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Plant Systematics and Evolution ISSN 0378-2697Volume 300Number 6 Plant Syst Evol (2014) 300:1409-1420DOI 10.1007/s00606-013-0970-z

Efficiency of ISSR and RAPD markersin genetic divergence analysis andconservation management of Justiciaadhatoda L., a medicinal plant

Amit Kumar, Priyanka Mishra, SubhashChandra Singh & Velusamy Sundaresan

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ORIGINAL ARTICLE

Efficiency of ISSR and RAPD markers in genetic divergenceanalysis and conservation management of Justicia adhatoda L.,a medicinal plant

Amit Kumar • Priyanka Mishra •

Subhash Chandra Singh • Velusamy Sundaresan

Received: 29 May 2013 / Accepted: 15 December 2013 / Published online: 7 January 2014

� Springer-Verlag Wien 2014

Abstract Genetic variation within and among population

is the basis for survival of the population both in short and

long term. Thus, studying the plant genetic diversity is

essential for any conservation program. Indigenous

medicinal plants like Justicia adhatoda L. which are facing

high rate of depletion from the wild population need

immediate attention. DNA-based dominant molecular

marker techniques, random amplification of polymorphic

DNA (RAPD) and inter-simple sequence repeat (ISSR)

were used to unravel the genetic variability and relation-

ships across thirty-two wild accessions of J. adhatoda L., a

valuable medicinal shrub widespread throughout the trop-

ical regions of Southeast Asia. Amplification of genomic

DNA using 38 primers (18 RAPD and 20 ISSR) yielded

434 products, of which 404 products were polymorphic

revealing 93.11 % polymorphism. The average polymor-

phic information content value obtained with RAPD and

ISSR markers was 0.25 and 0.24, respectively. Marker

index (RAPD = 3.94; ISSR = 3.53) and resolving power

(RAPD = 4.24; ISSR = 3.94) indicate that the RAPD

markers were relatively more efficient than the ISSR assay

revealing the genetic diversity of J. adhatoda. The Shan-

non diversity index obtained with RAPD and ISSR markers

was 0.40 and 0.38, respectively. The similarity coefficient

ranged from 0.26 to 0.89, 0.33 to 0.93 and 0.31 to 0.90 with

RAPD, ISSR and combined UPGMA dendrogram,

respectively. PCA derived on the basis of pooled data of

both the markers illustrated that the first three principal

coordinate components accounted 79.27 % of the genetic

similarity variance. The mantel test between two Jaccard’s

similarity matrices gave r = 0.901, showing the fit corre-

lation between ISSR- and RAPD-based similarities. Based

on the results, ex-situ methods may be the most suitable

and efficient measure for long-term conservation.

Keywords Justicia adhatoda � Conservation

implication � Genetic variability � Molecular markers �Marker parameters

Introduction

The use of plant-derived products in disease management

is an important breakthrough in the history of humankind.

The use of plants as medicines is as old as human civili-

zation itself and out of about 258,650 species of higher

plants reported from the world; more than 10 % are used to

cure ailing communities (Shinwari 2010). Justicia adhat-

oda L. (Synonym Adhatoda vasica Nees), belonging to the

family Acanthaceae, is a highly valuable medicinal shrub

grows wild along the roadside and railway tracks where the

availability of water is low, throughout the tropical regions

of Southeast Asia (Chakrabarty and Brantner 2001). Jus-

ticia adhatoda commonly known as Vasaka or Malabar nut

in India is a perennial, evergreen, gregarious, stiff and

highly branched shrub (1.0–2.5 m height) with bitter taste

(Patel and Venkata-Krishna-Bhatt 1984). Justicia adhatoda

leaves have been used extensively in Ayurvedic and Unani

Medicine primarily for respiratory disorders and used

locally for the last 2000 years in India (Atal 1980). The

A. Kumar � P. Mishra � V. Sundaresan (&)

Department of Plant Biology and Systematics, CSIR, Central

Institute of Medicinal and Aromatic Plants, Research Centre

(CIMAP), Bangalore 560065, India

e-mail: vsundaresan@cimap.res.in

S. C. Singh

Department of Taxonomy and Pharmacognosy, CSIR, Central

Institute of Medicinal and Aromatic Plants (CIMAP),

Lucknow 226015, India

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DOI 10.1007/s00606-013-0970-z

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respiratory benefits of vasaka are linked to the abundance

of quinazoline alkaloids in the plant’s leaves. Studies show

that these alkaloids viz. vasicine, vasicinone, vasicinol,

adhatodine, adhatonine, adhvasinone, anisotine and hy-

droxypeganine in the leaves contribute to the observed

medicinal properties of the plant.

The primary goal of conservation is to ensure potential

survival of the population both in short and in long term

which is achieved by the population through possible

adaptation to environmental changes indicated by the level

of genetic variation present in the population (Ellstrand and

Elam 1993; Frankel et al. 1995; Zaghloul et al. 2013).

