Genetic diversity of Iranian Acanthophyllum species collection using SCoT and SRAP markers

Abstract

A preliminary assessment of genetic diversity is an essential step toward elucidating the evolutionary relationships and identifying valuable germplasm resources within Acanthophyllum species. In this study, genetic diversity was explored in different species of the genus Acanthophyllum using SCoT and SRAP molecular markers for the first time. A total of 71 accessions from seven Acanthophyllum species (A. bracteatum, A. glandulosum, A. korshinskyi, A. microcephalum, A. sordidum, A. spinosum, and A. squarrosum (were collected from natural habitats of Iran. The genetic diversity was analyzed using SCoT (12) and SRAP (20) molecular markers. Genetic diversity parameters, indices, and population differentiation were evaluated. Population structure was investigated using AMOVA, PCoA, UPGMA-based clustering, and Bayesian analysis (STRUCTURE).Both marker systems exhibited high informativeness, generating 200 (SCoT) and 242 (SRAP) fragments. Polymorphic information content (PIC = 0.37), Shannon’s information index (I = 0.41), and average expected heterozygosity (He = 0.27) generated by SCoT primers were higher than those obtained from SRAP analysis (PIC = 0.34, I = 0.34, and He = 0.22). A. glandulosum, A. sordidum, and A. microcephalum displayed the highest levels of genetic diversity, while A. korshinskyi showed consistently low variation. The results of the STRUCTURE analysis indicated that the Acanthophyllum species could be grouped into four genetically distinct subpopulations. AMOVA indicated that most genetic variation (~ 86%) occurred within populations, a pattern supported by high gene flow estimates (Nm > 2.3), which reflects considerable genetic connectivity across geographic regions. Widespread admixture, especially in A. bracteatum, A. korshinskyi, and A. spinosum, points to historical hybridization events and shared ancestry.Both SCoT and SRAP markers proved highly efficient in revealing inter and intra-specific genetic diversity within the Acanthophyllum genus. These results provide a critical foundation for identifying the diversity of Acanthophyllum germplasm. They highlight A. glandulosum as a genetically rich resource, due to its high genetic diversity revealed by both marker systems, and identify A. korshinskyi as a conservation priority. Subsequent genome-wide studies are recommended to build upon these findings and to explore the adaptive genetic variation associated with environmental resilience.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-54076-0.

Introduction

The genus Acanthophyllum ranks as the third-largest genus within the family Caryophyllaceae. It comprises approximately 90–100 species distributed predominantly in the Irano-Turanian region¹. These perennial subshrubs are characterized by their cushion-forming growth habit and spiny leaves arranged in decussate pattern. Acanthophyllum species are primarily distributed across the arid and semi-arid landscapes of Asia². In Iran, they are particularly abundant in the northeastern provinces, especially Khorasan, and are locally referred to as “Chubak”³.

Acanthophyllum species have gained increasing interest due to their valuable pharmacological properties, attributed to their diverse secondary metabolites². The plant’s roots, in particular, are known to be a rich source of hydrocolloids and saponins^4,5. Saponins are recognized as valuable bioactive compounds with a wide array of medicinal applications, including hemolytic, antifungal, adjuvant, anti-inflammatory, antioxidant, antimicrobial, insecticidal, anti-diabetic, anticancer activities^2,6–8, as well as industrial applications due to their foam properties⁹.

Genetic diversity is a fundamental component of plant biodiversity, and its conservation is vital for the long-term survival of plant species, especially those that are endemic and threatened under changing environmental conditions¹⁰. Genetic diversity is influenced by several factors, including geographical variation, breeding systems, dispersal mechanisms, and life span^10,11. Assessing genetic variation is crucial for the conservation and sustainable utilization of plant genetic resources¹². Genetic diversity in plant germplasm is assessed using different biochemical, morphological, and molecular markers; however, DNA-level variation holds particular significance in genetic studies¹³.

Accurate species distinction within Acanthophyllum is a critical prerequisite for its effective conservation, particularly since Acanthophyllum species are considered as endemic species that are predominantly collected from natural habitats of Iran. Precise species identification remains a significant challenge due to the occurrence of morphological similarities among Acanthophyllum species, which often leads to taxonomic confusion. Furthermore, the current lack of comprehensive phytochemical profiling limits the utility of chemical markers for genetic differentiation. Consequently, these obstacles in morphological and phytochemical identification necessitate the use of molecular markers to resolve genetic relationships and ensure accurate germplasm identification among and between Acanthophyllum species.

The development of molecular marker techniques has revolutionized plant genetic studies over the past few decades¹⁴. In this context, molecular markers play an essential role in biodiversity conservation, identification of elite cultivars, quantitative trait loci (QTL) mapping. These markers also provide valuable insights into plant phylogeny, population structure, and evolutionary history, rendering them highly effective biotechnological tools for assessing genetic variation^15,16. Molecular marker assays offer high polymorphism and precision, while being independent of environmental influences and plant developmental stages¹⁷. Moreover, using molecular markers is crucial for the protection and sustainable use of medicinal plants^18,19.

PCR-based molecular markers have proven to be sensitive, reliable, rapid, and cost-effective tools for evaluating genetic stability, without being affected by environmental factors²⁰. Compared to traditional methods, DNA-based markers offer higher resolution in detecting genetic variations, allowing for more accurate assessment of genetic differences among individuals and populations²¹.

Due to the tremendous growth in the availability of public biological databases, the development of functional markers located within or near candidate genes has been greatly facilitated. This has initiated a shift from random DNA markers to gene-targeted markers, resulting in the creation of novel marker systems¹⁴.

Two molecular markers, including Start Codon Targeted (SCoT)²² and Sequence-Related Amplified Polymorphism (SRAP)²³, have been effectively utilized for assessing genetic diversity in plant species^24,25.

SCoT markers target the short, conserved regions adjacent to the ATG start codon in plant genes²², whereas SRAP markers are designed to amplify open reading frames (ORFs)²³. The SRAP technique offers a balance of simplicity, reliability, moderate throughput, and ease in sequencing selected bands and generates a moderate number of codominant markers²³, which can be used for various applications such as genetic diversity studies²⁶, QTL mapping²⁷, genetic map construction²⁷, cDNA fingerprinting, and map-based cloning²⁶. In contrast, SCoT markers are known for their reproducibility and high polymorphism²².

Compared to many other molecular markers, two relatively novel systems, SRAP and SCoT, have gained preference due to their minimal DNA requirement, clear and unambiguous banding patterns, high reproducibility²⁵, and high polymorphism, making them suitable for applications like QTL mapping and bulked segregant analysis. They also indicate the presence or absence of a particular allele, without differentiating between homozygous dominant and heterozygous individuals^22,23.

Furthermore, both SRAP and SCoT are gene-targeted markers that amplify functionally relevant genomic regions. This characteristic enables the discrimination of closely related species and the detection of hybridization or genetic admixture through additive or recombinant banding patterns, thereby ensuring a reliable assessment of germplasm integrity^22,23. These molecular markers (SCoT and SRAP) have been widely applied to assess genetic diversity in various medicinal plants with different reproductive biologies such as Carthamus tinctoriusL²⁸., Ferula asafoetida(Falc.) H. Karst²⁹., Peganum harmalaL²¹., Eruca sativaL³⁰., Paris polyphylla ³¹., Ocimum basilicum L. ¹⁹., and Haberlea rhodopensis Friv. ³². Their high reproducibility, low cost, and ability to detect DNA-level variation make them effective tools for evaluating genetic variation in plant germplasm. SRAP molecular markers have been successfully applied to assess genetic diversity in other genera of the Caryophyllaceae, such as Stellaria ³³ and Gypsophila ³⁴, demonstrating the high efficiency of this marker system for genetic diversity studies within this family. Previous studies have evaluated the molecular phylogeny of the genus Acanthophyllum ^1,35,36. Investigations have explored phylogenetic relationships within this genus, employing markers such as ITS and rps16^1,36. Additionally, research has utilized chloroplast DNA regions (specifically, markers like psbA-trnH and rpl32-trnL) along with other nuclear markers (EST24 and nrITS)³⁵. The gene-targeted nature of SCoT and SRAP markers allows for the detection of polymorphisms in functionally relevant regions of the genome. In cross-pollinated species like Acanthophyllum, this is particularly advantageous as it can reveal variations in coding or regulatory regions that directly influence important traits, providing deeper insights into the genetic architecture of diversity compared to anonymous markers³⁷.

