Investigating the genetic potential of Ashwagandha (Withania somnifera L. Dunal) germplasm through SCoT and EST-SSR molecular markers

Abstract

Ashwagandha (Withania somnifera L. Dunal) is a highly acclaimed medicinal herb belonging to the family Solanaceae. The plant has been rendered an essential component of the Indian medicinal system due to its high therapeutic potential, attributed to its rich profile of secondary metabolites, namely withanolides. Widespread cultivation of the herb across diverse agroclimatic conditions has resulted in the development of numerous varieties and landraces, making it very difficult to discriminate the elite germplasm using conventional approaches. To overcome these, we use two molecular marker systems, SCoT and EST-SSR, to study the genetic variation and population structure of 32 Withania accessions. A total of 113 and 26 reproducible bands were generated using 10 SCoT and 17 EST-SSR markers, respectively, of which 100 and 20 were polymorphic, with an average polymorphism rate of 88 and 85.3%. The mean polymorphism information content (PIC), Marker Index (MI), and Resolving Power (Rp) for SCoT and EST-SSR markers were 0.327 and 0.172, 2.98 and 0.282, and 7.24 and 1.14, respectively, indicating that SCoT markers are more informative than EST-SSR markers for genetic diversity assessment. Cluster analysis using Jaccard similarity coefficients revealed distinct genetic groupings: SCoT markers formed two clusters, and EST-SSR markers formed two main clusters, with some outliers. Population structure analysis using Bayesian clustering revealed two ancestral populations with high admixture. This is the first time SCoT markers were used to assess genetic diversity in Withania somnifera. Quantitative real-time (qRT-PCR) expression analysis of six withanolide biosynthetic pathway genes (GT, CAS, FPPS, HMGR, SE, SS) across four genotypes of Withania somnifera reveals genotype-specific differential expression, further supporting the diversity observed by molecular markers. Our results indicate that both markers are informative and can be used to detect distinct genetic variations within Withania genotypes, providing valuable insights for breeding and conservation efforts and enabling the development of improved cultivars suited to various agroclimatic conditions.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-51758-7.

Introduction

Ashwagandha (Withania somnifera L.) belongs to the Solanaceae family and is highly known for its extensive medicinal properties since ancient times. The economics and therapeutic ability of W. somnifera plant are well known by their trade, as the global market for ashwagandha is projected to expand to 11.4% from 2022 to 2029, with an estimated value of 102.72 million by USD in 2029¹ while, their medicinal property is recognized by biosynthesis of therapeutic alkaloids such as Withanolides, and Withaferin². Withanolides are a very important therapeutic class found in the roots and leaves, demonstrating antifungal and antibacterial activity. Meanwhile, their berries possess anti-inflammatory, anticancer, and anthelmintic properties^3–6. Other parts of the ashwagandha are also used to treat diseases like smallpox, whooping cough, fever, healing wounds, back pain, trachoma, rheumatism, infertility, toothache, snake bites, bronchitis, and skin infections^7–11. Although Withania somnifera is a small perennial shrub with drought-resistant wild cultivars in dry tropical regions¹². It is a diploid species with 48 chromosomes, exhibiting significant genetic variation due to cross-pollination, enabling adaptations to diverse environmental conditions and ecological niches¹³.

As a member of the Solanaceae, it is essential to note that this family is highly diverse, comprising 3000–4000 species, ranging from trees to perennial herbs, found in diverse habitats across aquatic and desert regions¹⁴. Therefore, the members of this family showed intense variation in adaptation, diversification, and conservation, which is more pertinent to exploring their genetics and genomics, as this will help characterize superior plant accessions from ecological diversification^15,16. As crops adapt to fluctuating environmental conditions that affect their physiology and metabolism, they represent underlying genetic diversity¹⁷. Therefore, it is essential to understand the genetic variation in W. somnifera to strengthen breeding efforts for crop improvement and improve the quality of its therapeutic metabolites¹⁸.

Molecular markers are frequently used tools in germplasm characterizations, population genetics, conservation, evolution, and crop improvement with breeding programs¹⁹. Consequently, DNA-based molecular marker technologies like simple sequence repeats (SSRs), inter-simple sequence repeats (ISSRs), random amplified polymorphic DNA (RAPDs), start codon-targeted polymorphism (SCoT), etc., have gained a dynamic momentum to study genetic diversity, taxonomical characterization, comparative genetics, evolutionary, and ecological related studies^20,21. These technologies have become more attractive because they are accurate, reliable, polymorphic, repeatable, and cost-effective, demonstrating strong discriminatory potential within and across species^15,22–24. Therefore, various molecular marker studies have been employed with SSR, AFLP, RAPD, ISSR, etc., in various Withania sp^25–30.