Assessment of the level and distribution of genetic diver-

sity within species may not only contribute to knowledge

of their evolutionary history and potential, but is also

critical to their conservation and management (Hamrick

and Godt 1996; Li and Ge 2006). The level of genetic

diversity within a given species and its populations

depends on the breeding system, past level of gene flow

among populations, the actual sizes of populations, geo-

graphic distributions, isolation, historical events, and

human impact (Loveless and Hamrick 1984; Hamrick and

Godt 1989).

Investigation on population genetic diversity using

various molecular markers is of great importance for the

genetic resource characterization, protection and sustain-

able utilization of important medicinal plants (Munoz et al.

2010; Sundaram and Purwar 2011). Applying DNA

molecular markers, for assessment of genetic variation in

plants has shown advantages over other markers based on

the phenotype; they are neutral, not related to age and

tissue type, not influenced by the environmental conditions,

feasibility, lower costs and more informative than mor-

phological markers (Marshall 1997). Random amplified

polymorphic DNA (RAPD) and inter-simple sequence

repeat (ISSR) are two of the most popular markers based

on polymerase chain reaction (PCR) have been widely used

in population genetic studies to characterize genetic

divergence within and among the populations or species of

various medicinal plants (Tripathi et al. 2012; Singh et al.

2011; Guo et al. 2006). The selection of RAPD and ISSR

was based on their relative technical simplicity, level of

polymorphism they detect, cost effectiveness, easily

applicable to any plant species and target those sequences

which are abundant throughout the eukaryotic genome and

are rapidly evolved. The RAPD technique, has been com-

monly employed technique shown to be advantageous over

morphologic and chemical markers, provides an unlimited

number of rapid inheritable genetic markers independent of

environmental effects, which can be used for genetic

diversity analysis (Ince et al. 2010; Williams et al. 1990)

and breeding purposes (Raina et al. 2001). Recent studies

indicated that ISSR could be able to produce more reliable

and reproducible bands because of the high annealing

temperature, longer sequence of ISSR primers and unlike

SSR markers, no prior knowledge of target sequences is

required for ISSR (Zietkiewicz et al. 1994; Kiani et al.

2012).

For establishing conservation programs it is important to

estimate genetic diversity of a species because the level of

genetic variability, a species contains, responds adaptively

to environmental changes (Qian et al. 2001). The survival

index of a species is greatly determined by the percentage

of polymorphism and the gene flow between the popula-

tions. Large-scale demands of high medicinal value leaves

for ayurvedic formulations and local use for fuel have

fragmented the populations of J. adhatoda (Gilani et al.

2011). Apart from the genetic diversity studies of Justicia

adhatoda using PBA (P450-based analog) markers (Gilani

et al. 2011), the information on the genetic diversity of

J. adhatoda is completely lacking. The present study was

undertaken with the aims of predicting the genetic diversity

of the populations of J. adhatoda; assessing the role and

efficiency of molecular markers in revealing genetic vari-

ation within and among populations using ISSR and

RAPD; and providing basic information for future biodi-

versity conservation and management programs of this

important medicinal species.

Materials and methods

Collection of plant materials

Thirty-two accessions of J. adhatoda collected from dif-

ferent geographical regions ranging from 150 to 5,000 ft

altitude in northern parts of India (Fig. 1; Table 1). All the

accessions are deposited in the herbarium at CSIR-Central

Institute of Medicinal and Aromatic Plants, Lucknow

(26�530N; 80�580E), Uttar Pradesh, India.

Isolation, quantification and electrophoresis of genomic

DNA

Genomic DNA was isolated from the leaves of accessions

that were listed in Table 1, by adopting the procedure

outlined by Khanuja et al. (1999) with necessary modifi-

cation. Leaves (3 g) were ground to a fine powder in liquid

nitrogen and extracted with hot CTAB (Cetyl Tri-methy-

lammonium Bromide) extraction buffer [100 mM Tris–Cl

(pH 8.0), 25 mM EDTA (ethylenediamine tetraacetic acid),

1.5 M NaCl, 2.5 % CTAB, 0.2 % v/v b-mercaptoethanol

and 2 % w/v PVP (Polyvinylpyrrolidone)]. The mixture

was incubated at 65 �C for 90 min followed by extraction

with chloroform:isoamyl alcohol (24:1). Isopropanol was

used to precipitate nucleic acid and washed with 80 %

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ethanol. The pellets were dissolved in high salt TE buffer

(1 M NaCl, 10 mM Tris–Cl (pH 8.0) and 1 mM EDTA).

The purity of genomic DNA isolated was quantified both

spectrometrically (Nano Drop, ND-1000, Nanodrop Tech-

nologies Wilmington, Delaware, USA) by measuring

absorbance at 260 nm and visualized under UV light after

electrophoresis on 0.8 % agarose gel. Stock DNA was

diluted to make a working solution of 25 ng/ll.

