Plant Systematics and Evolution

, Volume 299, Issue 9, pp 1637–1643

Comparison of SSR and cytochrome P-450 markers for estimating genetic diversity in Picrorhiza kurrooa L.

Authors

    • Biotechnology DivisionIndian Institute of Integrative Medicine (CSIR)
  • M. A. Hussain
    • Biotechnology DivisionIndian Institute of Integrative Medicine (CSIR)
  • A. Ahuja
    • Biodiversity and Applied Botany DivisionIndian Institute of Integrative Medicine (CSIR)
Original Article

DOI: 10.1007/s00606-013-0820-z

Cite this article as:
Katoch, M., Hussain, M.A. & Ahuja, A. Plant Syst Evol (2013) 299: 1637. doi:10.1007/s00606-013-0820-z
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Abstract

Picrorhiza kurrooa L., a high altitude medicinal plant, is known for its drug content called Kutkin. In the present study, DNA-based molecular marker techniques, viz. simple sequence repeats (SSR) and cytochrome P-450 markers were used to estimate genetic diversity in Picrorhiza kurrooa. Twenty five accessions of Picrorhiza kurrooa, collected from ten different eco-geographical locations were subjected to 22 SSR and eight cytochrome P-450 primer pairs, out of which 13 SSR markers detected mean 5.037 alleles with a mean polymorphic information content (PIC) of 0.7718, whereas eight cytochrome P-450 markers detected mean 5.0 alleles with a mean PIC of 0.7596. Genetic relationship among the accessions was estimated by constructing the dendrograms using SSR and cytochrome P-450 data. There was a clear consistency between SSR and cytochrome P-450 trees in terms of positioning of most Picrorhiza accessions. SSR markers could cluster various Picrorhiza kurrooa accessions based on their geographical locations whereas cytochrome P-450 markers could cluster few accessions as per their geographical locations. The Mantel test between SSR and cytochrome P-450 markers revealed a good fit correlation (r = 0.6405). The dendrogram constructed using the combined data of SSR and cytochrome P-450s depicted two clusters of accessions based on its eco-geographical locations whereas two clusters contained the accessions from mixed eco-geographical locations. Overall, the results of the present study point towards quiet high degree of genetic variation among the accessions of each eco-geographic region.

Keywords

Picrorhiza kurrooa L.Genetic diversityShort sequence repeats (SSR)

Introduction

Picrorhiza kurrooa Royle ex Benth being a well-known drug in the Ayurvedic system of medicine belongs to family Scrophulariaceae. It inhabits on higher altitudes in different parts of the world. In India, it is found in Himalayan region at an altitude of more than 3,000 m above mean sea level. Although the species favors cross pollination but in the absence of the agents/conditions, pollination will not occur. Some extent of self pollination is also possible (Raina et al. 2010). This plant has immense medicinal value and is used as cardiovascular, antihepatotoxic, choleretic, hypoglycemic, hypolipidemic, anti-inflammatory, antispasmodic, antitumor, antiviral, purgative, immuno-modulatory, antioxidant, neuritogenic, molluscicidal, leishmanicidal agent (Ghisalberti 1998).

Large number of chemical compounds have been isolated and identified which include picrosides I (Kitagawa et al. 1971), II (Wang et al. 1993), and three cucurbitacin glycosides (Stuppner et al. 1991), iridoid glycosides (Li et al. 2000), triterpenoids (Smit et al. 2000), phenolic glycosides (Wang et al. 2004), and phenylethanoid glycosides (Li et al. 1998). All these have been isolated from the rhizomes of this plant. Kim et al. (2006a) reported the isolation and structural elucidation of two phenylpropanoid glycosides, scrophulosides A and B. In a subsequent study, phytochemical analysis of rhizomes of this plant revealed three new iridoid glycosides, picrorosides A, B, and C, and a new cucurbitacin glycoside, scrophoside A (Kim et al. 2006b). Its pharmacological efficacy is attributed to its key metabolites picroside I, picroside II, picroside III, picroside IV, apocyanin, androsin, etc.

