Development of novel gene-based microsatellite markers for robust genotyping purposes in Lagenaria siceraria

Scientia Horticulturae 191 (2015) 15–24

Contents lists available at ScienceDirect

Scientia Horticulturae journal homepage: www.elsevier.com/locate/scihorti

Development of novel gene-based microsatellite markers for robust genotyping purposes in Lagenaria siceraria Bhawna a,b , M Z Abdin b , L Arya a , Chet Ram a , A K Sureja c , M Verma a,∗ a b c

Division of Genomic Resources, NBPGR, New Delhi, India Faulty of Science, Jamia Hamdard University, Hamdard Nagar, Delhi, India Division of Vegetable Science, IARI, New Delhi, India

a r t i c l e

i n f o

Article history: Received 19 February 2014 Received in revised form 1 May 2015 Accepted 5 May 2015 Keywords: Bottle gourd Microsatellite enriched library SSR markers Partial genome sequences Population structure

a b s t r a c t Bottle gourd [Lagenaria siceraria (Mol.) Standl.] is an easy growing climber that has made great impact due to its medicinal importance. For estimating genetic diversity among accessions in breeding programs, SSR markers are an essential tool, but for the species that have meager number of these markers available, their development is a challenging aspect. In the present study a microsatellite-enriched library was constructed from the genotype ‘Pusa Santushti’. Sequencing of 100 putative SSR-positive clones from a total of 220 clones provided 44 SSR repeats and primer pairs could be designed for a total of 40 SSRs. Of these, 30 (75%) primer pairs yielded scorable amplicons and seven (17.5%) primers showed polymorphism among 40 bottle gourd accessions. The 20 polymorphic alleles from seven polymorphic markers were used for UPGMA dendrogram and population structure analysis; both revealed that five populations are present in the 40 accessions that did not group consistent with their geographical origins probably due to gene flow. The results of this study provide 30 novel SSR markers that would be valuable in agriculture via plant breeding, phylogenetic relationships, cultivar identification and linkage mapping in cultivated bottle gourd as well as related cucurbits species. Significantly, 49 partial genome sequences were also isolated that can be helpful in further gene based studies in this vegetable crop. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Lagenaria siceraria [(Mol.) Standl, a diploid crop (2n = 2x = 22)], is known to be one of the most important crops (Shah and Seth, 2010; Saha et al., 2011; Nainwal et al., 2011; Rahman, 2003; Khayer et al., 2011) of the Cucurbitaceae family with a genome size of ∼334 Mb (Xu et al., 2011). It is originated in Africa, from where it got distributed and cultivated in Brazil, China, India and various European countries for vegetable purpose or medical practice. Human migration, genetic exchange or by-seeds floating across the oceans is considered as the primary reason for bottle gourd distribution from Africa to Asia, Europe and the Americas (Erickson et al., 2005). It is also used as one of the popular rootstocks for Cucurbitaceae (Han et al., 2009). Apart from its usage as vegetable, it is also well known for its decorative purposes as a bottle, utensil, musical instrument or pipe when it matures and its seeds are used as good source of oil and protein. Numerous health benefits are reported in bottle gourd including its anti-cancerous (Saha et al., 2011), Cardio

∗ Corresponding author. Tel.: +91 1125849459; fax: +91 1125842495. E-mail address: manjusha [email protected] (M. Verma). http://dx.doi.org/10.1016/j.scienta.2015.05.006 0304-4238/© 2015 Elsevier B.V. All rights reserved.

protective (Fard et al., 2008), diuretic, aphrodisiac, general tonic, antidote to certain poisons, cooling effect and laxative (Badmanaban et al., 2009), also suggested to increase the lactation in lactating mothers. It can also be used to cure pain, ulcers, fever, for pectoral cough, asthma and other bronchial disordersusing prepared syrup from the tender fruits (Upaganlawar and Balaraman, 2010). Moreover, bottle gourd exhibits abundant genetic and morphological variability (Given, 1987), this alone signifies its wide ecological adaptation. Since the advent of molecular markers they have been utilized to provide with requisite landmarks for elucidation of genetic variation. SSR markers have a wide range of applications, including phylogenetic analysis, DNA fingerprinting, detection of clonal variation, gene tagging, cultivar identification, detection of genomic instability, and assessment of hybridization (Xu et al., 2004). SSR markers are broadly used for high-throughput genotyping, map construction as they are valuable due to their high abundance, random presence within the genome, high polymorphism information content (PIC) and stable co-dominance (Fraser et al., 2004; Chen et al., 2006; Watanabe et al., 2011). The reproducibility, codominance, relative abundance and complete genome coverage of SSR markers have made them one of the most useful tools

