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Categorization of Saraca asoca and Polyalthia longifolia, using chemical and genetic fingerprinting associated with multivariate statistical analysis
Journal of Applied Research on Medicinal and Aromatic Plants xxx (xxxx) xxx–xxx
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Categorization of Saraca asoca and Polyalthia longifolia, using chemical and genetic fingerprinting associated with multivariate statistical analysis Poojadevi Sharma1, Sonal Sharma1, Sheetal Yadav, Anshu Srivastava, Astha Varma, ⁎ Kuldeep Luhana, Neeta Shrivastava B.V. Patel Pharmaceutical Education and Research Development (PERD) Centre, Ahmedabad, Gujarat, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Adulteration AFLP HPTLC Ashoka Chemical fingerprinting Genetic fingerprinting
Saraca asoca is a medicinally important tree of the Indian system of medicine, bark of which has therapeutic value against gynecological problems. Its morphological similarity with Polyalthia longifolia raise the concern of adulteration and substitution. Chemical and genetic profiles of each plant have been developed and compared, to develop the tool to be applied as the better identifier of the original. High performance liquid chromatography (HPTLC) was used to develop chemical fingerprint whereas genetic profiling was based on the Amplified fragment length polymorphism (AFLP) technique. Stem bark samples of each plant were collected from various geographical locations. For phytochemical analysis, successive extraction in three different solvents varying in polarity were performed and HPTLC plates were developed, subsequently scanned on three different wavelengths. AFLP was performed using six primers. Data were collected as the ‘0’ and ‘1’ for the banding pattern in HPTLC and AFLP, and subjected to statistical evaluations. Statistical analysis revealed 98.6% of polymorphism between S. asoca and P. longifolia based on the chemical data, whereas 97.1% polymorphism was obtained using genetic fingerprinting. Cluster analysis of both type of data grouped the S. asoca in the separate cluster, but it was observed that more discrete and reliable clustering was offered by the chemical analysis rather than the AFLP. Genetic diversity parameters suggested that high level of genetic diversity in P. longifolia caused distributed pattern of clustering, which was interrupting the absolute identification of the original. In context with S. asoca and P. longifolia, chemical fingerprinting has proved as the better classifier than genetic one, but the later method can be integrated and can be used as the supporting tool.
1. Introduction Saraca asoca (Roxb) De Wilde (Family:Leguminosae), ‘Asoka chhal’ the bark of asoca tree is used in many ayurvedic formulations for treating female gynaecological problems like uterine bleeding, menstrual disorders etc. Stem bark of the tree has oxytocic, uterotonic, antiinflammatory, anti-bacterial, anti-implantation, anti-tumour, anti-progestational, antiestrogenic, antimutagenic, anti-arthritic and anticancer properties (Ahmad et al., 2016a,b,c; Nag et al., 2015; Saravanan et al., 2011; Shahid et al., 2007, 2015; Singh et al., 2015). On account of its traditional usage, the bark is a highly traded raw drug, with more than 15000 metric tonnes reported to be consumed in the domestic market in India during 2007–2011 (Singh et al., 2015). Its high medicinal value has caused overharvesting of S. asoca, have led to the drastic reduction in the availability of the bark and hence, incorrect species are being sold as Saraca (Sumangala et al., 2013).
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Urumarudappa et al. (2016) reported presence of counterfeit material in more than 80% of the market samples of Asoca. Stem bark of a common ornamental tree species Polyalthia longifolia (Family-Annonaceae) is reported as the rampant adulterant and substituent of the Asoca crude drug. Both the plants share ambiguous vernacular names ‘Ashoka’ and have similar macroscopic characters to the greater degree (Mathew et al., 2005; Singh et al., 2015). Application of erroneous raw material for the final drug preparation can affect the pharmacological activities of the herbal drug or may be ineffectual for treating the particular disease. For authentication of the medicinal plants, both classical taxonomy (macroscopy-microscopy studies) as well as chemotaxonomy (phytochemical fingerprint and marker analysis) studies are on record (Beena and Radhakrishnan, 2012; Khatoon et al., 2008, 2009; Mathew et al., 2005; Raman et al., 2015). DNA based fingerprinting techniques have also been recommended for the authentication of herbal starting
Corresponding author. E-mail address: [email protected] (N. Shrivastava). Authors have equal contributions.
https://doi.org/10.1016/j.jarmap.2018.03.004 Received 8 May 2017; Received in revised form 8 March 2018; Accepted 11 March 2018 2214-7861/ © 2018 Elsevier GmbH. All rights reserved.
Please cite this article as: Sharma, P., Journal of Applied Research on Medicinal and Aromatic Plants (2018), https://doi.org/10.1016/j.jarmap.2018.03.004
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Table 1 Details of the stem bark sample collection. Sr No
S. asoca
1 2 3 4 5 6 7 8 9
P. longifolia
Geographic Locations
Voucher Number
Geographic Locations
Voucher Number
Anand, Gujarat Ahmedabad, Gujarat Dahod, Gujarat Gandhinagar, Gujarat Karamsad, Gujarat Rajpipla, Gujarat Vidyanagar, Gujarat Patna, Bihar Bhubaneswar, Orrisa
BVPPERD/PP/0611/29 BVPPERD/PP/0413/17 BVPPERD/PP/0413/19 BVPPERD/PP/0611/30 BVPPERD/PP/1013/20 BVPPERD/PP/0812/14 BVPPERD/PP/1013/21 BVPPERD/PP/0413/26 BVPPERD/PP/1213/24
Anand, Gujarat Ahmedabad, Gujarat Gandhinagar, Gujarat Karamsad, Gujarat Rajpipla, Gujarat Vadodara, Gujarat Valsad, Gujarat Khandva, MadhyaPradesh –
BVPPERD/PP/0611/31 BVPPERD/PP/0413/18 BVPPERD/PP/0611/32 BVPPERD/PP/1013/22 BVPPERD/PP/0812/15 BVPPERD/PP/1013/23 BVPPERD/PP/0611/33 BVPPERD/PP/1013/25
2. Material and methods
Table 2 Attributes of chemical and genetic diversity in S. asoca and P. longifolia. Attributes
Chemical fingerprints
Genetic Fingerprints
Total Number of bands Monomorphic Bands Polymorphic bands Percent polymorphism
212 3 209 98.6
103 3 100 97.1
2.1. Plant material Stem bark of Saraca asoca and Polyalthia longifolia were collected from different geographical regions of India. The samples consisted of 9 accessions of S. asoca and 8 accessions of P. longifolia (Table 1). Samples were collected and identified with the help of experts. Samples were obtained from the trees of age more than 5 years. Stem bark collection was carried out using the common hardware tools like chisel, in the month from April to December. Superficial incision were made to collect stem bark shavings which contained the outer dried cork cells as well as the secondary outer phloem. Each sample was assigned with the specific voucher specimen number and submitted at the herbarium division of the B.V. Patel Pharmaceutical Education and Research Development Centre, Ahmedabad, Gujarat, India (Table 1). Part of each stem bark sample was stored at −20 °C until used for further analysis.
material (Chen et al., 2014; Galimberti et al., 2013). Recently, many research studies aimed at development of tools and approaches for quality control of S. asoca, were reported (Joshi, 2016; Ketkar et al., 2015). Regarding, identification and authentication of original medicinal material, different phytochemical and DNA based methods were developed individualistically (Gahlaut et al., 2013; Kumar, 2016; Hegde et al., 2017a,b; Urumarudappa et al., 2016). In this paper both chemical fingerprinting and genetic fingerprinting using HPTLC and AFLP respectively, have been deployed with the objective to develop diagnostic tools for authentication of Saraca and for its discrimination with Polyalthia. We have worked on both the approaches in order to develop an integrated tool that can provide more reliability than the using solitary method. In addition, a comparative statistical analysis was also performed to boost the validity of fingerprint oriented approaches. The multivariate statistical analysis was also employed to evaluate the attributes of chemical and DNA fingerprints in order to designate the preferred technique which can be applied for discrete classification and discrimination for the Saraca medicinal material (Mavimbela et al., 2014; Zhaoyang et al., 2014). The paper will provide the platform to design the strategy for identification of S. asoca.