In this study, we focused exclusively on SCoT and SRAP markers. SCoT markers have demonstrated particular utility in population genetics studies due to their ability to target conserved genomic regions, including the translation initiation codon (ATG). Importantly, among common marker types such as AFLP, ISSR, RAPD, and SSR, SCoT markers have shown promise for population genetics³⁸. The choice of SRAP markers was further motivated by their proven effectiveness in revealing substantial polymorphism. Comparative analyses indicate that SRAP markers consistently yield a significantly higher percentage of polymorphic bands compared to RAPD and SSR markers³⁹. This heightened polymorphic potential is critical for capturing the full spectrum of genetic variation within and among landraces, especially when other markers might underestimate diversity. By employing both SCoT and SRAP markers, we aimed to leverage the established role of SCoT in population genetics and the high discriminatory power of SRAP for a comprehensive evaluation of the genetic diversity and population structure of Acanthophyllum, potentially uncovering finer genetic distinctions not fully resolved by other marker systems alone.

Natural features, reproductive mode, and breeding system have been identified as significant factors influencing genetic diversity levels. Generally, outcrossing species, such as Acanthophyllum, tend to exhibit greater genetic variation than selfing ones³⁷. The wide geographic distribution of Acanthophyllum across a vast range of climatic and ecological conditions further supports the hypothesis that this genus harbors a high level of genetic diversity in Iran. However, despite the considerable medicinal significance of this genus in Iran, comprehensive data on the genetic diversity and population structure of its various species remain largely unexplored.

This study aims to investigate the genetic diversity of Acanthophyllum species and accessions collected from diverse regions of Iran using SRAP and SCoT markers. Both marker systems are gene-targeted and enhance the detection of polymorphisms in coding or regulatory regions. In cross-pollinated species, these markers effectively reveal variation among individuals and populations, discriminate closely related taxa, and provide insights into hybridization, admixture, and evolutionary relationships. Specific objectives are: (i) to characterize genetic variation among accessions, (ii) to assess population structure using complementary markers, and (iii) to explore patterns of genetic similarity. The results are expected to support species conservation, facilitate the development of DNA fingerprinting tools for breeding and conservation monitoring, and ultimately promote sustainable utilization of Acanthophyllum genetic resources.

Materials & methods

Plant material and experimental design

Ethics statement

The collection and handling of plant seeds were conducted in accordance with all the relevant guidelines. In this study, a total of 71 different Acanthophyllum accessions were collected from various natural habitats across Iran between June and August 2022 (Fig. 1). The collected samples were identified as members of seven distinct species, i.e., A. bracteatum Boiss., A. glandulosum Bunge ex Boiss., A. korshinskyi Schischk., A. microcephalum Boiss., A. sordidum Bunge ex Boiss., A. spinosum C.A. Mey., and A. squarrosum Boiss., by Dr. A. Pirani, a botanist from the Department of Biology at Ferdowsi University of Mashhad, based on Flora Iranica⁴⁰. Voucher specimens were deposited at the Herbarium of the Department of Natural Resources at Isfahan University of Technology (HIUT). Accessions and their corresponding geographical origins are listed in Table 1, and the spatial distribution of the sampling sites is illustrated in Fig. 1.

Fig. 1.

Distribution map of the seventy-one Acanthophyllum accessions across different geographical regions of Iran.

Table 1.

Geographical origins of different accessions of Acanthophyllum from Iran.

