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Article

Development of Novel Genomic Simple Sequence Repeat (g-SSR) Markers and Their Validation for Genetic Diversity Analyses in Kalmegh [Andrographis paniculata (Burm. F.) Nees]

by
Ramesh Kumar
1,2,
Chavlesh Kumar
3,
Ritu Paliwal
1,
Debjani Roy Choudhury
1,
Isha Singh
4,5,
Ashok Kumar
6,
Abha Kumari
2 and
Rakesh Singh
1,*
1
Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi 110012, India
2
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, Uttar Pradesh, India
3
Division of Fruits and Horticultural Technology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
4
Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
5
School of Biomolecular and Biomedical Sciences, University College of Dublin, D04V1W8 Dublin, Ireland
6
Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi 110012, India
*
Author to whom correspondence should be addressed.
Plants 2020, 9(12), 1734; https://0-doi-org.brum.beds.ac.uk/10.3390/plants9121734
Submission received: 12 October 2020 / Revised: 5 November 2020 / Accepted: 17 November 2020 / Published: 9 December 2020
Browse Figures
Versions Notes

Abstract

:
Kalmegh (Andrographis paniculata (Burm. F.) Nees) is one of the most important medicinal plants and has been widely explored as traditional medicine. To exploit its natural genetic diversity and initiations of molecular breeding to develop novel cultivars or varieties, developments of genomic resources are essential. Four microsatellite-enriched genomic libraries—(CT)14, (GT)12, (AG)15 and (AAC)8—were constructed using the genomic DNA of A. paniculata. Initially, 183 recombinant colonies were screened for the presence of CT, GT, AG, and AAC microsatellite repeats, out of which 47 clones found positive for the desired simple sequence repeats (SSRs). It was found that few colonies had more than one desirable SSR. Thus, a sum of 67 SSRs were designed and synthesized for their validation among 42 A. paniculata accessions. Out of the 67 SSRs used for genotyping, only 41 were found to be polymorphic. The developed set of g-SSR markers showed substantial genetic variability among the selected A. paniculata accessions, with an average polymorphic information content (PIC) value of 0.32. Neighbor-joining tree analysis, population structure analysis, analysis of molecular variance (AMOVA), and principal coordinate analysis (PCoA) illustrated the considerable genetic diversity among them. The novel g-SSR markers developed in the present study could be important genomic resources for future applications in A. paniculata.

1. Introduction

The Kalmegh is an important medicinal crop species that is botanically known as Andrographis paniculate (Burm. F.) Nees and belongs to the family Acanthaceae [1]. This is an annual herb, about a meter in height, and bitter in taste, like Neem [2]; thus, Kalmegh is often called the king of bitter plants [3]. The plant is diploid (2n = 2x = 50) and found in both cultivated and wild forms in India [4]. A. paniculata is widely distributed in Asian countries like India, Sri Lanka, and China [5]. Andrographolide is one of the major bitter-tasting secondary metabolites derived from Kalmegh, a major bioactive substance responsible for the therapeutic interest [6]. This crop species is widely used as a traditional medicine in different parts of the world due to its versatile biological properties like immune-stimulatory [7], hepatoprotection [8], antibacterial [9], antimalarial [10], antithrombotic [11], antitumor [12], and anti-inflammatory [13]. Thus, this crop species has been used to treat various human diseases such as diabetes, hepatitis, leprosy, HIV, bronchitis, hypertension, cancer, and kidney disorders [14]. Currently, Kalmegh has been declared by the Indian National Medicinal Plants Board to be one of the most prioritized plant species among medicinal crop species for the exploitation of its potential use in human disease control and therapeutics [15].
Harnessing the genetic variability and development of the superior Kalmegh varieties with enhanced medicinal value is one of the prime objectives among researchers. Hence, genetic characterization of germplasm is one of the essential and primary steps in crop breeding programs. An in-depth characterization of germplasm allows effective selection of diverse parents in varietal improvement, besides helping efficient germplasm management in any crop species. Since distinguishing the genotypes based on morphological traits is time-consuming [16], the use of molecular markers can overcome its limitations. In the past, molecular markers like RAPD (randomly amplified polymorphic DNA) [3,4,17], AFLP (amplified fragment length polymorphism) [17], ISSR (inter simple sequence repeats), SCoT (start codon targeted polymorphism), and CBDP (CAAT box-derived polymorphism) [4] have been used for the genetic characterization of A. paniculate accessions. However, these dominant marker systems have low reproducibility and low consistency [18], which is a significant impediment for their further utilization in A. paniculate genomics and molecular breeding. Moreover, these dominant marker systems, namely, microsatellite or simple sequence repeat (SSR) markers, are one of the most preferred marker systems in studies of plant breeding and genetics due to the abundance in genomes, codominant natures, high reproducibility, multiallelic traits, and high transferability across the species [19]. Only the plastid genome sequence of A. paniculata has been carried out, and the whole genome of this crop is still not sequenced. It is, therefore, essential to develop microsatellite markers in A. paniculata.
Several methods have been employed to develop SSR markers in different plant species, and the microsatellite enrichment method is considered one of the most robust, reproducible, and cost-effective techniques [20]. This technique has been exploited for the development of SSR markers in several plant species including medicinal crops viz., Paeonia lactiflora [21], Centella asiatica [22], Tinospora cordifolia [23], and Bauhinia strychnifolia [24]. Therefore, the development of novel SSR markers was undertaken in A. paniculata using the microsatellite enrichment technique. The efficacy and informativeness of the developed SSRs were validated through genetic diversity studies among the A. paniculata accessions collected from different Indian states and maintained at ICAR-NBPGR, New Delhi, in the National Gene Bank.

