Salacia is a liana which belongs to family Celastraceae subfamily Salacioideae. In India,
Salacia is distributed in Karnataka,
Kerala, Tamil Nadu. Species of Salacia are
used in ayurveda for treatment of diabetes and obesity as polyherbal
preparation or churnas such as Madhujeevan Churna (Salunkhe and
Wachasundar 2009) and Diajith (Rajalakshmy et al.
2014).Root, bark and leaves of Salacia species contains active
ingredients which are anti-diabetic (Yoshikawa et al. 2001), anticancer (Yoshimi et al. 2001), antiviral agent effective against HIV, Herpes simplex
(Guha et al. 1996; Zheng
and Lu 1990). Salacia species contains
active compounds salacinol and kotalanol which have ?-glucosidase inhibitory
activity (Xie et al. 2011).
identification in the genus Salacia
is difficult when based solely on morphological characteristics. Although most
of the vegetative characters of the species within the genus are same differences
are observed in floral and some fruit characteristics (Udayan et al. 2012;
Udayan et al. 2013). Therefore, accurate methods of validation and
authentication is indispensable to ensure safe use and efficacy of extracted
and ISSR simple and quick techniques which does not require any prior knowledge
DNA sequence of the target organism. RAPD detects nucleotide sequence
polymorphisms, using a single primer of arbitrary nucleotide sequence where as
ISSR detects polymorphisms in identical
inter-microsatellite loci oriented in opposite direction, using primers which
are di, tri, tetra or penta nucleotide simple sequence repeats (Zietkiewicz et al.
1994). Inter transcribed spacer (ITS) having universal set of primers is a
popular choice for phylogenetic analyses (Alvarez and Wendel
In the present study two DNA markers
RAPD and ISSR, and a DNA barcoding region ITS was used to evaluate genetic
diversity within and among four Salacia
species-S. chinensis, S. macrosperma, S.
fruticosa, S. oblonga sampled from Western Ghats of Karnataka.
Materials and methods
samples were collected from various parts of western Ghats (Table 1 for details).
The 21 samples are grouped in four population of Salacia chinensis L., Salacia
macrosperma Wight., Salacia fruticosa
Lawson., Salacia oblonga Wall. ex
Wight & Arn.
isolation, RAPD and ISSR reaction
DNA was isolated according to Stange et al. (1998) protocol. DNA was quantified using NanoDrop 2000
Spectrophotometer (Thermo Fisher Scientific) and diluted to 25 ng for use in polymerase
chain reaction (PCR). Reaction mixture contained 100 uM of each dNTPs (Merck
biosciences), 5 uMole of primer (Sigma,USA), 0.5 Unit of Taq DNA polymerase
(Merck biosciences) and 1x Taq buffer (Merck biosciences) in a total volume of
20 ul. ISSR-PCR amplification was carried out for 40 cycles, with initial
denaturation for 5 minutes at 94°C, followed by cyclic process of denaturation
for 1-minute at 94oC, annealing at temperature standardized for each
primer (Table 2) for 1 minutes and extension at 72°C for 1 minutes, and final
extension at 72 °C for 5 minutes in Applied Biosystems Veriti Thermal Cycler. For
RAPD-PCR, the protocol was similar to ISSR except for the annealing temperature
which was 36°C for all the primers. For the PCR amplification of the ITS
sequence, primers ITS4-TCCTCCGCTTATTGATATGC and ITS5- GGAAGTAAAAGTCGTAACAAGG
designed by WHITE (1990) were used. ITS amplification was carried for 28
cycles with initial denaturation at 95°C for 1 minute 30 seconds, cyclic
process of denaturation at 95°C for 30 seconds, annealing at 42°C for 1 minute,
extension at72°C for 1 minute and final extension 72°C for 3 minutes. Amplified
products were separated in 1.8% agarose gel containing ethidium bromide using
1x TBE buffer. DNA fragments were visualized under UV light. The band patterns
were photographed using Gel Doc™ XR (Bio- Rad).
Phylogenetic analysis of ITS sequence
amplified products were sent to Chromous biotech, Bangalore for sequencing. The
sequence generated were submitted to NCBI database. For phylogenetic
analysis of ITS sequence MEGA 5 (Tamura et al. 2011) software was used. Nineteen samples from current
study and an outgroup sequence was used for sequence analysis. The multiple
sequence alignment was performed using CLUSTAL W, version 1.6 (Thompson et al. 2002). Using MEGA 5 best-fit Model-test was performed and
model with the lowest Bayesian Information Criterion (BIC) score was selected
for further analysis. The Maximum Likelihood tree was constructed using the
best fit model with least BIC score.
collection and Analysis
banding patterns obtained from RAPD and ISSR were scored as present (1) or
absent (0) and binary matrix was created for RAPD and ISSR primers. The
polymorphic information content (PIC) proposed by Roldàn-Ruiz et al.