2.2. Phytochemical analysis For phytochemical analysis, each stem bark accession was dried in oven at 45 °C. Dried samples were subjected to HPTLC (CAMAG Muttenz, Switzerland) analysis for chemical fingerprinting. Dried plant material was powdered and successively extracted with three different solvents viz. methanol, ethyl acetate and hexane. Samples were spotted on precoated TLC Silica gel 60 F254 plates (Merck KGaA, Darmstadt, Germany) of size 20 × 10 cm and thickness of 0.2-mm. Spotting was done using Linomat V Automatic Sample Spotter (CAMAG, Muttenz, Switzerland). The plates were developed in glass twin-trough TLC
Fig. 1. Scatter plot diagrams of PCo analysis (PCoA) for S. asoca and P. longifolia (A) Chemical fingerprinting (B) Genetic fingerprinting.
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SA, S. asoca; Pl, P. longifolia; AN- Anand, AB- Ahmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
chamber (20 × 10 × 4 cm; CAMAG) for methanol, ethyl acetate and hexane extracts in the solvent system Toluene:Ethyl acetate (6:4), Toluene:Ethyl acetate:Formic acid (6.5:2.5:1) and Toluene:Methanol (9:1), respectively. Plates were scanned at 254 nm and 366 nm. All the plates were derivatised with anisaldehyde and scanned at 525 nm. Scanning was done using TLC Scanner 3 linked to winCATS software (CAMAG). 2.3. DNA isolation and AFLP fingerprinting Stem bark samples of S. asoca and P. longifolia were subjected to DNA isolation for genetic fingerprint development (AFLP). DNA isolation was done using CTAB extraction method (Doyle and Doyle 1990). For AFLP analysis, extracted DNA was sequentially digested with EcoRI and MseI, and ligated with the adapters. Ligated DNA samples were undertaken for PCR amplification by following the programme as initial denaturation at 94 °C for 2 min and then 12 cycles of 94 °C for 30 s, 65 °C for 30 s and amplification on 72 °C with reduction in annealing temperature at the rate of 0.7 °C per cycle. This was followed by the constant annealing on 56 °C for 30 s up to remaining 30 cycles. Final extension was carried out on 72 °C for 5 min. Amplified fragments were resolved on 8% polyacrylamide gel and silver staining was used to visualize bands (An et al., 2009). Out of hundred primer combinations six (ECoR-TAC/Mse-TAGC, ECoR-AAC/Mse-GCG, ECoR-AAG/Mse-TCA, ECoR-ACT/Mse-CTA, ECoR-GC/Mse-GGA, ECoR-ACT/Mse-CTG) were finally selected for the AFLP analysis, on the basis of unambiguity and the reproducibility. 2.4. Data scoring and statistical analysis For, the both types of fingerprinting chemical and DNA, presence and absence of the bands were scored as 1 and 0 to prepare binary matrix (Milojković-Opsenica et al., 2013; Thul et al., 2011). Rf values were considered for data scoring of chemical fingerprinting. For, AFLP scoring, bands between size of 60–500 bp were counted without considering the band intensity difference. The binary matrices thus generated were subjected to hierarchial clustering and PCoA analysis. Jaccard similarity coefficient was computed for each type of data and dendrogram was generated using UPGMA algorithm with online tool Dendro UPGMA: A dendrogram construction utility (http://genomes. urv.cat/UPGMA/index.php, Last accessed on 8.7.16). Bootstrap analysis was performed using 100 replicates. GenAlex software was used for PCoA analysis. The AFLP binary matrix was also analysed to evaluate genetic diversity of S. asoca and P. longifolia. 3. Results and discussion Credibility of Ashok Chal based ayurvedic drug is on stake at national and international level due to problem of adulteration. Substitution of Saraca crude drug with Polyalthia species may alter its efficacy and therapeutic value. Efficient methods are required to correctly identify the asoca medicinal material. In the pharmacopoeias, the methods of correct identification of herbal material are rely upon the macroscopic, microscopic and the phytochemical analysis. DNA based methods are also recommended in recent versions of pharmacopoeias. Adulteration detection based on the chemical methods include chemical fingerprinting and/or use of the marker compounds. Chemical fingerprinting methods are beneficial when any information about the marker/reference compounds is lacking, as in the case of S. asoca and P. longifolia. Similar to the chemical fingerprinting, DNA fingerprinting can be used to initiate the genetic analysis, if any related sequence information is not available. Moreover, integration of DNA fingerprinting with the chemical patterning can give extensive information to develop tools for botanical identification. However, various environmental, biological and evolutionary factors can cause diversity in the patterns which can affect the distinguishing ability of both types of
a
0.2 0.195 0.174 0.184 0.197 0.124 0.2 0.103 0.173 0.336 0.302 0.388 0.354 0.267 0.276 0.408 1 0.157 0.192 0.183 0.15 0.242 0.154 0.244 0.143 0.15 0.346 0.354 0.333 0.331 0.297 0.307 1 0.185 0.17 0.17 0.157 0.203 0.15 0.187 0.138 0.168 0.347 1 0.264 0.403 0.426 0.286 0.382 1 SA (AN) SA (AB) SA (DH) SA (GN) SA (KM) SA (RJ) SA (VN) SA (OR) SA (PT) PL (AB) PL (AN) PL (GN) PL (KM) PL (RJ) PL (VL) PL (KA) PL (VD)
1
0.414 1
0.314 0.397 1
0.211 0.328 0.367 1
0.209 0.361 0.277 0.333 1
0.267 0.376 0.31 0.368 0.73 0.397 1
0.26 0.357 0.419 0.281 0.432 0.475 0.447 1
0.265 0.328 0.344 0.267 0.266 0.373 0.268 0.39 1
0.174 0.266 0.236 0.202 0.211 0.182 0.224 0.159 0.202 1
0.211 0.227 0.185 0.162 0.237 0.221 0.25 0.196 0.196 0.276 0.367 1
0.147 0.188 0.202 0.176 0.18 0.167 0.195 0.165 0.176 0.322 0.342 0.33 1
0.125 0.14 0.18 0.136 0.146 0.15 0.186 0.149 0.157 0.279 0.285 0.239 0.322 1
0.144 0.169 0.202 0.168 0.164 0.171 0.214 0.159 0.19 0.243 0.305 0.257 0.289 0.776 1
PL (VD) PL (AN) SA (RJ) SA (KM) SA (GN) SA (DH) SA (AB) SAa (AN)
Table 3 Jaccard similarity coefficient for S. asoca and P. longifolia for the chemical fingerprint.