2. Materials and Methods

2.1. Plant Materials

Seeds of 42 A. paniculata accessions were collected from the National Gene Bank ICAR-NBPGR, New Delhi, which were earlier collected from different geographical regions of Indian states (Figure 1). The collected seeds were sown in the experimental field at Issapur Research Farm (situated at 28.24 N latitude and 76.50 E longitude and an elevation of 190.7 m above sea level). The details on the A. paniculata accessions and their collection places are given in Table 1.

2.2. Plant Genomic DNA (gDNA) Isolation

The young and healthy leaves of each accession were collected at 45 days after sowing, snap-frozen, and stored at −80 °C for further use. The genomic DNA was isolated, following the CTAB method described by Doyle and Doyle [25], with minor alterations. Since A. paniculata is rich in phenolic compounds, 3% polyvinylpyrrolidone (PVP) was also used to reduce the phenolics and facilitate quality gDNA extraction. To eliminate RNA contamination, 2.5 U of RNaseA enzyme (Himedia, West Chester, PA, USA) was used. The DNA quality was evaluated on 0.8% agarose gel, and its concentration was determined using a NanoDrop instrument (Thermo Fisher Scientific, Waltham, MA, USA).

2.3. Construction of the Microsatellite-Enriched Library

The microsatellite-enriched genomic libraries in A. paniculata were developed using the altered biotin-capture method, as suggested by Fischer and Bachmann [26]. The gDNA of A. paniculata accession IC111291 (1000 ng) was digested using the restriction enzyme Sau3A1 (New England Bio Labs, Knowl Piece Wilbury Way Hitchin UK) by incubation at 37 °C for 2 h and, later, inactivating it at 65 °C for 20 min. The restriction digestion of gDNA and the adapter-ligation of DNA were done as suggested by Bloor et al. [27].
SSR-containing DNA fragments were acquired by hybridization reaction of an adaptor-attached DNA fragment with prewashed (1X washing buffer and 2X washing buffer, respectively) streptavidin-coated magnetic beads and 3′-biotinylated oligonucleotide probes ((CT)14, (GT)12, (AG)15 and (AAC)8) at 60 °C for 30 min, with frequent shaking at 5 min intervals in 6X SSC buffer(Saline Sodium Citrate buffer). After the hybridization reaction, the magnetic beads were separated using the magnetic stand and the incubation of hybridization products in 2X SSC and 1XSSC, respectively, followed by final boiling at 95 °C for 15 min in TE buffer (Tris-EDTA). The concentration of enriched DNA was enhanced by performing a PCR reaction [27]. Thereafter, the PCR-amplified products were ligated into pCR 2.1 Cloning Vector (Thermo Fisher Scientific, Waltham, MA, USA) overnight at 16 °C and transformed into E. coli (Escherichia coli) DH5α competent cells. As per the X-gal/IPTG (5-Bromo-4-chloro-3-indolyl β-D-galactopyranoside (X-gal)/ Isopropyl-β-D-thiogalactoside (IPTG))selection method, a total of 183 white colonies were screened, from which 119 positive clones were selected while performing colony PCR (using M13 universal primer). The plasmid DNA from selected positive clones was extracted using a plasmid isolation kit (Zymo Research, Irvine, California, USA). The plasmids were subsequently sequenced, along with M13 primers, using Sanger’s dideoxy sequencing approach (Macrogen Inc., Seoul, Korea).

2.4. SSR Finding and Primer Designing

After the trimming of vector sequences, the sequencing results of positive clones were searched for desirable microsatellite repeats using an online SSR finder tool (http://www.csufresno.edu/ssrfinder/). The microsatellite primer pairs were designed based on the sequences flanking the SSR motifs using an online tool, Primer 3.0 input version 0.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/). Finally, primer pairs were designed in the range of 18–25 nucleotides having an amplicon size ranging from 100 to 500 base pairs.

2.5. Polymerase Chain Reaction

A set of 67 developed SSR primers were selected for genetic diversity analysis, and these primers were amplified on 42 A. paniculata accessions, out of which 41 were found reproducible and polymorphic. The gDNA of selected 42 accessions was isolated, and its final working concentrations were kept at 10 ng/µL. The PCR reaction was performed in the total volume of 25 µL containing 7 µL gDNA (70 ng) as a template, 2.5 µL of 10X DreamTaq buffer, 3 µL of 2.5 mM MgCl2, 2.5 µL of 2.5 mM dNTPs, 0.8 µL of each primer (10 nmol), and 0.4 µL of DreamTaq DNA polymerase enzyme (Thermo Scientific, Waltham, MA, USA), with 8.8 µL Milli-Q water added to make the final volume. PCR amplification was performed in a thermocycler (Gstorm, Essex, England) using following the PCR cycle: initial denaturation at 94 °C for 4 min, followed by 36 cycles of denaturation at 94 °C for the 30 s, annealing temperature (standardize by gradient PCR) for 45 s, extension at 72 °C for 2 min, and a final extension at 72 °C for 10 min. The PCR products were checked on 4% metaphor agarose gel (Lonza, Rockland, ME, USA) for 4 h at a constant supply of 120 V, and gel images were captured using a gel documentation system (Alpha Imager®, Bengaluru, Karnataka, India).

