African Journal of
Biotechnology

  • Abbreviation: Afr. J. Biotechnol.
  • Language: English
  • ISSN: 1684-5315
  • DOI: 10.5897/AJB
  • Start Year: 2002
  • Published Articles: 12512

Full Length Research Paper

Genetic diversity in Nepalese population of Swertia chirayita (Roxb. Ex Fleming) H. Karst based on inter-simple sequence repeats (ISSR) markers

Surendra Neupane
  • Surendra Neupane
  • Molecular Biotechnology Unit, Nepal Academy of Science and Technology (NAST), Khumaltar, Lalitpur, Nepal
  • Search for this author on:
  • Google Scholar
Jaishree Sijapati
  • Jaishree Sijapati
  • Molecular Biotechnology Unit, Nepal Academy of Science and Technology (NAST), Khumaltar, Lalitpur, Nepal
  • Search for this author on:
  • Google Scholar
Tribikram Bhattarai
  • Tribikram Bhattarai
  • Central Department of Biotechnology, Tribhuvan University, Kirtipur, Kathmandu, Nepal
  • Search for this author on:
  • Google Scholar
Sangita Shrestha
  • Sangita Shrestha
  • Molecular Biotechnology Unit, Nepal Academy of Science and Technology (NAST), Khumaltar, Lalitpur, Nepal
  • Search for this author on:
  • Google Scholar


  •  Received: 28 December 2016
  •  Accepted: 20 March 2017
  •  Published: 19 April 2017

 ABSTRACT

Swertia chirayita is a highly valued but vulnerable medicinal plant species of Nepal. Its populations are declining in natural habitats due to over exploitation. Twenty-seven inter-simple sequence repeats (ISSR) primers were used to assess the genetic diversity and population genetic structure of 42 genotypes representing six natural populations of S. chirayita, from Nepal. Of the total 479 bands amplified by 27 ISSR primers, 473 (98.18%) were polymorphic, indicating very high level of genetic diversity at species level. Percentage polymorphism value for different primers ranged from 83.3 to 100% with an average of 98.18%. Polymorphism information content (PIC) value ranged from 0.88 to 0.93 with an average of 0.91. Cluster analysis performed with NTSYS pc statistical package using Jaccard’s similarity coefficients generated from ISSR binary data matrix showed that, all 27 ISSR primers separated 42 individuals into two major clusters and six sub clusters at the similarity level of 0.24. The average value of Nei’s genetic diversity (H) and Shannon’s information index (I) equaled 0.276 and 0.423, respectively at species level. The coefficient of genetic differentiation (GST) amongst populations of S. chirayita was found to be high (0.548) with restricted gene flow (Nm=0.4829). Analysis of molecular variance showed that genetic diversity within populations is slightly higher (50.9%) than among populations (47.6%). The present genetic diversity assessment of S. chirayita populations has been of immense importance to understand the cause of its vulnerability and has furnished valuable insights for its conservation and sustainable utilization.

 

Key words: Polymerase chain reaction, inter-simple sequence repeats (ISSR), genetic diversity, polymorphism, population genetics.


 INTRODUCTION

Nepal is home to medicinal and aromatic plants (MAPs) diversity  and  over  1950  medicinal   plants   have   been stated till date (Ghimire, 2008). Among them, 30 species of   Swertia   (Gentianaceae),    including    varieties    are reported in Nepal (Press et al., 2000). Swertia chirayita (Roxb. ex Flem) Karst, the most valuable and dominant species in trade, is indigenous to the temperate Himalayas and is abundant in 54 districts in Nepal (Barakoti et al., 2013) throughout the altitude ranging from 600 to 5600 m (Rijal, 2010). Because of its high need in national and international markets, natural populations have been threatened and species as such has been considered vulnerable medicinal plant in Himalayan region including Nepal (Nepal, 2004; Purohit et al., 2013). Together with S. chirayita, eight other species of Swertia [Swertia angustifolia Buch.-Ham ex D. Don, Swertia dilatata C. B. Clarke, Swertia nervosa (G. Don) C. B. Clarke, Swertia racemosa (Griseb.) C.B. Clarke, Swertia ciliata (D. Don ex G. Don) B. L. Burtt, Swertia multicaulis D. Don, Swertia alata (Royle ex D. Don) C.B. Clarke and Swertia tetragona Edgew] are also traded for their medicinal properties with the name ‘Chiraito’ or ‘Chiretta’ (Barakoti, 2002). Among these nine species, S. chirayita is considered superior in medicinal quality and is in high demand in trade nationally and internationally (Barakoti, 2002; Joshi, 2008). Shrestha et al. (2016) have reported that S. chirayita is the mostly used species by the indigenous people of Sankhuwasabha district of Nepal proving the efficacy and importance in ethnomedical research. Nepal trades more than 45% of the world’s total traded volume of S. chirayita (Barakoti, 2004). Nevertheless, Nepal uses only 1% of this volume and the rest is exported to various countries including India, Italy, France, Switzerland, Sri Lanka, Bangladesh, Pakistan, China, Germany, Singapore and the United States of America (Phoboo et al., 2008). Numerous studies have reported many chemical compounds in S. chirayita such as chiratinin, terpenoids, iridoids gentianine, amarogentin, amaroswerin, xanthones, secoiridoid, glycosides and urosilic acid (Bajpai et al., 1991; Joshi and Dhawan, 2005; Khanal et al., 2015; Kumar and Chandra, 2015; Kshirsagar et al., 2016, 2017). These compounds and their derivatives possess antihepatotoxic, antileishmanial, anticarcinogenic, antioxidant, anti-inflammatory, antidiabetic, antimalarial and antihelmithic properties (Bajpai et al., 1991; Ray et al., 1996; Saha et al., 2004; Iqbal et al., 2006; Balaraju et al., 2009; Chen et al., 2011; Nagalekshmi et al., 2011; Phoboo et al., 2013; Zhou et al., 2015; Lad and Bhatnagar, 2016). Recently, Tupe et al. (2017) have reported that S. chirayita showed antiglycating activity which plays crucial role in antidiabetic effects. Also, S. chirayita possess less inhibitory effect on the drug metabolizing isoenzymes CYP3A4 and CYP3D6 as well as inhibits the β-glucuronidase and helps in hepatoprotection (Ahmmed et al., 2016; Karak et al., 2017).
 