In the present study, molecular markers such as expressed sequence tag-based microsatellites (SSR) and start codon targeted polymorphism (SCoT) have been employed in various Withania accessions to characterize superior accessions. EST-SSR markers are located in gene-specific regions, provide functional insights into gene-level polymorphism, and help identify and characterize important traits^16,31,32. Furthermore, SCoT markers characterize start-codon target regions, which are small, conserved regions following the ATG start codon in genes, thereby enabling efficient detection of variation among genotypes. SCoT markers have several advantages over traditional markers, including their ability to amplify gene-rich regions, simplicity, dominance, high reproducibility, and cost-effectiveness³³. Therefore, we utilized the EST-SSR and SCoT marker system to delineate genetic variability and population structure, which will help characterize superior accessions with therapeutic potential among various Withania somnifera accessions from different geographical locations. The major withanolides found in Withania include Withaferin A, Withanolide A, and Withanone, which are putatively produced through the mevalonate pathway and downstream of the triterpenoid pathway. Understanding the genes associated with chemo-diversity is crucial in delineating the withanolide biosynthetic pathway and potentially enhancing the biosynthesis of specific withanolides³⁴. Consequently, investigating the expression of key genes involved in the withanolide biosynthetic pathway in superior genotypes is significant for validation.

Material and method

Plant material

Thirty-two Withania somnifera accessions were used for diversity analysis. The seeds of different accessions comprised 30 breeding lines, and two released varieties were kindly provided by the AICRP on medicinal and aromatic plants, Department of Genetics and Plant Breeding, Rajasthan College of Agriculture, MPUAT, India, Krishi Vigyan Kendra, Banswara, and Department of Biotechnology, Jawaharlal Nehru Krishi Vishwavidhyalaya, Jabalpur, India (Supplementary Table 1). All these accessions were further submitted and maintained by the AICRP on medicinal and aromatic plants at the MPUAT Centre.

DNA isolation

Genomic DNA from 32 accessions was isolated from young leaf tissue of 21-day-old seedlings. Approximately 100 mg of young fresh leaves were taken for the extraction of total genomic DNA from all the accessions using a Plant Genomic DNA Extraction kit (HiMedia, India) in accordance with the manufacturer’s guidelines, and the DNA quality and quantity were assessed using agarose gel (0.8%) electrophoresis and spectrophotometrically using Nanodrop. DNA was stored at −70 °C till further use and diluted to 25 ng/µL prior to PCR amplification.

SCoT PCR

According to Collard and Mackill (2009)³⁵, 36 primer sets were custom-synthesized (Supplementary Table 2). These are 18-mer primers having GC content between 50% and 72%. Amplification was performed using 5 µl of 2X PCR buffer (Jumpstart ready mix-P1107) with 1µL of 25 ng of template DNA and 1 µL of primer in a reaction volume of 10 µL and conducted using a thermal cycler (Biorad) for an initial denaturation at 94 °C for 3 min, followed by 40 cycles consisting of denaturation at 94 °C for 1 min, annealing at 50–57 °C for 1 min, and extension at 72 °C for 2 min with final extension for 5 min at 72 °C. Agarose gel electrophoresis was used to separate the PCR products on a 1.5% agarose gel for 2 h at 80 V, and the Gel Documentation system (Eppendorf, USA) was used to capture images under UV light. The size of the amplified products was assessed using a 100 bp − 3 kb DNA Ladder (GeNei).

EST-SSR PCR

EST sequences retrieving, ESTs assembling, and microsatellites identification

For microsatellite deciphering of Withania somnifera, EST sequences were downloaded in Fasta format from the National Center for Biotechnology Information (NCBI). Further, the CAP3 (Galaxy Version 2.0.0) (http://mobyle.pasteur.fr/cgi-bin/portal.py#forms::cap3) program was used to assemble ESTs to minimize sequence redundancy. Identification of microsatellites was performed using MISA (Microsatellite identification tool) software (MISAv2.1) (http://pgrc.ipk-gatersleben.de/misa/) and following the SSR detection criteria of 6, 5, 4, 3, and 2 repeat units for di-, tri-, tetra-, penta-, and hexa nucleotides, respectively. An online program, BatchPrimer3, was used to design forward and reverse SSR primer pairs for selected EST sequences containing microsatellites. A total of 252 primers were designed, of which 20 were used for this study (Supplementary Table 3). Primers were selected according to the following criteria: primer length (18–24 bp), the number of repeated units, and the amplified product length (100–300 bp).

EST-SSR PCR

The amplification reaction was carried out as per SCOT PCR, varying the annealing temperature from 58 to 62 °C according to the primer, and the amplified products were separated on a 3% MetaPhor agarose gel along with a 1 kb DNA ladder (GeNei), using a horizontal gel electrophoresis unit for 3 h at 70 V. The Gel Documentation system (Eppendorf, USA) was used to document the gel.

Real-time expression analysis

Extraction of RNA and Preparation of cDNA libraries

The 21-day-old plantlets grown under in vitro conditions were used for gene expression studies of different withanolide pathway genes using quantitative real-time RT-PCR (Bio-Rad “CFX96 Real-Time PCR System”). Tissues were ground to a fine powder in liquid nitrogen, and total RNA was extracted using the Spectrum Plant Total RNA Kit (Sigma–Aldrich, USA) and treated with RNase-free DNase I according to the manufacturer’s instructions. The quality and quantity of total RNA were analyzed by agarose gel electrophoresis (0.8%) and spectrophotometry. A double-stranded (ds) cDNA library was prepared from pooled total RNA using a double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s recommendations. The PCR cycling conditions comprised an initial priming step at 25 °C for 5 min, reverse transcription at 46 °C for 30 s, and enzyme inactivation at 5 °C for 5 min 20 s.