2.6. Data Scoring and Statistical Analyses

The amplified PCR products of each primer pair among the A. paniculata accessions were scored using PyElph 1.4 [28]. The genetic diversity statistics viz. the dominant allele frequency, gene diversity, heterozygosity, and polymorphic information content (PIC) were calculated using Power Marker 3.5 [29]. An unrooted neighbor-joining (N-J) tree was generated, and the genetic distances between the A. paniculata accessions were also estimated using Power Marker 3.5 software [29]. Model-based population structure analysis was performed using STRUCTURE software version 2.3.4 [30]; the software was run multiple times by setting k (the number of populations) from 2 to 10, the length of burn-in period and number Markov Chain Monte Carlo (MCMC) replications were set at 100,000 for each run for all 42 genotypes to evaluate the number of populations [31]. An online tool, Structure Harvester (http://taylor0.biology.ucla.edu), was used to calculate the most probable genetic population groups of the studied A. paniculate accessions. Principal coordinate analysis (PCoA), analysis of molecular variance (AMOVA), and the Mantel test were done using the program GenAlEx 6.5 [32].

3. Results and Discussion

3.1. Development of SSR Markers from Enriched Genomic Libraries

To obtain the microsatellite-enriched libraries, the genomic DNA of A. paniculata (IC 111291) was digested with restriction enzyme Sau3A1, which was further enriched with four types of 3′ biotinylated oligonucleotide probes ((CT)14, (GT)12, (AG)15 and (AAC)8). Altogether, 183 recombinant colonies were screened for the presence of CT, GT, AG, and AAC repeats, of which 119 were confirmed as positive clones (65%) through colony PCR and submitted for Sanger sequencing. The SSR finder tool was used to identify the perfect SSR markers, and 47 positive clones (39%) with perfect microsatellite repeats were identified. It was noticed that a few positive clones had more than one microsatellite repeat, and thus, a total of 67 primer pairs were developed (Table 2). The developed and synthesized microsatellite markers had motif-length groups, varying from monomer to hexamer, and their occurrence percentage varied from 1.49 to 85.07 (Figure 2). The tetramer and hexamer motif-length groups had a 1.49% occurrence; trimer had 2.98%, monomer and pentamer had 4.47%, while the dimer motif-length group had a maximum occurrence of 85.07%. Earlier, Wee et al. [33] sequenced 192 clones, and 102 colonies were obtained with desirable SSRs. Furthermore, Kaliswamy et al. [34] also reported that di- and trinucleotide repeats had more occurrences in the Acanthaceae family, which is similar to our findings. In addition to that, Lagercrantz et al. [35] and La Rota et al. [36] noticed that GA/CT microsatellite motifs are more abundant than the CA/GT motif in the plant species, which is similar to the present investigation. Marker-assisted breeding essentially requires a robust and informative marker system in the crop of interest [37]. Microsatellite markers are one of the choicest marker systems in molecular breeding of crop species due to its versatile applications in crop genetics and breeding, including cultivar identification [38], genetic diversity assessment [39], genetic mapping [40], gene tagging [41], gene flow [42], and molecular evolution studies [43] on plant species. In A. paniculate, the availability of microsatellite markers is lacking, which is a major limitation for its marker-assisted breeding. The screening of microsatellite-enriched libraries and the sequencing of microsatellite-positive clones are effective methods for the development of SSR markers [44].

3.2. Validation of g-SSR Loci and Genetic Diversity Statistics

A genetic diversity study among 42 A. paniculata accessions was performed using the 67 genomic SSR loci developed from four microsatellite-enriched libraries. The developed g-SSRs were screened for their amplification among the A. paniculata accessions, out of which 41 SSRs were found to be polymorphic (Table 3). These SSRs had substantial variations in allele number, which ranged from 2 to 8 with an average of 3.95 alleles per locus, and allele sizes, which ranged from 100 to 870 bp (Figures S4–S6). Similarly, Geng et al. [45] also recorded a range in the number of alleles, from 2 to 8, in Acanthus ilicifolius, which is congruent with our results. The PIC value varied from 0.09 for primer Ando4-36-2 to 0.38 for primer Ando5-29, with an average of 0.32. The observed heterozygosity was calculated as 0.00 for several markers, and the highest value was 0.21 for the marker Ando5-12-1, with a mean value of 0.02. Gene diversity (expected heterozygosity) ranged from 0.10 (Ando4-36-2) to 0.50 (Ando5-29, Ando5-26-2, Ando5-14-2, Ando4-27-2, Ando4-26, and Ando2-31-2), with a mean value of 0.40. Similarly, Geng et al. [45] calculated the observed and expected heterozygosity in Acanthus ilicifolius using SSR markers, which ranged from 0.200 to 0.875 and 0.227 to 0.798, respectively. Furthermore, Suárez-Montes et al. [46] also calculated the observed and expected heterozygosity values among the Aphelandra aurantiaca genotypes, ranging from 0.22 to 0.96 and 0.20 to 0.87, respectively, which is higher than the present study. The present investigation also deciphered large differences between the observed and expected heterozygosity, which indicates that the selected population of A. paniculata deviates from Hardy Weinberg’s equilibrium, which might be due to inbreeding, population bottleneck, or random genetic drift [17,45].