NO.	Accessions abbreviations	Collection area	Cluster Group (SCoT)	Cluster Group (SRAP)	Voucher numbers
1	A. bracteatum (A.br1)	Qasrcham, Shahreza, Isfahan, Iran	Group I	Group I	100 (HIUT)
2	A. bracteatum (A.br2)	Cheshme Sard, Semirom, Isfahan, Iran	Group III	Group I	101 (HIUT)
3	A. bracteatum (A.br3)	Podeh, Dehaqan, Isfahan, Iran	Group I	Group I	102 (HIUT)
4	A. bracteatum (A.br4)	Gonharan, Isfahan, Iran	Group I	Group I	103 (HIUT)
5	A. bracteatum (A.br5)	Borujen, Chaharmahal and Bakhtiari, Iran	Group I	Group I	104 (HIUT)
6	A. bracteatum (A.br6)	Sefidasht, Chaharmahal and Bakhtiari, Iran	Group I	Group III	105 (HIUT)
7	A. bracteatum (A.br7)	Robat Sefid, Razavi Khorasan, Iran	Group III	Single accession	106 (HIUT)
8	A. glandulosum (A.gl1)	Kesheh, Isfahan, Iran	Group III	Group II	108 (HIUT)
9	A. glandulosum (A.gl2)	Ab Senjad, Natanz, Isfahan, Iran	Group III	Group II	109 (HIUT)
10	A. glandulosum (A.gl3)	Borujen, Chaharmahal and Bakhtiari, Iran	Group III	Group II	110 (HIUT)
11	A. glandulosum (A.gl4)	Gandoman, Chaharmahal and Bakhtiari, Iran	Group III	Group II	111 (HIUT)
12	A. glandulosum (A.gl5)	Borujen, Chaharmahal and Bakhtiari, Iran	Group III	Group II	112 (HIUT)
13	A. glandulosum (A.gl6)	Ardakan, Sepidan, Fars, Iran	Group III	Group II	113 (HIUT)
14	A. glandulosum (A.gl7)	Chalehgah, Sepidan, Fars, Iran	Group III	Group II	114 (HIUT)
15	A. glandulosum (A.gl8)	Chalehgah, Sepidan, Fars, Iran	Group III	Group II	115 (HIUT)
16	A. glandulosum (A.gl9)	Bon Rud, Sepidan, Fars, Iran	Group III	Group II	116 (HIUT)
17	A. glandulosum (A.gl10)	Sepidan, Fars, Iran	Group III	Group II	117 (HIUT)
18	A. glandulosum (A.gl11)	Ardakan, Sepidan, Fars, Iran	Group III	Group II	118 (HIUT)
19	A. glandulosum (A.gl12)	Kang to Zoshk road, Razavi Khorasan, Iran	Group III	Group II	119 (HIUT)
20	A. glandulosum (A.gl13)	Dehbar, Razavi Khorasan, Iran	Group III	Group II	120 (HIUT)
21	A. glandulosum (A.gl14)	Farizi village, Razavi Khorasan, Iran	Group III	Group II	121 (HIUT)
22	A. glandulosum (A.gl15)	Amrudak, Razavi Khorasan, Iran	Group III	Group II	122 (HIUT)
23	A. glandulosum (A.gl16)	Hesar, Razavi Khorasan, Iran	Group III	Group II	123 (HIUT)
24	A. glandulosum (A.gl17)	Hesar, Razavi Khorasan, Iran	Group III	Group II	124 (HIUT)
25	A. glandulosum (A.gl18)	Zoshk, Razavi Khorasan, Iran	Group III	Group II	125 (HIUT)
26	A. glandulosum (A.gl19)	Binalud Heights, Razavi Khorasan, Iran	Group III	Group II	126 (HIUT)
27	A. glandulosum (A.gl20)	Farizi Dareh Darmeh, Razavi Khorasan, Iran	Group III	Group II	127 (HIUT)
28	A. glandulosum (A.gl21)	Kohgiluyeh and Boyer Ahmad, Iran	Group III	Group II	128 (HIUT)
29	A. glandulosum (A.gl22)	Kohgiluyeh and Boyer Ahmad, Iran	Group III	Group II	129 (HIUT)
30	A. glandulosum (A.gl23)	Amrudak, Razavi Khorasan, Iran	Group III	Group II	130 (HIUT)
31	A. korshinskyi (A.ko1)	Golbahar, Razavi Khorasan, Iran	Group IV	Group VI	132 (HIUT)
32	A. korshinskyi (A.ko2)	Sadd-e Ţoroq, Razavi Khorasan, Iran	Group V	Group VI	133 (HIUT)
33	A. korshinskyi (A.ko3)	Robat Sefid, Razavi Khorasan, Iran	Group IV	Group VI	134 (HIUT)
34	A. korshinskyi (A.ko4)	Robat Sefid, Razavi Khorasan, Iran	Group IV	Group V	135 (HIUT)
35	A. korshinskyi (A.ko5)	Golbahar, Razavi Khorasan, Iran	Group IV	Group V	136 (HIUT)
36	A. microcephalum (A.mi1)	Qarneh, Isfahan, Iran	Group I	Group IV	138 (HIUT)
37	A. microcephalum (A.mi2)	Kesheh, Tar Road, Isfahan, Iran	Group II	Group III	139 (HIUT)
38	A. microcephalum (A.mi3)	Kahrouye, Shahreza, Isfahan, Iran	Group II	Group III	140 (HIUT)
39	A. microcephalum (A.mi4)	Bardkan, Semirom, Isfahan, Iran	Group II	Group III	141 (HIUT)
40	A. microcephalum (A.mi5)	Dezej, Dehaqan, Isfahan, Iran	Group II	Group III	142 (HIUT)
41	A. microcephalum (A.mi6)	Kordesofla, Tiran and Karvan, Isfahan, Iran	Group II	Group III	143 (HIUT)
42	A. microcephalum (A.mi7)	Goldare, Najaf Abad, Isfahan, Iran	Group V	Group VII	144 (HIUT)
43	A. microcephalum (A.mi8)	Seng-e Sefid, Khansar, Isfahan, Iran	Group II	Group III	145 (HIUT)
44	A. microcephalum (A.mi9)	Borujen, Chaharmahal and Bakhtiari, Iran	Group II	Group III	146 (HIUT)
45	A. microcephalum (A.mi10)	Gandoman, Chaharmahal and Bakhtiari, Iran	Group II	Group III	147 (HIUT)
46	A. microcephalum (A.mi11)	Borujen, Chaharmahal and Bakhtiari, Iran	Group II	Group III	148 (HIUT)
47	A. microcephalum (A.mi12)	Sefidasht, Chaharmahal and Bakhtiari, Iran	Group II	Single accession	149 (HIUT)
48	A. sordidum (A.so1)	Sereshk, Nayin, Isfahan, Iran	Group III	Group II	150 (HIUT)
49	A. sordidum (A.so2)	Taminan, Nayin, Isfahan, Iran	Group I	Group IV	151 (HIUT)
50	A. sordidum (A.so3)	Sereshk, Nayin, Isfahan, Iran	Group III	Group II	152 (HIUT)
51	A. sordidum (A.so4)	Akbarabad, Nayin, Isfahan, Iran	Group I	Group IV	153 (HIUT)
52	A. sordidum (A.so5)	Sereshk, Nayin, Isfahan, Iran	Group I	Group IV	154 (HIUT)
53	A. sordidum (A.so6)	Akbarabad, Nayin, Isfahan, Iran	Group III	Group II	155 (HIUT)
54	A. sordidum (A.so7)	Abchuiyeh, Nayin, Isfahan, Iran	Group I	Group IV	156 (HIUT)
55	A. sordidum (A.so8)	Mayan-e Sofla, Razavi Khorasan, Iran	Group I	Group IV	157 (HIUT)
56	A. sordidum (A.so9)	Dehbar, Razavi Khorasan, Iran	Group I	Group IV	158 (HIUT)
57	A. sordidum (A.so10)	Robat Sefid, Razavi Khorasan, Iran	Group I	Group IV	159 (HIUT)
58	A. sordidum (A.so11)	Kang to Zoshk road, Razavi Khorasan, Iran	Group III	Group II	160 (HIUT)
59	A. sordidum (A.so12)	Zoshk, Razavi Khorasan, Iran	Group I	Group IV	161 (HIUT)
60	A. spinosum (A.sp1)	Qarneh, Isfahan, Iran	Group III	Group II	162 (HIUT)
61	A. spinosum (A.sp2)	Tangleh, Shahreza, Isfahan, Iran	Group III	Group II	163 (HIUT)
62	A. spinosum (A.sp3)	Kahrouye, Shahreza, Isfahan, Iran	Group III	Group II	164 (HIUT)
63	A. spinosum (A.sp4)	Cheshme Sard, Semirom, Isfahan, Iran	Group III	Group II	165 (HIUT)
64	A. spinosum (A.sp5)	Gonharan, Tiran and Karvan, Isfahan, Iran	Group III	Group II	166 (HIUT)
65	A. spinosum (A.sp6)	Gonharan, Tiran and Karvan, Isfahan, Iran	Group III	Group II	167 (HIUT)
66	A. squarrosum (A.sq1)	Tangleh, Shahreza, Isfahan, Iran	Group V	Group V	168 (HIUT)
67	A. squarrosum (A.sq2)	Dezej, Dehaqan, Isfahan, Iran	Group V	Group V	169 (HIUT)
68	A. squarrosum (A.sq3)	Kordesofla, Tiran and Karvan, Isfahan, Iran	Group V	Group V	170 (HIUT)
69	A. squarrosum (A.sq4)	Goldare, Najaf Abad, Isfahan, Iran	Group V	Group VII	171 (HIUT)
70	A. squarrosum (A.sq5)	Khansar, Isfahan, Iran	Group V	Group VII	172 (HIUT)
71	A. squarrosum (A.sq6)	Laijand, Khansar, Isfahan, Iran	Group I	Group II	173 (HIUT)

Molecular markers analysis

Extracting genomic DNA

The genomic DNA was extracted from young leaves using a modified CTAB protocol developed by Murray and Thompson⁴¹. The concentration of genomic DNA was assessed using a NanoDrop™ spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). Subsequently, the extracted DNA was diluted to a working concentration of 50 ng/µL.

Molecular markers amplification

SCoT–PCR amplification

The molecular analysis of SCoT markers was performed according to the method of Collard and Mackill²², with minor modifications, using twelve polymorphic SCoT primers. The characteristics of these primers are summarized in Table S1. All primer combinations were initially screened on a subset of samples to optimize the amplification conditions. Each PCR reaction was conducted in a final volume of 10 µL, consisting of 5 µL Master Mix (Amplicon), 1 µL genomic DNA (50 ng), 1 µL primers (10 µmol/L), and 3 µL double-distilled water (ddH₂O). Thermal cycling was carried out using a Senso quest MyCycler PCR system (Senso quest, Germany), starting with an initial denaturation at 94 °C for five minutes, followed by 35 cycles of denaturation at 94 ^°C for 35 s, annealing at 53 ^°C for 60 s, and extension at 72 ^°C for 2 min. A final extension was performed at 72 ^°C for seven minutes. The amplified products were separated by electrophoresis on a 2% (w/v) agarose gel (Sigma) using 1× TBE buffer and were visualized by staining with DNA Safe Stain.

SRAP–PCR amplification

The molecular analysis of SRAP markers was conducted following the protocol of Li and Quiros²³, with slight modifications. All SRAP primer combinations were initially tested on a subset of samples for optimization. Each PCR reaction was performed in a final volume of 10 µL, comprising 5 µL of Master Mix (Amplicon), 1 µL genomic DNA (50 ng), primers at a concentration of 10 µmol/L, and 3 µL of ddH₂O. Amplification was carried out using a gradient thermocycler (Senso quest, Germany) under the following conditions: an initial denaturation at 94 °C for 2 min, followed by 45 cycles of 94 °C for 1 min, 38 ^°C for 45 s, and 72 ^°C for 1 min. This was followed by another 45 cycles of 94 ^°C for 1 min, 53^°C for 45 s, and 72 ^°C for 1 min, ending with a final extension at 72 ^°C for 7 min. The characteristics of the SRAP markers are provided in Table S2. Amplified products were separated using 2.5% (w/v) Sigma agarose gel electrophoresis with 1× TBE buffer, and visualized by staining with DNA Safe Stain.