Real-time RT-PCR analysis

Real-time RT-PCR was performed in a 20 µL reaction containing 5 µL cDNA, 0.4 µM of each gene-specific primer (Supplementary Table 4), and 10 µL of 2X SYBR Green JumpStart Taq ReadyMix (Sigma, USA). All reactions were performed in triplicate, and the actin gene was used as a housekeeping reference for normalization.

Data analysis

All amplified DNA fragments were scored manually as present (1) or absent (0), and data analysis was done using SIMUQUAL Jaccard’s similarity coefficient as well as NTSYS-PC 2.2e (Numerical Taxonomy and Multivariate Analysis System) software. The dendrogram was created using the UPGMA method using Similarity coefficient values. Polymorphism information content (PIC), marker index (MI), resolving power (RP), and effective multiple ratios (EMR) were measured as suggested by Jain et al.³⁶. Population structure analysis was done using STRUCTURE 2.3.4. software with varying values of the number of clusters (K). To determine the optimal K value, the burn-in is set to 10,000 iterations, and the Monte Carlo Markov Chain (MCMC) is run for 200,000 replicates. Structure Harvester was used to evaluate population structure using the ΔK method⁴⁵. The RT-PCR data were calculated from the threshold cycle (Ct) of the amplification curve. The relative gene expression level was assessed using the 2^–ΔΔCT method, where fold change values less than 1 indicate downregulation and values greater than 1 indicate upregulation relative to the control. The sizes of the amplified fragments were confirmed by gel electrophoresis.

Results

DNA amplification and polymorphism in Withania somnifera

In the present study, genomic DNA from 32 distinct accessions of Withania somnifera was used, and genetic variation and population structure were explored using SCoT and EST-SSR markers, as these marker technologies are well known for their functional application at the gene level. Therefore, a total of 56 molecular markers comprising 36 SCoT markers and 20 EST-SSR (WSSSR) markers were employed to identify DNA polymorphism, genetic differentiation, and population structure among 32 different accessions of Withania somnifera, which were collected from distinct geographical locations of Rajasthan and Madhya Pradesh states of India (Fig. 1; Supplementary Fig. 1). Out of 36 markers SCoT markers, 10 SCoT markers were selected due to their unique and consistent DNA amplification profiles, and primer polymorphism ranged from 63% (SCoT 31) to 100% (SCoT12, SCoT30, SCoT33) with an average polymorphism of 88%. A total of 113 amplicons were generated by SCoT markers, with sizes ranging from 130 bp to 1.7 kb, whereas the distribution of amplicons varied from 7 (SCoT 13) to 16 (SCoT 29) with an average of 11.3 amplicons per primer (Fig. 2). Similarly, for EST-SSR markers, novel 17 out of 20 EST-SSR markers showed successful amplification profiles, and primer polymorphism ranged from 50% (WSSSR8) to 100% (WSSSR1, 3,5,7,10,11,12,13,14,17,18,19,20) with an average polymorphism of 85.3%. The amplicon’s size ranged from 149 bp to 283 bp in size, and a total of 26 amplicons were produced, with a mean of 1.53 amplicons per primer.

Fig. 1.

The sampling points of Withania somnifera germplasm resources used in the study.

Fig. 2.

DNA profile of Withania somnifera germplasm generated with (a) SCOT-09 and (b) EST-SSR-8 (genotype numbering as given in Supplementary Table 1).

SCoT marker analysis also revealed the presence of seven unique bands (bands present in particular genotypes but absent in others) in five genotypes: PWS-14, PWS-15, UWS-57, UWS-58, and RVA-100, using five SCoT primers (SCoT 9, SCoT 14, SCoT 30, SCoT 31, and SCoT 35) (Table 1). The size of these unique bands ranged from 440 to 1500 bp. Li et al.³⁷ reported that accessions with diverse DNA profiles are more likely to produce novel alleles, with certain genotypes producing more unique bands, indicating higher genetic variability or unique genetic traits. These unique alleles indicate exclusive genetic variation and are used to assess gene flow between populations.

Table 1.

Unique alleles detected by 5 SCoT primers as shown by 5 different genotypes of Withania somnifera.

S. No.	Primer code	Total No. of Unique band	Genotype	Size of band (bp)
1	SCoT 9	1	UWS-57	1030
2	SCoT 14	2	UWS-58	800, 1000
3	SCoT 30	1	UWS-58	1500
4	SCoT 31	1	RVA-100	580
5	SCoT 35	2	PWS-14 PWS-15	870 440
Total		7

Marker efficiency in Withania somnifera

The effectiveness of SCoT and EST-SSR markers was assessed in 32 Withania somnifera accessions using PIC, MI, and RP. For SCoT markers, the PIC value ranged from 0.250 (SCoT 31) to 0.400 (SCoT 33) with an average PIC value of 0.327, and the mean MI value was 2.98 which ranged from 0.81 (SCoT 31) to 4.80 (SCoT 33) in the present study while the average of RP value was 7.24 which was varied from 3.63 (SCoT 12) to 14.00 (SCoT 31) (Table 2). Similarly, for EST-SSR markers, the PIC value ranged from 0 (WSSSR 6 & 16) to 0.482 (WSSSR 8) with an average of 0.172, and the marker index ranged from 0 (WSSSR 6 and 16) to 0.688 (WSSSR 10) with a mean of 0.282 in the present study. The mean value of RP was 1.14, ranging from 0.88 (WSSSR18) to 2.38 (WSSSR1) for WSSSR markers (Table 3). Therefore, these values reflected the potential of the markers and demonstrated their significant discriminatory power in detecting genetic variations.