Owing to its medicinal importance of Picrorhiza kurrooa, conservation and utilization of Picrorhiza germplasm in crop improvement programs received attention in recent years (Ved et al. 2003). For plant conservation and crop improvement through breeding program, knowledge of available genetic diversity is required. Therefore, there is an interest in determining the genetic diversity in Picrorhiza kurrooa and as such there is no report on the genetic diversity of Picrorhiza germplasm. During last two decades, various molecular markers have been successfully developed and used to overcome the problem of phenotypic diversity. With advances in PCR techniques, new genetic markers like random amplified polymorphism DNA (RAPD) (Rafalski and Tingey 1993), inter simple sequence repeats (ISSR) (Zietkiewicz et al. 1994), amplified fragment length polymorphisms (AFLPs) (Vos et al. 1995) and simple sequence repeats (SSRs) also called microsatellites (Powell et al. 1996), came into existence. These DNA markers are present throughout the plant genome (Wu and Tanksley 1993) and are easier to reproduce and analyze. These markers are multi-allelic and locus specific. High level of polymorphism and their co-dominant nature have made SSRs as ideal markers for studying genetic diversity in plants (Morgante and Olivieri 1993; Plaschke et al. 1995). In higher plants cytochrome P-450 mono-oxygenases play an important role in defense mechanism and the biosynthesis of secondary metabolites (Hallahan and West 1995). Analysis of this multi-gene family also provides an efficient tool to study the genetic diversity in higher plant species (Yamanaka et al. 2003).

Therefore, the present study aimed to address genetic diversity among germplasm of Picrorhiza kurrooa L., in India using SSR and cytochrome P-450 mono-oxygenase markers. Beside, other concerns were: (1) evaluation of discriminating power of SSR and cytochrome P-450 mono-oxygenase primers for the analysis of the genetic diversity efficiently among its accessions, as very limited information about the application of DNA markers is available in the literature for Picrorhiza species, (2) investigation of inter relationship among 25 accessions of Picrorhiza collected from different geographical regions of India.

Materials and methods

Plant material

Twenty five accessions of Picrorhiza kurrooa were collected from ten agro-ecological regions viz. Kedarnath, Uttarkashi, Kullu, Manali, Palampur, Zhanskar, Srinagar, Yarica, Gurez, Manipur, representing four states in India namely Uttarakhand (UK), Himachal Pradesh (HP), Jammu and Kashmir (J&K) and Manipur (MN) (Table 1).
Table 1

Details of place of collection of various accessions of Picrorhiza kurrooa in India

S. no.

Accession code no.

Place of collection

Geographical location

1.

3 (1)

Manipur (MN)

25°N 94°E

2.

1 (42)

Kedarnath (Uttarakhand)

30.7°N 79°E

3.

1 (1)

Kedarnath (Uttarakhand)

30.7°N 79°E

4.

1 (3)

Kedarnath (Uttarakhand)

30.7°N 79°E

5.

1 (8)

Kedarnath (Uttarakhand)

30.7°N 79°E

6.

1 (19)

Kedarnath (Uttarakhand)

30.7°N 79°E

7.

10 (2)

Uttarkashi (Uttarakhand)

30.7°N 78°E

8.

10 (3)

Uttarkashi (Uttarakhand)

30.7°N 78°E

9.

10 (4)

Uttarkashi (Uttarakhand)

30.7°N 78°E

10.

11 (11)

Kullu (HP)

31.9°N 77°E

11.

11 (15)

Kullu (HP)

31.9°N 77°E

12.

12 (15)

Manali (HP)

32°N 77°E

13.

12 (18)

Manali (HP)

32°N 77°E

14.

IHBT94

Palampur (HP)

32°N 76°E

15.

IHBT100

Palampur (HP)

32°N 76°E

16.

2 (1)

Darcha, Zanskar, Ladakh (J&K)

33.8°N 76°E

17.

2 (10)

Darcha, Zanskar, Ladakh (J&K)

33.8°N 76°E

18.

2 (11)

Darcha, Zanskar, Ladakh (J&K)

33.8°N 76°E

19.

2 (2)

Darcha, Zanskar, Ladakh (J&K)

33.8°N 76°E

20.

2 (14)

Darcha, Zanskar, Ladakh (J&K)

33.8°N 76°E

21.

Pk-1S

Srinagar, Kashmir (J&K)

34.5°N 74°E

22.

Pk-2S

Srinagar, Kashmir (J&K)

34.5°N 74°E

23.