SA (VN)
SA (OR)
SA (PT)
PL (AB)
PL (GN)
PL (KM)
PL (RJ)
PL (VL)
PL (KA)
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Fig. 2. UPGMA based dendrograms of S. asoca and P. longifolia for (A) Chemical fingerprinting (B) Genetic fingerprinting.SA, S. asoca; Pl, P. longifolia; AN- Anand, ABAhmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
fingerprinting (Galimberti et al., 2013; Smillie and Khan 2010; Zhaoyang et al., 2014). In accordance with the above lines, both chemical and DNA fingerprints were generated and the data were compared in order to provide better platform to develop methods to discriminate S. asoca and P. longifolia medicinal material. Phytochemical analysis was done using HPTLC and DNA fingerprints based on AFLP technique were developed. The techniques chosen in the present study are less resource intensive as compared to other advanced and efficient techniques, used in the other reports (McGregor et al., 2000; Nybom et al., 2014). When considering traditional herbal drug market, it is important to remember that the key suppliers of herbal drug materials are resource limited developing countries and cannot afford the implementation of such costly high throughput analytical techniques for routine quality control (Hoareau and DaSilva, 1999; Kaale et al., 2011; Nybom et al., 2014; Sahoo and Manchikanti, 2013). In practical terms, the situation warrants use of analytical techniques which are low cost and have ease of applicability. Keeping the perspective in mind, we chose to evaluate HPTLC and AFLP fingerprinting for Saraca and Polyalthia bark discrimination. HPTLC chemical fingerprinting based quality control approach is simple, reliable and can successfully demonstrate both overall phytochemical similarity and differences between various plant samples. This approach obviates the need of purification of variety of chemical markers specific to particular targeted plant which in itself is again a huge exercise. Furthermore, few selected chemical markers cannot reflect the complexity of metabolome of herbal drug material which a simple HPTLC fingerprint can successfully convey in form of a simple visual image. Also, HPTLC analysis can be employed for multiple sample analysis in single run of solvent development (Ram et al., 2011). Similarly, DNA molecular profiling technique, AFLP, also has widespread applicability owing to its simplicity, low cost and high reproducibility feature. More importantly, it does not require any prior genomic DNA sequence information of target plant under analysis. Ideally, primers combinations once designed can be used for any plant in question (Nybom et al., 2014; Paun and Schönswetter, 2012). Bark samples of both plants were collected from various locations and dried for phytochemical analysis (Table 1). Regarding HPTLC fingerprinting, three different solvents were used for the extraction of polar, non-polar and medium polar compounds. Methanol, ethyl acetate and hexane extract was used for the polar compounds, medium polar and non-polar compounds, respectively. For the each solvent, plates were developed and scanned on all the recommended wavelengths 254 nm, 366 nm and 525 nm (for the plates derived with
anisaldehyde). This was done to quench data related to each possible type of compound present in S. asoca and P. longifolia. Bands on the similar Rf values, if present, were counted as 1 and absence was counted as 0. Phytochemical analysis coupled with the statistical evaluation showed that total number of bands generated was 212, out of which maximum number of bands obtained in ethylacetate extraction (74) and minimum number of bands obtained in methanol extraction (66). In hexane extracts total 72 bands were obtained. The total number of polymorphic bands were 209 and the percent polymorphism was equal to 98.6%, between S. asoca and P. longifolia (Table 2). As per the percent polymorphism, Polyalthia (95.93%) is slightly more chemically diverse than Saraca (94.21%). PCoA was performed to validate the application of chemical fingerprinting to discriminate S. asoca from P. longifolia. Results showed the grouping of both the plants in the two separate clusters (Fig. 1A). However, statistical analysis of the chemical data, obtained by analysing the three different solvent extracts individually, clustered S. asoca in a single different group; but In comparison to the methanol and the ethylacetate extracts the chemical data from the hexane extract gave tight clustering of the Saraca, (Supplementary Fig. 1). Hexane extracts can be used to generate signature HPTLC fingerprints for the Saraca which can differentiate it from the most potent substitute P. longifolia. Binary matrix computed for the chemical data was also analysed for hierarchical clustering. UPGMA based dendrogram was generated which also clearly distinguished S. asoca from the P. longifolia (Table 3, Fig. 2A). The similar analysis was performed with the genetic fingerprint data. Bark of the Saraca is used as the medicinal material, so stem bark was used as the source of DNA for AFLP fingerprinting. DNA from each bark samples were isolated and subjected to qualitative and quantitative analysis. The 260/280 ratio for the S. asoca was obtained between 1.2–1.6 and for P. longifolia it was obtained between 1.3–1.9. This PCR amenable DNA was subjected to the AFLP analysis. Initially, 100 primer pairs were screened to determine the appropriate discriminating primer pair combinations. Finally, six primer pairs were selected on the basis of percent polymorphism between S. asoca and the P. longifolia, as well as the unambiguity in the fingerprints. Total 103 bands were generated and out of which 100 bands were found polymorphic (Table 2). Being the members of different families, as obvious, high level of polymorphism (97.03%) was found between these two plants. Jaccard similarity coefficient was computed and UPGMA based dendrogram was build (Table 4). Dendrogram based on the genetic fingerprinting clustered both the plants separately into two individual groups but with a little ambiguity (Fig. 2B). The similar results was obtained with PCoA
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method. In the PCoA analysis also, Polyalthia individuals exhibited distributed pattern of clustering in comparison to Saraca samples, indicating high level of genetic diversity between Polyalthia populations (Fig. 1B). Estimation of genetic diversity of the individual plants proved the S. asoca is less diverse than the P. longifolia. Percent polymorphism between Saraca individuals was 55.71 whereas Polyalthia had 95% of polymorphism (Table 5). Higher level of genetic similarity between Saraca individuals were also evident in the Jaccard similarity coefficient related to different populations, which was between the ranges of 0.600–0.923 (Table 4). Similar lower level of genetic diversity in Saraca was estimated using RAPD which was reported by Senapati et al. (2012). In this study, concern about the immediate need to step forward for the conservation of the Saraca was raised. Similarly, in another research, informative microsatellite markers have been isolated for managing the conservation policies for Saraca (Sumangala et al., 2013). Low abundance of original botanical can worsen the adulteration problem. On the other side, Jaccard similarity coefficient for Polyalthia fell in the range of minimum 0.173 to the maximum 0.896 (Table 4). Results showed that the genetic data is sufficiently diverse, and simultaneously, conserve in Saraca individuals to satisfy its separate clustering; but Polyalthia has shown distributed pattern due to high level of genetic diversity which can interrupt the absolute authentication of Saraca, if only genetic fingerprinting is used. It also inhibited us to find the species specific unique marker band in either Saraca or Polyalthia in order to develop SCAR marker for the identification of the original. Moreover, from comparison of PCoA plots and dendrograms for the chemical and the genetic data, it was exhibited that chemical fingerprints can give more discrete clustering without intersecting the authentication process for the Saraca (Figs. 1 and 2). Thus, the study suggests that the chemical fingerprinting can provide more reliable platform over the genetic data to develop an authentication tool regarding S. asoca and P. longifolia. Nevertheless, more number of samples are required to validate the high genetic diversity of Polyalthia and its effect on the identification process, but at this point of time, genetic fingerprinting can only be used as supporting tool with the chemical analysis for absolute categorization of S. asoca and its adulterant P. longifolia.
SA, S. asoca; Pl, P. longifolia; AN- Anand, AB- Ahmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
0.263 0.247 0.254 0.243 0.243 0.244 0.211 0.243 0.194 0.303 0.259 0.225 0.239 0.304 0.262 0.293 1 0.38 0.405 0.406 0.333 0.333 0.358 0.364 0.382 0.329 0.269 0.246 0.389 0.384 0.321 0.268 1 0.254 0.235 0.262 0.194 0.25 0.304 0.232 0.25 0.237 0.274 0.173 0.379 0.35 0.537 1 0.325 0.293 0.304 0.274 0.274 0.392 0.324 0.342 0.284 0.333 0.254 0.431 0.446 1 0.587 0.583 0.492 0.517 0.517 0.578 0.627 0.627 0.556 0.304 0.241 0.896 1 0.571 0.567 0.45 0.5 0.5 0.515 0.557 0.557 0.509 0.29 0.203 1 0.159 0.172 0.153 0.109 0.109 0.209 0.169 0.206 0.123 0.321 1 0.307 0.292 0.323 0.271 0.29 0.338 0.288 0.306 0.242 1 0.673 0.706 0.6 0.694 0.766 0.632 0.692 0.66 1 0.763 0.868 0.704 0.727 0.696 0.78 0.923 1 0.793 0.868 0.673 0.727 0.727 0.75 1 0.8 0.808 0.611 0.837 1 SA (AN) SA (AB) SA (DH) SA (GN) SA (KM) SA (RJ) SA (VN) SA (PT) SA (OR) PL (AB) PL (AN) PL (GN) PL (KM) PL (RJ) PL (VL) PL (KA) PL (VD)
1
0.873 1
0.627 0.655 1
0.768 0.808 0.611 1
0.677 0.733 0.617 0.639 0.639 1
SA (PT) SA (VN) SA (RJ) SA (KM) SA (GN) SA (DH) SA (AB) SA (AN)
Table 4 Jaccard similarity coefficient for S. asoca and P. longifolia for the AFLP fingerprint.