3.3. Cluster Analysis

The unrooted N-J tree was constructed based on 41 developed SSR loci, which clustered all the 42 A. paniculata accessions into three major clusters (Figure 3). Earlier, Wijarat et al. [17] clustered 58 A. paniculata accessions into two major clusters using SSR markers that are lower than our findings. The genetic distance between the A. paniculata accessions ranged from 0.010–0.810, with an average of 0.400. The minimum genetic distance (0.010) was estimated between accessions IC 111291 & IC 211295 and IC 412436 & IC 421432, while the maximum (0.810) was between IC 471917 & IC 471891. Cluster A contained four accessions of A. paniculata, out of which two samples were from Uttar Pradesh, one each from Kerala, and one from Maharashtra. Cluster B constituted seven individuals of A. paniculata, out of which two were from Uttar Pradesh and one sample each from Andhra Pradesh, Kerala, Madhya Pradesh, Assam, and Tamil Nadu. Cluster C was further divided into two subclusters; subcluster C-1 contained 12 individuals, while C-2 had 19 individuals. Subcluster C-1 showed a tight grouping of four samples from Madhya Pradesh (IC 471890, IC 471891, IC 471892, and IC 471893) and two samples from Uttar Pradesh (IC 111287 and IC 342139). In subcluster C-2, there was a close grouping of two genotypes collected from Chhattisgarh (IC 421436 and IC 421432), and three tight groups from Himachal Pradesh were observed (Tight Group 1—IC 471,915 and IC 471917, Tight Group 2—IC 471912 and IC 471913, Tight Group 3—IC 471916 and IC 471918). Furthermore, A. paniculate accessions viz. IC 471890, IC 471891, IC 471892, and IC 471893 were in Cluster C-1 while IC 421431, IC 421435, IC 264272, IC 421442, IC 421432 and IC 421436 were in Cluster C2, grouped according to their natural habitat, which is possibly due to less human intervention in their natural habitats. Thus, a few of the A. paniculate accessions were tightly grouped according to their habitat and agro-geographical regions, while most of the accessions did not group according to their habitat and agro-geographical regions, which might be due to gene flow in the form of either gamete or genotype.

3.4. Population Structure

Model-based population structure analysis was utilized to rebuild the genetic relationship among 42 A. paniculata accessions using 41 developed SSR markers. Structure Harvester identified three genetic populations in the present set of A. paniculate accessions (Figure 4, Figure 5, Figures S1 and S2). The individuals with a probability score of more than 0.80 are considered genetically pure accession, while a score of <0.80, as an admixture accession. Population I showed eight pure accessions (IC471891, IC 471890, IC 471892, IC 471889, IC 471893, IC 400519, IC 399612, IC 437223) and nine admixed accessions (IC 421442, IC 111287, IC 264272, IC 342140, IC 421432, IC 342141, IC 421431, IC 421435, IC 342139). Population II showed ten pure accessions (IC 210635, IC 342137, IC 111291, IC 211295, IC 342135, IC 111288, IC 111290, IC 333252, IC 342134, IC 342136) and two admixed accessions (IC 342138, IC 421436). Population III showed twelve pure accessions (IC 471895, IC 471913, IC 471912, IC 471919, IC 471917, IC 471915, IC 471914, IC 471894, IC 471896, IC 471918, IC 111286, IC 471916) and one admixed accession (IC 421397). The genetic population differentiation of plant species is the consequence of various processes such as mating strategies, selection, mutations, and gene flow [47]. The genetic population differentiation among the A. paniculata accessions might be due to their mating behavior since the crop is self-pollinated and up to 4% cross-pollination occurs through insects [4,17]. The mean Fst value of Population I, Population II, and Population III were 0.4847, 0.5563, and 0.5090, respectively, and the mean alpha value was 0.1075 (Table S2). The allele-frequency divergence between Population I and Population II, Populations II and III, and Populations I and III were 0.2288, 0.1639, and 0.2277, respectively (Table S3). The population structure study indicated the genetic differentiation of A. paniculata accessions, which amply suggested that the developed gSSR markers were suitable for population structure studies.

3.5. AMOVA, PCoA, and Mantel Test

An analysis of molecular variance (AMOVA) was undertaken using the three genetic population groups of A. paniculate, as deciphered by model-based population structure analysis. The AMOVA illustrated 22% variance among the populations, with 77% variance among the individuals and 1% variance within the individuals of the populations (Table 4 and Figure S3). The first three principal coordinate analyses (PCoA) explained 34.88% cumulative variance, whereas the first, second and third axes explained 14.16%, 11.81%, and 8.91% of genetic variation, respectively (Table S4). Furthermore, the grouping of the A. paniculata accessions is depicted in three colors on the coordinates, supplementing the results of the model-based population structure analysis (Figure 6). AMOVA and PCoA explained the substantial genetic diversity among the A. paniculate accessions. Furthermore, the Mantel test was performed to obtain the correlation between genetic distance and geographical distance of A. paniculata accessions. Overall, a correlation coefficient with a low value (Rxy = 0.046) was observed (Table S5, Figure S7), indicating very little correlation between the genetic and geographical distances of A. paniculata accessions [48]. This might be due to the gene flow of A. paniculata accessions, in the form of either genotype or gamete.