Table 2.

DNA amplification profile and polymorphism generated in Withania somnifera using 10 SCoT primers.

S. No.	Primer	Tm	TB	PB	Β	PIC	EMR	MI	Rp
1	SCoT 7	50	15	14	0.93	0.307	13.07	4.01	9
2	SCoT 9	50	12	9	0.75	0.318	6.75	2.14	7.75
3	SCoT 12	50	8	8	1.00	0.359	8.00	2.88	3.63
4	SCoT 13	50	7	6	0.86	0.371	5.14	1.91	3.94
5	SCoT 14	50	12	11	0.92	0.274	10.08	2.76	7.44
6	SCoT 29	50	16	13	0.81	0.337	10.56	3.56	9.56
7	SCoT 30	50	12	12	1.00	0.353	12.00	4.24	5.94
8	SCoT 31	50	8	5	0.63	0.259	3.13	0.81	14.00
9	SCoT 33	50	12	12	1.00	0.400	12.00	4.80	4.31
10	SCoT 35	50	11	10	0.91	0.295	9.09	2.68	6.88
Total			113	100	0.88

Tm =Temperature of melting, TB = Total no. of bands, PB= Polymorphic bands, PIC= Polymorphic information content, EMR=Effective Mean Ratio, MI= Marker index, Rp= Resolving power, beta = polymorphism percentage.

Table 3.

DNA amplification profile and polymorphism generated in Withania somnifera using 17 EST-SSR primers.

Primer	Tm	TB	PB	Β	PIC	EMR	MI	Rp
WSSSR 1	60	3	3	1	0.165	3	0.495	2.38
WSSSR 2	60.5	2	2	1	0.162	2	0.324	1.63
WSSSR 3	59	1	1	1	0.065	1	0.065	0.94
WSSSR 5	59	1	1	1	0.117	1	0.117	0.88
WSSSR 6	62	1	0	0	0	0	0	1.00
WSSSR 7	59	1	1	1	0.117	1	0	0.88
WSSSR 8	62	2	1	0.5	0.482	0.5	0.117	1.19
WSSSR 10	58	2	2	1	0.344	2	0.241	1.00
WSSSR 11	60	3	1	1	0.37	3	0.688	1.50
WSSSR 12	62	1	1	1	0.061	1	1.11	0.94
WSSSR 13	61	1	2	1	0.117	1	0.061	0.88
WSSSR 14	59	2	0	1	0.256	2	0.117	1.25
WSSSR 16	58	1	0	0	0	0	0.512	1.00
WSSSR 17	59	1	1	1	0.061	1	0	0.94
WSSSR 18	60.5	1	1	1	0.219	1	0.219	0.75
WSSSR 19	58	1	1	1	0.117	1	0.117	0.88
WSSSR 20	61	2	2	1	0.272	2	0.544	1.31
Total		26	20	0.85

Tm =Temperature of melting, TB = Total no. of bands, PB= Polymorphic bands, PIC= Polymorphic information content, EMR=Effeective mean ratio, MI= Marker index, Rp=Resolving power, beta = polymorphism percentage.

Measures of Genetic Variation in Withania somnifera

Genetic variation and diversity at the genome level were evaluated among 32 accessions of W. somnifera using SCoT and EST-SSR marker systems. Thereupon, the Observed allele (Na), Effective allele (Ne), Heterozygosity (He), Shannon’s Information index (I), and Nei’s gene diversity (H) parameters were measured in Withania somnifera. According to SCoT markers, the Na varied from 1.62 (SCoT-31) to 2 (SCoT-12, 30, and 33) with an average of 1.88, followed by the mean value of Ne was 1.48, which varied from 1.28 (SCoT-13) to 1.69 (SCoT-33) and He varied from 0.33 (SCoT-31) to 0.79 (SCoT-13) with a mean of 0.58. Furthermore, the H varied from 0.21(SCoT-31) to 0.400 (SCoT-33), with a mean of 0.29, while I varied from 0.31 (SCoT-31) to 0.59, with an average of 0.43 (SCoT-33). In addition, Nei’s statistics of genetic diversity were examined using parameters such as total species diversity (Ht), diversity within the population (Hs), coefficient of gene differentiation (Gst), and gene flow (Nm) in the W. somnifera accession using SCoT markers. Thereupon, the grand mean values of Ht, Hs, Gst, and Nm were 0.291, 0.249, 0.144, and 2.9874, respectively (Table 4). Similarly, for EST-SSR markers, Na ranged from 1 (WSSSR 7) to 2 (WSSSR 16) with an average of 1.89, followed by the mean distribution of Ne, which was 1.39, varying from 1 (WSSSR 7) to 1.96 (WSSSR 16), and the mean He was 0.312, which varied from 0 (WSSSR 7 and 17) to 0.96 (WSSSR 12). Also, the H varied from 0 (WSSSR 7) to 0.49 (EST-SSR 16) with a mean of 0.25, while the mean distribution of I varied from 0 to 0.68, with a mean of 0.40. Moreover, the statistics of Nei’s Analysis of genetic diversity were calculated through EST-SSR markers, while the grand mean values of Ht, Hs, Gst, and Nm were 0.235, 0.205, 0.130, and 3.344, respectively (Table 5).