The knowledge of plant genetic diversity in their natural habitat, sustainable utilization, is important for efficient management of plant genetic resources (Mondini et al., 2009).   Thus,   information   on   genetic    diversity    and geographical distribution of plant species in wild conditions are essential for formulating conservation strategy (Wang et al., 2011). Analysis of the genetic diversity and population genetic structure using various molecular marker techniques is necessary to endorse the ‘vulnerable’ status of any species (Godt and Hamrick, 1998). Thus, molecular tools play important role in  exploring genetic diversity in endangered species for the formulation conservation strategies (Kim et al., 2005).
 
PCR-based inter-simple sequence repeats (ISSR) markers have been widely employed for studying population genetics of various plant species, including several medicinal plants such as Solanum tuberosum, Neopicrorhiza scrophulariiflora and Dendrobium spp. (Bornet et al., 2002; Wang et al., 2009; Liu et al., 2011). Limited molecular studies have been carried out in Swertia species of the world. In a study including ISSR markers, 98.7% polymorphism was found among 19 genotypes of Swertia spp. (13 of S. chirayita and 2 each of Swertia cordata, Swertia paniculata and Swertia purpurascenes) collected from the temperate Himalayas of India (Joshi and Dhawan, 2007). In the investigation of endangered endemic species, Swertia przewalskii of the Qinghai-Tibet plateau using RAPD and ISSR analysis, Zhang et al. (2007) observed the significant genetic differentiation based on different measures including analysis of molecular variance (AMOVA) (52% for RAPD and 56% for ISSR). Misra et al. (2010) used amplified fragment length polymorphism (AFLP) to produce DNA fingerprints for Swertia spp. In the study, 19 accessions (two of S. chirayita, three of Swertia augustifolia, two of Swertia bimaculata, five of S. ciliata, five of S. cordata and two of S. alata) from India were used in the study by employing 46 selected AFLP primer pairs. The species-specific markers were identified for all six Swertia spp. which can be used to authenticate drugs. Another study revealed polymorphism of up to 99% among various species of Swertia using 16 ISSR primers (Tamhankar et al., 2009). They found Swertia lurida to be the closest to S. chirayita. Another study on Swertia tetraptera showed high genetic diversity within species and low genetic diversity among populations (Yang et al., 2011). Also, Samaddar et al. (2015) reported on some RAPD markers that can be used for fingerprinting analysis and implementation of genetic diversity study on Swertia spp.
 
In the context of Nepal, initiation of molecular characterization of Swertia spp. has been reported with the phylogeny of 11 Nepalese Swertia spp. such as S. augustifolia, S. chirayita, S. ciliata, S. dilatata, S. lurida, Swertia macrosperma, S. multicaulis, S. nervosa, S. paniculata, Swertia pedicellata and S. racemosa (Joshi, 2008, 2011). The internal transcribed spacer (ITS) and chloroplast (trnL-F) regions were analyzed together with distance, parsimony and Bayesian analysis. The result indicated that ITS fragment can be used in identification as a barcoding marker for Swertia. The optimization of the RAPD-PCR  conditions  (Shrestha  et  al.,  2011)  and genetic diversity of 34 accessions of S. chirayita including six outlier species were assessed in Nepal (Shrestha et al., 2013). Of the total 285 amplified bands scored for S. chirayita, 263 (92.8%) were polymorphic.
 
The objectives of this study were to assess the genetic diversity, unravel the genetic variation within and among populations and provide future recommendations necessary for conservation and sustainable utilization of S. chirayita, the highly valuable and vulnerable medicinal plant species of Nepal.


 MATERIALS AND METHODS

Genetic material and genotypic data
 
Forty-two samples of S. chirayita were collected in silica gel from six different locations of Nepal: nine from Phulchowki, four from Kaski, six from Sankhuwasabha, eight from Terhathum, seven from Nagarjun, eight from Ilam along with four outlier accessions from Kaski and Sankhuwasabha districts (Figure 1, Plate 1 and Table 1). Genomic DNA was extracted by using hexadecyltrimethyl ammonium bromide (CTAB) method (Graham et al., 1994). Quantification and purity assessments of DNA were assessed by using a UV Biophotometer (EPPENDORF AG 22331, Germany). ISSR-PCR reaction parameters were optimized in 20 µl reaction volume containing 50 ng of genomic DNA, 2.4 µl of MgCl2 (3.0 mM), 2.0 µl of 10X Taq polymerase reaction buffer [100 mM Tris-HCl (pH 8.8 at 25°C), 500 mM KCl, 0.8% (v/v) Nonidet P40], 1 U Taq DNA polymerase (Thermo Scientific company), 0.3 mM of dNTPs and 0.6 µM of each primer.
 