(2000), marker index (MI) described by Varshney et al. (2007) and resolving power (RP) by Prevost and Wilkinson
(1999) of each marker was calculated and multiplex ratio was
calculates as product of total number monomorphic and polymorphic loci/
number of assays.
(Yeh et al. 1999)was used to calculate various paraments such as
percentage of polymorphic band, observed number of alleles (na), effective
number of alleles (ne), Shannon’s information index (I) and Nei’s gene
diversity (H) total heterozygosity (Ht), average heterozygosity (Hs) and gene
flow (Nm) between the populations and among the individuals within each
population. The similarity matrix was subjected to cluster analysis by
unweighted pair group method for arithmetic mean (UPGMA) and a dendrogram was
(Peakall and Smouse
2006) was also used to calculate Principal Coordinates Analysis (PCoA) that
plots the relationship between distance matrix elements based on their first
two principal coordinates. The product-moment correlation (r) based on Mantel Z
value was computed to measure the degree of relationship between similarity
index matrices produced by any two-marker systems. The RAPD, ISSR and ITS data
were subjected to analysis of molecular variance (AMOVA), as described by .
ISSR analysis details
In present study, initially 40
RAPD primers that is 2 set of Operon primer kits OPG and OPR (20 primer from
each kits) were used to detect genetic polymorphism of S. oblonga, S. fruticosa, S. chinensis and S. macrosperma. Out of the 40 RAPD primers, 10 primers i.e. OPG-02,
14, -16, -17, -18, -19 and OPR-02, -03, -07, -08 showed reproducible amplified
DNA polymorphism. All the chosen primers amplified fragments across the 21
samples, with the number of amplified fragments ranging from 4 to 12. Minimum number
of loci were seen in the primer OPG18 (4 bands) and maximum bands were observed
in primer OPG17-12 bands. From the ten primers, a total of 76 loci were
generated of which 70 were polymorphic, making polymorphism generated by RAPD
makers to be 92.11%. Multiplex ratio of RAPD analysis was calculated to be 7.6.
Cumulative resolving power of 10 RAPD primer was 54.67.
While in ISSR analysis 10 primers
produced 67 loci of which 61 bands were polymorphic, accounting for 91.04% of
polymorphism. Number of loci varied from minimum of four in primer ISSR 5 to
maximum of nine in ISSR 10. Multiplex ratio of ISSR analysis is calculated to
be 6.8. Cumulative resolving power of 10 ISSR primer was 58.48. The marker
index for RAPD and ISSR was 6.54 and 5.45 respectively.
Observed number of alleles,
effective number of alleles, Nei’s genetic diversity, Shannon’s information
index, for 21 samples of Salacia
species analyzed using ten each of RAPD
and ISSR primer were found to be 1.9211, 1.4537, 0.2785, 0.4294 and 1.9104,
1.5108, 0.2988, 0.4509 respectively. Total genotype diversity among population (Ht) was estimated to be 0.2713 while within population
diversity (Hs) was estimated to be 0.1514 for RAPD and for ISSR Ht was 0.3055
and Hs was 0.1222. Mean coefficient of gene differentiation (Gst) value for
RAPD was 0.4418 and ISSR was 0.5999. Suggesting that 55.8% and 40.1 % of the
genetic diversity resided within the population as per RAPD and ISSR markers.
Estimates of gene flow in the population for RAPD and ISSR were 0.6318 and
0.3334 respectively. (Table 3).
Dendrogram and PCoA of RAPD and ISSR
In RAPD dendrogram, 21
samples of Salacia grouped into two clusters
(Cluster 1 and 2). Cluster 1 contained S.
chinensis SC1 to SC5 and cluster 2 was further divided into two
sub-clusters (sub-cluster 1 & 2). In cluster 2, sub-cluster 1 contained all
samples of S. macrosperma along with
two samples of S. fruticosa SF1 &
SF3 (Fig 1) and sub-cluster 2 contained three remaining samples of S. fruticosa along with S. oblonga samples. The cumulative total
variation of three principle components accounted for 65.68 % of variation.
Dendrogram of ISSR data
showed that the samples clearly grouped into four clusters (I, II III and IV)
of its respective species S. chinensis,
S. macrosperma, S. fruticosa, S. oblonga. For ISSR analysis cumulative
total variation of three principle components accounted for 74.05% of the
variation. The results of RAPD and ISSR PCoA analysis were comparable to the
cluster analysis (Fig 2).