Table 4.

Genetic diversity indices of Withania somnifera accessions using SCoT markers.

Marker	Sample Size	Na	Ne	He	H	I	Ht	Hs	Gst	Nm
SCoT-9	32	1.6667	1.3716	0.3741	0.2210	0.3312
SCoT-7	32	1.9333	1.5437	0.4954	0.3151	0.4751
SCoT-13	32	1.8571	1.2812	0.7976	0.2080	0.3467
SCoT-31	32	1.6250	1.3549	0.3311	0.2075	0.3084
SCoT-12	32	2.0000	1.6878	0.6094	0.3868	0.5664
SCoT-14	32	1.9167	1.3562	0.6541	0.2264	0.3583
SCoT-29	32	1.8125	1.3719	0.6465	0.2316	0.3597
SCoT-33	32	2.0000	1.6934	0.6025	0.3998	0.5861
SCoT-35	32	1.9091	1.5011	0.6170	0.2887	0.4310
SCoT-30	32	2.0000	1.6112	0.7157	0.3444	0.5112
Mean		1.8761	1.4807	0.584337	0.2842	0.4291	0.2907	0.249	0.1434	2.9874
STDEV		0.3309	0.3491	0.145203	0.1758	0.2386	0.0308	0.0241

The number of alleles (Na), the effective number of alleles (Ne), Nei’s gene diversity Index (H), Shannon’s information index (I), the total genetic diversity (Ht), population genetic diversity (Hs), the coefficient of genetic differentiation (Gst), Gene flow (Nm).

Table 5.

Genetic diversity indices of Withania somnifera accessions using EST-SSR markers.

Markers	Sample Size	Na	Ne	He	H	I	Ht	Hs	Gst	Nm*
WSSSR1	32	2	1.1911	0.3740	0.1524	0.2734
WSSSR2	32	2	1.2490	0.9565	0.1969	0.3461
WSSSR3	32	2	1.2703	0.0615	0.2128	0.3690
WSSSR5	32	2	1.2703	0.0615	0.2128	0.3690
WSSSR6	32	2	1.5221	0.1211	0.3430	0.5267
WSSSR7	32	1	1	0	0	0
WSSSR8	32	2	1.1981	0.0605	0.1419	0.2289
WSSSR10	32	2	1.6849	0.5347	0.4064	0.5964
WSSSR11	32	2	1.6361	0.7331	0.3743	0.5578
WSSSR12	32	2	1.2208	0.9648	0.1808	0.3262
WSSSR13	32	2	1.2703	0.0615	0.2128	0.3690
WSSSR14	32	2	1.3753	0.0913	0.2686	0.4373
WSSSR16	32	2	1.9630	0.5693	0.4906	0.6837
WSSSR17	32	1	1	0	0	0
WSSSR18	32	2	1.2167	0.0615	0.1781	0.3225
WSSSR19	32	2	1.8215	0.2344	0.4510	0.6433
WSSSR20	32	2	1.4863	0.4124	0.3216	0.4999
Mean		1.8889	1.3892	0.3117	0.2539	0.4007	0.2352	0.2046	0.1301	3.3443
STDEV		0.3203	0.2744	0.3306	0.1419	0.1970	0.0204	0.0175

The number of alleles (Na), the effective number of alleles (Ne), Nei’s gene diversity Index (H), Shannon’s information index (I), the total genetic diversity (Ht), population genetic diversity (Hs), the coefficient of genetic differentiation (Gst), Gene flow (Nm).

Population structure in Withania somnifera

Molecular variance was measured in the Withania somnifera populations using an AMOVA analysis, with P < 0.001. Significantly, increased genetic variance was observed with EST-SSR markers at the population level, whereas the opposite was observed with SCoT markers (Fig. 3). Further, the spatial arrangement of Withania somnifera accessions was measured using Principal Coordinate Analysis (PCoA) analysis to identify relationships between plant samples. Hence, the first 3 PCoA retained 28.87% total variance through SCoT markers, and EST-SSR displayed 45.47% of the total variance obtained by the first 3 PCoA. Moreover, the distribution pattern among Withania accessions was distinct at different coordinate positions. In plot A, the largest variation of proportion was observed in co-ordinate 1. There is a clear separation between PWS and UWS accessions vs. P-series (P1-P5) and RVA-100. PWS and UWS accessions are predominantly on the left side, whereas P-series and RVA-100 are on the right side, indicating clear genetic divergence within accessions. PWS accessions are mostly situated in the left quadrants, while the UWS series is scattered from the upper middle to the right of the quadrants. Additionally, the P-series shows greater diversity and predominance on the right side of the coordinate.

Fig. 3.

Percentage of molecular variance (AMOVA) in Withania somnifera population based on (A) SCoT markers (B) EST-SSR Markers.

Plot B revealed further sub-clustering between accessions. The UWS series revealed greater genetic variability within groups, as they are widely dispersed across all quadrants of plots. Moderate clustering is observed in PWS accessions, and P-series accessions occupy positions in the lower quadrants. Both varieties, JA-20 and RVA-100, are placed in intermediate positions, indicating a mixed or transitional genetic background. The overall PCoA also showed close and distant relationships and clustering among the Withania accessions, supporting the clustering pattern obtained by both marker systems (Fig. 4).