 
 
 
PCR cycling conditions described by Tamhankar et al. (2009) produced finest ISSR profiles for S. chirayita. The PCR program comprised of an initial denaturation step at 94°C for 2 min followed by 45 cycles of 95°C for 30 s, 51°C for 45 s and 72°C for 2 min and final extension of 72°C for 5 min. Amplification of DNA was performed using BIOER Xp thermal cycler [BIOER Technology Co. LTD, Taiwan, China]. The PCR amplified ISSR fragments were assessed by gel electrophoresis using 1.5% agarose (Promega Co.) in 1X TAE stained with ethidium bromide (10 mg/ml solution, Promega Co.) buffer at 50 V (4.2 V/cm) in EMBI TEC (Santiago, CA) gel tank for 2 h 30 min. The gel documentation was done using Gel Doc system (IN GENIUS, Syngene Bioimaging, UK). 100 ISSR primers (UBC primer, University of British Columbia, Oligonucleotide Synthesis Laboratory, Vancouver, British Columbia, Canada) were employed to screen against DNA of S. chirayita from Nagarjun. Twenty-seven UBC primers (Table 2) that engendered reproducible and scorable bands were selected for the ISSR profiling. Both polymorphic and monomorphic bands were scored as “1” for presence and “0” for absence whereas, the failure in amplification was scored as “9”, as an indicator of missing data (Jaccard, 1908). The size of the ISSR-PCR products was determined using Gene rulerTM100 bp plus DNA ladder (Thermo Scientific Company) (Plate 1).
 
 
 
Genetic diversity and clustering analysis
 
The reproducible bands scored across all samples were included in the analysis. The binary data matrix was investigated using MS-Excel 2007 for the assessment of total number of bands (TNB), number of polymorphic bands (NPB), percent polymorphism (PP), polymorphic information content (PIC), band informativeness (IB), and resolving power (RP) for each primer. These are calculated   by, PP = NPB/TNB engendered by each primer; PIC = 1- Σ(Pij)2, where Pij denotes the frequency of the ith pattern revealed by the jth primer added throughout patterns revealed by the primers (Botstein et al., 1980); Rp = ΣIB, where IB is the band informativeness with IB = 1-[2×(0.5-P)], where P denotes the proportion of accessions containing the band (Prevost and Wilkinson, 1999). The similarity indices were computed using a similarity for qualitative data (SIMQUAL) computer algorithm via NTSYS-PC (Numerical Taxonomy and Multivariate System, version 2.21i, Exeter software; Setauket, New York, USA).
 
The program POPGENE 1.31 was used to estimate intra and inter-population genetic variation. The parameters used were Nei’s gene diversity index (He), Shannon’s information index (Ho), the observed number of alleles (Na) and the effective number of alleles (Ne). Nei’s gene diversity statistics along with total genetic diversity (HT), genetic diversity within populations (HS), and the extent of genetic differentiation among populations (Gst) was estimated to study the genetic construction (Nei, 1978). Gst was calculated using formula as (HT - HS)/HT. The gene flow among populations (Nm) was calculated using formula (1 - Gst)/4Gst by Slatkin and Barton (1989). GenAlEx ver 6.5  was used for analysis of molecular variance (AMOVA) and Mantel test done to estimate the genetic variation among and within populations (Peakall and Smouse, 2012). AMOVA was tested by nonparametric randomization tests using 999 permutations in variation attribute. 3D plot of the distribution of all S. chirayita accessions was constructed with the analysis of Eigen vector for the illustration of variation as compared to the dendrogram using Jaccard’s similarity matrix using NTSYS-PC.
 
Principal coordinates analysis (PCA) was performed through Nei’s genetic distance (Nei, 1978) matrix of populations using GenAlEx software (Peakall and Smouse, 2012). Finally, Fst was calculated employing Tools for Population Genetic Analysis (TFPGA) through 1000 permutations (Miller, 1997).


 RESULTS

ISSR analysis
 
The total number of bands (TNB), number of polymorphic bands (NPB), percentage polymorphism (PP), amplicon size range, PIC, IB and RP values of the 27 ISSR primers used to generate ISSR profiles of S. chirayita accessions are  presented in Table 2. The 27 selected primers generated altogether, 479 unambiguous and reproducible bands, of which 473 (98.18%) were polymorphic, the sizes ranged from 150 to 3000 bp. The numbers of bands varied from 8 to 24, with an average of 18 bands per primer. Polymorphism information content (PIC) score of primers under study ranged from 0.805 (UBC830) to 0.938 (UBC836) with an average of 0.91. The average band informativeness (IB) of the 27 primers was 0.54 and it ranged from 0.31 (UBC 812) to 0.80 (UBC 895). Whereas, the resolving power (RP) ranged from 3.43 for primer UBC 830 to 14.95 for primer UBC 841 with an average of 10.47.
 