SA (OR)
PL (AB)
PL (AN)
PL (GN)
PL (KM)
PL (RJ)
PL (VL)
PL (KA)
PL (VD)
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4. Conclusion The current study presents statistical evaluation of chemical and genetic fingerprinting for validating their application in authenticating S. asoca and for its discrimination with P. longifolia. The present research can be used for planning the scheme for authentication of genuine S. asoca starting herbal material and can be adopted by herbal drug industry for quality control
Conflict of interest Authors declare that they have no conflict of interest.
Acknowledgements Authors are thankful to Gujarat State Biotechnology Mission (GSBTM), Gujarat, India for their financial support. Authors also wish to thank following personnel and institutes for helping in stem bark collection i.e Dr. Virendra Rana, DMAPR; Dr. Mahesh Patel, AAU, Anand; Dr. Aruna Joshi, MS University, Vadodara; Dr. KJ Mehta, Navjivan Science College; Dr. Farzin Parabia, SPU University; Dr. Sheetal Anadjiwala, Valsad; IKICP, ADIT Campus, Karamsad; Jawaharlal Nehru Udhyan, Gandhinagar; Govt. Medicinal Botanical Garden, Rajpipla, Dr. Kislaya Singh, Patna; Mr. Ashutosh Behuria, Orrisa. 5
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Table 5 Total number of bands, polymorphic bands, percent polymorphism obtained with 6 AFLP primer combinations on 17 accessions of S. asoca (SA) and P. longifolia (PL). Primers
ECoR-TAC/Mse-TAGC ECoR-AAC/Mse-GCG ECoR-AAG/Mse-TCA ECoR-ACT/Mse-CTA ECoR-GC/Mse-GGA ECoR-ACT/Mse-CTG Total
Total bands
Monomorphic bands
Polymorphic bands
Percent polymorphism
SA
PL
SA
PL
SA
PL
SA
PL
11 13 11 11 15 9 70
17 18 14 17 19 15 100
3 5 4 9 3 7 31
1 1 1 2 0 0 5
8 8 7 2 12 2 39
16 17 13 15 19 15 95
72.73 61.54 63.64 18.18 80.00 22.22 55.71
94.12 94.44 92.86 88.24 100 100 95
Appendix A. Supplementary data
Mavimbela, T., Viljoena, A., Vermaaka, I., 2014. Differentiating between Agathosma betulina and Agathosma crenulata—a quality control perspective. Journal of Applied Research on Medicinal and Aromatatic Plants 1, e-8–e-14. McGregor, C.E., Lambert, C.A., Greyling, M.M., Louw, J.H., Warnich, L., 2000. A comparative assessment of DNA fingerprinting techniques (RAPD ISSR, AFLP and SSR) in tetraploid potato (Solanum tuberosum L.) germplasm. Euphytica 113, 135–144. Milojković-Opsenica, D., Ristivojević, P., Andrić, F., Trifković, J., 2013. Planar chromatographic systems in pattern recognition and fingerprint analysis. Chromatographia 76, 1239–1247. Nag, D., Ghosh, M., Mukherjee, A., 2015. Antimutagenic and genoprotective effects of Saraca asoca bark extract. Toxicology and Industrial Health 31, 696–703. Nybom, H., Weising, K., Rotter, B., 2014. DNA fingerprinting in botany: past, present, future. Investigative Genetics 5, 1. Paun, O., Schönswetter, P., 2012. Amplified fragment length polymorphism: an invaluable fingerprinting technique for genomic, transcriptomic, and epigenetic studies. In: Sucher, N., Hennell, J., Carles, M. (Eds.), Plant DNA Fingerprinting and Barcoding. Methods in Molecular Biology (Methods and Protocols). Humana Press, pp. 75–87. Ram, M., Abdin, M.Z., Khan, M.A., Jha, P., 2011. HPTLC fingerprint analysis: a quality Control for authentication of herbal phytochemicals. In: Srivastava, M. (Ed.), Highperformance Thin-layer Chromatography (HPTLC). Springer, Berlin, Heidelberg, pp. 105–116. Raman, V., Sagi, S., Galal, A.M., Avula, B., Viljoen, A., Khan, I.A., 2015. Adulteration in commercial buchu dietary supplements: analyses of commercial and authentic buchu samples and comparative studies of Agathosma betulina and Agathosma crenulata by microscopy and HPTLC. South African Journal of Botany 100, 122–131. Sahoo, N., Manchikanti, P., 2013. Herbal drug regulation and commercialization: an Indian industry perspective. The Journal of Alternative and Complementary Medicine 19, 957–963. Saravanan, S., Babu, N.P., Pandikumar, P., Ignacimuthu, S., 2011. Therapeutic effect of Saraca asoca (Roxb) Wilde on lysosomal enzymes and collagen metabolism in adjuvant induced arthritis. Inflammopharmacology 19, 317–325. Senapati, S.K., Das, G.K., Aparajita, S., Rout, G.R., 2012. Assessment of genetic variability in the Asoka Tree of India. Biodiversity 13, 16–23. Shahid, M., Shahzad, A., Malik, A., Anis, M., 2007. Antibacterial activity of aerial parts as well as in vitro raised calli of the medicinal plant Saraca asoca (Roxb) de Wilde. Canadian Journal of Microbiology 53, 75–81. Shahid, A.P., Salini, S., Sasidharan, N., Padikkala, J., Raghavamenon, A.C., Babu, T.D., 2015. Effect of Saraca asoca (Asoka) on estradiol-induced keratinizing metaplasia in rat uterus. Journal of Basic and Clinical Physiology and Pharmacology 26, 509–515. Singh, S., Anantha Krishna, T.H., Kamalraj, S., Kuriakose, G.C., Valayil, J.M., Jayabaskaran, C., 2015. Phytomedicinal importance of Saraca asoca (Ashoka): an exciting past, an emerging present and a promising future. Current Science 109, 1790–1801. Smillie, T.J., Khan, I.A., 2010. A comprehensive approach to identifying and authenticating botanical products. Clinical Pharmacology and Therapeutics 87, 175–186. Sumangala, R.C., Shaanker, R.U., Dayanandan, S., Vasudeva, R., Ravikanth, G., 2013. Identification of novel microsatellite markers for Saraca asoca: a medicinally important tree species in India. Journal of Genetics 92, e93–5. Thul, S.T., Srivastava, A.K., Singh, S.C., Shanker, K., 2011. Genetic and chemical diversity of high mucilaginous plants of Sida complex by ISSR markers and chemical fingerprinting. Molecular Biotechnology 49, 77–81. Urumarudappa, S.K.J., Gogna, N., Newmaster, S.G., Venkatarangaiah, K., Subramanyam, R., Saroja, S.G., Gudasalamani, R., Dorai, K., Ramanan, U.S., 2016. DNA barcoding and NMR spectroscopy-based assessment of species adulteration in the raw herbal trade of Saraca asoca (Roxb.) Willd, an important medicinal plant. International Journal of Legal Medicine 130, 1457–1470. Zhaoyang, Z., Xiaodong, L., Sheng, L., 2014. Comparative genetic and chemical profiling performed on Alstonia scholaris in China and its implications to standardization of Traditional Chinese Medicine. Journal of Medicinal Plants Research 8, 301–306.
Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jarmap.2018.03.004. References Ahmad, F., Misra, L., Gupta, V.K., Darokar, M.P., Prakash, O., Khan, F., Shukla, R., 2016a. Synergistic effect of (+)-pinitol from Saraca asoca with beta-lactam antibiotics and studies on the in silico possible mechanism. Journal of Asian Natural Product Research 18, 172–183. Ahmad, F., Misra, L., Tewari, R., Gupta, P., Gupta, V.K., Darokar, M.P., 2016b. Isolation and HPLC profiling of chemical constituents of Saraca asoca stem bark. Indian Journal of Chemistry 55, 353–361. Ahmad, F., Misra, L., Tewari, R., Gupta, P., Mishra, P., Shukla, R., 2016c. Anti-inflammatory flavanol glycosides from Saraca asoca bark. Natural Product Research 30, 489–492. An, Z.W., Xie, L.L., Cheng, H., Zhou, Y., Zhang, Q., He, X.G., Huang, H.S., 2009. A silver staining procedure for nucleic acids in polyacrylamide gels without fixation and pretreatment. Analytical Biochemistry 391, 77–79. Beena, C., Radhakrishnan, V.V., 2012. Quality assessment evaluation of the market samples of important ayurvedic drug asoka bark. Annals of Phytomedicine 1, 95–98. Chen, S., Pang, X., Song, J., Shi, L., Yao, H., Han, J., Leon, C., 2014. A renaissance in herbal medicine identification: from morphology to DNA. Biotechnology Advances 32, 1237–1244. Gahlaut, A., Shirolkar, A., Hooda, V., Dabur, R., 2013. A rapid and simple approach to discriminate various extracts of Saraca asoca [Roxb.], De. Wild using UPLC-QTOFMS and multivariate analysis. Journal of Pharmacy Research 7, 143–149. Galimberti, A., De Mattia, F., Losa, A., Bruni, I., Federici, S., Casiraghi, M., Martellos, S., Labra, M., 2013. DNA barcoding as a new tool for food traceability. Food Research International 50, 55–63. Hegde, S., Hegde, H.V., Jalalpure, S.S., Peram, M.R., Pai, S.R., Roy, S., 2017a. Resolving identification issues of Saraca asoca from its adulterant and commercial samples using phytochemical markers. Pharmacognosy Magazine 13 (2), S266–S272. Hegde, S., Pai, S.R., Roy, S., 2017b. Combination of DNA isolation and RP-HPLC analysis method for bark samples of Saraca asoca and its adulterant. 3 Biotech 7, 208. Hoareau, L., DaSilva, E.J., 1999. Medicinal plants: a re-emerging health aid. Electronic Journal of Biotechnology 2, 3–4. Joshi, R.K., 2016. E,E-α-Farnesene rich essential oil of Saraca asoca (Roxb.) Wilde flower. Natural Product Research 30, 979–981. Kaale, E., Risha, P., Layloff, T., 2011. TLC for pharmaceutical analysis in resource limited countries. Journal of Chromatography A 1218, 2732–2736. Ketkar, P.M., Shraddha, U.N., Sandeep, R.P., Rajesh, K.J., 2015. Determining seasonal changes in three major phenolic compounds from bark of Saraca asoca in comparison with local market sample using RP-HPLC analysis. National Academy Science Letters 38, 403–407. Khatoon, S., Singh, N., Srivastava, N., Rawat, A.K.S., Mehrotra, S., 2008. Chemical evaluation of seven Terminalia species and quantification of important polyphenols by TLC. Journal of Planar Chromatography—Modern TLC 21, 167–171. Khatoon, S., Singh, N., Kumar, S., Srivastava, N., Rathi, A., Mehrotra, S., 2009. Authentication and quality evaluation of an important ayurvedic drug—Ashoka bark. Journal of Scientifica and Industrial Research 68, 393–400. Kumar, V., 2016. Rapid molecular authentication of Ashoka [Saraca asoca (Roxb.), de Wild], vulnerable medicinal plant species and its adulterant Polyalthia longifolia Benth by the development of SCAR markers and multiplex-PCR. Research Journal of Biotechnology 11, 59–68. Mathew, S., Mathew, G., Joy, P.P., Skari, B.P., Joseph, T.S., 2005. Differentiation of Saraca asoca crude drug from its adulterant. Ancient Science of Life 24, 174–178.
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Categorization of Saraca asoca and Polyalthia longifolia, using chemical and genetic fingerprinting associated with multivariate statistical analysis Poojadevi Sharma1, Sonal Sharma1, Sheetal Yadav, Anshu Srivastava, Astha Varma, ⁎ Kuldeep Luhana, Neeta Shrivastava B.V. Patel Pharmaceutical Education and Research Development (PERD) Centre, Ahmedabad, Gujarat, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Adulteration AFLP HPTLC Ashoka Chemical fingerprinting Genetic fingerprinting
Saraca asoca is a medicinally important tree of the Indian system of medicine, bark of which has therapeutic value against gynecological problems. Its morphological similarity with Polyalthia longifolia raise the concern of adulteration and substitution. Chemical and genetic profiles of each plant have been developed and compared, to develop the tool to be applied as the better identifier of the original. High performance liquid chromatography (HPTLC) was used to develop chemical fingerprint whereas genetic profiling was based on the Amplified fragment length polymorphism (AFLP) technique. Stem bark samples of each plant were collected from various geographical locations. For phytochemical analysis, successive extraction in three different solvents varying in polarity were performed and HPTLC plates were developed, subsequently scanned on three different wavelengths. AFLP was performed using six primers. Data were collected as the ‘0’ and ‘1’ for the banding pattern in HPTLC and AFLP, and subjected to statistical evaluations. Statistical analysis revealed 98.6% of polymorphism between S. asoca and P. longifolia based on the chemical data, whereas 97.1% polymorphism was obtained using genetic fingerprinting. Cluster analysis of both type of data grouped the S. asoca in the separate cluster, but it was observed that more discrete and reliable clustering was offered by the chemical analysis rather than the AFLP. Genetic diversity parameters suggested that high level of genetic diversity in P. longifolia caused distributed pattern of clustering, which was interrupting the absolute identification of the original. In context with S. asoca and P. longifolia, chemical fingerprinting has proved as the better classifier than genetic one, but the later method can be integrated and can be used as the supporting tool.
1. Introduction Saraca asoca (Roxb) De Wilde (Family:Leguminosae), ‘Asoka chhal’ the bark of asoca tree is used in many ayurvedic formulations for treating female gynaecological problems like uterine bleeding, menstrual disorders etc. Stem bark of the tree has oxytocic, uterotonic, antiinflammatory, anti-bacterial, anti-implantation, anti-tumour, anti-progestational, antiestrogenic, antimutagenic, anti-arthritic and anticancer properties (Ahmad et al., 2016a,b,c; Nag et al., 2015; Saravanan et al., 2011; Shahid et al., 2007, 2015; Singh et al., 2015). On account of its traditional usage, the bark is a highly traded raw drug, with more than 15000 metric tonnes reported to be consumed in the domestic market in India during 2007–2011 (Singh et al., 2015). Its high medicinal value has caused overharvesting of S. asoca, have led to the drastic reduction in the availability of the bark and hence, incorrect species are being sold as Saraca (Sumangala et al., 2013).