Pk-1Y

Yarica, Kashmir (J&K)

34.5°N 74°E

24.

Pk-3Y

Yarica, Kashmir (J&K)

34.5°N 74°E

25.

Gurez

Gurez, Kashmir (J&K)

34.6°N 74.8°E

DNA extraction

Young and fresh leaves (0.2 g) of various accessions were collected and used for genomic DNA extraction (Ahmad et al. 2004).

SSR analysis

Twenty two primer pairs from rice microsatellite loci available on http://www.dna-res.kazusa.or.jp/3/4/02/html/ described by Miyao et al. (1993) were used for this study. These primers represent SSR loci in eight different chromosomes of rice. Details about sequence, melting temperature and microsatellite motifs are presented in Table 2. SSR profiles were generated as described by Kumar et al. (2007).
Table 2

Comparitive analysis of SSRs and Cytochrome P450s of Picrorhiza kurrooa. The number of polymorphic alleles (NPA), Percent polymorphic alleles (%PA), Shannon’s diversity (H) and polymorphic information content (PIC) were investigated in 25 Picrorhiza kurrooa accessions

 

Primer (5′–3′)

Tm (oC)

NPA

% PA

H

PIC

SSR

PL 581 TTTATCCGCGTCCCTAGCTT (F)

PL 582 GCCGCCGGGGTCACAGGTCA (R)

62

10

100 %

2.07

0.8267

PL 579 ATCTCTTCGCAGATCCACCT (F)

PL 580 GCTGAGACGCGCGGGTCGGA (R)

60

9

100 %

1.89

0.8267

PL 585 CGTGCTCGTGGATCCCCATC (F)

PL 586 CACCGTCGAATCGAATCCAA (R)

59

6

75 %

1.79

0.7367

PL 557 CGCCGAAGTGGTAGAGGCAA (F)

PL 558 TGCAGGAGGCAGGAGGAGAA (R)

55

6

75 %

1.64

0.8867

PL 589 ATATTTCCAGCCAGCCGCAT (F)

PL 590 GGGCCGTGCCGTGCCTCACC (R)

60

6

85.7 %

1.52

0.8067

PL 563 GAATCCGATCCATCCATTGG (F)

PL 564 CACGAACACGCGACGCACGA (R)

60

5

71 %

1.36

0.740

PL 559 GCAAATGCATATGCAGTGAT (F)

PL 560 CCCGTGCTAGCTGCAGCCTT (R)

55

6

85.7 %

1.34

0.770

PL 573 CTCCCCCTCCTCCTCCTCCC (F)

PL 574 CGACCGGCCGGAATGGATGC(R)

60

4

80 %

1.22

0.7567

PL 595 GGGATGATCATCTCCGATGC (F)

PL 596 CCCTTCTCCCACTCTTCTCC (R)

60

4

66.6 %

1.06

0.7267

PL 567 GTCAGGCCCAATCGGCAAAT (F)

PL 568 GAGGAGGGCTGCGTGCAGAC (R)

60

6

100 %

0.99

0.8533

PL 555 ACCCCAGCGCGATCCGAGGT (F)

PL 556 AGCGGCGTCTCCAGCCCGAA (R)

60

2

66.6 %

0.59

0.6967

PL 583 GAGAGGTTTCCGATACCCTT (F)

PL 584 TCGGCCTCTCGCCCCCCGA (R)

55

3

100 %

0.57

0.7033

PL 561 CGCCGCCGTACTGCTCCATC (F)

PL 562 GCGGAGGAGACCTGCGGGT (R)

60

2

40 %

0.30

0.7033

   

Mean 5.037

80.4

16.4

Mean 0.7718

Cyt-P450

PL 454 (F) GATGGTCTTCCGCGGTA (F)

PL 455 (R) CACTGGAAGGCGTGCA (R)

62.3

63.6

5

100

0.96

0.787

PL 456 (F) CGGCTTGCTCATGGA (F)

PL 457 (R) GAGAAATAGGTGGGTGGA (R)

62.1

57

6

100

1.59

0.92

PL 458 (F) GACCCAAGCAACGTCA (F)

PL 459 (R) GTGGGTTATGGCCCACA (R)

59.2

63.0

5

71

1.13

0.81

PL 460 (F) GACGTGCCACTCTGCA (F)