Genetic diversity within populations
 
The percentage of polymorphic loci (PPB) ranged from 51.42 to 78.21%, with an average of 70.20% in individual populations (Table 3). Nei’s gene diversities (H) varied from 0.174 to 0.270, with an average of 0.245, and Shannon’s indices (I) ranged from 0.264 to 0.407, with an average of 0.366. In this study, the high genetic diversity was found in Sankhuwasabha populations (H and I values of 0.270 and 0.407, respectively), while low genetic diversity was found in Kaski populations (H and I values of 0.174 and 0.264, respectively). The genetic diversity of populations from high to low ranked as follows: Sankhuwasabha > Ilam > Terhathum > Phulchowki > Nagarjun > Kaski. It was also evidenced from the number (Na) and effective number of alleles (Ne) (Table 3). At species level, the H and I values equaled 0.276 and 0.423, respectively, and the Na and Ne values equaled 1.986 and 1.451, respectively.
 
 
Genetic construction of populations
 
The considerable level of genetic differentiation was observed among various populations of S. chirayita studied. The total gene diversity (HT) and gene diversity within populations (HS) were 0.278 and 0.120, respectively. The coefficient of genetic differentiation (Gst) amongst inter-populations of S. chirayita 0.548 indicated 54.8% variation in inter-populations and 45.2% variation within the populations (Table 4). AMOVA showed 48.0% genetic variation in inter-populations (Table 5); which strongly supports the result shown in genetic differentiation of S. chirayita as it was affected more in inter-population units. The gene flow was estimated to be 0.482. The correlation between genetic distance and geographical distance (r) value was found to be 0.418 (p<0.001), which indicated no significant correlation between the two matrices, based on genetic and geogra-phical distances. FST value was found to be 0.6529. 
 
 
Cluster analysis based on the ISSR genotyping profiles
 
The results from Mantel test (Matrix comparison) using NTSYS-PC (Version 2.21i) showed that the correlation between Jaccard and Dice similarity matrices was the highest and significant (0.99252) (Table 6 and Plate 1). Clustering based on unweighted pair group method of arithmetic averages (UPGMA) for Jaccard coefficient was observed to give a high  cophenetic  correlation  value  of 0.96875 and comparatively lowest cophenetic correlation value of 0.94904 was observed for UPGMA clustering using simple matching coefficient. Because of their highest correlation value and comparison of standard chart of goodness of fit, Jaccard’s coefficient of similarity with UPGMA clustering method was the best for studying relationship among S. chirayita accessions. In the study, Jaccard similarity with UPGMA yielded the highest correlation coefficient value but the difference between Jaccard, Dice and simple matching  coefficients  was  not so far (0.96875, 0.95197 and 0.94904 respectively). From this test, the three coefficients were in the decreasing order as J > D > SM (Table 7). Consensus indices (CI) were calculated for each combination of coefficient and UPGMA clustering for the evaluation of trees constructed from UPGMA clustering by genetic similarity coefficients. Highest Consensus fork index (CIc = 0.9805) was found for Jaccard and Dice coefficients. The CIc values for J and SM and D and SM were lower (CIc = 0.7500) (Table 8).
 
 
Based on the Jaccard’s similarity matrix (01-0.86) clarified the genetic relationship of S. chirayita accessions from geographically diverse population. Individuals (42) were found separated into two major clusters (Clusters I and II) and six sub clusters A, B, C, D, E and F at the similarity level of 0.24 (Figure 2). The sub clusters A and B were separated at the similarity coefficient level of 0.27. Likewise, the clusters C and D were separated from A and B at the similarity coefficient of 0.25. The sub-cluster A contained accessions from Phulchowki and Kaski. All accessions (except F9) from Phulchowki were clustered at the similarity coefficient level of 0.65, while F9 accession was clustered at the similarity coefficient level of 0.425. Likewise, all the individuals from Kaski were clustered as the similarity coefficient level of 0.68. The sub cluster B contained accessions from Nagarjun and were clustered at the coefficient level of 0.62. The sub cluster C contained accessions from Sankhuwasabha (S1 and S2), whereas sub cluster D contained accessions from both Sankhuwasabha (S4, S5, S6, S7 and S8) and Terhathum. The accessions from Sankhuwasabha, S1 and S2 were separated from rest of the individuals at the similarity coefficient level of 0.35, while the rest of individuals were clustered at the level of 0.675. The accessions T7 and T6 were separated from the rest of the individuals of Terhathum at similarity coefficient level of 0.52 and 0.50, respectively. The sub clusters E and F contained accessions from Ilam. The individuals of Ilam were clustered at the similarity coefficient level of 0.49 except the accession I4 at 0.345. The PCoA analysis was carried out based on the Euclidean matrix. In the plot, first (percentage of variance = 18.33%) and second (percentage of variance = 16.90%) axis with a cumulative variance of 35.23% was seen. The PCoA plot supports the result of dendrogram by clustering the individuals according to their geographical locations (Figure 3). It showed the congruence with 3D-plot of the distribution of all S. chirayita accessions (Figure 4).
 