Data analysis

Molecular data analysis

Amplified SRAP and SCoT fragments were scored in a binary matrix as either present (1) or absent (0) for each sample, based on the relative size determined using a 100 bp DNA ladder^22,23. Diversity indices were calculated using GenAlEx 6.51b2⁴² and POPGENE version 1.32³⁹. Bands that were unclear or indistinct were excluded from scoring.

Genetic diversity analysis

The diversity parameters were calculated using GenAlEx 6.51b2⁴² and POPGENE version 1.31⁴³, including: number of observed alleles (Na), number of effective alleles (Ne)⁴⁴, polymorphic information content ⁴⁵, expected heterozygosity (H), Shannon’s information index (I)⁴⁶, number of polymorphic loci (NPL), percentage of polymorphic loci (PPL), and gene flow (Nm) as described by Slatkin and Barton⁴⁷.

Genetic structure analysis

Analysis of molecular variance (AMOVA) was conducted to quantify the distribution of genetic variation within and among populations using both marker systems, following the method developed by Excoffier et al.⁴⁸. Cluster analysis was based on Jaccard’s similarity index, and a dendrogram was generated using the unweighted pair group method with arithmetic mean (UPGMA) via NTSYS-pc software. Principal Coordinate Analysis (PCoA) was conducted to generate 3D eigenvectors using the DCENTER module of NTSYS version 2.0⁴⁹, providing complementary insights into population clustering.

Population structure was further examined using the Markov chain Monte Carlo (MCMC) simulation method in STRUCTURE HARVESTER version 2.3.4⁵⁰. The admixture model with correlated allele frequencies was tested across a range of K values (2–10)⁵¹. The most probable number of clusters (optimal K) was determined using the online tool Structure Harvester version 6.0⁵² based on the ΔK method.

Results and discussion

The assessment of genetic diversity is a prerequisite for efficient genetic resource management, species conservation in natural habitats, the identification of suitable parental combinations, analysis of inter- and intra-species variation, phylogenetic studies, and the establishment of fingerprinting systems across plant species^53,54. Evaluating the genetic diversity of Acanthophyllum germplasm provides valuable insights into the genetic structure of this genus. In this study, we conducted a comprehensive analysis of the genetic diversity and population structure of Acanthophyllum species and their accessions, which provides a foundation for future breeding and phylogenetic investigations.

Phylogenetic relationships within the genus Acanthophyllum have previously been examined using a limited set of molecular markers, including ITS and rps16 markers^1,36, as well as chloroplast regions (psbA–trnH and rpl32–trnL)³⁵ and two nuclear markers (EST24 and nrITS)³⁵. In contrast, the combined use of SCoT and SRAP markers-though extensively applied in other medicinal plants such as Carthamus tinctorius ²⁸, Peganum harmala ²¹, Eruca sativa³⁰, Paris polyphylla³¹, Ocimum basilicum¹⁹, Haberlea rhodopensis³² and Iris reticulated ⁵⁵-has not been reported for Acanthophyllum, highlighting the novelty of the present study.

Genetic diversity assay with molecular marker

Among the examined SCoT primers, 12 produced polymorphic patterns (Table 2). A total of 200 bands were amplified, of which 199 were polymorphic (Table 2). The number of bands per primer ranged from 15 (SCoT27) to 18 (SCoT19 and SCoT21), averaging 16.66 bands per primer. The number of polymorphic amplicons varied between 15 (SCoT27) and 18 (SCoT19 and SCoT21), with an average of 16.58 polymorphic fragments per primer. The percentage of polymorphism ranged from 94.12% (SCoT3) to 100% (for all remaining primers) (Table 2). The PIC values ranged from 0.36 (SCoT18, SCoT19, SCoT21, SCoT22, SCoT31, and SCoT35) to 0.39 (SCoT36), with an average of 0.37 (Table 2), indicating a moderate level of polymorphism for this marker system (Fig. S1 A).

Table 2.

The extent of polymorphism of different SCoT and SRAP primers used in evaluating the genetic diversity of Acanthophyllum accessions.

No.	Primer	No. TB b	No. PB c	PPB d (%)	PIC
SCoT
1	SCoT-3	17	16	94.12	0.37
2	SCoT-12	17	17	100	0.38
3	SCoT-18	16	16	100	0.36
4	SCoT-19	18	18	100	0.36
5	SCoT-21	18	18	100	0.36
6	SCoT-22	16	16	100	0.36
7	SCoT-27	15	15	100	0.38
8	SCoT-30	17	17	100	0.37
9	SCoT-31	16	16	100	0.36
10	SCoT-32	17	17	100	0.37
11	SCoT-35	17	17	100	0.36
12	SCoT-36	16	16	100	0.39
Total		200	199
Average			16.58	99.51	0.37
NO.	Primer Combination	No. TB b	No. PB c	PPB d (%)	PIC
SRAP
1	Me1 + Em1	14	14	100	0.35
2	Me1 + Em2	14	14	100	0.34
3	Me1 + Em3	12	12	100	0.33
4	Me1 + Em6	12	12	100	0.35
5	Me2 + Em1	12	12	100	0.35
6	Me2 + Em2	16	16	100	0.34
7	Me2 + Em4	10	10	100	0.36
8	Me2 + Em6	12	12	100	0.34
9	Me3 + Em1	10	10	100	0.34
10	Me3 + Em3	13	13	100	0.33
11	Me3 + Em4	13	13	100	0.31
12	Me3 + Em6	13	13	100	0.37
13	Me4 + Em2	11	11	100	0.35
14	Me4 + Em3	10	9	90	0.35
15	Me4 + Em4	11	11	100	0.37
16	Me4 + Em5	12	12	100	0.36
17	Me5 + Em3	11	11	100	0.36
18	Me5 + Em4	10	10	100	0.36
19	Me5 + Em5	13	13	100	0.36
20	Me5 + Em6	13	13	100	0.33
Total		242	241
Average			12	99.5	0.34

b: number of total bands, c: number of polymorphic bands, d: percentage of polymorphic band.

Analysis based on SRAP markers indicated that 20 out of 21 primer combinations produced polymorphic bands (Table 2). The total number of bands generated per primer set ranged from 10 (Me3-Em1, Me4-Em3, and Me5-Em4) to 16 (Me2-Em2) (Table 2). Similarly, the number of polymorphic bands varied between 9 (Me4-Em3) and 16 (Me2-Em2) (Table 2). Out of 242 amplified fragments, 241 were polymorphic, yielding an average of 12.05 polymorphic bands per primer combination. The percentage of polymorphism across primer combinations ranged from 90% (Me4-Em3) to 100% (for all others), with an overall average of 99.5%. The mean PIC value, reflecting the informativeness of the markers, was 0.34. The lowest PIC (0.31) was observed for the combination Me3-Em4, while the highest (0.37) was detected for Me3-Em6 and Me4-Em4 (Table 2; Fig. S1B).

The studied Iranian Acanthophyllum species exhibited high genetic diversity, a pattern consistent with reports from other Caryophyllaceae genera, such as Dianthus⁵⁶ and Gypsophila and Silene³⁴. The observed genetic diversity indices revealed moderate PIC values of 0.37 and 0.34 for SCoT and SRAP markers, respectively. These values fall within the range considered indicative of moderate polymorphism (0.25 < PIC < 0.5), demonstrating the informativeness of both marker systems for assessing genetic diversity in Acanthophyllum accessions. For SCoT markers, the PIC value (0.37) aligns with those reported in other caryophyllaceous taxa, such as Allochrusa (PIC = 0.32)⁵⁷ and Stellaria media L. (PIC = 0.34)⁵⁸. The PIC value obtained for SRAP markers also indicates a moderate level of polymorphism⁵⁹, similar to previous reports for Stellaria L. (PIC = 0.4)³³ and Gypsophila and Silene (PIC = 0.88)³⁴. Consequently, these results collectively indicate that both SRAP and SCoT markers were effective for detecting genetic variation within the Acanthophyllum germplasm. However, SCoT markers proved to be more informative than SRAP markers for assessing genetic diversity in this genus. Based on genetic diversity indices, A. glandulosum and A. sordidum exhibited the highest values for polymorphic loci and diversity parameters in the SCoT analysis, while A. microcephalum and A. glandulosum showed the highest values in the SRAP analysis.