PL 461 (R) ACCCTAGGCTAAGGTGGA (R)

60.8

58.6

3

60

0.513

0.663

PL 462 (F) CCACCTTGACGACCCAA (F)

PL 463 (R) TGGCCCACATATTCACCA (R)

63.4

63.5

6

85

1.42

0.82

PL 464 (F) ACGTGCCACTCTGCAA (F)

PL 465 (R) ACCCTAGGCTAAGGTGGA (R)

60.1

58.6

5

100

1.30

0.847

PL 466 (F) GGGCCATAACCCACGA (F)

PL 467 (R) ATTGGAGCGCCGGTGA (R)

63.1

65.5

5

71

1.259

0.75

PL 468 (F) CCTGTACGACCCAAGCA (F)

PL 469 (R) TGGCCCACATATTCACCA (R)

60.9

63.5

5

83

0.54

0.48

   

Mean 5.0

 

8.712

Mean 0.7596

The number of polymorphic alleles (NPA), Percent polymorphic alleles (% PA), Shannon’s diversity (H) and polymorphic information content (PIC) were investigated in 25 Picrorhiza kurrooa accessions

Cytochrome P-450 analysis

Cytochrome P-450 sequence primers (Table 3) were derived from cytochrome P-450 sequences of family CYP78 (Larkin 1994). Cytochrome P-450 profiles were generated as described by Ahmad et al. (2009).

In both SSR and cytochrome P-450 analysis, a control PCR reaction containing all components, but no genomic DNA was run with each primer to check for contamination. All the PCR results were tested for reproducibility by at least three times. Bands that did not show fidelity were eliminated for statistical analysis.

Statistical analysis

Discriminating power (Dj) of each primer, i.e. the probability that the two randomly chosen accessions from the sample of 25 accessions have different banding patterns and, thus, are distinguishable from one another, was estimated (Tessier et al. 1999). Genetic diversity was estimated by Shannon index (Lewontin 1972). To investigate phenetic relationships among accessions, the binary matrix was used to cluster individuals using procedure of NTSYS-PC2.1 (Rohlf 1993). A dendrogram was constructed based on Dice coefficient’s similarity data applying the unweighted pair group method (UPGMA). The robustness and validity of clustering pattern were tested by Bootstrap analyses of 1,000 bootstrap samples using the software WINBOOT (Yap and Nelson 1996). To investigate the correlation between the two marker data, a Mantel test was conducted using the COPH and MAXCOMPS program. Polymorphism information content (PIC) values were calculated to evaluate diverse level of each SSR marker according to Anderson et al. (1993) using the formula PIC = 1 − ΣPij2 where Pij is the frequency of the j-th allele (marker) for the i-th SSR locus.

Results and discussion

Genetic diversity was estimated among germplasm of Picrorhiza kurrooa L., in India using SSR and cytochrome P-450 mono-oxygenase markers.

Out of 22 SSR primer pairs, only 13 primer pairs generated distinct band patterns among 25 accessions of Picrorhiza sp. collected from ten eco-geographical locations of four states (Table 2). These 13 SSR primer pairs generated 84 markers, out of which 69 were found polymorphic (82.14 %) with the mean of 5.037 alleles; the number of alleles ranged from 2 to 10 per accession and the size varied between 0.1 and 3 kb. Whereas eight cytochrome P-450 primer pairs generated 48 markers, out of which 40 were found polymorphic (83 %) with the mean of 5.0 alleles; the number of alleles ranged from 3 to 6 per accession and the size varied below 1 kb.

Genetic diversity in terms of mean Shannon index per primer using SSR markers was recorded to be 1.26, ranging from 0.30 (PL-460 and PL-461) to 2.07 (PL-456 and PL-457) with a total genetic diversity (H) of 16.4, whereas cytochrome P-450 markers revealed 1.089 mean Shannon index per primer, ranging from 0.513 (PL-460 and PL-461) to 1.59 (PL-456 and PL-457) with a total genetic diversity (H) of 8.712. Higher Shannon index value for SSR markers in comparison to cytochrome P-450 markers suggested a higher discrimination power of SSR markers. Agro-ecological region wise total genetic diversity (H) based on both markers was 3.97 Kedarnath, 5.9 Uttarkashi, 9.0 Kullu, 6.24 Manali, 8.66 Palampur, 2.44 Zhanskar, 11.44 Srinagar and 9.7 Yarica. State wise total genetic diversity (H) was 13.95 (UK), 17.23 (HP) and 22.24 (J&K). Results in terms of genetic diversity reflected a high genetic differentiation among genotypes from same locations per se.