 
 


 DISCUSSION

The genetic polymorphism observed in the present study for S. chirayita was very high (that is, 98.18%) as compared to the result from 13 Indian S. chirayita genotypes from temperate Himalaya (42.5%) using ISSR marker (Joshi and Dhawan, 2007). When over 50% of the total genetic variation existed within populations, six populations of species should retain 95% of their  genetic diversity (Hamrick and Murawski, 1991). The investigation showed substantial genetic differentiation among S. chirayita populations. However, RAPD based analysis of 34 accessions of S. chirayita (used in this present research) revealed 92.28% polymorphism (Shrestha et al., 2013). S. tetraptera, an endemic species of Qinghai-Tibetan Plateau revealed 98.9% polymorphism using ISSR fingerprinting (Yang et al., 2011) and S. przewalskii from the same region showed 56% polymorphism using ISSR marker (Zhang et al., 2007). Genetic polymorphism shown with ISSR analysis for Neopicrorhiza scrophulariiflora also showed high level of polymorphism of 100% (Liu et al., 2011), likewise, 100% for Dendrobium studies (Wang et al., 2009). The high level of both polymorphism and reproducibility is observed using ISSR-PCR technique because of the use of longer primers which increases stringency in annealing temperatures than in RAPD-PCR technique (Kojima et al., 1998).
 
The present study revealed Jaccard’s similarity coefficient values ranging from 0.1 to 0.86. This values suggest the presence of high genetic diversity within S. chirayita species of six populations. Similar result was observed among the S. chirayita species collected from temperate Himalaya of India as Jaccard’s coefficient was observed in the range of 0.68 to 0.97 (Joshi and Dhawan, 2007). Although, there was a different cluster pattern in PCoA, the Mantel test revealed no significant correlation between genetic distance and geographical distance (r = 0.418; p < 0.001). This might be due to clustering of accessions from Phulchowki and Kaski together, which are approximately 200 km apart. The accessions from Ilam were grouped together in a separate cluster.
 
For the formulation of conservation strategies, understanding of the diversity of threatened species is quintessential (Kareem et al., 2012). The species should possess enough genetic variability as it plays important role in adaption in the changing environment (Schaal et al., 1991). The polymorphism study can be shown in terms of the Nei’s genetic diversity (H), Shannon’s information index (I), total heterozygosity (HT), average heterozygosity (HS), coefficient of population differentiation (GST) and gene flow (Nm) (Zhao et al., 2006). The value of gene differentiation (GST) ranging < 0.05, 0.05 to 0.15 and > 0.15 is grouped as low, medium and high population differentiation, respectively (Nei, 1978). Also, the value of gene flow (Nm) < 1 which denotes less than one migrant per generation into a population is the threshold value at which the differentiation occurs in population in a significant amount (Slatkin and Barton, 1989). When the condition arises where Nm is found to be less than one, it is suggested that the diversity maintained in the population is prone to genetic drift (Wright, 1949). High GST value (0.5485) and the low Nm value (0.4829) were observed in the present study and showed rapid genetic differentiation among the six populations of S. chirayita. The reason behind the high level of population differentiation can be the geographic separation of the populations (Hogbin and Peakall, 1999). The genetic variation in S. chirayita could be due to genetic drift within the population. Also, effect of the gene flow among inter-populations of S. chirayita is not significant. The genetic heterogeneity showing index, the Shannon’s index was observed to be the highest (0.416) for accession from Sankhuwasabha along with high Nei’s gene diversity index (0.280), whereas the lowest Shannon’s index (0.0.264) was found for Kaski accession with low Nei’s gene diversity index (0.175) and PPB (51.42%). Study of species level diversity showed high level of genetic differentiation with 98.18 of PPB and high Shannon’s index of 0.423, which shows the high polymorphism in the S. chirayita populations.
 
The data on the genetic structure of S. chirayita obtained in the present study suggest that the differentiation coefficients (GST=0.5485 and FST=0.6529) are higher than  the  average  coefficients  of  outcrossing species (GST=0.22 and FST=0.27) and similar to the average coefficients of inbreeding species (GST=0.59 and FST=0.65) (Nybom, 2004). High genetic differentiation in this species may suggest that the individual populations have been reproductively isolated and there is little current gene flow between them. The result is in agreement with Soumendra et al. (2009) in which S. chirayita has demonstrated the self-pollination in natural pollination of formation of seeds. However, it contrasts with the aspect of high genetic diversity observed among the S. chirayita individuals of different populations that indicate the existence of significant cross-pollination among the individuals in the population. The natural environment of the plants from Gentian family shows the probability of out crossing by 16 to 20% (Dudash, 1990). The pollinators such as bees and insects are responsible for cross pollination as they collect nectar from nectar glands (Khoshoo and Tandon, 1963). The morphological reason behind the self-pollination can be the structure pattern of androecium and gynoecium (Kulkarni et al., 2005). In S. chirayita, the distance between the anther sac and stigma is less which creates the condition for self-pollination (Proctor et al., 1996). Also, the various studies revealed that S. chirayita is mostly cross pollinated with potential of self-pollination (Shah et al., 2011; Raina et al., 2013).


 CONCLUSION

S. chirayita is considered a highly valued medicinal plant of Nepal. In the present investigation, microsatellite based ISSR technique was employed for the assessment of existing genetic diversity among six S. chirayita populations from eastern, central and western regions of Nepal. The conservation of S. chirayita populations in situ to preserve its genetic diversity is suggested. The findings provide insights into important genetic information for formulating and effecting conservation strategies and cultivation of S. chirayita. Easily identifiable and confusion with the other Swertia spp.  twinned with capacity of possessing chemical compounds from the early stage are the main reasons for premature harvesting of S. chirayita. This has resulted into the major problem as seeds could not be dispersed in the natural environment. In vitro tissue culture technique to be followed in order to reduce harvesting pressure on wild populations of S. chirayita is suggested. Additionally, to implement effective conservation strategies of S. chirayita, it is crucial to understand species pollination, breeding system associated with the genetic structure.