For ITS analysis, 19 samples of
current study and an outgroup Pristimera
preussii belonging to sub-family Hippocrateoideae was used to construct
phylogenetic tree. Two samples SM1 and SM14 produced faint bands and could not
be sequenced. Sequence alignment of 20 samples resulted in overall sequence
length of 752 bp, of which 221 bp (29.38%) were conserved, 503 bp (66.88%) were
variable sites and 103 bp (13.69%) were parsimony informative sites. Three
major clades were observed from ML tree. Clade 1 contained all the samples of S. macrosperma along with samples of S. oblonga which were nested with-in the
clade. Clade 2 and 3 contained S.
chinensis and S. fruticosa
samples respectively (Fig 3).
Comparative analysis of population
Values of observed number of
alleles, effective number of alleles, Nei’s genetic diversity, Shannon’s
information index of each population were compared to observed diversity and
degree of polymorphism with-in the population (Table 3 and 4). In comparison,
the RAPD values were marginally higher than the ISSR except in the S. fruticosa population. Significant
differences were observed in all the parameters. Highest percentage of
polymorphism and highest polymorphic loci were seen in S. fruticosa population in RAPD analysis. In RAPD, ISSR and ITS analysis
high degree of polymorphism was seen in S.
macrosperma and S. fruticosa population
followed by S. chinensis population. Although
only two samples are in S. oblonga
population, RAPD, ISSR and ITS analysis detected polymorphism of 15.79%,16.42%,23.36%
respectively. Also, in parameters such as Ht, Hs, Gst and Nm significant
differences in value were observed (Table 4). The level of polymorphism
revealed by RAPD was (41.45%±10%) which was higher than ISSR (33.58%±6.52%) and
ITS (25.50%±17.25%). The polymorphism of each population of S. chinensis, S. macrosperma, S. fruticosa and S. oblonga from RAPD was35.53%,
55.26%, 59.21%, 15.79%and ISSR was32.84%,
For comparative analysis of ITS
with the RAPD and ISSR, sequences data of ITS was analyzed in GenAlEx. Before
exporting the data, the outgroup sequence and sequence SF5 were removed. Only
polymorphic nucleotide positions were converted to numeric codes (A=1, C=2,
G=3, T=4, hypen/colon=5) and 137 sites showed the polymorphism which were used
for the further analysis. The polymorphism of each population of S. chinensis, S. fruticosa, S. macrosperma
and S. oblonga from ITS analysis was
6.57%,19.71%,24.82%,23.36% and overall polymorphism was 18.61%±4.16%. For ITS coefficient
of evolutionary differentiation was 0.797which indicated that 20.3% of the
genetic diversity resided within the population. Tajima’s D neutrality tests were
performed to check whether genus Salacia
populations followed a neutral model of evolution with constant population size
over time. The observed values of Tajima’s D neutrality tests were -1.089757
for S. macroperma and S. oblonga population, -1.105205 for S. fruticosa and -0.174749 for S. chinensis and-1. 181277 for all the
19 samples. After removing sample SF5 since it showed high divergence,
neutrality test was performed for 18 sample of Salacia which gave observed value of 0.606285.
AMOVA, which helps in
partitioning of the overall variations among groups and among populations
within the group were performed for RAPD, ISSR and ITS data matrices. From RAPD,
39% of molecular variance was found among population while, within the population
this value was found to be 61% indicating that there were more variations
within the population. While in ISSR, 55% molecular variance was found among
population and 45%within the population. For ITS sequence analysis 80%
variances was among the population and 20% variance was within population which
was similar to coefficient of evolutionary differentiation. (Table5).
Nei genetic pairwise
distance of Salacia species was found
to be > 0.5 for RAPD, ISSR and ITS sequence. But in ITS sequence analysis,
the pair-wise distance between the S. oblonga
and S. macrosperm was 0.061
suggesting that they are very closely related. In addition, the pair-wise
distance and identity of S. oblonga
and S. fruticosa was 0.915 and 0.088
indicating that they are highly dissimilar. (Table 6).
Statistical comparative analysis
Mantel test was employed to
determine the coefficient of correlation between the genetic distance matrices
generated by RAPD and ISSR markers. The coefficient of correlation between RAPD
and ISSR marker was R2=0.3781, r=0.614 which is high. This value signifies
that there was considerable correlation between RAPD and ISSR genetic distances
matrices. Twenty-one samples grouped into two clusters in RAPD dendrogram
whereas in ISSR dendrogram four cluster were observed. Comparing RAPD
dendrogram with ISSR dendrograms we can notice that S. oblonga was an Operational Taxonomic Units (OTU). In all
analysis, results of cluster analysis were comparable to PCoA.
Mantel test was also employed to
analyze the ‘goodness of fit’ for each marker system. This was done by
comparing cophenetic similarity matrices of genetic distance with cophenetic
similarity matrices with the Nei’s Genetic Distance for each marker technique.