Genetic diversity indices for studied species

SRAP and SCoT molecular markers were used to calculate various genetic diversity indices including Na, Ne, He, I, NPL, PPL, gene flow, and Nei’s index (Table 3). For SCoT markers, the highest Na (1.92) was recorded in A. glandulosum, while the lowest (1.25) was observed in A. korshinskyi. The greatest values of Ne (1.54), I (0.48), He (0.32), and UHe (0.33) were found in A. sordidum, whereas the lowest Ne (1.33), I (0.30), He (0.20), and UHe (0.22) were associated with A. korshinskyi. In terms of polymorphism, the highest NPL (189) and PPL (94.5%) were detected in A. glandulosum, while the lowest NPL (120) and PPL (60%) were observed in A. korshinskyi (Table 3).

Table 3.

Different genetic variation parameters for Acanthophyllum species through SCoT and SRAP molecular markers.

Population	Marker	Naa	Genetic diversity indices
	SCoT		Neb	Ic	Hed	UHee	NPLf	PPLg (%)	Nmh
A. bracteatum		1.72	1.51	0.45	0.3	0.32	167	83.50
A. glandulosum		1.92	1.47	0.44	0.28	0.29	189	94.50
A. korshinskyi		1.25	1.33	0.3	0.2	0.22	120	60.00
A. microcephalum		1.78	1.48	0.43	0.29	0.3	176	88.00
A. sordidum		1.88	1.54	0.48	0.32	0.33	187	93.50
A. spinosum		1.63	1.47	0.41	0.27	0.3	154	77.00
A. squarrosum		1.54	1.39	0.37	0.24	0.26	149	74.50
Multi species		1.67	1.45	0.41	0.27	0.28	163.14	81.57	2.31
	SRAP	Naa	Neb	Ic	Hed	UHee	NPLf	PPLg(%)	Nmh
A. bracteatum		1.51	1.38	0.35	0.23	0.25	177	73.14
A. glandulosum		1.69	1.38	0.36	0.23	0.24	200	82.64
A. korshinskyi		1.20	1.30	0.28	0.18	0.20	139	57.44
A. microcephalum		1.67	1.41	0.38	0.25	0.26	202	83.47
A. sordidum		1.62	1.40	0.37	0.24	0.25	192	79.34
A. spinosum		1.32	1.35	0.32	0.21	0.23	150	61.98
A. squarrosum		1.27	1.34	0.31	0.20	0.22	151	62.40
Multi species		1.47	1.37	0.34	0.22	0.24	173	71.49	2.42

^a observed number of alleles,^b effective number of alleles, ^c shannon’s information index, ^d expected heterozygosity, ^e unbiased expected heterozygosity, ^f number of polymorphic loci, ^g percentage of polymorphic loci, ^h gene flow.

For SRAP markers, the highest Na (1.69) was identified in A. glandulosum, and the lowest (1.20) was again observed in A. korshinskyi. The highest Ne (1.41) occurred in A. microcephalum, while the lowest (1.30) was also associated with A. korshinskyi. Nei’s gene diversity (He) ranged from 0.25 (A. microcephalum) to 0.18 (A. korshinskyi), with an average of 0.22. Similarly, UHe peaked at 0.26 in A. microcephalum and reached its lowest value (0.20) in A. korshinskyi. Shannon’s index, which reflects the distribution of alleles across polymorphic loci, ranged from 0.38 in A. microcephalum to 0.28 in A. korshinskyi, with an overall mean of 0.34. The highest NPL (202) and PPL (83.47%) were observed in A. microcephalum, while the lowest NPL (139) and PPL (57.44%) were recorded in A. korshinskyi (Table 3). Here, Iranian Acanthophyllum species exhibited high genetic and allelic richness, supported by elevated PIC values and an exceptionally high overall polymorphism rate (99.5%). However, it is important to contextualize these high diversity indices by considering the biological characteristics of the Acanthophyllum genus and the limitations of the marker system. Acanthophyllum species are predominantly cross-pollinated, a reproductive system that typically maintains high levels of heterozygosity and diversity within populations⁶⁰. Furthermore, SCoT and SRAP markers, being dominant and multi-locus, are highly effective at detecting polymorphism in gene-rich regions, which may contribute to the observed high diversity values. Nevertheless, these estimates are inherently sensitive to sampling design²⁴. A comparative analysis of diversity indices across Acanthophyllum species reveals distinct patterns of genetic variation within the genus. While SCoT markers identified A. glandulosum as possessing the highest allelic richness (Na = 1.92) and polymorphism (PPL = 94.5%), SRAP analysis highlighted A. microcephalum, which exhibited the highest effective number of alleles (Ne = 1.41) and PPL (83.47%). This discrepancy suggests that different genomic regions are targeted by these marker systems, revealing a more complex distribution of genetic diversity than a single marker could capture. Specifically, the high Shannon’s index (I = 0.48) and Nei’s gene diversity (He = 0.32) in A. sordidum (based on SCoT data) further confirm its strong adaptive potential. In contrast, A. korshinskyi displayed uniformly low diversity across both marker systems, with the lowest PPL values (57.44% to 60%) and reduced Na and Ne. This species also consistently formed a distinct, species-specific cluster in both SCoT and SRAP dendrograms.

Genetic diversity indices, (e.g., Na, Ne, and PPL), are inherently sensitive to sample size¹². In this study, the limited number of accessions for A. korshinskyi (n = 5) may have influenced the estimation of these parameters and, consequently, the inferred level of genetic diversity. Therefore, the lower diversity observed for this species should be interpreted with caution. Additionally, the limited number of loci scored by SCoT and SRAP markers, compared to genome-wide approaches, may not fully capture the extent of genome-wide diversity or provide complete resolution of the population structure^61,62. Additional sampling from populations with larger population sizes and across diverse geographical regions is required to robustly assess the genetic structure of A. korshinskyi and to determine whether the observed pattern reflects a true biological signal rather than a sampling artifact. Nevertheless, species with lower diversity indices, such as A. korshinskyi, may be vulnerable to reduced adaptability due to their small accession sizes or historical bottlenecks, which could accelerate genetic erosion if appropriate conservation strategies are not implemented ^63,64.

Principal coordinate analysis for Acanthophyllum accessions

Genetic relationships and variation among Acanthophyllum accessions were visualized using PCoA based on SCoT and SRAP marker data (Fig. 2a and b). For the SCoT marker, the first three principal coordinates collectively explained 13.91% of the total molecular variation (Coord1 = 6.16%, Coord2 = 4.39%, Coord3 = 3.35%). Based on this analysis (Fig. 2a), the accessions from different species were separated into five distinct genetic groups. Group I comprised a mix of A. br, A. mi, and A. so accessions, while Group II was dominated by A. mi along with A. sq and one A. br accession. Group III was the largest, containing primarily A. br, A. sp and A. gl accessions alongside several A. so samples. Group IV consisted exclusively of A. ko accessions, and Group V included A. sq and A. mi.

Fig. 2.

Biplot derived from the PCoA of the Acanthophyllum accessions based on SCoT (a) and SRAP (b) molecular markers.

For the SRAP marker, the first three coordinates accounted for 13.08% of the total variation (Coord1 = 5.5%, Coord2 = 4.11%, Coord3 = 3.48%). The PCoA plot assigned the accessions to six different groups (Fig. 2b). Group I included specific A. br accessions, while Group II was characterized by a large cluster of A. gl and A. sp, along with A. so and A. sq. Group III showed a mixture of A. br, A. sp, and A. mi, whereas Group IV consisted of A. mi and A. so. Group V comprised A. ko, A. mi, and A. sq, and Group VI contained A. mi and A. sq accessions. These results highlight meaningful clustering where species such as A. ko formed distinct groups, while others exhibited interspecific admixture, reflecting the genetic structure revealed by the markers.

All accessions of A. korshinskyi were grouped into a specific cluster (SCoT: Group IV; SRAP: Group V), forming a distinct, species-specific cluster in both analyses. The PCoA results revealed distinct clustering patterns between the two marker systems. Analysis based on SCoT markers identified five genetic groups, with Group III as the largest cluster (containing 37 accessions from four species), while Group IV contained only five accessions from a single species. In contrast, SRAP markers delineated six genetic groups; Group II formed the largest cluster (32 accessions from five species), whereas Group I comprised only four accessions from one species.