PCR Amplification

A set of 40 ISSR primers procured from the University of

British Columbia (UBC set No. 9), and 30 RAPD primers

procured from Operon Technologies Inc (USA) was

screened for their repeatable amplification. 20 ISSR and 18

RAPD Primers which produced good bands and amplifi-

cation profile were selected. For each primer, 25 ll ampli-

fication reaction contained 19 Taq DNA polymerase buffer

(with MgCl2) (GeNeiTM), 50 ng of genomic DNA, 1U of

Taq DNA polymerase (GeNeiTM), dNTP 200 lM each

(dATP: dTTP: dCTP: dGTP in 1:1:1:1 parts) (GeNeiTM)

and 0.5 lM primer. Amplifications were carried out using a

DNA thermal cycler (Applied Biosystems, Bio-Rad) by

optimizing the protocol, with the following parameters:

initial denaturation for 5 min at 94 �C followed by 45

cycles, denaturation of 1 min at 94 �C, annealing of 1 min

at 36 �C for RAPD and at 48–60 �C for ISSR, extension at

72 �C for 2 min, with final extension at 72 �C for 7 min.

Amplified PCR products were resolved by electrophoresis

on 1.2 % agarose gel in 1.0 % tris–acetate EDTA buffer

stained with 0.001 % EtBr using a Minipack-250 electro-

phoresis system (GeNeiTM) at 50 V. A double-digested kDNA (EcoRI and HindIII) and 2-Log DNA ladder were

used to assess the size of DNA bands. Gel photographs were

taken by means of Box EF2 (Syngene Pvt. Ltd.) gel docu-

mentation system supported by Gene Sys software.

Statistical analysis

Reproducible ISSR and RAPD products were manually

scored for band presence (1) or absence (0) for each

accession and a binary qualitative data matrix was con-

structed. Initially, the potential of both the markers for

estimating genetic variability of J. adhatoda was examined

by measuring the marker informativeness through the

counting of bands. Primer banding characteristics such as

number of total bands (TB), number of polymorphic bands

(PB) and percentage of polymorphic bands (PPB) were

obtained. To analyze the suitability of both the markers to

evaluate genetic profiles of J. adhatoda, the performance of

the markers was measured using three parameters: poly-

morphic information content (PIC), marker index (MI) and

resolving power (RP). The PIC value for each locus was

calculated using formula (Roldan-Ruiz et al. 2000);

PICi = 2fi (1 - fi), Where PICi is the polymorphic infor-

mation content of the locus i, fi is the frequency of the

amplified fragments and 1 - fi is the frequency of non-

amplified fragments. The frequency was calculated as the

Fig. 1 Geographic map showing the collection sites of 32 accessions of Justicia adhatoda. Numbers indicate the collection site as listed in Table 1

Efficiency of ISSR and RAPD markers in genetic divergence analysis 1411

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ratio between the number of amplified fragments at each

locus and the total number of accessions (excluding miss-

ing data). The PIC of each primer was calculated using the

average PIC value from all loci of each primer. Effective

multiplex ratio was calculated using formula; EMR

(effective multiplex ratio) = n 9 b, where n is the average

number of fragments amplified by accession to a specific

system marker (multiplex ratio) and b is estimated from the

number of polymorphic loci (PB) and the number of non-

polymorphic loci (MB); b = PB/(PB ? MB). Marker

index for both the markers was calculated to characterize

the capacity of each primer to detect polymorphic loci

among the genotypes. Marker index for each primers was

calculated as a product of polymorphic information content

and effective multiplex ratio (Varshney et al. 2007);

MI = EMR 9 PIC. The resolving power (RP) of each

primer was calculated as (Prevost and Wilkinson 1999);

RP = RIb, Where Ib represents the informative fragments.

The Ib can be represented on a scale of 0/1 by the fol-

lowing formula; Ib = 1 - (2 9 |0.5 - pi|), where pi is the

proportion of accessions containing the ith band.

The data matrix of both markers was then converted into

genetic similarity matrix using Jaccard coefficient (Jaccard

1908) in SPSS 17.0 (SPSS Inc.) and NTSYS-PC 2.02j

(Rohlf 1998). The genetic relatedness among the 32 indi-

viduals of three populations was analyzed using unweigh-

ted pair group method with arithmetic average (UPGMA)