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.



 REFERENCES

Ahmmed SM, Mukherjee PK, Bahadur S, Harwansh RK, Kar A, Bandyopadhyay A, Al-Dhabi NA, Duraipandiyan V (2016). CYP450 mediated inhibition potential of Swertia chirata: An herb from Indian traditional medicine. J. Ethnopharmacol. 178:34-39.
Crossref
 

Bajpai MB, Asthana R, Sharma N, Chatterjee S, Mukherjee S (1991). Hypoglycemic effect of swerchirin from the hexane fraction of Swertia chirayita. Planta Med. 57(2):102-104.
Crossref

  
 

Balaraju K, Maheswaran R, Agastian P, Ignacimuthu S (2009). Egg hatchability and larvicidal activity of Swertia chirata Buch.-Hams. ex Wall. against Aedes aegypti L. and Culex quinquefasciatus Say. Indian J. Sci. Technol. 2(12):46-49.

  
 

Barakoti T (2002). Commercial Cultivation and Production Management of Chiraito: Scheme Guide. ARS Pakhribas, Nepal Agricultural Research Council, Nepal. Pp.1-50.

  
 

Barakoti T (2004). Attempts Made for Domestication, Conservation and Sustainable Development of Chiretta (Swertia chirayita). Nepal Agriculture Research Centre (NARC), Dhankuta.

  
 

Barakoti TP, Tiwari NN, Yonzon M (2013). Quality of Chiretta (Swertia chirayita) in Cultivated and Wild Samples Collected from Different Districts of Nepal. Nepal J. Sci. Technol. 13(2):57-62.
Crossref

  
 

Bornet B, Goraguer F, Joly G, Branchard M (2002). Genetic diversity in European and Argentinian cultivated potatoes (Solanum tuberosum subsp. tuberosum) detected by inter-simple sequence repeats (ISSRs). Genome 45(3):481-484.
Crossref

  
 

Botstein D, White RL, Skolnick M, Davis RW (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32(3):314.

  
 

Chen Y, Huang B, He J, Han L, Zhan Y, Wang Y (2011). In vitro and in vivo antioxidant effects of the ethanolic extract of Swertia chirayita. J. Ethnopharmacol. 136(2):309-315.
Crossref

  
 

Dudash MR (1990). Relative fitness of selfed and outcrossed progeny in a self-compatible, protandrous species, Sabatia angularis L.(Gentianaceae): a comparison in three environments. Evolution 44(5):1129-1139.
Crossref

  
 

Ghimire S (2008). Medicinal plants in the Nepal Himalaya: Current issues, sustainable harvesting, knowledge gaps and research priorities. Medicinal Plants in Nepal: an Analogy of Contemporary Research. Pp. 25-44.

  
 

Godt M, Hamrick J (1998). Allozyme diversity in the endangered pitcher plant Sarracenia rubra ssp. Alabamensis (Sarraceniaceae) and its close relative S. rubra ssp. rubra. Am. J. Bot. 85(6):802-802.
Crossref

  
 

Graham GC, Mayers P, Henry R (1994). A simplified method for the preparation of fungal genomic DNA for PCR and RAPD analysis. BioTechniques 16(1):48-50.

  
 

Hamrick J, Murawski DA (1991). Levels of allozyme diversity in populations of uncommon neotropical tree species. J. Trop. Ecol. 7(3):395-399.
Crossref

  
 

Hogbin PM, Peakall R (1999). Evaluation of the contribution of genetic research to the management of the endangered plant Zieria prostrata. Conserv. Biol. 13(3):514-522.
Crossref

  
 

Iqbal Z, Lateef M, Khan MN, Jabbar A, Akhtar MS (2006). Anthelmintic activity of Swertia chirata against gastrointestinal nematodes of sheep. Fitoterapia 77(6):463-465.
Crossref

  
 

Jaccard P (1908). Nouvelles recherches sur la distribution florale. Bull. Soc. Vard. Sci. Nat. 44:223-270.

  
 

Joshi K (2008). Swertia L.(Gentianaceae) in Nepal: Ethnobotany and Agenda for sustainable management. Ethnobot. Leafl. 12:1-6.

  
 

Joshi K (2011). Molecular Differentiation and Phylogeny of Swertia (Gentianaceae) of the Himalayan Region, Nepal. Int. J. Biochem. Biotechnol. 7(2):265-277.

  
 

Joshi P, Dhawan V (2005). Swertia chirayita-an overview. Curr. Sci. Bangalore 89(4):635

  
 

Joshi P, Dhawan V (2007). Analysis of genetic diversity among Swertia chirayita genotypes. Biol. Plant. 51(4):764-768
Crossref

  
 

Karak S, Nag G, De B (2017). Metabolic profile and β-glucuronidase inhibitory property of three species of Swertia. Rev. Bras. Farmacogn. 27(1):105-111.
Crossref

  
 

Kareem VA, Rajasekharan P, Ravish B, Mini S, Sane A, Kumar TV (2012). Analysis of genetic diversity in Acorus calamus populations in South and North East India using ISSR markers. Biochem. Syst. Ecol. 40156-40161

  
 

Khanal S, Shakya N, Thapa K, Pant DR (2015). Phytochemical investigation of crude methanol extracts of different species of Swertia from Nepal. BMC Res. Notes 8(1):821.
Crossref

  
 

Khoshoo T, Tandon S (1963). Cytological, morphological and pollination studies on some Himalayan species of Swertia. Caryologia 16(2): 445.