Fig. 4.

Principal coordinate analysis (PCoA) of Withania somnifera Population based on (A) SCoT markers (B) EST-SSR markers.

To further evaluate, the Jaccard’s index similarity coefficient was calculated for Withania accessions (Supplementary Tables 5 and 6). Therefore, the similarity coefficient for SCoT ranged from 0.35 (PWS-61 and RVA-100) to 0.81 (PWS-3 and PWS-38), indicating moderate to high genetic diversity among genotypes, whereas the similarity coefficient for EST-SSR varied from 0.57 (PWS-35 and UWS-57) to 1.00 (PWS-10 and PWS-24, PWS-40 and P-1), indicating narrow genetic diversity compared to SCoT markers. Moreover, phylogenetic relationships were inferred using a UPGMA dendrogram of the Withania accessions (Fig. 5). For SCoT markers, Withania germplasms are primarily grouped into two clusters. Cluster I is the biggest cluster, having 29 genotypes, further divided into two groups, A and B. Group A is further divided into two subgroups, Ia and Ib. The subgroup Ia had 23 accessions, while subgroup Ib included only 3 genotypes based on their closeness. Group B is distinguished into two subgroups, Ic and Id. Subgroup Ic comprised 2 accessions, i.e., PWS-14 and PWS-27, whereas Subgroup Id comprised a single accession, i.e., UWS-58. Cluster II contained only three genotypes: Group C with a single accession, P2, and Group D with two accessions, P4 and RVA-100. In the EST-SSR dendrogram, the genotypes were grouped into two main clusters, i.e., cluster I and cluster II, with 17 and 12 genotypes, respectively, while genotypes PWS-14, PWS-18, and PWS-35 were out-grouped. Cluster I comprised two groups, A and B. Group A was further subdivided into subgroups Ia and Ib, each containing 6 and 8 accessions, respectively. As well, Group B comprised only three accessions, Subgroup Ic with two accessions (PWS3 & PWS-19) and Subgroup Id with only one accession (PWS-15). Cluster II comprised two groups, C and D. Group D had only 2 accessions in each subgroup, Ig and Ih, while Group C had 10 accessions; IIf had only 3 accessions, and IIe had 7 accessions. They were distinguished from Cluster I accessions. Lastly, the population makeup of Withania accessions was ascertained by combining information from SCoT and EST-SSR markers using the STRUCTURE software, employing the Bayesian clustering method. The K fraction was estimated to range from K1 to K10. The maximum value of ΔK was observed at K = 2 with a 0.988 mean similarity score, suggesting that the overall Withania accessions were grouped into two subpopulations (Fig. 6) (Supplementary Fig. 2). The best value of K2 was obtained at 50,000 burning and 100,000 MCMC.

Fig. 5.

Dendrogram generated for 32 Withania somnifera genotypes using UPGMA cluster analysis based on Jaccard similarity coefficient for (A) SCoT markers (B) EST-SSR markers.

Fig. 6.

Population structure bar plot of 32 Withania somnifera genotypes at K = 2.

Real-time expression analysis of Withania genotype

To investigate the effect of biosynthetic pathway genes on the transcriptional regulation of withanolides, quantitative real-time PCR (QRT-PCR) was performed across four genotypes of Withania somnifera. The expression of six key pathway genes, Glucosyltransferase (GT), Cycloartenol synthase (CAS), Farnesyl diphosphate synthase (FPPS), Squalene synthase (SS), Squalene epoxidase (SE), and 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGR), and one housekeeping reference gene, viz. Actin was analyzed (Fig. 7; Supplementary Fig. 3). In the case of Glucosyltransferase (GT), compared to control (JA-20), significant upregulation was detected in PWS-18 whereas UWS-57 (0.18-fold) and P-3 (0.18-fold) showed slight downregulation. The expression levels of the Cycloartenol synthase (CAS) gene directly impact withanolide production. Among them, substantial upregulation was observed in P-3 (7.81-fold) and UWS-57 (3.39-fold), compared to JA20, suggesting strong transcriptional activation in these genotypes. In contrast, PWS-18 shows marked downregulation (0.07-fold). Both FPPS and SS encode key enzymes that regulate upstream flux in the isoprenoid biosynthesis pathway, thereby directly influencing the production of withanolides in Withania somnifera. Notably, in both FPPS and SS genes, a substantial level of upregulation was observed in PWS-18 (4.22- and 3.04-fold), followed by P-3 (1.35- and 1.54-fold), while UWS-57 showed slight downregulation (0.86- and 0.85-fold) compared to the control. Our findings suggest that genotypes PWS-18 and P-3 have an enhanced capacity for isoprenoid biosynthesis. For tested genotypes, Squalene Epoxidase (SE) showed significant downregulation compared to JA-20, with fold change values of UWS-57 (0.28), followed by PWS-18 (0.03) and P-3 (0.03). This consistent downregulation pattern across all genotypes suggests regulation or bottlenecks in metabolic pathways for the conversion of squalene to Oxidosqualene. For the HMGR gene, substantial downregulation was observed in PWS-18 (0.82-fold), UWS-57 (0.11-fold), and P-3 (0.07-fold) compared to JA20.

Fig. 7.