based on pairwise Nei’s (1973) genetic distance. Bootstrap

analysis (Felsenstein 1985) using UPGMA search with

1,000 replicates was performed to obtain the confidence of

Table 1 Details of germplasm collected for genetic diversity analysis

S. no. Accession no. Place of collection State Latitude Longitude Altitude (feet)

1 12486 Kukrail forest (Lucknow) Uttar Pradesh N26�53043.000 E80�58056.000 402

2 12487 Allahabad Uttar Pradesh N25�24048.500 E81�50058.900 316

3 12488 Kaushambhi Uttar Pradesh N26�53039.400 E80�58059.400 393

4 11159 Mughalsarai Uttar Pradesh N25�16015.600 E83�07004.800 269

5 14561 Varanasi Uttar Pradesh N25�19045.600 E82�59015.300 248

6 11157 Gorakhpur Uttar Pradesh N26�44000.700 E83�21008.200 272

7 11158 Raebareli Uttar Pradesh N26�13051.800 E81�14026.300 366

8 12489 Haldwani Uttarakhand N29�12052.300 E79�32006.200 1410

9 12490 Almora Uttarakhand N29�35055.800 E79�40014.800 5240

10 12491 Someshwar Uttarakhand N29�47028.100 E79�36009.700 4640

11 12492 Kathgodam Uttarakhand N29�16002.100 E79�32046.900 1703

12 12493 Bhimtal Uttarakhand N29�18042.400 E79�33031.800 2937

13 12494 Pantnagar Uttarakhand N29�01056.000 E79�28035.600 686

14 12495 Bhawali Uttarakhand N29�27040.800 E79�31039.300 5258

15 12496 Kaushani Uttarakhand N29�50023.400 E79�35047.900 5318

16 12497 Lalkuan Uttarakhand N29�04038.900 E79�31001.400 815

17 11160 Buxar Bihar N25�33010.800 E83�58011.400 203

18 11164 Hilsa Bihar N25�20026.300 E85�17042.500 148

19 11161 Ara Bihar N25�33001.400 E84�40014.100 213

20 11155 CIMAP-I (Lucknow) Uttar Pradesh N26�53036.400 E80�58051.700 384

21 11156 CIMAP-II (Lucknow) Uttar Pradesh N26�53036.400 E80�58044.800 396

22 12498 Bareilly Uttar Pradesh N28�19056.600 E79�24041.500 566

23 7000 Patna Bihar N25�34038.800 E85�04049.600 171

24 12485 Barabanki Uttar Pradesh N26�54000.600 E81�06009.200 405

25 12499 Mirzapur Uttar Pradesh N29�04038.900 E79�31001.400 815

26 12500 Kanpur Uttar Pradesh N26�29050.900 E80�27033.400 388

27 11151 Fatehpur Uttar Pradesh N25�55035.600 E80�49027.200 381

28 11152 Jhansi Uttar Pradesh N25�27048.900 E78�37014.400 728

29 11153 Unchahar Uttar Pradesh N25�55002.400 E81�17039.500 369

30 11154 Faizabad Uttar Pradesh N26�46014.200 E82�08030.400 332

31 11162 Pratapgarh Uttar Pradesh N25�53029.600 E81�55016.200 308

32 11163 Kannauj Uttar Pradesh N27�02088.500 E79�54034.000 458

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the tree using the software Treecon (Van De Peer and

Wachter 1994). The data matrix was used to determine the

genetic diversity, genetic differentiation and gene flow

using the software POPGENE (Yeh et al. 1999) ver. 1.32.

Population differentiation was analyzed for polymorphism

between populations by Gst. Indirect estimation of the

amount of gene flow between populations was made from

Gst values (Nei 1987) using the formula; Nm = 0.5 (1 -

Gst)/Gst (McDermott and McDonald 1993), where Nm is the

number of migrants per generation. Analysis of molecular

variance (AMOVA) (Excoffier et al. 1992) was used to

calculate variation among and within population using

GenAlEx (Peakall and Smouse 2006) ver. 6.41. Further,

principal component analysis (PCA) was performed to

highlight the resolving power of the ordination based on

similarity coefficient of data realized from RAPD and ISSR

average similarity indices using SPSS statistics 17.0 soft-

ware (SPSS Inc.). Mantel test (Mantel 1967) was per-

formed using 10,000 permutations carried out in

XLSTAT�-Pro (Version 7.5, 2004, Addinsoft Inc.,

Brooklyn, NY, USA); the significance level was set at

a = 0.05, to compute the matrix correlation (r) between

the similarity matrices generated from different assays to

test the goodness of fit between RAPD and ISSR markers.

Results

RAPD Profiling

Out of 30 RAPD primers tested, 18 primers (Table 2) of

10 bp produce clear and reproducible band. The number of

products generated by these decamer RAPD primers was

found to range from 7 to 16 with primer OPT 06, OPT 18,

OPJ 01 giving the maximum (16) and primer OPT 09, OPT

15 giving the minimum number of amplicon (7). A total of

208 products produced in different size ranging from 150

to 2,500 bp out of which 196 (94.2 %) products were

polymorphic and 12 (5.8 %) products were monomorphic.