Using 13 SSR primer pairs on the set of 25 accessions, 110 SSR patterns were obtained. The number of banding patterns (Tp) ranged from 4 in primer pair PL-583 and PL-584 to 13 in prime pair PL-557 and PL-558 with an average of 8 banding patterns. The PIC value ranged from 0.6967 to 0.8867 with a mean of 0.7718. Whereas eight cytochrome P-450 primer pairs, generated 67 banding patterns on the set of 25 accessions. The number of banding patterns (Tp) ranged from 6 in primer pair PL-468 and PL-469 to 14 in prime pair PL-456 and PL-457 with an average of 8 banding patterns. The PIC value ranged from 0.48 to 0.92 with a mean of 0.7596. Results suggested that SSR markers generated higher allelic number and higher mean PIC value than cytochrome P-450 markers, showing a higher discrimination power.

Dice’s similarity coefficients were separately calculated using the SSR and cytochrome P-450 data. The pair-wise comparison of similarity coefficients generated a mean similarity coefficient of former is 0.7659, whereas latter generated a higher value of 0.7974, again reflecting the polymorphic differences between the two markers. Combination of the two data gave a moderate value of 0.78.

To investigate the genetic relationship among 25 accessions of Picrorhiza collected from different geographical regions in India, dendrograms were separately constructed based on the SSR or cytochrome P-450 data, and both the markers revealed a high similarity in dendrogram topologies (Fig. 1a, b). SSR markers divided P. kurrooa accessions into two major clades (Fig. 1a). Clade 1 contained the accessions of Kedarnath, Uttarkashi (UK) and Zanskar (J&K). Accessions from Kedarnath (UK) and Zanskar (J&K) grouped collectively into clade 1.1 but accessions from Uttarkashi (UK) belonged to a separate clade 1.2. There is no chance of immigration of germplasm from one geographical region (Kedarnath) to other (Zhanskar) because of their geographical position, i.e. in between them there is another state, Himachal Pradesh. Although accessions from Kedarnath and Uttarkashi belong to same state (UK), but they grouped separately suggesting there is sufficient variation among their accessions.
https://static-content.springer.com/image/art%3A10.1007%2Fs00606-013-0820-z/MediaObjects/606_2013_820_Fig1_HTML.gif
Fig. 1

a Dendrogram showing two clusters I and II depicting the relationship among 25 accessions of Picrorhiza kurrooa. The tree was constructed based on the SSR markers using UPGMA method. b Dendrogram showing six clusters I–VI depicting the relationship among 25 accessions of Picrorhiza kurrooa. The tree was constructed based on the cytochrome P-450 markers using UPGMA method. c Dendrogram showing six clusters I–IV depicting the relationship among 25 accessions of Picrorhiza kurrooa. The tree was constructed based on the combined data of SSR markers and cytochrome P-450 markers using UPGMA method

Clade 2 comprised the accessions from HP and J&K. Accessions in this clade also grouped into the state-specific subclades. This cluster comprised two subclades. Subclade 2.1 contained the accessions from J&K while subclade 2.2 had the accessions from HP. But within a subclade, all the accessions of particular agro-ecological regions were not clustered in distinct group reflecting a high genetic differentiation among genotypes from same locations per se. Accession 11(15) from Kullu (HP) was found to be highly variable within its subclade. The accession 3(1), which belonged to a separate eco-geographical zone, i.e. Manipur, was observed to be outside the two clusters, indicating that it is highly genetically different. This might be because of entirely different climatic conditions. Overall, the study indicates the narrow range of the coefficient 0.66–1.0 to depict the Picrorhiza sp. accessions. This might be attributed to its propagation mostly by stolon segments.

Recently, microsatellite sequences were successfully isolated from Picrorhiza kurrooa (Hussain et al. 2009). They are yet to be utilized for the assessment of genetic diversity. The present study and similar studies on Hypericum (Smelcerovic et al. 2006; Verma et al. 2008) and Cymbopogon (Kumar et al. 2007) suggested that SSR markers are of immense importance in studying intra- and inter-specific genetic diversity.