At the accession level, PCoA delineated accessions into five distinct groups for SCoT markers and six groups for SRAP markers. This pattern reflects substantial genetic variation and admixture among accessions of the same species from different geographical regions-a phenomenon similarly observed in Ferula asafoetida²⁹ and Nigella sativa⁶⁵. For the studied accessions, the relatively low cumulative variance explained by the first three axes (SCoT: 13.91%; SRAP: 13.08%) for both marker systems indicates that genetic variation is broadly distributed across the entire Acanthophyllum genome. Overall, the PCoA results highlight both the distinctiveness of A. korshinskyi and the extensive genetic diversity present among the other species. Although the PCoA axes explained only a fraction of the total variance, they may still capture a broad representation of genetic diversity and subtle differentiation among the groups³⁰.

Principal coordinate analysis of Acanthophyllum species

Genetic relationships and variation among Acanthophyllum species were assessed using PCoA based on SCoT and SRAP marker data. For the SCoT marker, the first two principal coordinates collectively explained 51.19% of the total molecular variation (Coord1 = 26.33%, Coord2 = 24.86%), capturing over half of the genetic structure. The analysis separated the species into four distinct genetic groups. Group I included A. bracteatum, and A. sordidum. Group II comprised A. microcephalum and A. squarrosum. Group III included A. glandulosum and A. spinosum and Group IV comprised A. korshinskyi.

For the SRAP marker, the first two coordinates accounted for 45.97% of the total variation (Coord1 = 26.13%, Coord2 = 19.84%). The PCoA plot also resolved four distinct genetic groups, though with a slightly different composition. Group I included A. bracteatum. Group II comprised A. glandulosum and A. spinosum. Group III included A. microcephalum and A. squarrosum. Group IV comprised A. sordidum and Group V included A. korshinskyi.

The results of the PCoA analysis were consistent with the genetic diversity indices. Other species, such as A. bracteatum, A. glandulosum, and A. sordidum, showed partial overlap in clustering, reflecting shared genetic backgrounds likely influenced by common ancestry and historical gene flow. This finding aligns with previous reports in dominant and wild species of Carthamus²⁸ and Docynia delavayi (Franch.) C.K. Schneid⁶⁶.

Genetic distances based on SCoT and SRAP molecular markers

Analysis of genetic similarity revealed distinct patterns among the studied species. For SCoT markers (Table S3), the lowest Nei’s genetic similarity (0.87) was observed between A. bracteatum and A. korshinskyi, while the highest value (0.94) was found between A. bracteatum and A. sordidum, A. glandulosum and A. sordidum, and A. spinosum and A. glandulosum (Fig. 3a). Conversely, SRAP data (Table S3) showed a generally higher overall level of genetic similarity. The lowest similarity (0.91) was observed between A. microcephalum and A. spinosum and between A. spinosum and A. korshinskyi, while the highest value (0.95) was recorded between A. microcephalum and A. squarrosum (Fig. 3b.).

Fig. 3.

Heat-map graph (similarity matrix) showing Nei’s genetic identity (above diagonal) based on SCoT (a) and SRAP (b) molecular marker.

Cluster analysis based on Acanthophyllum accessions

The cluster analysis based on the SCoT marker categorized the accessions into 5 distinct groups (І, ІІ, ІІІ, IV, V) (Fig. 4a). Group I (15 accessions, 21.12%), including A. br1, A. br3, A. br4, A. br5, A. br6, A. mi1, A. so2, A. so4, A. so5, A. so7, A. so8, A. so9, A. so10, A. so12, A. sq6. Group II (10 accessions, 14.08%) including A. mi2, A. mi3, A. mi4, A. mi5, A. mi6, A. mi8, A. mi9, A. mi10, A. mi11, A. mi12. Group III (35 accessions, 49.29%) including A. br2, A. br7, A. sp1, A. sp2, A. sp3, A. sp4, A. sp5, A. sp6, A. gl1, A. gl2, A. gl3, A. gl4, A. gl5, A. gl6, A. gl7, A. gl8, A. gl9, A. gl10, A. gl11, A. gl12, A. gl13, A. gl14, A. gl15, A. gl16, A. gl17, A. gl18, A. gl19, A. gl20, A. gl21, A. gl22, A. gl23, A. so1, A. so3, A. so6, A. so11. Group IV (4 accessions, 5.63%) comprised: A. ko1, A. ko3, A. ko4, A. ko5. Group V (7 accessions, 9.85%) including A. ko2, A. mi7, A. sq1, A. sq2, A. sq3, A. sq4, A. sq5. The cluster analysis based on the SRAP marker categorized the accessions into 7 distinct groups (І, ІІ, ІІІ, IV, V, VI, VII) and two single accessions (A. br7 and A. mi12) where each group represented the accessions from different provinces (Fig. 4b). Group I (5 accessions, 7.04%) included A. br1, A. br2, A. br3, A. br4 and A. br5. Group II (34 accessions, 47.88%) comprised A. gl1, A. gl2, A. gl3, A. gl4, A. gl5, A. gl6, A. gl7, A. gl8, A. gl9, A. gl10, A. gl11, A. gl12, A. gl13, A. gl14, A. gl15, A. gl16, A. gl17, A. gl18, A. gl19, A. gl20, A. gl21, A. gl22, A. gl23, A. sp1, A. sp2, A. sp3, A. sp4, A. sp5, A. sp6, A. so1, A. so3, A. so6, A. so11, A. sq6. Group III (10 accessions, 14.08%) included A. br6, A. mi2, A. mi3, A. mi4, A. mi5, A. mi6, A. mi8, A. mi9, A. mi10, A. mi11. Group IV (9 accessions, 12.67%) comprised A. mi1, A. so2, A. so4, A. so5, A. so7, A. so8, A. so9 and A. so10 and A. so12. Group V (5 accessions, 7.04%) comprised A. ko4, A. ko5, A. sq1, A. sq2 and A. sq3. Group VI (3 accessions, 4.22%) comprised A. ko1, A. ko2 and A. ko3. Group VII (3 accessions, 4.22%), included A. mi7, A. sq4 and A. sq5. The SRAP marker analysis showed a different but equally distinct clustering pattern: A. bracteatum accessions were predominantly grouped in Group I. The majority of A. spinosum and A. glandulosum accessions co-clustered in Group II, suggesting a close genetic relationship between these two species. A. microcephalum accessions were primarily grouped in Group III.

Fig. 4.

The UPGMA dendrogram showing the genetic relationship among Acanthophyllum accessions based on combined data of SCoT (a) and SRAP (b) molecular markers.

According to cluster analysis based on SCoT markers, the accessions were divided into five major groups, whereas analysis using SRAP markers resolved seven groups and two single-accession clusters. In both analyses, the majority of accessions from each species co-clustered, indicating strong genetic cohesion and a shared genetic background within species. Cluster analysis based on SCoT markers revealed the high discriminatory power of the mixture of the markers in distinguishing accessions from distinct geographical regions. The majority of accessions from each species were predominantly assigned to a primary cluster: A. sordidum accessions clustered mainly in Group I, A. microcephalum accessions formed a distinct, species-specific cluster in Group II. The majority of A. glandulosum and A. spinosum accessions co-clustered in Group III, and A. korshinskyi and A. squarrosum accessions were primarily assigned to Group IV and Group V, respectively. The grouping of accessions from different species into a common sub-cluster may be attributed to shared ancestry, interspecific gene flow, or historical hybridization ^67,68.

Cluster analysis of Acanthophyllum accessions based on SCoT and SRAP markers revealed clear species-specific patterns. A. sordidum accessions primarily formed a distinct cluster, A. microcephalum accessions constituted a separate group, and A. glandulosum and A. spinosum accessions largely co-clustered, indicating a close genetic relationship between these two species. These results highlight the particularly close association between A. spinosum and A. glandulosum. This pattern of species-specific clustering is consistent with findings reported in Brassica napusL.⁶⁹, Quercus glauca Bürger ex Blume ⁷⁰., Crocus sativus L. ⁷¹. and Asteraceae species⁷².