The percentage of polymorphism ranged from 80 % for

OPT 05 to 100 % for OPT 01, OPT 02, OPT 04 (Fig. 2),

OPT 09, OPT 12, OPT 16, OPT 17, OPT 18 and OPT 19

with an average of 94.2 % polymorphism per primer. To

determine PIC values of each primer, the mean of PIC

values is analyzed for all loci. High PIC value of 0.36 (OPT

01) and low PIC value of 0.13 (OPT 05), with an average

value of PIC per primer 0.25 were obtained. The effective

multiplex ratio depends on the fraction of polymorphic

fragments (b). In this study, the highest effective multiplex

ratio (EMR) 20.42 was observed with the primer OPT 04

and the lowest effective multiplex ratio (EMR) 9.68 was

observed with the primer OPT 05 with an average EMR of

15.09 per primer. To determine the general usefulness of

the system of markers used, the MI (marker index) for each

RAPD primer was calculated. The highest MI was

observed with the primer OPT 04 (6.89) and lowest in the

primer OPT 05 (1.34), with an average MI of 3.94 per

primer was obtained. The resolving power (RP) is a

parameter that indicates the discriminatory potential of the

primers chosen. The highest RP value was observed with

the primer OPT 18 (7.37) and the lowest with the primer

OPT 15 (1.75) with an average RP of 4.24 per primer

(Table 2). The Nei’s gene diversity (h) and Shannon index

(I) among all the populations were estimated as 0.25

(SD = 0.14) and 0.40 (SD = 0.19), respectively. Mean

coefficient of gene differentiation (Gst) value 0.30 indicated

a high level of population differentiation while estimate of

gene flow (Nm) in the population was found as 1.28

(Table 4). Of the total genetic diversity, AMOVA analysis

revealed a very high 92.2 % variance occurred within

populations and 7.8 % variance occurred among popula-

tions with a high significance (P \ 0.001).

The similarity matrix developed using the NTSYS-PC

2.02j (Rohlf 1998) software showed that Jaccard’s simi-

larity index ranged from 0.26 to 0.89 with mean value of

0.63. The UPGMA algorithm was used for grouping all

accessions based on their genetic distances. Dendrograms

representing most probable genetic relationship between

accessions corresponding to RAPD are presented in Fig. 3.

Bootstrapping values represent percentage out of 1,000

replications, and demonstrated the reliability of tree

topology. It was observed that the accessions from three

different northern states of India were grouped into two

major clusters. Cluster I has 26 accessions, all from the

plain region. Cluster-II has six accessions comprising five

accessions from hilly regions and one from lower hilly area

of Uttarakhand. Cluster I again sub clusters into four

clusters with few outliers. Subcluster Ia comprised the

accessions from both Bihar and Uttar Pradesh. All the

accessions of subcluster Ib and subcluster Ic comprised

different places of Uttar Pradesh. Subcluster Id comprised

one accessions from Uttar Pradesh and three accessions

from lower hilly area of Uttarakhand (Fig. 3). The most

closely related genotypes were Mirzapur and Kanpur with

the highest similarity index (0.892) and the most distantly

related genotypes were Faizabad and Almora with the

lowest similarity index (0.263). PCA derived on the basis

of RAPD data illustrated that the first three principal

coordinate components accounted for 67.19, 8.41 and

4.31 % variation, respectively.

ISSR profiling

The initial screening of the ISSR primers for clear and

repeatable band profiles showed that out of 40 primers

screened, 20 primers (Table 3) of 15–18 bp, of which all of

Efficiency of ISSR and RAPD markers in genetic divergence analysis 1413

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them were anchored at 30 end for increased specificity

yielded amplification products. The number of products

generated by these arbitrary primers was found to range

from 8 to 17 of different sizes ranging from 150 to

3,500 bp. The primer UBC 844 gave the maximum (17)

and primer UBC 810, UBC 823, UBC 834 giving the

minimum number of amplicon (8). These twenty primers

generated a total of 226 products with an average of 11.3

products per primer, of which 208 (92.0 %) products with

an average of 10.4 products per primer were polymorphic

and 18 (8 %) products were monomorphic. The percentage

of polymorphism ranged from 80 % for primers UBC 809

and UBC 825 to 100 % for primers UBC 807, UBC 828,

UBC 835, UBC 844, UBC 845 and UBC 848 with an

average of 92 % polymorphism per primer. In this study,

high PIC value of 0.36 for primer UBC 845 and low PIC

value of 0.17 for primer UBC 866 and UBC 881, with an

average value of PIC per primer 0.24 was obtained. The

highest effective multiplex ratio (EMR) 17.73 was

observed with the primer UBC 862 and the lowest 9.29 was

observed with the primer UBC 823 with an average EMR

of 14.39 per primer. The highest MI was observed with the

primer UBC 845 (6.09) and lowest in the primer UBC 881

(2.06) with an average MI of 3.53 per primer was obtained,

while the highest RP value was observed with the primer

UBC 843 (8.56) and the lowest with the primer UBC 855

(1.93) with an average RP of 3.94 per primer was obtained

(Fig. 4; Table 3). The Nei’s gene diversity (h) and Shannon

Table 2 Marker parameters calculated for each RAPD primer used with Justicia adhatoda