Genetic interrelationship analysis based on cytochrome P-450 data generated a dendrogram (Fig. 1b) with six major clusters having similarity coefficient values ranging from 0.67 to 1.0. Clade 1 represented the accessions from two eco-geographical zones viz. Kedarnath (UK), and Darcha, Zhanskar (J&K). These accessions were grouped into two subclades at around 92 % similarity level. Subclade 1.1 again comprised the mixed accessions of Kedarnath (UK) and Darcha, Zanskar (J&K) similar to SSR analysis. In this group, cytochrome P-450 pattern of two accessions 2(2) and 2(10) was found exactly similar; similarly three other accessions 1(3), 1(19) and 2(1) were again showed to be similar but different profile with previous one. This suggests similarity in this multi-gene family across the accessions, although these accessions showed variable SSR pattern. Subclade 1.2 comprised two accessions 2(11), 2(14) from Darcha, Zhanskar (J&K). Accessions from Kullu and Manali (HP) 11(11), 11(15), 12(15), 12(18) were clustered into state-specific clade 2 at 85 % similarity level, similarly accessions from Uttarkashi 10(2), 10(3), 10(4) (UK) were clustered into region-specific clade 3 at 92 % similarity level. Clade 4 had two accessions belonging to two different eco-geographical locations, one from Manipur (MN) 3(1) and other from Palampur (HP) (ihbt94). Both of these two zones lie on two extreme ends of India, one at eastern side (MN) and the other at north-western side (HP). Similarly clade 5 comprised the two accessions, ihbt100 (Palampur, HP) and Pk2s (Srinagar). Again accessions from J&K (pk1s (Srinagar), pk1y, pk2y (yarica), Gurez were clustered into state-specific clade 6 at 67 % similarity level. Similar to SSR analysis, within a clade/subclade, all the accessions of particular agro-ecological regions were not clustered in distinct group reflecting a high genetic differentiation among genotypes from same locations per se. These differences between two trees could be due to the differences in targeted DNA regions or genomic coverage rate of the different marker systems.

The present study and similar studies on many other taxonomic families (Yamanaka et al. 2003) and Tinospora (Ahmad et al. 2009) suggested that cytochrome P-450 markers are of immense importance in studying intra- and inter-specific genetic diversity.

Genetic interrelationship analysis based on combined data of SSR and cytochrome P-450 generated a dendrogram which showed four major clades. Clade 2 and 4 were region/state-specific as previous one contained the accessions of Uttarkashi while latter one contained the accessions of Kashmir (J&K). The other two clades 1 and 3 contained the mixed accessions. Cluster I had accessions belonging to two different eco-geographical locations, Kedarnath (UK) and Darcha, Zanskar (J&K) while cluster III had accessions belonging to five different eco-geographical locations Manipur (MN), Kullu (HP), Manali (HP), Palampur (HP) and Srinagar (J&K).

The correlation between SSR and cytochrome P-450 similarity matrices was compared by Mantel test and revealed a moderate fit between the two data (r = 0.6405). As expected, the general dendrogram (Fig. 1c) that was constructed using the combined data of the two markers was very similar to those obtained separately with each marker. This combined dendrogram clearly separated the accessions according to their geographical origins, demonstrating the importance of both dataset in the genetic relationship studies. The dendrogram from SSR data was the most congruent with the general dendrogram as shown by the Mantel test (r = 0.932) between similarity matrices generated by SSR data and the combined data of two markers, whereas correlation value in terms of Mantel test between cytochrome P-450 and general similarity matrices was r = 0.87456.

In conclusion, the results of the present study suggested that there is quiet high degree of genetic variation among the accessions of each eco-geographic region estimated by SSR and cytochrome P-450 markers. SSR markers revealed higher polymorphism among the accession set in comparison to cytochrome P-450 markers. This study will serve the purpose of germplasm curators to utilize the diverse accessions for cost-effective conservation and further utilization in crop improvement programmes.

Acknowledgments

Authors are grateful to the Director, Indian Institute of Integrative Medicine (CSIR) Jammu, India for giving platform, financial and technical support for accomplishing this present research work. This is IIIM communication No. IIIM/1543/2013.

Copyright information

© Springer-Verlag Wien 2013