⁎
1
Urumarudappa et al. (2016) reported presence of counterfeit material in more than 80% of the market samples of Asoca. Stem bark of a common ornamental tree species Polyalthia longifolia (Family-Annonaceae) is reported as the rampant adulterant and substituent of the Asoca crude drug. Both the plants share ambiguous vernacular names ‘Ashoka’ and have similar macroscopic characters to the greater degree (Mathew et al., 2005; Singh et al., 2015). Application of erroneous raw material for the final drug preparation can affect the pharmacological activities of the herbal drug or may be ineffectual for treating the particular disease. For authentication of the medicinal plants, both classical taxonomy (macroscopy-microscopy studies) as well as chemotaxonomy (phytochemical fingerprint and marker analysis) studies are on record (Beena and Radhakrishnan, 2012; Khatoon et al., 2008, 2009; Mathew et al., 2005; Raman et al., 2015). DNA based fingerprinting techniques have also been recommended for the authentication of herbal starting
Corresponding author. E-mail address: [email protected] (N. Shrivastava). Authors have equal contributions.
https://doi.org/10.1016/j.jarmap.2018.03.004 Received 8 May 2017; Received in revised form 8 March 2018; Accepted 11 March 2018 2214-7861/ © 2018 Elsevier GmbH. All rights reserved.
Please cite this article as: Sharma, P., Journal of Applied Research on Medicinal and Aromatic Plants (2018), https://doi.org/10.1016/j.jarmap.2018.03.004
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Table 1 Details of the stem bark sample collection. Sr No
S. asoca
1 2 3 4 5 6 7 8 9
P. longifolia
Geographic Locations
Voucher Number
Geographic Locations
Voucher Number
Anand, Gujarat Ahmedabad, Gujarat Dahod, Gujarat Gandhinagar, Gujarat Karamsad, Gujarat Rajpipla, Gujarat Vidyanagar, Gujarat Patna, Bihar Bhubaneswar, Orrisa
BVPPERD/PP/0611/29 BVPPERD/PP/0413/17 BVPPERD/PP/0413/19 BVPPERD/PP/0611/30 BVPPERD/PP/1013/20 BVPPERD/PP/0812/14 BVPPERD/PP/1013/21 BVPPERD/PP/0413/26 BVPPERD/PP/1213/24
Anand, Gujarat Ahmedabad, Gujarat Gandhinagar, Gujarat Karamsad, Gujarat Rajpipla, Gujarat Vadodara, Gujarat Valsad, Gujarat Khandva, MadhyaPradesh –
BVPPERD/PP/0611/31 BVPPERD/PP/0413/18 BVPPERD/PP/0611/32 BVPPERD/PP/1013/22 BVPPERD/PP/0812/15 BVPPERD/PP/1013/23 BVPPERD/PP/0611/33 BVPPERD/PP/1013/25
2. Material and methods
Table 2 Attributes of chemical and genetic diversity in S. asoca and P. longifolia. Attributes
Chemical fingerprints
Genetic Fingerprints
Total Number of bands Monomorphic Bands Polymorphic bands Percent polymorphism
212 3 209 98.6
103 3 100 97.1
2.1. Plant material Stem bark of Saraca asoca and Polyalthia longifolia were collected from different geographical regions of India. The samples consisted of 9 accessions of S. asoca and 8 accessions of P. longifolia (Table 1). Samples were collected and identified with the help of experts. Samples were obtained from the trees of age more than 5 years. Stem bark collection was carried out using the common hardware tools like chisel, in the month from April to December. Superficial incision were made to collect stem bark shavings which contained the outer dried cork cells as well as the secondary outer phloem. Each sample was assigned with the specific voucher specimen number and submitted at the herbarium division of the B.V. Patel Pharmaceutical Education and Research Development Centre, Ahmedabad, Gujarat, India (Table 1). Part of each stem bark sample was stored at −20 °C until used for further analysis.
material (Chen et al., 2014; Galimberti et al., 2013). Recently, many research studies aimed at development of tools and approaches for quality control of S. asoca, were reported (Joshi, 2016; Ketkar et al., 2015). Regarding, identification and authentication of original medicinal material, different phytochemical and DNA based methods were developed individualistically (Gahlaut et al., 2013; Kumar, 2016; Hegde et al., 2017a,b; Urumarudappa et al., 2016). In this paper both chemical fingerprinting and genetic fingerprinting using HPTLC and AFLP respectively, have been deployed with the objective to develop diagnostic tools for authentication of Saraca and for its discrimination with Polyalthia. We have worked on both the approaches in order to develop an integrated tool that can provide more reliability than the using solitary method. In addition, a comparative statistical analysis was also performed to boost the validity of fingerprint oriented approaches. The multivariate statistical analysis was also employed to evaluate the attributes of chemical and DNA fingerprints in order to designate the preferred technique which can be applied for discrete classification and discrimination for the Saraca medicinal material (Mavimbela et al., 2014; Zhaoyang et al., 2014). The paper will provide the platform to design the strategy for identification of S. asoca.
2.2. Phytochemical analysis For phytochemical analysis, each stem bark accession was dried in oven at 45 °C. Dried samples were subjected to HPTLC (CAMAG Muttenz, Switzerland) analysis for chemical fingerprinting. Dried plant material was powdered and successively extracted with three different solvents viz. methanol, ethyl acetate and hexane. Samples were spotted on precoated TLC Silica gel 60 F254 plates (Merck KGaA, Darmstadt, Germany) of size 20 × 10 cm and thickness of 0.2-mm. Spotting was done using Linomat V Automatic Sample Spotter (CAMAG, Muttenz, Switzerland). The plates were developed in glass twin-trough TLC
Fig. 1. Scatter plot diagrams of PCo analysis (PCoA) for S. asoca and P. longifolia (A) Chemical fingerprinting (B) Genetic fingerprinting.