S. no. Primer T (�C) TB PB MB PPB (%) PIC EMR MI RP

1 OPT 01 36 12 12 0 100 0.36 14.50 5.24 6.37

2 OPT 02 36 13 13 0 100 0.25 13.69 3.55 4.62

3 OPT 03 36 9 8 1 88.88 0.23 17.67 4.17 3.06

4 OPT 04 36 14 14 0 100 0.33 20.42 6.89 7.12

5 OPT 05 36 10 8 2 80 0.13 9.68 1.34 1.81

6 OPT 06 36 16 15 1 93.75 0.24 11.30 2.77 5.43

7 OPT 08 36 11 10 1 90.90 0.21 11.81 2.54 3.06

8 OPT 09 36 7 7 0 100 0.28 15.00 4.20 2.56

9 OPT 12 36 13 13 0 100 0.25 17.84 4.49 4.37

10 OPT 13 36 10 9 1 90 0.25 16.38 4.17 3.12

11 OPT 14 36 10 9 1 90 0.21 10.71 2.32 2.81

12 OPT 15 36 7 6 1 85.71 0.18 16.65 3.04 1.75

13 OPT 16 36 14 14 0 100 0.30 15.78 4.79 5.68

14 OPT 17 36 8 8 0 100 0.22 19.12 4.33 2.56

15 OPT 18 36 16 16 0 100 0.30 15.75 4.82 7.37

16 OPT 19 36 11 11 0 100 0.28 16.63 4.67 4.06

17 OPT 20 36 11 9 2 81.81 0.26 15.84 4.22 4.31

18 OPJ 01 36 16 14 2 87.5 0.26 12.96 3.42 6.31

Total 208 196 12

Avg./primer 11.55 10.88 0.66 94.23 0.25 15.09 3.94 4.24

T (�C) annealing temperature, TB total band, PB polymorphic band, MB monomorphic band, PPB (%) percentage polymorphic band (%), PIC

polymorphic information content, EMR effective multiplex ratio, MI marker index, RP resolving power of primer

Fig. 2 RAPD profile of different genotypes of Justicia adhatoda produced with primer OPT 04. Lane M is 2-log DNA ladder and lanes 1–32

represent different J. adhatoda genotypes as listed in Table 1

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Table 3 Marker parameters calculated for each ISSR primer used with Justicia adhatoda

S. no. Primer T (�C) TB PB MB PPB (%) PIC EMR MI RP

1 UBC 807 52 10 10 0 100 0.32 15.90 5.24 4.81

2 UBC 809 60 10 8 2 80.0 0.19 14.24 2.80 3.00

3 UBC 810 52 8 7 1 87.5 0.21 15.75 3.36 2.12

4 UBC 823 54 8 7 1 87.5 0.25 9.29 2.34 3.18

5 UBC 825 60 10 8 2 80.0 0.18 17.68 3.22 2.18

6 UBC 826 52.5 10 9 1 90.0 0.18 15.75 2.96 2.56

7 UBC 828 52 10 10 0 100 0.24 16.40 3.98 3.25

8 UBC 834 52.5 8 7 1 87.5 0.29 10.06 3.01 3.87

9 UBC 835 52 15 15 0 100 0.22 13.46 2.97 4.25

10 UBC 841 52 12 11 1 91.7 0.27 16.34 4.41 4.37

11 UBC 843 53 15 14 1 93.3 0.35 15.12 5.43 8.56

12 UBC 844 54 17 17 0 100 0.27 12.58 3.43 6.75

13 UBC 845 53 12 12 0 100 0.36 16.91 6.09 7.06

14 UBC 848 52.5 10 10 0 100 0.32 15.00 4.86 4.87

15 UBC 855 53 9 8 1 88.9 0.18 11.95 2.19 1.93

16 UBC 856 53 16 15 1 93.7 0.21 11.89 2.53 4.31

17 UBC 862 57.2 10 9 1 90.0 0.18 17.73 3.29 2.43

18 UBC 866 57.2 14 12 2 85.7 0.17 14.75 2.58 3.06

19 UBC 876 48 10 9 1 90.0 0.25 15.39 3.92 3.43

20 UBC 881 58.7 12 10 2 83.3 0.17 11.59 2.06 2.81

Total 226 208 18

Avg./primer 11.3 10.4 92.0 0.24 14.39 3.53 3.94

T (�C) annealing temperature, TB total band, PB polymorphic band, MB monomorphic band, PPB (%) percentage polymorphic band (%), PIC

polymorphic information content, EMR effective multiplex ratio, MI marker index, RP resolving power of primer

Fig. 3 Dendrogram of Justicia

adhatoda based on genetic

distance obtained from RAPD

markers using the UPGMA

method. Numbers on branches

correspond to bootstrap values

(1,000 replications)

Efficiency of ISSR and RAPD markers in genetic divergence analysis 1415

123

Author's personal copy

index (I) among all the populations were estimated as 0.24

(SD = 0.15) and 0.38 (SD = 0.20), respectively. Mean

coefficient of gene differentiation (Gst) value 0.31 indicated

a high level of population differentiation, while estimate of

gene flow (Nm) in the population was found as 1.30

(Table 4). AMOVA analysis revealed a very high 91.7 %

variance occurred within populations and 8.3 % variance

occurred among populations with a high significant

(P \ 0.001). Jaccard’s similarity index ranged from 0.33

(Mirzapur and Almora) to 0.94 (Patna and Buxar) with

mean value of 0.66 suggesting high level of genetic vari-

ability in the species. The dendrogram obtained from ISSR

profiles showed two major clusters (Fig. 5), placing

twenty-six accessions from plain region in first and the rest

in the second cluster. Cluster I again sub clusters into four

clusters with few outliers. All the accessions of subcluster

Ia and subcluster Ic comprised different places of Uttar

Pradesh. Subcluster Ib comprised the accessions from both

Bihar and Uttar Pradesh. Subcluster Id comprised three

accessions from lower hilly area of Uttarakhand. PCA

derived on the basis of ISSR data illustrated that the first

three principal coordinate components accounted for 68.59,

5.84 and 4.37 % variation, respectively.