It revealed values higher than 0.80 for all the markers used RAPD (r = 0.827,
P = 0.01), ISSR (r = 0.816, P = 0.01) thus confirming their authenticity and
very good fit of PCA clustering.
DNA markers have been used to
evaluate genetic diversity in various plant species. In general, RAPD is
increasingly being employed in genetic research owing to its speedy process and
simplicity. On the other hand, ISSR marker has high potential to reveal polymorphism
at intra- and intergenomic level to determine diversity than compared RAPDs
(Zietkiewicz et al., 1994).
In current study, we have compared
the applicability of ISSRs and RAPDs as genetic markers to characterize the Salacia species. The only reports on
genetic diversity on genus Salacia was
carried out by Priya et al. (2016) who used RAPD molecular markers to asses diversity of
samples collected from Wayanad region in Kerala. In the present study, an
attempt has been made to examine the level of genetic variation within Salacia species sampled in the Western
Ghats of Karnataka.
From numbers and values obtained
in the current study it was quite that obvious RAPD is a better marker than ISSR
in evaluating diversity of Salacia
species. However, on careful observation it can be observed that RAPD marker was
not able to differentiate S. oblonga
samples and it was grouped within S.
fruticosa samples. This could be attributed to the fact that the putatively
similar bands originating from RAPD analysis in different individuals may not
necessarily have to be homologous, although they may be of same size in base
pairs which in-turn results the erroneous calculation of genetic relationships (Fernandez et al. 2002). This also explains the fact that Nei’s genetic
distance and identity between S. oblonga and
S. fruticosa were considerably high
which was contrary to observation seen in dendrogram and PCA. Resolving power
of ISSR was marginally higher than RAPD. Also, the differences in clustering
pattern in RAPD and ISSR markers may also be attributed to differences in
overall number of loci and their coverage of the overall genome, which would
affect reliable estimates of genetic relationships among samples (Loarce et al. 1996).
In both RAPD and ISSR analysis S. macrosperma had high polymorphism
within the population which was apparent as the samples were collected from
many different locations. However, in case of S. chinensis, S. fruticosa, and S.
oblonga populations the samples were collected from one location. Despite
the samples within the population originating from one location, a considerable
high rate of polymorphism was observed which was in correlation with the
observations made earlier by Priya et al. (2016).Similarly, diversity evaluation of Memecylon species collected from western
Ghats of Karnataka by Ramasetty et al. (2016) using RAPD,ISSR and barcoding genes found high level
of polymorphism in RAPD (65.4%) and ISSR
analysis (68.5%). RAPD and ISSR markers were also able to effectively detect
low polymorphism variation in Garcinia
xanthochymus species sampled across various states of western Ghats(Anerao et al. 2017) which suggest that RAPD and ISSR are efficient
markers in for diversity analysis.
In the study by Dev et al. (2015) ITS2 region showed highest interspecific divergence
and 100% efficiency for species identification by nearest distance method when
compared to rbcL, matK and trnH-psbA barcoding regions. The authors also
observed reciprocal monophyly among S. fruticosa,
S. chinensis, S. agasthiamalana and S.
macrosperma in the phylogenetic tree generated from the combined dataset, which
was also observed in our current results. The high divergence of S. fruticosa sample SF5 can be
attributed to amplification of ITS pseudogene as it is identified by its high
rate of substitution especially in the ITS2 region. Furthermore, this fact was validated
by AMOVA, since as compared to RAPD and ISSR, ITS had highest percent of
variance (80%) in detecting interspecific or among the species divergence whereas
the RAPD had the lowest (39%).From the AMOVA it can be seen that there was
considerable variation within and among Salacia
species. The variation within the species may due to presence of infrageneric
variation in Salacia species.
Evidence can be seen from discovery of variety kakkayamana in S. oblonga (Udayan et al. 2014). Also, the high variation among groups was due to the
component of genetic variance, as new species S. agasthiamalana (Udayan et al. 2012) S. vellaniana (Udayan et al. 2013) were discovered in western Ghats of Kerala. From the study of Dev et al. (2015) of Salacia
species sampled from Kerala, S. oblonga
and variety kakkayamana showed 100%
homology, while S. fruticosa, S.
vellaniana, S. chinensis, S. malabarica, S. agasthiamalana samples formed monophyletic group and S. macrosperma and S. beddomei were closely
related sister species as per the phylogram. The results of Tajima’s D
neutrality tests were negative for all the Salacia
species population suggesting excess of rare alleles within the population,
which may suggest population expansion. However, when the sample SF5 was
removed and all the individual samples were analyzed across species samples,
there exist an equilibrium.
study of RAPD, ISSR and ITS for Salacia
species has given an insight into the efficiency of each technique in detecting
diversity within and among the population sampled in the western Ghats of