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SA, S. asoca; Pl, P. longifolia; AN- Anand, AB- Ahmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
chamber (20 × 10 × 4 cm; CAMAG) for methanol, ethyl acetate and hexane extracts in the solvent system Toluene:Ethyl acetate (6:4), Toluene:Ethyl acetate:Formic acid (6.5:2.5:1) and Toluene:Methanol (9:1), respectively. Plates were scanned at 254 nm and 366 nm. All the plates were derivatised with anisaldehyde and scanned at 525 nm. Scanning was done using TLC Scanner 3 linked to winCATS software (CAMAG). 2.3. DNA isolation and AFLP fingerprinting Stem bark samples of S. asoca and P. longifolia were subjected to DNA isolation for genetic fingerprint development (AFLP). DNA isolation was done using CTAB extraction method (Doyle and Doyle 1990). For AFLP analysis, extracted DNA was sequentially digested with EcoRI and MseI, and ligated with the adapters. Ligated DNA samples were undertaken for PCR amplification by following the programme as initial denaturation at 94 °C for 2 min and then 12 cycles of 94 °C for 30 s, 65 °C for 30 s and amplification on 72 °C with reduction in annealing temperature at the rate of 0.7 °C per cycle. This was followed by the constant annealing on 56 °C for 30 s up to remaining 30 cycles. Final extension was carried out on 72 °C for 5 min. Amplified fragments were resolved on 8% polyacrylamide gel and silver staining was used to visualize bands (An et al., 2009). Out of hundred primer combinations six (ECoR-TAC/Mse-TAGC, ECoR-AAC/Mse-GCG, ECoR-AAG/Mse-TCA, ECoR-ACT/Mse-CTA, ECoR-GC/Mse-GGA, ECoR-ACT/Mse-CTG) were finally selected for the AFLP analysis, on the basis of unambiguity and the reproducibility. 2.4. Data scoring and statistical analysis For, the both types of fingerprinting chemical and DNA, presence and absence of the bands were scored as 1 and 0 to prepare binary matrix (Milojković-Opsenica et al., 2013; Thul et al., 2011). Rf values were considered for data scoring of chemical fingerprinting. For, AFLP scoring, bands between size of 60–500 bp were counted without considering the band intensity difference. The binary matrices thus generated were subjected to hierarchial clustering and PCoA analysis. Jaccard similarity coefficient was computed for each type of data and dendrogram was generated using UPGMA algorithm with online tool Dendro UPGMA: A dendrogram construction utility (http://genomes. urv.cat/UPGMA/index.php, Last accessed on 8.7.16). Bootstrap analysis was performed using 100 replicates. GenAlex software was used for PCoA analysis. The AFLP binary matrix was also analysed to evaluate genetic diversity of S. asoca and P. longifolia. 3. Results and discussion Credibility of Ashok Chal based ayurvedic drug is on stake at national and international level due to problem of adulteration. Substitution of Saraca crude drug with Polyalthia species may alter its efficacy and therapeutic value. Efficient methods are required to correctly identify the asoca medicinal material. In the pharmacopoeias, the methods of correct identification of herbal material are rely upon the macroscopic, microscopic and the phytochemical analysis. DNA based methods are also recommended in recent versions of pharmacopoeias. Adulteration detection based on the chemical methods include chemical fingerprinting and/or use of the marker compounds. Chemical fingerprinting methods are beneficial when any information about the marker/reference compounds is lacking, as in the case of S. asoca and P. longifolia. Similar to the chemical fingerprinting, DNA fingerprinting can be used to initiate the genetic analysis, if any related sequence information is not available. Moreover, integration of DNA fingerprinting with the chemical patterning can give extensive information to develop tools for botanical identification. However, various environmental, biological and evolutionary factors can cause diversity in the patterns which can affect the distinguishing ability of both types of
a
0.2 0.195 0.174 0.184 0.197 0.124 0.2 0.103 0.173 0.336 0.302 0.388 0.354 0.267 0.276 0.408 1 0.157 0.192 0.183 0.15 0.242 0.154 0.244 0.143 0.15 0.346 0.354 0.333 0.331 0.297 0.307 1 0.185 0.17 0.17 0.157 0.203 0.15 0.187 0.138 0.168 0.347 1 0.264 0.403 0.426 0.286 0.382 1 SA (AN) SA (AB) SA (DH) SA (GN) SA (KM) SA (RJ) SA (VN) SA (OR) SA (PT) PL (AB) PL (AN) PL (GN) PL (KM) PL (RJ) PL (VL) PL (KA) PL (VD)
1
0.414 1
0.314 0.397 1
0.211 0.328 0.367 1
0.209 0.361 0.277 0.333 1
0.267 0.376 0.31 0.368 0.73 0.397 1
0.26 0.357 0.419 0.281 0.432 0.475 0.447 1
0.265 0.328 0.344 0.267 0.266 0.373 0.268 0.39 1
0.174 0.266 0.236 0.202 0.211 0.182 0.224 0.159 0.202 1
0.211 0.227 0.185 0.162 0.237 0.221 0.25 0.196 0.196 0.276 0.367 1
0.147 0.188 0.202 0.176 0.18 0.167 0.195 0.165 0.176 0.322 0.342 0.33 1
0.125 0.14 0.18 0.136 0.146 0.15 0.186 0.149 0.157 0.279 0.285 0.239 0.322 1
0.144 0.169 0.202 0.168 0.164 0.171 0.214 0.159 0.19 0.243 0.305 0.257 0.289 0.776 1
PL (VD) PL (AN) SA (RJ) SA (KM) SA (GN) SA (DH) SA (AB) SAa (AN)
Table 3 Jaccard similarity coefficient for S. asoca and P. longifolia for the chemical fingerprint.
SA (VN)
SA (OR)
SA (PT)
PL (AB)
PL (GN)
PL (KM)
PL (RJ)
PL (VL)
PL (KA)
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Fig. 2. UPGMA based dendrograms of S. asoca and P. longifolia for (A) Chemical fingerprinting (B) Genetic fingerprinting.SA, S. asoca; Pl, P. longifolia; AN- Anand, ABAhmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
fingerprinting (Galimberti et al., 2013; Smillie and Khan 2010; Zhaoyang et al., 2014). In accordance with the above lines, both chemical and DNA fingerprints were generated and the data were compared in order to provide better platform to develop methods to discriminate S. asoca and P. longifolia medicinal material. Phytochemical analysis was done using HPTLC and DNA fingerprints based on AFLP technique were developed. The techniques chosen in the present study are less resource intensive as compared to other advanced and efficient techniques, used in the other reports (McGregor et al., 2000; Nybom et al., 2014). When considering traditional herbal drug market, it is important to remember that the key suppliers of herbal drug materials are resource limited developing countries and cannot afford the implementation of such costly high throughput analytical techniques for routine quality control (Hoareau and DaSilva, 1999; Kaale et al., 2011; Nybom et al., 2014; Sahoo and Manchikanti, 2013). In practical terms, the situation warrants use of analytical techniques which are low cost and have ease of applicability. Keeping the perspective in mind, we chose to evaluate HPTLC and AFLP fingerprinting for Saraca and Polyalthia bark discrimination. HPTLC chemical fingerprinting based quality control approach is simple, reliable and can successfully demonstrate both overall phytochemical similarity and differences between various plant samples. This approach obviates the need of purification of variety of chemical markers specific to particular targeted plant which in itself is again a huge exercise. Furthermore, few selected chemical markers cannot reflect the complexity of metabolome of herbal drug material which a simple HPTLC fingerprint can successfully convey in form of a simple visual image. Also, HPTLC analysis can be employed for multiple sample analysis in single run of solvent development (Ram et al., 2011). Similarly, DNA molecular profiling technique, AFLP, also has widespread applicability owing to its simplicity, low cost and high reproducibility feature. More importantly, it does not require any prior genomic DNA sequence information of target plant under analysis. Ideally, primers combinations once designed can be used for any plant in question (Nybom et al., 2014; Paun and Schönswetter, 2012). Bark samples of both plants were collected from various locations and dried for phytochemical analysis (Table 1). Regarding HPTLC fingerprinting, three different solvents were used for the extraction of polar, non-polar and medium polar compounds. Methanol, ethyl acetate and hexane extract was used for the polar compounds, medium polar and non-polar compounds, respectively. For the each solvent, plates were developed and scanned on all the recommended wavelengths 254 nm, 366 nm and 525 nm (for the plates derived with