4. Conclusions

Based on this study, it can be concluded that the novel set of g-SSR primer pairs developed in the present study were found to be efficient for molecular characterization of A. paniculate accessions. Thus, it can be added as new genomic resources for A. paniculata and further utilized in germplasm management and basic population genetics and plant-breeding studies.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/2223-7747/9/12/1734/s1. Figure S1: Analysis of molecular variance (AMOVA) of 42 accessions of Andrographis paniculata based on SSR marker data. Figure S2: Plot of mean likelihood L (K) and variance per K value from STRUCTURE on SSR dataset. Figure S3: Table output of the Evanno method results; yellow highlight is performed dynamically on the Structure Harvester online tool and shows the highest value in the Delta K column. Figure S4: Gel image of primer Ando4-34-1 among the 42 accessions of A. paniculate; M = 100 bp marker; polymorphic bands are indicated with a yellow arrow. Figure S5: Gel image of primer Ando2-40-2 among the 42 accessions of A. paniculata. Figure S6: Gel image of primer Ando4-26 among the 42 accessions of A. paniculata. Figure S7: Relationship between genetic and geographic distances for 42 A. paniculata accessions. Table S1: Nanodrop DNA quantification result of 42 A. paniculata accessions. Table S2: Mean value of Fst1, Fst2, Fst3, and alpha concluded from a model-based approach. Table S3: Allele-frequency divergence among populations of A. paniculata accessions. Table S4: Percentage of variation explained by the first three axes among the A. paniculata accessions. Table S5: Mantel results for geographic distance vs. genetic distance.

Author Contributions

R.S. conceived and designed the experiments; R.K., C.K., D.R.C., and R.P. performed the experiments; R.K., I.S., and R.S. analyzed the data; A.K. (Ashok Kumar) contributed reagents/materials/analysis tools; R.K., A.K. (Abha Kumari), and R.S. contributed to the writing of the manuscript. All authors have read and agreed to the final version of the manuscript.

Funding

The research work was fully supported by the ICAR-National Bureau of Plant Genetic Resources, New Delhi.

Acknowledgments

We are grateful to the Indian Council of Agricultural Research (ICAR), New Delhi, for financial support, and the University Grant Commission (UGC) for providing fellowship to the senior author. We are also thankful to the Director, ICAR-NBPGR, New Delhi, and the In-charge Head, Division of Genomic Resources, NBPGR, New Delhi, for providing research facilities.

Conflicts of Interest

There is no conflict of interest for this manuscript.