Heatmap representation of genotype-specific expression profiles of key withanolide biosynthetic genes in Withania somnifera based on ΔΔCT values obtained from qRT-PCR analysis.

The ΔΔ C_T values from the real-time PCR analysis reveal distinct gene expression profiles among the different genotypes. PWS-18 and P-3 genotypes exhibit notable upregulation of multiple genes, particularly CAS and FPPS, suggesting potential genetic variants for targeted breeding and research. These findings provide valuable insights into the genetic regulation of key compounds in root and shoot tissues, guiding future studies on gene function and expression in Withania somnifera.

Discussion

Withania somnifera is a traditional crop in the Indian Ayurvedic and Unani systems of medicine and has been used for 5000 years for its therapeutic and medicinal properties. It is officially listed as a drug in the Indian Pharmacopeia³⁸. Genetic diversity and evolutionary history significantly impact metabolite profiles, which, in turn, alter metabolite composition and concentration. Molecular markers are widely regarded as the most effective tools for analyzing genetic polymorphism at the DNA level. These molecular markers play a crucial role in germplasm characterization, parentage determination, diversity analysis, gene mapping, genetic distance estimation, and marker-assisted selection^39–42. The genetic diversity uncovered by these markers could confer long-term environmental adaptation and directly impact the metabolite profile. Variation in metabolite profiling is directly linked to the genetic makeup and the environmental conditions to which the species has adapted.

Advancements in molecular biology have led to the exploration of different marker technologies for genetic characterization within the Withania genus. With the advent of high-throughput sequencing methods like SCoT, SNAPs, etc., they offer higher reproducibility and are functionally associated with genes. Together, these new molecular techniques provide enhanced resolution of genetic fingerprinting, genome organization, adaptive traits, and the possibility of trait-linked selection in breeding programs.

During the current investigation, two gene-based marker systems, i.e., SCoT and EST-SSR, are employed. A comparative evaluation of these two markers for assessing genetic variation and phylogenetic relationships among Withania populations was conducted. Earlier, various studies have emphasized the potential of SCoT and EST-SSR markers for reliable, reproducible results across several plant species, including sugarcane, rose, capsicum, chickpea, curcuma sp., Banana, opium, and chili^{15,24,45–50}. Similar results were also observed across various plant species when a combination of SCoT and EST-SSR markers was used alongside another marker system^51–53.

In the present study, 36 SCoT and 20 EST-SSR markers were employed to optimize PCR and assess genetic diversity in 32 Withania somnifera accessions. SCoT markers generated 113 scorable bands with an average of 11.3 amplicons per primer, whereas EST-SSR markers produced 26 alleles with an average of 1.56 alleles per primer. Our results are consistent with earlier studies on Withania using other marker systems, such as ISSR and RAPD analysis^26,43,44. The average polymorphism percentages for SCoT and EST-SSR were 88% and 85.3%, respectively. Our research is comparable to several other plant species in which high SCoT-based polymorphism was observed, viz. 88.98% in common bean⁵⁴, 96.18% in orchids⁵⁵, and 75% in Kalmegh⁵⁶. Similarly, a higher percentage of polymorphism in EST-SSRs is also observed in several plant species, 88.5% in Curcuma sp⁵⁷. and 51.15% in Rhododendron arboreum⁵⁸.

Further, marker efficiency was assessed using key parameters, including PIC, MI, and RP. For instance, the PIC for the SCoT marker varied from 0.250 to 0.400, with a mean PIC of 0.327. Similarly, for the EST-SSR marker, the average PIC value is 0.172, with a range of 0 to 0.482, similar to PIC values obtained in earlier studies, such as 0.81 for the ISSR marker, 0.84 for the SCoT marker⁵⁹, and 0.63 for the CDDP marker⁶⁰. A high polymorphism rate reflects significant genetic diversity within genotypes, underscoring the robustness of markers in genetic analysis. PIC, which measures a marker’s ability to distinguish between genotypes, was a critical indicator of marker effectiveness⁵². RP as a measure of primer efficacy, emphasizing its importance in assessing genetic diversity⁶¹. The average RP values were 7.24 and 1.14 using SCoT and EST-SSR markers, respectively. Our results are quite comparable to those of studies on the same marker system in other plant species^62,63. In the present study, higher PIC, RP, and polymorphism percentages were obtained with the SCoT maker, indicating greater genetic diversification in the gene’s promoter regions and, in turn, higher polymorphism within the Withania population. Both marker systems were found to resolve differently; it could be due to their difference in targeting different regions of the genome for resolution, as suggested by Poczai et al.⁶⁴ The reduced polymorphism and resolving power of EST-SSR markers indicate that they are better suited for specialized gene investigations than for general diversity assessments, even though they provide functional insights because of their relationship with transcribed regions³².