Combined data analysis for RAPD and ISSR

The DNA fingerprinting methods used in this study

revealed polymorphism independent of each other from

different genomic regions. The RAPD profiles usually

represent widely distributed portions of the genome, while

the ISSR profiles are generated from microsatellite-rich

regions of the genome. Thus, these methods involve

regions having substantially different evolutionary histo-

ries and have different genome coverage (Verma and

Rana 2013). In the present analysis, a total of 38 primers

were considered resulted in 434 fragments, of which 404

were polymorphic revealing 93.11 % polymorphism

across all the accessions of J. adhatoda. The mean PIC

value, MI value and RP value observed for all the 38

primers were 0.25, 3.73 and 4.09, respectively. The

minimum genetic distance (0.31) among the accessions

was observed between Faizabad and Almora, whereas the

maximum genetic distance (0.90) was observed between

Patna and Ara based on the cumulative data for both

molecular marker used in the study. The dendrogram

based on the pooled data clearly showed that all the

accessions grouped into two major clusters (Fig. 6). PCA

(Fig. 7) derived on the basis of pooled data of both the

molecular markers illustrated that the first three principal

coordinate components accounted for 68.82, 6.54 and

3.91 % variation, respectively, accounted for 79.27 % of

the genetic similarity variance. Estimates of matrix cor-

relation coefficient (r) value between the genetic distances

were highest (r = 0.901) for RAPD versus ISSR (Fig. 8).

These values revealed that RAPD and ISSR data have

good correlation, and are best fit to each other.

Discussion

J. adhatoda is depleting from the wild population due to its

uses in indigenous medicines, so that information on

genetic structure and the diversity of populations are

essential for their conservational program. Life history

traits like reproductive mode, seed dispersal mechanism,

Fig. 4 ISSR patterns of Justicia adhatoda generated by primer UBC 844. Lane M is double-digested k DNA (EcoRI and HindIII) DNA ladder

and lanes 1–32 represent different Justicia adhatoda genotypes as listed in Table 1

Table 4 Summary of various genetic diversity indices analyzed in

Justicia adhatoda populations

S.

no.

Mean value with RAPD

(std. deviation)

Diversity

indices

Mean value with ISSR

(std. deviation)

1 1.93 (0.17) Na 1.91 (0.23)

2 1.40 (0.30) Ne 1.39 (0.31)

3 0.25 (0.14) h 0.24 (0.15)

4 0.40 (0.19) I 0.38 (0.20)

5 0.26 (0.02) Ht 0.25 (0.02)

6 0.18 (0.01) Hs 0.17 (0.01)

7 0.30 Gst 0.31

8 1.28 Nm 1.30

Na observed number of alleles, Ne expected number of alleles, h Nei’s

gene diversity, I Shannon’s information index of genetic diversity, Ht

heterozygosity, Hs average heterozygosity, Gst degree of genetic

differentiation, Nm estimate of gene flow

1416 A. Kumar et al.

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and geographic range have greatest influence on the levels

and distribution of genetic diversity of plant species.

Generally, geographically widespread species tend to

maintain more genetic diversity than species with small

geographical ranges (Hamrick and Godt 1996). In the

present study, it is observed that genetic diversity within

population at high altitude was greater than that of popu-

lations at lower altitude. Studies show that genetic varia-

tions occurred along elevation gradients because

topographical heterogeneity of alpine plants habitat causes

Fig. 6 Dendrogram of Justicia

adhatoda based on genetic

distance obtained from

cumulative data using the

UPGMA method. Numbers on

branches correspond to

bootstrap values (1,000

replications)

Fig. 5 Dendrogram of Justicia

adhatoda based on genetic

distance obtained from ISSR

markers using the UPGMA

method. Numbers on branches

correspond to bootstrap values

(1,000 replications)

Efficiency of ISSR and RAPD markers in genetic divergence analysis 1417

123

Author's personal copy

substantial changes in the environment (Ohsawa and Ide

2008; Byars et al. 2009). It is also reported that at different

altitudes a strong isolation of populations may be occurred

because of drastic differences in phenology between higher

and lower altitudes and mountain barriers which restricts

the gene flow between the populations resulting in complex

and varied genetic variations (Arnaud-Haond et al. 2006;

Liu et al. 2012) which is supported by this study revealing

high genetic diversity in the accessions from hilly region of

Uttarakhand compared to the accessions from plain region

of Bihar and Uttar Pradesh. In general, outcrossing species

have higher levels of genetic diversity than selfing and

clonal plants (Rossetto et al. 1995). J. adhatoda is self

incompatible, and a very high level of geitonogamy is

found in this species. Two species of carpenter bees, Xy-

locopa verticalis and Xylocopa sp. are reported as the

effective pollinators (Shivanna 2009). The pollination

system is expected to play a major role in determining

plant reproductive success in the face of environmental

variation, and shaping its pattern of genetic diversity.