anisaldehyde). This was done to quench data related to each possible type of compound present in S. asoca and P. longifolia. Bands on the similar Rf values, if present, were counted as 1 and absence was counted as 0. Phytochemical analysis coupled with the statistical evaluation showed that total number of bands generated was 212, out of which maximum number of bands obtained in ethylacetate extraction (74) and minimum number of bands obtained in methanol extraction (66). In hexane extracts total 72 bands were obtained. The total number of polymorphic bands were 209 and the percent polymorphism was equal to 98.6%, between S. asoca and P. longifolia (Table 2). As per the percent polymorphism, Polyalthia (95.93%) is slightly more chemically diverse than Saraca (94.21%). PCoA was performed to validate the application of chemical fingerprinting to discriminate S. asoca from P. longifolia. Results showed the grouping of both the plants in the two separate clusters (Fig. 1A). However, statistical analysis of the chemical data, obtained by analysing the three different solvent extracts individually, clustered S. asoca in a single different group; but In comparison to the methanol and the ethylacetate extracts the chemical data from the hexane extract gave tight clustering of the Saraca, (Supplementary Fig. 1). Hexane extracts can be used to generate signature HPTLC fingerprints for the Saraca which can differentiate it from the most potent substitute P. longifolia. Binary matrix computed for the chemical data was also analysed for hierarchical clustering. UPGMA based dendrogram was generated which also clearly distinguished S. asoca from the P. longifolia (Table 3, Fig. 2A). The similar analysis was performed with the genetic fingerprint data. Bark of the Saraca is used as the medicinal material, so stem bark was used as the source of DNA for AFLP fingerprinting. DNA from each bark samples were isolated and subjected to qualitative and quantitative analysis. The 260/280 ratio for the S. asoca was obtained between 1.2–1.6 and for P. longifolia it was obtained between 1.3–1.9. This PCR amenable DNA was subjected to the AFLP analysis. Initially, 100 primer pairs were screened to determine the appropriate discriminating primer pair combinations. Finally, six primer pairs were selected on the basis of percent polymorphism between S. asoca and the P. longifolia, as well as the unambiguity in the fingerprints. Total 103 bands were generated and out of which 100 bands were found polymorphic (Table 2). Being the members of different families, as obvious, high level of polymorphism (97.03%) was found between these two plants. Jaccard similarity coefficient was computed and UPGMA based dendrogram was build (Table 4). Dendrogram based on the genetic fingerprinting clustered both the plants separately into two individual groups but with a little ambiguity (Fig. 2B). The similar results was obtained with PCoA
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method. In the PCoA analysis also, Polyalthia individuals exhibited distributed pattern of clustering in comparison to Saraca samples, indicating high level of genetic diversity between Polyalthia populations (Fig. 1B). Estimation of genetic diversity of the individual plants proved the S. asoca is less diverse than the P. longifolia. Percent polymorphism between Saraca individuals was 55.71 whereas Polyalthia had 95% of polymorphism (Table 5). Higher level of genetic similarity between Saraca individuals were also evident in the Jaccard similarity coefficient related to different populations, which was between the ranges of 0.600–0.923 (Table 4). Similar lower level of genetic diversity in Saraca was estimated using RAPD which was reported by Senapati et al. (2012). In this study, concern about the immediate need to step forward for the conservation of the Saraca was raised. Similarly, in another research, informative microsatellite markers have been isolated for managing the conservation policies for Saraca (Sumangala et al., 2013). Low abundance of original botanical can worsen the adulteration problem. On the other side, Jaccard similarity coefficient for Polyalthia fell in the range of minimum 0.173 to the maximum 0.896 (Table 4). Results showed that the genetic data is sufficiently diverse, and simultaneously, conserve in Saraca individuals to satisfy its separate clustering; but Polyalthia has shown distributed pattern due to high level of genetic diversity which can interrupt the absolute authentication of Saraca, if only genetic fingerprinting is used. It also inhibited us to find the species specific unique marker band in either Saraca or Polyalthia in order to develop SCAR marker for the identification of the original. Moreover, from comparison of PCoA plots and dendrograms for the chemical and the genetic data, it was exhibited that chemical fingerprints can give more discrete clustering without intersecting the authentication process for the Saraca (Figs. 1 and 2). Thus, the study suggests that the chemical fingerprinting can provide more reliable platform over the genetic data to develop an authentication tool regarding S. asoca and P. longifolia. Nevertheless, more number of samples are required to validate the high genetic diversity of Polyalthia and its effect on the identification process, but at this point of time, genetic fingerprinting can only be used as supporting tool with the chemical analysis for absolute categorization of S. asoca and its adulterant P. longifolia.
SA, S. asoca; Pl, P. longifolia; AN- Anand, AB- Ahmedabad, DH- Dahod, GN- Gandhinagar, KA- Khandva, KM- Karamsad, RJ- Rajpipla, VD- Vadodara, VL- Valsad, VN- Vidyanagar, PT- Patna, OR- Orrisa.
0.263 0.247 0.254 0.243 0.243 0.244 0.211 0.243 0.194 0.303 0.259 0.225 0.239 0.304 0.262 0.293 1 0.38 0.405 0.406 0.333 0.333 0.358 0.364 0.382 0.329 0.269 0.246 0.389 0.384 0.321 0.268 1 0.254 0.235 0.262 0.194 0.25 0.304 0.232 0.25 0.237 0.274 0.173 0.379 0.35 0.537 1 0.325 0.293 0.304 0.274 0.274 0.392 0.324 0.342 0.284 0.333 0.254 0.431 0.446 1 0.587 0.583 0.492 0.517 0.517 0.578 0.627 0.627 0.556 0.304 0.241 0.896 1 0.571 0.567 0.45 0.5 0.5 0.515 0.557 0.557 0.509 0.29 0.203 1 0.159 0.172 0.153 0.109 0.109 0.209 0.169 0.206 0.123 0.321 1 0.307 0.292 0.323 0.271 0.29 0.338 0.288 0.306 0.242 1 0.673 0.706 0.6 0.694 0.766 0.632 0.692 0.66 1 0.763 0.868 0.704 0.727 0.696 0.78 0.923 1 0.793 0.868 0.673 0.727 0.727 0.75 1 0.8 0.808 0.611 0.837 1 SA (AN) SA (AB) SA (DH) SA (GN) SA (KM) SA (RJ) SA (VN) SA (PT) SA (OR) PL (AB) PL (AN) PL (GN) PL (KM) PL (RJ) PL (VL) PL (KA) PL (VD)
1
0.873 1
0.627 0.655 1
0.768 0.808 0.611 1
0.677 0.733 0.617 0.639 0.639 1
SA (PT) SA (VN) SA (RJ) SA (KM) SA (GN) SA (DH) SA (AB) SA (AN)
Table 4 Jaccard similarity coefficient for S. asoca and P. longifolia for the AFLP fingerprint.
SA (OR)
PL (AB)
PL (AN)
PL (GN)
PL (KM)
PL (RJ)
PL (VL)
PL (KA)
PL (VD)
P. Sharma et al.
4. Conclusion The current study presents statistical evaluation of chemical and genetic fingerprinting for validating their application in authenticating S. asoca and for its discrimination with P. longifolia. The present research can be used for planning the scheme for authentication of genuine S. asoca starting herbal material and can be adopted by herbal drug industry for quality control
Conflict of interest Authors declare that they have no conflict of interest.
Acknowledgements Authors are thankful to Gujarat State Biotechnology Mission (GSBTM), Gujarat, India for their financial support. Authors also wish to thank following personnel and institutes for helping in stem bark collection i.e Dr. Virendra Rana, DMAPR; Dr. Mahesh Patel, AAU, Anand; Dr. Aruna Joshi, MS University, Vadodara; Dr. KJ Mehta, Navjivan Science College; Dr. Farzin Parabia, SPU University; Dr. Sheetal Anadjiwala, Valsad; IKICP, ADIT Campus, Karamsad; Jawaharlal Nehru Udhyan, Gandhinagar; Govt. Medicinal Botanical Garden, Rajpipla, Dr. Kislaya Singh, Patna; Mr. Ashutosh Behuria, Orrisa. 5
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Table 5 Total number of bands, polymorphic bands, percent polymorphism obtained with 6 AFLP primer combinations on 17 accessions of S. asoca (SA) and P. longifolia (PL). Primers
ECoR-TAC/Mse-TAGC ECoR-AAC/Mse-GCG ECoR-AAG/Mse-TCA ECoR-ACT/Mse-CTA ECoR-GC/Mse-GGA ECoR-ACT/Mse-CTG Total
Total bands
Monomorphic bands
Polymorphic bands
Percent polymorphism
SA
PL
SA
PL
SA
PL
SA
PL
11 13 11 11 15 9 70
17 18 14 17 19 15 100
3 5 4 9 3 7 31
1 1 1 2 0 0 5
8 8 7 2 12 2 39
16 17 13 15 19 15 95
72.73 61.54 63.64 18.18 80.00 22.22 55.71
94.12 94.44 92.86 88.24 100 100 95
Appendix A. Supplementary data
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