  
 

Kim S-C, Lee C, Santos-Guerra A (2005). Genetic analysis and conservation of the endangered Canary Island woody sow-thistle, Sonchus gandogeri (Asteraceae). J. Plant Res. 118(2):147-153.
Crossref

  
 

Kojima T, Nagaoka T, Noda K, Ogihara Y (1998). Genetic linkage map of ISSR and RAPD markers in Einkorn wheat in relation to that of RFLP markers. Theor. Appl. Genet. 96(1):37-45.
Crossref

  
 

Kshirsagar P, Gaikwad N, Pai S, Bapat V (2017). Optimization of extraction techniques and quantification of swertiamarin and mangiferin by using RP-UFLC method from eleven Swertia species. S. Afr. J. Bot. 10:881-889.
Crossref

  
 

Kshirsagar PR, Pai SR, Nimbalkar MS, Gaikwad NB (2016). RP-HPLC analysis of seco-iridoid glycoside swertiamarin from different Swertia species. Nat. Prod. Res. 30(7):865-868.
Crossref

  
 

Kulkarni R, Sreevalli Y, Baskaran K (2005). Allelic differences at two loci govern different mechanisms of intraflower self-pollination in self-pollinating strains of periwinkle. J. Hered. 96(1):71-77.
Crossref

  
 

Kumar V, Chandra S (2015). LC-ESI/MS determination of xanthone and secoiridoid glycosides from in vitro regenerated and in vivo Swertia chirayita. Physiol. Mol. Biol. Plants 21(1):51-60.
Crossref

  
 

Lad H, Bhatnagar D (2016). Amelioration of oxidative and inflammatory changes by Swertia chirayita leaves in experimental arthritis. Inflammopharmacology 24(6):363-375.
Crossref

  
 

Liu X-L, Qian Z-G, Liu F-H, Yang Y-W, Pu C-X (2011). Genetic diversity within and among populations of Neopicrorhiza scrophulariiflora (Scrophulariaceae) in China, an endangered medicinal plant. Biochem. Syst. Ecol. 39(4):297-301.
Crossref

  
 

Miller MP (1997). Tools for population genetic analyses (TFPGA) 1.3: A Windows program for the analysis of allozyme and molecular population genetic data. Computer software distributed by author. 4:157.

  
 

Misra A, Shasany A, Shukla A, Darokar M, Singh S, Sundaresan V, Singh J, Bagchi G, Jain S, Saikia D (2010). AFLP markers for identification of Swertia species (Gentianaceae). Genet. Mol. Res. 9(3):1535-1544.
Crossref

  
 

Mondini L, Noorani A, Pagnotta MA (2009). Assessing plant genetic diversity by molecular tools. Diversity 1(1):19-35.
Crossref

  
 

Nagalekshmi R, Menon A, Chandrasekharan DK, Nair CKK (2011). Hepatoprotective activity of Andrographis paniculata and Swertia chirayita. Food Chem. Toxicol. 49(12):3367-3373.
Crossref

  
 

Nei M (1978). Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89(3):583-590.

  
 

Nepal I (2004). National register of medicinal & aromatic plants. IUCN Nepal Country Office for His Majesty's Government of Nepal, Ministry of Forests and Soil Conservation.

  
 

Nybom H (2004). Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Mol. Ecol. 13(5):1143-1155.
Crossref

  
 

Peakall R, Smouse PE (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28(19):2537-2539.
Crossref

  
 

Phoboo S, Jha P, Bhowmik P (2008). Biology and Phytochemistry of Swertia chirayita. Medicinal Plants in Nepal: Anthology of Contemporary Research, Ecological Society (ECOS), Kathmandu. Pp. 203-211.

  
 

Phoboo S, Pinto MDS, Barbosa ACL, Sarkar D, Bhowmik PC, Jha PK, Shetty K (2013). Phenolic‐Linked Biochemical Rationale for the Anti‐Diabetic Properties of Swertia chirayita (Roxb. ex Flem.) Karst. Phytother. Res. 27(2):227-235.
Crossref

  
 

Press JR, Shrestha KK, Sutton DA (2000). Annotated checklist of the flowering plants of Nepal. Natural History Museum Publications.

  
 

Prevost A, Wilkinson M (1999). A new system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Theor. Appl. Genet. 98(1):107-112.
Crossref

  
 

Proctor M, Yeo P, Lack A (1996). The natural history of pollination. HarperCollins Publishers.

  
 

Purohit K, Vijay B, Tiwari D, Tiwari A, Andola CH, Negi SJ, Chauhan SR (2013). Swertia chirayita (Roxb. ex Fleming) Karsten on the verge of extinction in the Himalayan region. Curr. Sci. 104(2):161.