Clustering analyses using SCoT and SRAP markers revealed varying degrees of dispersion among accessions of the seven Acanthophyllum species. Accessions of A. glandulosum and A. microcephalum exhibited the highest dispersion, suggesting substantial genetic diversity and potential gene flow. Accessions of A. bracteatum, A. sordidum, A. spinosum, and A. squarrosum displayed intermediate patterns, with some forming distinct clusters while others were distributed across multiple groups. These findings indicate that species with greater accession dispersion, such as A. glandulosum and A. microcephalum, may offer valuable genetic resources for breeding programs. In contrast, species like A. korshinskyi require careful management to conserve their limited genetic variability. The strategic selection of parental lines with the greatest genetic distance, as identified in this study, could be employed to maximize heterosis and introduce novel, desirable traits into cultivated accessions⁷³.

Cluster analysis based on Acanthophyllum species

Cluster analysis categorized the surveyed species into four distinct groups (I–IV) for each marker. For the SCoT marker, Group I contained A. bracteatum, A. glandulosum, and A. sordidum (~ 42.85%), Group II comprised A. microcephalum and A. squarrosum (~ 28.57%), Group III included A. spinosum (~ 14.28%), and Group IV consisted of A. korshinskyi (~ 14.28%) (Fig. 5a). For the SRAP marker, Group I included A. bracteatum (~ 14.28%), Group II comprised A. microcephalum and A. squarrosum (~ 28.57%), Group III included A. glandulosum, A. sordidum and A. spinosum (~ 42.85%), and Group IV comprised A. korshinskyi (~ 14.28%) (Fig. 5b). Both SCoT and SRAP markers revealed strong species-specific genetic structuring in Acanthophyllum. Notably, A. microcephalum and A. squarrosum consistently clustered together across both marker systems, reflecting their close genetic affinity. The observed variation in the clustering of other species, such as A. glandulosum and A. sordidum^22,23, highlights subtle differences in genetic relationships captured by each marker type. The resulting clusters likely reflect a combination of historical gene flow and conserved genomic regions, a phenomenon consistent with previous reports in Panicum virgatumRoxb. ex Steud⁷⁴. and Carthamus tinctorius²⁸. Similar patterns of intra and inter-species genetic dispersion have been reported in other plant species, including Eruca sativa ³⁰ and Paris polyphylla ³¹.

Fig. 5.

The UPGMA dendrogram showing the genetic relationship among Acanthophyllum species based on data obtained from SCoT (a) and SRAP (b) molecular markers.

The discrepancies observed between SCoT and SRAP clustering patterns-both for species and accessions, as seen in the contrasting placement of A. bracteatum and A. glandulosum-may be attributed to differences in mutation rates and recombination events ^75,76. It should be emphasized that marker type can fundamentally influence such discrepancies ^75,76, which aligns with the marker-dependent clustering observed here. This divergence can be attributed to the fact that SCoT markers typically reveal higher polymorphism in regulatory and coding regions, whereas SRAP markers primarily target ORFs and intron–exon junctions in Acanthophyllum species, a pattern consistent with findings in Capparis spinosa ⁷⁷, Paris polyphylla ³¹ and reticulated iris  ⁵⁵.

The Mantel test⁷⁸ revealed a weak but statistically significant positive correlation between genetic and geographic distances for both marker systems (SRAP: r = 0.099, P = 0.010; SCoT: r = 0.072, P = 0.035), indicating isolation by distance across the studied Acanthophyllum species. The low correlation coefficients indicate that geographic separation contributes only modestly to genetic differentiation. Instead, species-specific divergence and the outcrossing reproductive system are likely the primary factors shaping population structure^67,79–82. Furthermore, the resolution of the marker systems and the specific sampling locations may limit the ability to detect stronger spatial patterns^30,83.

Analysis of molecular variance (AMOVA)

AMOVA analysis was performed to quantify the genetic differentiation among Acanthophyllum populations. The majority of genetic variation was attributed to within-species rather than among-species differences (Table 4). AMOVA revealed that the predominant proportion of genetic variation in Iranian Acanthophyllum species resides within species (approximately 86% for SCoT and 87% for SRAP), with only 13–14% distributed among species. This pattern, which highlights a high level of intraspecific diversity, aligns with findings reported in Capparis spinosa L. and other genera such as Ziziphus Mill. ⁸⁴., Nigella L. ⁶⁵., Carthamus L. ⁶¹., Reynoutria Houtt¹⁴., and Origanum L. ⁸⁵. Genetic diversity within species is governed by a combination of evolutionary and ecological forces, including breeding system, gene flow, effective population size, and natural selection^24,65,86. In Acanthophyllum, the relatively high levels of variation within species may be attributed to historical admixture among geographically separated accessions, a predominantly outcrossing reproductive system, perennial life form, long-term adaptation to heterogeneous native environments, pollen-mediated gene exchange, and seed dispersal. Although the reproductive biology of Acanthophyllum has not been specifically investigated, a predominantly outcrossing mating system is consistent with findings from related caryophyllaceous genera such as Silene ⁸⁷. Insect-mediated pollination by bees, butterflies, and nocturnal moths is common within this genus. Studies on Silene⁸⁸ and Oenothera harringtonii ⁸⁹ have demonstrated that nocturnal pollinators reduce genetic isolation by facilitating long-distance pollen dispersal in the Caryophyllaceae family-a finding consistent with the patterns observed here. The predominance of within-population diversity (86–87%) suggests that Acanthophyllum populations have not yet undergone severe anthropogenic bottlenecks, highlighting the need for proactive conservation measures and the initiation of early domestication programs.

Table 4.

The results of analysis of molecular variance through molecular markers of SCoT and SRAP in Acanthophyllum accessions.

SCoT				SRAP
Source of variation	Among species	Within  species s	Total	Among species	Within species	Total
dfa	6	64	70	6	64	70
Sum of square	521.903	2230.576	2752.479	504.41	2241.51	2745.92
Variance components	5.446	34.853	40.299	5.12	35.02	40.15
Percentage of variation	14%	86%	100%	13%	87%	100%
Gstb	_	_	0.18	_	_	0.17
Nmc	_	_	2.31	_	_	2.42

^aDF: Degrees of freedom; ^b Gst: Coefficient of genetic differentiation; ^c Nm: estimate of gene flow from Gst e.g., Nm = 0.5(1 - Gst)/Gst.

The Gst values were relatively low at 0.18 for SCoT and 0.17 for SRAP (Table 4). The substantial gene flow observed across the studied Acanthophyllum species (Nm = 2.31 for SCoT; Nm = 2.42 for SRAP) confirms significant genetic connectivity. According to Wright’s⁹⁰ criterion, species with Nm > 1-including A. bracteatum, A. glandulosum, A. microcephalum, A. sordidum, A. squarrosum, and A. spinosum-show no strong tendency for population differentiation. In contrast, species with Nm < 1 exhibit higher susceptibility to genetic drift⁹⁰. A low average Nm value indicates restricted gene exchange among populations, resulting in pronounced genetic differentiation within such species⁹¹. Although A. korshinskyi exhibited Nm values near the mean, it maintained adequate gene flow to prevent severe genetic differentiation. An average Nm > 2 further indicates that gene flow is robust enough to effectively counteract genetic drift, a pattern characteristic of outcrossing species⁹² and consistent with Acanthophyllum being predominantly cross-pollinated. This level of genetic connectivity may be influenced by the degree of isolation and effective population size within species.

Structure analysis

Many factors-such as evolutionary events, breeding system, environmental conditions, and human activities—shape the extent and pattern of genetic diversity in a plant species⁹³. The population structure of Acanthophyllum accessions was inferred using a Bayesian model implemented in the STRUCTURE software. For data generated with SCoT primers, the optimal number of clusters was determined to be four (K = 4), based on the highest ΔK value calculated by STRUCTURE HARVESTER (Fig. 6a). At K = 4, 30.98% of the accessions (22 out of 71) were assigned to four distinct genetic clusters (designated STR1a, STR1b, STR1c, and STR1d), while the remaining 69.01% (49 accessions) were identified as admixed. The accessions of A. bracteatum, A. korshinskyi, and A. spinosum exhibited the highest degree of admixture, each at 100% (Table 5). Similarly, analysis of SRAP data also identified four genetic clusters as optimal (highest ΔK at K = 4; Fig. 6b). Under this model, 50.7% of the accessions (36 out of 71) were assigned to four distinct clusters (STR2a, STR2b, STR2c, and STR2d) (Fig. 6b), while the remaining 49.3% (35 accessions) showed admixed ancestry. Among the admixed accessions, those of A. sordidum displayed the highest admixture percentage (75%).