Further genetic variability was assessed using factors such as Na, Ne, He, H, I, Ht, and Hs, which are important for genetic characterization within Withania accession. For SCoT and EST-SSR markers, the average values of Na, Ne, and He were 1.88, 1.48, 0.58, and 1.89, 1.39, 0.312, respectively. Similarly, the values of H and I were 0.29 and 0.43, and 0.25 and 0.40 for SCoT and EST-SSR, respectively. A substantial degree of genetic variation and effective polymorphism was found within the Withania population using both marker systems; additionally, population size is directly linked to genetic differentiation, with larger populations exhibiting higher genetic variation and heterozygosity⁶⁵. Our results are consistent with earlier reports on cucumber, where Ne, He, and I were 1.41, 0.25, and 0.27, respectively⁶². Similarly, Wang et al.⁷² reported Na and Ne values of 1.98 and 1.36, respectively, in the Chinese medicinal plant Pinellia turnata. In addition, the Nei gene diversity analysis was performed using parameters like Ht, Hs, Gst, and Nm. The grand average values for these parameters were 0.291 and 0.235, 0.249 and 0.205, 0.144 and 0.130, 2.987 and 3.344 for SCoT and EST-SSR markers, respectively. Gst and Nm are important parameters for characterizing population genetic structure. Gst quantifies the total genetic difference present within the population. The high Gst value (Gst > 0.15) suggests greater differentiation and limited gene flow among populations, whereas moderate and low Gst (< 0.5) indicate genetic similarity and intermixing within populations. Our results for Gst and Nm indicate moderate to extensive gene flow. Previously, Saboori et al.⁵⁷ reported an Nm value of 0.60 for date palm, indicating moderate gene flow, whereas Haq et al.²⁴ reported Gst and Nm values of 0.296 and 1.189, respectively, indicating higher gene flow among populations. This pattern suggests local adaptation of cultivars, either through cultural selection or environmental conditions. Moreover, the presence of high Hs along with significant Ht indicates a gradual accumulation of genetic variations due to the distribution, habitat, or medicinal use of a particular landrace.

Further characterization of population structure was performed using AMOVA, UPGMA, Jaccard’s similarity coefficient, and STRUCTURE. AMOVA analysis shows a maximum of 93% and 94% variation within the population and 7% and 6% among the population using SCoT and EST-SSR markers, respectively. The UPGMA-based cluster analysis shows two different clusters with the SCoT marker and two clusters with some outliers in EST-SSR. PCoA clustering also shows a similar pattern, revealing genetic diversity among accessions, possibly due to differences in geographical origin, genetic drift, or population admixture. A similar difference in clustering patterns was observed when two different marker systems were used in potato, chickpea, and snake melon^51,53,66. Earlier studies on bamboo, sponge gourd, wheat, cotton, sugar beet, and muskmelon^{67–70,72,73,74}. also obtained similar clustering patterns. Moreover, a similar clustering pattern within the Withania accession is obtained using Bayesian clustering with the STRUCTURE software. Bayesian clustering uses genetic information to infer population genetic structure and divide a population into clusters based on genetic similarity, admixture, and allele frequency; it also helps identify evolutionary relationships among populations^71,75.

Quantitative real-time PCR analysis of six genes in the withanolide biosynthetic pathway across four diverse genotypes based on phytochemical profiling revealed genotype-specific expression profiles. Higher upregulation of GT, FPPS, and SS is observed in PWS-18, while higher expression of CAS is present in P-3 and UWS-57. In contrast, downregulation was observed across all three genotypes compared to the control for SE and HMGR. These differences in gene transcription levels are genotype-specific, suggesting that differences in withanolide accumulation among genotypes are due to underlying genetic variation⁷⁶. Our findings are consistent with previous studies demonstrating that different chemotypes of Withania somnifera exhibit varying withanolide concentrations, correlated with differential expression of pathway genes^77–80.

To further understand the biological significance of the differences in expression levels, we compared the molecular marker-based clustering results with the CT values from real-time expression analysis. The dendrogram derived from marker data groups these genotypes into clusters. Genotypes UWS-57 and P-3 demonstrated significant upregulation of FPPS, SS, and HMGR, which are more closely related compared to JA-20, which shows relatively lower expression levels. Similarly, PWS-18 and P-3 show differential expression patterns of CAS and FPPS, suggesting allelic variation at their loci, as also supported by polymorphism data from molecular markers. This positive association between biosynthetic gene expression profiles and marker-based genetic diversity analysis underscores the utility of integrating these strategies to identify elite genotypes with enhanced biochemical potential for targeted breeding programs and varietal development.

Conclusion

In our study, the SCoT marker was used for the first time to analyze genetic diversity in Withania somnifera germplasm. Along with SCoT, EST-SSR markers are also used to analyze genetic polymorphism. Both marker systems produce significant genetic diversity. The SCoT marker showed higher discrimination capacity between varieties than EST-SSR. Moreover, the DNA profile generated by both markers shows significant genetic diversity. These results can effectively be used for the characterization and identification of genotypes, which can be applied in plant breeding programs for molecular marker-assisted breeding and better utilization of germplasm. The full potential of this medicinally significant species may be unlocked through an additional study combining molecular markers with cutting-edge genomic methods. Thus, the findings of the current study highlight the importance of molecular marker-based diversity analysis to support conservation and targeted breeding to enhance the medicinal value of Withania somnifera.

Electronic Supplementary Material

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Author contributions

Trapti Mandliya: Writing – original draft, Data curation.Shamshad Ul Haq: Writing – original draft, Data curation.Rohit Jain: Writing – review & editing, ValidationDevendra Jain: Writing – review & editing, Validation, Supervision, Conceptualization.

Data availability

No data were used for the research described in the article. The data that support the findings of this study are available from the first author, [TM], upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The whole or part of this article has not been sent/published in any other journal.