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Plants 09 01734 g001 550
Figure 1. Andrographis paniculata accessions collected from different geographical regions of India.
Figure 1. Andrographis paniculata accessions collected from different geographical regions of India.
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Plants 09 01734 g002 550
Figure 2. SSR motif-length groups and their percentage occurrence.
Figure 2. SSR motif-length groups and their percentage occurrence.
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Plants 09 01734 g003 550
Figure 3. Neighbor-joining (N-J) phylogenetic tree showing the grouping of 42 accessions of Andrographis paniculata based on the data of 41 SSR markers.
Figure 3. Neighbor-joining (N-J) phylogenetic tree showing the grouping of 42 accessions of Andrographis paniculata based on the data of 41 SSR markers.
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Plants 09 01734 g004 550
Figure 4. Estimation of the number of populations using LnP(D)-derived Delta k from 2 to 10 using SSR data.
Figure 4. Estimation of the number of populations using LnP(D)-derived Delta k from 2 to 10 using SSR data.
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Plants 09 01734 g005 550
Figure 5. Model-based population structure analysis of 42 accessions of Andrographis paniculata.
Figure 5. Model-based population structure analysis of 42 accessions of Andrographis paniculata.
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Plants 09 01734 g006 550
Figure 6. Principal coordinate analysis (PCoA) based on model-based population structure.
Figure 6. Principal coordinate analysis (PCoA) based on model-based population structure.
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Table
Table 1. The details of Andrographis paniculata accessions studied.
Table 1. The details of Andrographis paniculata accessions studied.
Sl.No.Accession No.HabitatCollection SiteCollection YearAgro Ecological Regions
1IC 111286DisturbedHaidargarh, Faizabad, Uttar Pradesh1992Gangatic Plains
2IC 111287DisturbedKatarnia Forest, Gonda, Uttar Pradesh1992Gangatic Plains
3IC 111288NaturalBastiForest, Basti, Uttar Pradesh1992Gangatic Plains
4IC 111290CultivatedRegional Research Lab (RRL), Jorhat, Assam1992N.E. Region
5IC 111291DisturbedShola Forest, Thiruananthapuram, Kerala1992Western Ghats
6IC 210635CultivatedGarden, Namakkal, Tamil Nadu1997Eastern Ghats
7IC 211295NaturalAnanthagiri Forest, Visakhapatnam, Andhra Pradesh1997Eastern Ghats
8IC 333252DisturbedJamli, Barwani, Madhya Pradesh1999Eastern Ghats
9IC 342134NaturalSuparia Forest, Bahraich, Uttar Pradesh1995Gangatic Plains
10IC 342135NaturalSatrikh Forest, Barbanki, Uttar Pradesh1995Gangatic Plains
11IC 342136CultivatedGarden, Saharanpur, Uttar Pradesh1994Gangatic Plains
12IC342137CultivatedRegional Research Centre (RRC), Pune, Maharashtra1995Western Ghats
13IC 342138CultivatedRegional Research Centre (RRC), Thiruananthapuram, Kerala1995Western Ghats
14IC 342139CultivatedRegional Research Centre (RRC), Jhansi, Uttar Pradesh1995Gangatic Plains
15IC 342140CultivatedGarden, RRC-Kolkata, West Bangal1995N. E. Region
16IC 342141CultivatedNarendra Dev University, Faizabad, Uttar Pradesh1995Gangatic Plains
17IC 399612NaturalKashidih Forest, East Singhbhum, Jharkhand1999Gangatic Plains
18IC 400519CultivatedAdumalleshwaram, Chitradurg, Karnataka2001Western Ghats
19IC 437223CultivatedYS Parmar University, Solan, Himachal Pradesh2003Western Himalaya
20IC 471889NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
21IC 471890NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
22IC 471891NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
23IC 471892NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
24IC 471893NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
25IC 471894NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
26IC 471895NaturalDindori Forest, Amarkantak, Madhya Pradesh2003Eastern Ghats
27IC 471912DisturbedNauni Forest, Solan, Himachal Pradesh2002Western Himalaya
28IC 471913DisturbedNauni Forest, Solan, Himachal Pradesh2002Western Himalaya
29IC 471914DisturbedOchghat, Solan, Himachal Pradesh2002Western Himalaya
30IC 471915DisturbedBatalGhatti, Solan, Himachal Pradesh2002Western Himalaya
31IC 471916DisturbedOchghat, Solan, Himachal Pradesh2002Western Himalaya
32IC 471917DisturbedKunihar, Solan, Himachal Pradesh2002Western Himalaya
33IC 471918DisturbedBatalGhatti, Solan, Himachal Pradesh2002Western Himalaya
