table 2.
Cross-species amplification: size range of reproductible bands and number of alleles for 13 microsatellite loci developed for Carex curvula across 15 populations of Carex. IA = inconsistent amplification; NA = non-existent amplification.
Species/Population | VG139 | VG175 | VG152 | VG100 | VG174 | G165 | VG168 | G110 | VG119 | VG203 | VG108 | VG131 | VG153 |
---|
Carex pauciflora | 126 | 261 | 141 | 73 | 165 | NA | 203 | 99 | 113 | NA | 90 | 120 | 143 |
Carex pyrenaica | 118 | 173–178 | 141 | 73 | 166 | NA | 188–197 | 90 | 107–122 | 259 | 109 | 118 | 171 |
Carex nardina | 118 | 185 | 159 | 79 | 151 | NA | 166 | 97 | 110 | 194 | 105 | 124 | 145 |
Carex parallela | 142 | 171 | 133 | 75 | 167 | 220 | 177 | 94–97 | 105–110 | 206–216 | 193 | NA | 138–166 |
Carex baldensis | 142–148 | 149 | 134–160 | 90–97 | 146 | 146 | 157 | 86 | 109–122 | 196–223 | 94–100 | 119 | 131–136 |
Carex maritima | 139 | 242 | 158–184 | 80 | 164 | NA | 156–170 | 97 | 105 | 248–257 | 224–259 | 119 | 137–138 |
Carex pulicaris | 143 | 203 | 143 | 86 | 163 | 92 | NA | 87 | 61 | 193 | 100–103 | 117–118 | 143 |
Carex microglochin | 147 | NA | 139 | 79–80 | 158 | 168 | 150 | 87 | 100 | 182 | 87 | 130 | 145 |
Carex rupestris | 149–151 | 271–274 | 132–139 | 73–88 | 160–170 | 165–178 | 168–174 | 86–92 | 93–109 | 208 | 98 | 119–122 | 149–152 |
Carex dacica | 137–175 | NA | 151 | 83–92 | 160 | 147–161 | 161–165 | 87–96 | 107–114 | 382–401 | 91–96 | 113 | 132–134 |
Carex nigra | NA | NA | 152 | 77–93 | 157–161 | 165–171 | 145–193 | 89–92 | 107 | 368–398 | 96–113 | 117 | 134 |
Carex dioica | 118–142 | 203 | 138 | 87 | 164–167 | NA | 175–179 | 86–98 | 128–130 | NA | 198–199 | 122–125 | 145 |
Carex simpliciuscula | 142 | 393 | 141 | 92 | 167 | NA | 161 | 111 | 107–109 | 207 | 107 | 120 | 140 |
Carex myosuroides (1) | 151 | 103–134 | IA | IA | 169 | 185 | 153 | 93 | 103 | 242 | 90–98 | 124 | 140 |
Carex myosuroides (2) | 136–153 | 103 | IA | IA | 159 | 185 | 153 | 99 | 103 | 242–244 | 98–102 | 124 | 140 |
No. of alleles | 17 | 13 | 13 | 12 | 14 | 10 | 19 | 12 | 13 | 17 | 18 | 11 | 14 |
Table 3.
Average number of different alleles (NA); average number of private alleles (NAP); observed heterozygosity (Ho); expected heterozygosity (He); and percentage of polymorphic loci (%P) for 13 microsatellite loci developed for Carex curvula across 15 populations of Carex sp.
Table 3.
Average number of different alleles (NA); average number of private alleles (NAP); observed heterozygosity (Ho); expected heterozygosity (He); and percentage of polymorphic loci (%P) for 13 microsatellite loci developed for Carex curvula across 15 populations of Carex sp.
Species/Population | NA | NAP | He | Ho | %P |
---|
Carex pauciflora | 0.846 | 0.308 | 0.000 | 0.000 | 0.00 |
Carex pyrenaica | 1.231 | 0.692 | 0.115 | 0.192 | 23.08 |
Carex nardina | 0.923 | 0.462 | 0.000 | 0.000 | 0.00 |
Carex parallela | 1.231 | 0.846 | 0.154 | 0.308 | 30.77 |
Carex baldensis | 1.538 | 1.077 | 0.250 | 0.462 | 53.85 |
Carex maritima | 1.308 | 0.846 | 0.192 | 0.308 | 38.46 |
Carex pulicaris | 1.077 | 0.692 | 0.067 | 0.115 | 15.38 |
Carex microglochin | 1.000 | 0.615 | 0.038 | 0.000 | 7.69 |
Carex rupestris | 2.077 | 1.385 | 0.404 | 0.308 | 84.62 |
Carex dacica | 1.846 | 1.385 | 0.317 | 0.385 | 69.23 |
Carex nigra | 1.462 | 0.846 | 0.192 | 0.269 | 38.46 |
Carex dioica | 0.846 | 0.692 | 0.038 | 0.077 | 7.69 |
Carex simpliciuscula | 1.000 | 0.385 | 0.029 | 0.038 | 7.69 |
Carex myosuroides (1) | 1.000 | 0.154 | 0.077 | 0.077 | 15.38 |
Carex myosuroides (2) | 1.077 | 0.385 | 0.096 | 0.077 | 23.08 |
Mean | 1.231 | 0.718 | 0.131 | 0.174 | 27.69 |
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