  
 

Raina R, Patil P, Sharma YP, Rana RC (2013). Reproductive biology of Swertia chirayita–a temperate critically endangered medicinal plant. Caryologia 66(1):12-20.
Crossref

  
 

Ray S, Majumder HK, Chakravarty AK, Mukhopadhyay S, Gil RR, Cordell GA (1996). Amarogentin, a naturally occurring secoiridoid glycoside and a newly recognized inhibitor of topoisomerase I from Leishmania donovani. J. Nat. Prod. 59(1):27-29.
Crossref

  
 

Rijal DP (2010). Taxonomic study of some medicinally important species of Swertia L. (Gentianaceae) in Nepal. Botanica Orientalis 6:18-24.
Crossref

  
 

Saha P, Mandal S, Das A, Das PC, Das S (2004). Evaluation of the anticarcinogenic activity of Swertia chirata Buch. Ham, an Indian medicinal plant, on DMBA‐induced mouse skin carcinogenesis model. Phytother. Res. 18(5):373-378.
Crossref

  
 

Samaddar T, Than MMM, Jha TB, Jha S (2015). Cytogenetic and DNA fingerprinting analysis in three species of Swertia from Eastern Himalaya. Caryologia 68(3):207-216.
Crossref

  
 

Schaal BA, Leverich WJ, Rogstad SH (1991). Comparison of methods for assessing genetic variation in plant conservation biology. Genetics and conservation of rare plants.14:123-134.

  
 

Shah IA, Sharma Y, Raina R, Rana R (2011). Pollination studies in Swertia chirayita–a critically endangered medicinal plant of Western Himalayas. Open Access J. Med. Aromat. Plants 2(2):14-17.

  
 

Shrestha JC, Bhattarai T, Sijapati J, Rana N, Maharjan J, Rawal D, Raskoti B, Shrestha S (2013). Assessment of Genetic Diversity in Nepalese Populations of Swertia chirayita (Roxb. Ex Fleming) H. Karst Using RAPD-PCR Technique. Am. J. Plant Sci. 4(8):1617-1628.
Crossref

  
 

Shrestha N, Shrestha S, Koju L, Shrestha KK, Wang Z (2016). Medicinal plant diversity and traditional healing practices in eastern Nepal. J. Ethnopharmacol. 192:292-301.
Crossref

  
 

Shrestha S, Sijapati J, Rana N, Malla D, Regmi P, Raskoti B (2011). Optimization of RAPD-PCR conditions for the study of genetic diversity in Nepal's Swertia chirayita (Roxb. Ex Fleming) H. Karst. Himalayan J. Sci. 6(8):35-40.
Crossref

  
 

Slatkin M, Barton NH (1989). A comparison of three indirect methods for estimating average levels of gene flow. Evolution 43(7):1349-1368.
Crossref

  
 

Soumendra C, Mukherjee D, Dasgupta T (2009). Cytological study on chromosome behaviour and new report on nature of mode of pollination of Swertia chirayita, a high value endangered medicinal plant of North Eastern Himalayan region. Caryologia 62(1):43-52.
Crossref

  
 

Tamhankar S, Ghate V, Raut A, Rajput B (2009). Molecular profiling of" Chirayat" complex using Inter Simple Sequence Repeat (ISSR) markers. Planta Med. 75(11):1266-1270.
Crossref

  
 

Tupe RS, Kemse NG, Khaire AA, Shaikh SA (2017). Attenuation of glycation-induced multiple protein modifications by Indian antidiabetic plant extracts. Pharm. Biol. 55(1):68-75.
Crossref

  
 

Wang B, Shi L, Ruan Z, Deng J (2011). Genetic diversity and differentiation in Dalbergia sissoo (Fabaceae) as revealed by RAPD. Genet. Mol. Res. 10:114-120.
Crossref

  
 

Wang HZ, Feng SG, Lu JJ, Shi NN, Liu JJ (2009). Phylogenetic study and molecular identification of 31 Dendrobium species using inter-simple sequence repeat (ISSR) markers. Sci. Hortic. 122(3):440-447.
Crossref

  
 

Wright S (1949). The genetical structure of populations. Ann. Eugen. 15(1):323-354.
Crossref

  
 

Yang L, Zhou G, Chen G (2011). Genetic diversity and population structure of Swertia tetraptera (Gentianaceae), an endemic species of Qinghai-Tibetan Plateau. Biochem. Syst. Ecol. 39(4):302-308.
Crossref

  
 

Zhang D, Chen S, Chen S, Zhang D, Gao Q (2007). Patterns of genetic variation in Swertia przewalskii, an endangered endemic species of the Qinghai-Tibet Plateau. Biochem. Genet. 45(1-2):33-50.
Crossref

  
 

Zhao WG, Zhang JQ, Wang YH, Chen TT, Yin YL, Huang YP, Pan YL, Yang YH (2006). Analysis of genetic diversity in wild populations of mulberry from western part of Northeast China determined by ISSR markers. J. Genet. Mol. Biol. 17(4):196-203.

  
 

Zhou NJ, Geng CA, Huang XY, Ma YB, Zhang XM, Wang JL, Chen JJ (2015). Anti-hepatitis B virus active constituents from Swertia chirayita. Fitoterapia 100:27-34.
Crossref

  

 




Copyright © 2025 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

Back to Vol. 16 No. 16
Back to articles
SUBSCRIBE TO RSS
SUBMIT MANUSCRIPT
CONFERENCES
          */?>