34IC 471919DisturbedBatalGhatti, Solan, Himachal Pradesh2002Western Himalaya
35IC 471896NaturalMandla Forest, Mandla, Madhya Pradesh2003Eastern Ghats
36IC 421397NaturalMachkot, Bastar, Chhattisgarh2001Eastern Ghats
37IC 421431NaturalBarasur Forest, Dantewada, Chhattisgarh2001Eastern Ghats
38IC 421432NaturalBarasur Forest, Dantewada, Chhattisgarh2001Eastern Ghats
39IC 421435NaturalGreedam, Dantewada, Chhattisgarh2001Eastern Ghats
40IC 421436NaturalMuchnar, Dantewada, Chhattisgarh2001Eastern Ghats
41IC 421442NaturalDantewara Forest, Dantewada, Chhattisgarh2001Eastern Ghats
42IC 264272NaturalIshapur, Dalsinghsarai, Bihar2001Gangatic Plains
Table
Table 2. Details of 67 novel genomic simple sequence repeat (SSR) loci developed through microsatellite-enriched genomic libraries.
Table 2. Details of 67 novel genomic simple sequence repeat (SSR) loci developed through microsatellite-enriched genomic libraries.
S.NoPrimer IDForward Primer Sequence (5′-3′)Reverse Primer Sequence (5′-3′)Repeat MotifExpected Product Size (bp)
1Ando4-2CTCTCTCTCGCAGCTCTCTCTCCTTCGGATCAGTTAGCCCCT(CT)7386
2Ando6-3AGCTTCGGATCAGTTAGTCCCTGCTCCCTCTCAGTGTCTCTCTC(AG)6378
3Ando2-3-2GCTTCGGATCAGTTAGTCCCTTAGCTCTCTCTCGCAGCTCTCT(AG)6388
4Ando2-3-3GAGAGACCTGCGAGAGAGAGAGGAACCAGGCAGAACCAATAATC(GA)6241
5Ando2-6-2GCTTCGGATCAGTTAGTCCCTTCTCTCTCTCGCAGCTCTCTCTC(GA)7387
6Ando2-9GATTATGTGGGAATCTTGGGTGATATAGGTGGGCGATAAACCG(A)15228
7Ando2-12AACAAGGTTACACTCTCCGACCCTCGATCCTATTCAGTTCCACC(A)13388
8Ando2-23TTCTTTTCTGTGTAATCGTCGCCTAAGCGTTGCTCCATTTCTTC(A)10188
9Ando2-24-3CGGCTCTCTCTCAGTCTCTCTCCTTCGGGTCAGTTAGTCCCTT(CT)7375
10Ando2-30-2AGCTTCGGATCAGTTAGTCCCTCGCTCGTAGTCTCTCTCTCACA(AG)6258
11Ando2-31-2ATTGATGCCCAAAGAGAAGAAGCTCTCCCTATCTCGCACTATCG(AG)6250
12Ando2-32-2AGCTTCGGATCAGTTAGTCCCTCAGCTCTCCCTCAGTCTCTCTC(AG)6378
13Ando2-40-2CTCTCTCTCTCTCTCCACAGCCATGACCCTCAACATAGCGTTTT(TC)18316
14Ando 4-2-1ATGACCCTCAACATAGCGTTTTCTCTCTCTCTCTCTCCACAGCC(GA)13318
15Ando4-3-2AGCTTCGGATCTCTCTCCACTATGACCCTCAACATAGCGTTTT(TC)9383
16Ando4-4-2CTTCGGGTCAGTTAGTCCCTTCAGCTCCCTCTCAGTCTCTCTC(AG)6374
17Ando4-11-2AGCTTCGGATCTCTCTCCACTATGACCCTCAACATAGCGTTTT(TC)9379
18Ando2-3AGCTTCGGATCAGTTAGTCCCTTCTCTATCTCGCATTCTCTCCC(AG)6478
19Ando2-21GCCCAAAGAGAAATAGCTGAGACTATGACCATGATTACGCCAAG(GA)6298
20Ando2-30-1GCTTCGGATCAGTTAGTCCCTTCGCAGCTCTCTCTCAGTCTCTC(AG)6381
21Ando2-30-3AGCTTCGGATCAGTTAGTCCCTTATCTCGCACTCTCTCTCTGGC(GA)7479
22Ando2-31-1CTTCGGATCGGTTAGTCCCTCTCTCTCTCGCAGCTCTCTCTC(AG)6390
23Ando2-31-3CTTCGGATCGGTTAGTCCCTTATCTCGCACTCTCTCTCTGGC(GA)7479
24Ando4-4-1CTTCGGGTCAGTTAGTCCCTTCAGCTCCCTCTCAGTCTCTCTC(AG)6374
25Ando4-4-3CTTCGGGTCAGTTAGTCCCTTTCTCTATCTCGCACTCTCTCCC(GA)7477
26Ando4-9CCAGTCCTTTTCTGCTGTTACCAGCTTCGATCAATTTCCAAGG(AG)10173
27Ando4-9-2GCTTCGGATCAAAATACTCAGCCTCTCTTTATGGCCTATCCCCT(AGGGAG)5298
28Ando4-21AGCTTCGGATCTCTCTCCACTATGACCCTCAACATAGCGTTTT(TC)12383
29Ando4-26AGCTTCGGATCGTAGGGTTTTCTGTATGTGTGCTCAACCTCC(TC)14235
30Ando4-27ATGACCCTCAACATAGCGTTTTCTCTCTCTCTCTCTCCACAGCC(GA)19318
31Ando4-27-2ATGACCCTCAACATAGCGTTTTAGCTTCGGATCTCTCTCCACT(GA)13385
32Ando4-31AGCTTCGGATCAGTTAGTCCCTTTTCCCTCTCTATCTCGCACTC(AG)6489
33Ando4-32AGCTTCGGATCAGTTAGTCCCTGCAGTCTCTCTCGCAACTCTCT(AG)6449
34Ando4-32-1AGCTTCGGATCAGTTAGTCCCTTGCAGCTCTCTCTCTCTCAGTTT(GA)6499
35Ando4-34-1CTCTCTCTCTCTCTCCACAGCCCAACCTCCATCATCTGAACAAA(TC)18253
36Ando4-34-2AGCTTCGGATCTCTCTCCACTATGACCCTCAACATAGCGTTTT(TC)12385
37Ando4-35-1CAACCTCCATCATCTGAACAAACTCTCTCTCTCTCTCCACAGCC(GA)19251
38Ando4-35-2ATGACCCTCAACATAGCGTTTTAGCTTCGGATCTCTCTCCACT(GA)13385
39Ando4-36AGCTTCGGATCAGTTAGTCCCTTATCTCGCACTCTCTCTCTGGC(GA)6471
40Ando4-36-2AGCGATAGTGCGTGATAGGGGGCCTCTCTCAGTTACAGTCTCC(GA)6276
41Ando4-39AGCTTCGGATCGTAGGGTTTTCTGTATGTGTGCTCAACCTCC(TC)13233
42Ando4-41CTTCGGGTCAGTTAGTCCCTTTCTCTATCTCGCACTCTCTCCC(GA)7479
43Ando4-42AATTCCCACAGCAGAGAGAGAGGTTTCTGACTTTTCACGTTCCC(GA)14331
44Ando4-43/1CTCTCTCTCTCTCTCCACAGCCTGACCCTCAACATAGCGTCTTA(TC)19317
45Ando4-43/2AGCTTCGGATCTCTCTCCACTTGACCCTCAACATAGCGTCTTA(TC)12382
46Ando5-1TAACCGAGCATCTCTCTCTGCTTCAATGGGTATCTGTGTTTTGG(TCT)4120
47Ando5-8GCTTCGGATCTAACACAACCTCGAAAAGGGTTCTCCTCCAGTTT(TCTT)3187
48Ando5-10TTGATGCCCAAAGAGAAATAGCGTTACAGTCTCCCTTGCAGCTC(AG)6487
49Ando5-12GAGCGATAGTGCGAGATAGGGGTTACAGTCTCCCTTGCAGCTC(GA)8270
50Ando5-12-1CTTCGGATCAGTTAGCCCCTGTCTTGCACCCACTCTCTCTCT(GA)6319
51Ando5-13CTCTCTCTCTCTCTCCACAGCCAAGCGGGATTGATTTACAACAC(TC)15388
52Ando5-13-2AGCTTCGGATCTCTCTCCACTAAGCGGGATTGATTTACAACAC(TC)15459
53Ando5-14AGCTTCGGATCAGTTAGTCCCTTATCTCGCACTCTCTCTCTGGC(GA)7473
54Ando5-14-2AGCTTCGGATCAGTTAGTCCCTCGCACTCTCTCAGTTTTCCTCT(AG)6345
55Ando5-19GAAGACCCTAATCGAAACATCGAAAGAACCTCCGCTCATAACAG(TCTTC)2264