Fig. 6.

Estimation of sub-populations for STR1 based on SCoT (a) marker at K = 4 by Evano table (A). Estimation of sub-populations for STR2 based on SRAP (b) marker at K = 4 by Evano table (A).

Table 5.

Distribution of Acanthophyllum accessions in four different sub-populations resulted from structure analysis.

STR1 (SCoT)							STR2 (SRAP)
Population groups	ACC	STR1 (a)	STR1 (b)	STR1 (c)	STR1 (d)	Ad	STR2 (a)	STR2 (b)	STR2 (c)	STR2 (d)	Ad
A. bracteatum	7					7				5	2
A. glandulosum	23			12		11		11		1	11
A. korshinskyi	5					5			3		2
A. microcephalum	12	1	5			6			1	4	7
A. sordidum	12			1	2	9	2	1			9
A. spinosum	6					6				2	4
A. squarrosum	6	1				5			5	1	0
Total	71	2	5	13	2	49	2	12	9	13	35

ACC: The number of accessions in each population group. Ad: Admixture.

High levels of genetic admixture were confirmed by STRUCTURE analysis, AMOVA, and cluster analysis (Fig. 6; Table 5). Bayesian clustering analysis using STRUCTURE revealed a complex genetic composition among Acanthophyllum species for both SCoT and SRAP markers. The optimal number of genetic clusters was four (K = 4), indicating the presence of multiple subpopulations within the genus. A significant proportion of accessions demonstrated mixed genetic ancestry- 69% in the SCoT analysis and 49.3% in the SRAP analysis-indicating substantial gene flow. This genetic mixing was especially evident in A. bracteatum, A. korshinskyi, and A. spinosum, implying interspecific hybridization, shared evolutionary origins among these species²⁵, or ongoing gene flow among closely related species of the genus Acanthophyllum, facilitated by overlapping geographical distributions and the absence of strong reproductive isolation barriers. The notably high admixture ratio observed in A. sordidum (75% based on SRAP markers) suggests significant historical gene flow between species or the influence of selective pressures that have preserved shared genetic variation. Comparable genetic patterns have been documented in taxa such as Ferula asafoetida ²⁹ and Reynoutria species ¹⁴, where both gene exchange and selective forces played key roles in sustaining genetic diversity and adaptive capacity⁹⁴. However, it is essential to acknowledge the limitations of the marker systems used in this study. As SCoT and SRAP are dominant markers, they do not distinguish between homozygous and heterozygous states, which can affect the accuracy of individual ancestry assignments Furthermore, the number of loci scored, while sufficient for general diversity assessment, represents only a fraction of the genome and may not capture the full complexity of the population structure. Therefore, while the observed admixture patterns are robust, the specific cluster assignments and admixture proportions should be interpreted as estimates reflecting broad-scale genetic relationships rather than precise genealogical histories ^14,61. These admixtures highlight the dynamic nature of the genetic structure in Acanthophyllum, indicating that geographic, ecological, and anthropogenic influences, along with historical migratory patterns, have likely played important roles in shaping the present germplasm configuration and population fragmentation⁹⁵. The co-occurrence of distinct genetic clusters and admixed accessions emphasizes the need to incorporate both pure and hybrid accessions in conservation and breeding strategies. Genetically unique accessions can improve the precision of trait selection and introgression efforts, whereas admixed groups serve as valuable reservoirs of allelic variation essential for adaptation to varying environmental conditions^30,96.

Regarding conservation strategies, population genetic diversity is maintained at higher levels in situ rather than ex situ for outcrossing species⁹⁷. This strategy may also be suitable for the Acanthophyllum genus. As no prior information regarding the population genetic structure of Acanthophyllum is available, a more comprehensive study with broader sampling, covering the entire geographical distribution range of the genus, can provide a clearer image of its global genetic structure. Consequently, the origin of the Iranian gene pools of Acanthophyllum could be elucidated.

The genetic similarity patterns obtained from Nei’s identity matrix were fully congruent with the clustering patterns revealed by PCoA and STRUCTURE analyses. Species pairs exhibiting the highest Nei’s identity-e.g., A. bracteatum and A. sordidum, A. glandulosum and A. sordidum, A. microcephalum and A. squarrosum-consistently co-clustered in dendrograms generated from both SCoT and SRAP data, affirming their close phylogenetic affinity. In contrast, A. korshinskyi, which demonstrated the lowest Nei’s similarity in most comparisons, formed a distinct cluster in the UPGMA, PCoA, and STRUCTURE (at K = 4) analyses, underscoring its significant genetic divergence.

The consistent results across all analytical methods validate the robustness of the dataset. Overall, SCoT and SRAP markers proved effective in revealing both inter- and intra-species genetic diversity, confirming the utility of these molecular tools for genetic characterization in Acanthophyllum. Consequently, future research should incorporate genome-wide approaches (e.g., whole-genome sequencing) to fully assess the resilience and adaptive capacity of Acanthophyllum species in the face of environmental challenges such as climate change⁹⁸.

Limitations

Although this study evaluated a broad number of accessions across seven different species of Acanthophyllum, several limitations should be acknowledged. The use of molecular markers such as SRAP and SCoT was challenging, as conserved and universal primers for these markers have not been identified in Acanthophyllum. Employing a broader range of molecular markers might have provided more comprehensive insights into the genetic diversity of this genus. Additionally, the unequal number of sampled accessions among species represents a limitation. This sampling imbalance, due to time and logistical constraints, could partially affect the observed patterns of genetic diversity indices and the comparisons between species.

Conclusion

This study provides the first comprehensive evaluation of the molecular characterization of the genetic resources of Iranian Acanthophyllum species. Four analytical approaches-clustering, PCoA, STRUCTURE, and AMOVA - were employed to elucidate the genetic relationships and population structure of seven Acanthophyllum species (71 accessions) using SCoT and SRAP markers. The studied accessions, collected from various habitats of the Acanthophyllum genus, revealed high genetic diversity. These findings also demonstrate a higher degree of genetic variation within species than between species. Both marker systems proved effective in discriminating genotypes, as SCoT demonstrated slightly higher diversity indices compared to SRAP. Both markers consistently identified the A. bracteatum and A. korshinskyi as the most divergent species, reflecting divergence and patterns of gene flow. The observed population structure in Acanthophyllum species indicates four distinct ancestral gene pools, a pattern that can be explained by mechanisms such as shared ancestral polymorphism, historical gene flow, and incomplete lineage sorting. These findings suggest that species with higher genetic diversity, such as A. glandulosum and A. microcephalum, are valuable for breeding programs, while species with limited genetic variability, like A. korshinskyi, require careful conservation strategies. Expanding future studies to include additional accessions will further support these insights and enhance their applications in evolutionary and breeding research. Subsequent genome-wide studies and molecular breeding approaches are recommended to build upon these findings and to explore adaptive genetic variation related to environmental flexibility in Acanthophyllum.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Author contributions

F.B. performed all the experiments section; wrote the initial draft of the article; P. G. supervised the experiment, data analysis, manuscript writing, editing and final review of manuscript. H. B. supervisor and manuscript editing; A.P. advisor, supporting experimental section, editing manuscript and reviewing.

Funding

The author(s) declare that financial support was received from the Research Council of the Iran National Science Foundation (INSF) for funding this project under grant number [4025857].

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Statement specifying permissions

For this study, we acquired permission to study “Acanthyphyllum genus” issued by the Agricultural and Natural Resources Ministry of Iran.

Statement on experimental research and field studies on plants

The collected samples of all of all “Acanthyphyllum species and their accessions comply with relevant institutional, national, and international guidelines and domestic legislation of Iran.