Nearby populations of J. adhatoda that are pollinated by

bees may share many alleles because genes can flow easily

between sites, leading to such species genetically similar.

The result of the present study using RAPD and ISSR

markers revealed high level of genetic diversity with an

average Nei’s genetic diversity were estimated as 0.25 and

0.24, respectively, which were very slightly higher than

for, dicotyledons (0.165), long-lived perennial plants

(0.180), widespread (0.183) (Hamrick and Godt 1996)

when compared to outcrossing nature of J. adhatoda.

The Gst value of 0.31 for both RAPD and ISSR indicated

that the majority of variation is found within populations

and minimum among populations. AMOVA analysis with a

high significance (P \ 0.001) showed that a very higher

proportion of the genetic variation resided within popula-

tions (RAPD = 92.2 %; ISSR = 91.7 %), and a very lower

degree of genetic variation resulted from differentiation

among populations (RAPD = 7.8 %; ISSR = 8.3 %).

Similar result was also reported using PBA (P450-based

analog) molecular marker in J. adhatoda (Gilani et al.

2011). A value of estimate gene flow Nm \ 1, Nm [ 1 and

Nm [ 4 classified as low, moderate and extensive gene

flow, respectively. The value obtained for Nm (estimate

gene flow) based on the mean Gst (Nm = 1.28 for RAPD;

Nm = 1.30 for ISSR) indicates moderate gene flow among

these wild populations of J. adhatoda (since Nm [ 1, i.e.,

Fig. 7 Two-dimensional plots

obtained using principal

component analysis based on

cumulative data of RAPD and

ISSR

Fig. 8 Mantel test showing correlation between RAPD and ISSR

marker

1418 A. Kumar et al.

123

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more than one migrant per generation into a population).

Even a moderate rate of gene flow among populations (one

individual every a few generations) can ‘‘link’’ the gene

pools of two populations. The present study on the wild

populations of J. adhatoda revealed a high level of genetic

variation at the species level, with RAPD = 94.2 %;

ISSR = 92.0 % of loci being polymorphic, with [70 %

polymorphism. Results indicated the presence of wide

genetic variability among different genotypes of the plant.

The present amplification revealed polymorphism inde-

pendent of each other from different genomic regions. The

RAPD profiles usually represent widely distributed portions

of the genome, while the ISSR profiles are generated from

microsatellite-rich regions of the genome. Cluster analysis

based on all the three DNA marker profiles broadly grouped

all the 32 accessions into two clusters. Clustering of isolates

remained more or less the same in RAPD, ISSR and com-

bined data of RAPD and ISSR. Dendrograms show clear

pattern of clustering according to the locations from where

germplasms were collected. Population of Uttar Pradesh

and Bihar are clustered into one because of close geo-

graphic proximity or may be due to similar climatic con-

ditions. The correlation between Jaccard’s similarity values

obtained from different marker techniques was high

(r = 0.901). This indicates the fit correlation in between

ISSR- and RAPD-based similarities.

The molecular genetic tools used in this study were

compared for their efficiency to generate polymorphism

across the accessions of J. adhatoda. Both techniques were

informative with regard to the amount of polymorphism

detected. However, on the basis of higher PIC values

(RAPD = 0.25; ISSR = 0.24), Shannon diversity index

(RAPD = 0.40; ISSR = 0.38), percent polymorphism

(RAPD = 94.2; ISSR = 92.0), marker index (RAPD =

3.94; ISSR = 3.53), and resolving power (RAPD = 4.24;

ISSR = 3.94), the RAPD markers were marginally more

informative than ISSR in the assessment of genetic diver-

sity in J. adhatoda. The similar results are reported in

Caldesia grandis (Chen et al. 2006), Dalbergia sissoo (Arif

et al. 2009). This may be because of the fact that two

marker techniques targeted different portions of the gen-

ome. It also revealed the use of both RAPD and ISSR

markers in the genetic diversity studies for further con-

servation program.

Because of its valuable medicinal properties and high

demand, Justicia adhatoda needs more attention toward the

conservation. As the plant is widely used in traditional and

Ayurvedic systems of medicine in India, an ex-situ con-

servation program seems to be the most suitable and effi-

cient measure for long-term conservation. Understanding

population structure of J. adhatoda will provide critical

base-line information for developing sustainable manage-

ment strategies. In the present study, molecular variances

for within population were more than among population,

which implied the need to conserve more individuals in any

population. In addition, it would be wise to save popula-

tions in different regions to limit population decline caused

by large-scale environmental catastrophes.

Acknowledgments The authors wish to express their sincere thanks

to Director, CSIR-CIMAP (Lucknow), for providing the facilities to

carry out this research. The financial support from the Council of

Scientific and Industrial Research (CSIR), New Delhi, India through

the projects (XIIth FYP) ChemBiO (BSC-0203) and BioprosPR

(BSC-0106) is gratefully acknowledged.

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