56Ando5-23AGCTTCGGATCAGTTAGTCCCTGCTCTCTCTCTCGCAGTTTCTC(AG)6500
57Ando5-26ATTCGGTCATTCTTAGCCCTCTTCAATGGGTATCTGTGTTTTGG(TCT)4158
58Ando5-26-2ACCGAGCATCTCTCTCTGCTATTTCGGATCTGTCCTGTGTTTC(AACTC)2224
59Ando5-29CTTCGGATCAGTTAGTCCCTTCTCTCTATCTCGCAGCTCTCCTT(GA)6413
60Ando5-29-2AGCTTCGGATCAGTTAGTCCCTTCTCTCTCTCCCTATCTCGCAC(AG)6281
61Ando5-30GACAACACATTCCTCAAAAGCCAGCTTCGGATCTGGTCTAACG(TC)8141
62Ando5-31CTTCGGGTCAGTTAGTCCCTTTCCCTCTCTATCTCGCACTCTC(GA)6481
63Ando5-31-2CGAGCGATAGTGCGTGATATCTCCCTCTCCCAGTCTCTC(GA)6324
64Ando5-36CTTCGGGTCAGTTAGTCCCTTTCTCTCTCGCAGGTCTCTCTCT(GA)7325
65Ando5-37CTCCTTGACTATCTTTGGCCTGTTATGTCTCTGATGATGGGTCG(TCTTC)2136
66Ando5-38CTCTCTCTCTCTCTCCACAGCCATGACCCTCAACATAGCGTTTT(TC)19318
67Ando5-38-2AGCTTCGGATCTCTCTCCACTATGACCCTCAACATAGCGTTTT(TC)14387
Table
Table 3. Details of allele number, major allele frequency, gene diversity, heterozygosity, and PIC values of developed g-SSRs.
Table 3. Details of allele number, major allele frequency, gene diversity, heterozygosity, and PIC values of developed g-SSRs.
Sl.No.Primer IDTa x(°C)Allele Size Range (bp)Allele NoMajor Allele FrequencyGene Diversity
(Expected
Heterozygosity)		Observed
Heterozygosity		PIC y
1.Ando 4-245.0160–17020.810.3100.26
2.Ando 2-24-365.4360–40040.710.4100.33
3.Ando 2-30-259.9270–30040.630.4500.35
4.Ando 4-4-260.9370–39020.760.3600.30
5.Ando 4-3-260.9390–49060.750.3500.28
6.Ando 4-11-260.9390–41020.850.2600.22
7.Ando 4-2-160.9320–36040.700.410.190.33
8.Ando 4-40-262.0310–32020.710.4200.33
9.Ando 2-32-240.9380–39020.680.4400.34
10.Ando 2-31-240.9250–26020.530.5000.37
11.Ando 2-2141.9720-76020.890.1900.17
12.Ando 4-9-240.9780–82040.700.4200.33
13.Ando 4-2159.3410–82040.720.4100.32
14.Ando 4-2650.9240–25020.540.5000.37
15.Ando 4-2745.6310–34040.840.2500.21
16.Ando 4-27-243.0390–40020.520.5000.37
17.Ando 4-3140.9290–36080.660.4100.32
18.Ando 4-3240.4210–36060.720.3900.31
19.Ando 4-32-148.4360–45080.730.3600.29
20.Ando 4-34-154.3310–32020.690.4300.34
21.Ando 4-34-254.3320–43080.690.4000.31
22.Ando 4-35-154.3220–28040.610.470.150.36
23.Ando 4-35-243.0320–41060.640.4500.35
24.Ando 4-36-243.0230–34040.950.1000.09
25.Ando 4-3940.9140-26040.770.360.060.29
26.Ando 4-4140.9620–68060.660.3900.30
27.Ando 4-43/140.4320–35040.580.4900.37
28.Ando 4-43/240.9390–43040.680.4100.32
29.Ando 5-151.9420–45040.640.4500.35
30.Ando 5-1040.1260–49040.660.440.100.35
31.Ando 5-1249.3100–22080.820.3000.25
32.Ando 5-12-151.9370–56040.540.490.210.37
33.Ando 5-1358.4390–42040.760.3700.30
34.Ando 5-13-251.9390–51040.640.4400.34
35.Ando 5.1451.9330–36040.660.4300.34
36.Ando 5-14-240.4390–41020.520.5000.37
37.Ando 5-26-254.3850–87020.530.5000.37
38.Ando 5-2954.3420–44020.500.5000.38
39.Ando 5-3040.4250–35040.650.4100.32
40.Ando 5-31-240.4330–55040.650.450.120.35
41.Ando 5-3751.9210–29040.700.4100.33
Mean3.950.680.400.020.32
x Ta = annealing temperature. y PIC = polymorphic information content.
Table
Table 4. Summary of analysis of molecular variance of 41 genomic SSRs among 42 Kalmegh accessions.
Table 4. Summary of analysis of molecular variance of 41 genomic SSRs among 42 Kalmegh accessions.
SourcedfSSMSEstimated Variance%
Among Populations2310.537155.2684.45322%
Among Individual391250.95232.07615.87777%
Within Individual4213.50.3210.3211%
Total831574.988 20.651100%
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Kumar, R.; Kumar, C.; Paliwal, R.; Roy Choudhury, D.; Singh, I.; Kumar, A.; Kumari, A.; Singh, R. Development of Novel Genomic Simple Sequence Repeat (g-SSR) Markers and Their Validation for Genetic Diversity Analyses in Kalmegh [Andrographis paniculata (Burm. F.) Nees]. Plants 2020, 9, 1734. https://0-doi-org.brum.beds.ac.uk/10.3390/plants9121734

AMA Style

Kumar R, Kumar C, Paliwal R, Roy Choudhury D, Singh I, Kumar A, Kumari A, Singh R. Development of Novel Genomic Simple Sequence Repeat (g-SSR) Markers and Their Validation for Genetic Diversity Analyses in Kalmegh [Andrographis paniculata (Burm. F.) Nees]. Plants. 2020; 9(12):1734. https://0-doi-org.brum.beds.ac.uk/10.3390/plants9121734

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Kumar, Ramesh, Chavlesh Kumar, Ritu Paliwal, Debjani Roy Choudhury, Isha Singh, Ashok Kumar, Abha Kumari, and Rakesh Singh. 2020. "Development of Novel Genomic Simple Sequence Repeat (g-SSR) Markers and Their Validation for Genetic Diversity Analyses in Kalmegh [Andrographis paniculata (Burm. F.) Nees]" Plants 9, no. 12: 1734. https://0-doi-org.brum.beds.ac.uk/10.3390/plants9121734

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