16

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

for detecting genetic diversity, genetic linkage mapping, association mapping and evolution analysis (Jiao et al., 2012). Despite bottle gourd being an important tropical vegetable crop, having abundant health benefits and proven for climate change adaptation strategies (Chimonyo and Modi, 2013), very limited molecular marker studies have been reported to study its genotyping, such as ISSR, SNP, SSR, RAPD (Bhawna et al., 2014; Xu et al., 2011; Bhawna et al., 2015; Sarao et al., 2014; Srivastava et al., 2014; Decker-Walters et al., 2001 respectively), but no study has been reported to characterize bottle gourd populations by constructing enriched SSR genomic library. To preserve the genetic richness of this crop, efforts must be directed towards the achievement of reliable collection and conservation strategies. A good knowledge of breeding material related to genetic variability is a prerequisite for an effective crop improvement program. With this view, we envisaged the present study to understand the molecular diversity among the varieties and collected accession of L. siceraria by using newly developed SSR markers via using genomic library. In the present study, we collected 40 L. siceraria accession from the northeast and northern region of India. The objectives of the present study were: (1) to develop SSR enriched library (2) to investigate the SSR markers developed for molecular characterization of bottle gourd; (3) to assess genetic diversity and population structure in the accession of bottle gourd with a view to further develop improved strategies for conservation and (4) submit partial genomic resources in this crop for future use in gene characterization. 2. Material and methods 2.1. Plant materials Forty accessions of bottle gourd were collected from two different agro ecological zones (northern and north-eastern) of India that differ in rainfall, soil type and temperature conditions. Of the 40 accessions, 12 were from northeast India and 28 from north India. These were sown in the fields of IARI, New Delhi, India, during the month of March–May 2012 with sub-humid conditions. After cultivation all leaf samples were stored at −80 ◦ C, until the extraction and isolation of DNA. All the information regarding IC (Indigenous Collection) numbers, place of collection and origin of these cultivars/accession are provided in Table 1. 2.2. DNA extraction CTAB (Cetyltrimethyl ammonium bromide) protocol (SaghaiMaroof et al., 1984) was used to isolate DNA from 5 g leaf samples. DNA samples were treated with 4 ␮l RNase A (50 mg/ml) at 37 ◦ C for 1 h, and purified by phenol: chloroform: isoamyl alcohol mixture in order to remove RNA. Then the DNA samples were quantified in a Nanodrop at 260 nm and the purity of samples estimated by A260/A280 ratio and DNA quality was verified by electrophoresis on 0.8% agarose gel. Finally, the stock DNA samples were diluted to 30 ng/␮l of uniform concentration for genotyping purposes. 2.3. Construction of microsatellite-enriched library and primer design The genotype ‘Pusa Santusthi’ which is known as high yielding variety with high nutritional quality value having light green, smooth, and pear shaped fruit; with fruit set at low as well as high temperature, was used to construct the SSR-enriched genomic DNA libraries (Bloor et al., 2001). Highly concentrated (∼500 ng/␮l) DNA was digested by frequent cutter restriction enzymes (Hae III, Sau3A, RsaI) and then incubated at 37 ◦ C for 3 h. All the steps required to construct library viz adaptor-ligated, required fragment excision,

washing of streptavidin coated magnetic beads, incubation of beads at specific hybridization temperature, requsite PCR and its success check, cloning, transformation, selection of recombinants, isolation of plasmid DNA were done by following Bloor et al. (2001) protocol. The plasmid DNA/clones were sent for custom sequencing. The sequences were submitted in WEBSAT software (http://wsmartins. net/websat/), for detecting the presence of simple sequence repeats primers of 18–22 base length and product sizes of 150–250 bp. Then the flanking primer pairs were designed. The sequences were also analyzed through multalign (http://multalin.toulouse.inra.fr/ multalin/) for redundancy removal. 2.4. Validation of the developed SSR primers SSR primers were tested for amplification and PCR reactions were carried out in a final volume of 25 ␮L containing 1 unit Taq DNA polymerase, 100 ␮M dNTP mixtures, 10 mM Tris HCl, 1.5 mM MgCl2 , 0.4 ␮M primer and 50 ng DNA. In a Bioer thermocycler (Hangzou, P. R. China) amplification was performed at 94 ◦ C for 5 min, followed by 35 cycles of 30 s at 94 ◦ C, 40 s at annealing temperature (Table 2), 45 s at 72 ◦ C and a final extension of 5 min at 72 ◦ C. The amplification products were resolved in 3% metaphor gel followed by ethidium bromide staining and visualized with a gel documentation system syngene, USA; Fig. 1). 2.5. Statistical analysis The 40 developed SSR primers were screened and out of them 30 yielded clear and intense bands. The sequences of the primers developed in this study and their annealing temperatures are mentioned in Table 2. Seven primers showed polymorphisms which were further used for profiling of the 40 selected accession. The consistently amplified bands were scored for each accession in a binary matrix as 1 (presence) or 0 (absence) for analysis with NTSYS PC 2.1. The bands were also scored as alleles with A, B, C denomination for analysis with Popgene 1.31 and allele sizing was done for Structure program. The polymorphism information content (PIC) of all microsatellite loci was calculated as described by Weir (1990): PIC = 1 − Pi 2 , where Pi is the rate of recurrence of the ith allele in the studied accession. A pairwise genetic similarity matrix between accession was estimated using Jaccard’s coefficient, and a dendrogram was constructed based on UPGMA (Unweighted pair group method by arithmetic mean) hierarchical clustering method by using the NTSYS-PC 2.01 software package. The data were also analyzed with POPGENE v.1.32 (Yeh et al., 1999) to estimate different genetic diversity parameters, observed number of alleles (na), effective number of alleles (ne), percentage of polymorphic loci (P), Shannon’s information index (I). To examine population genetic divergence Nei’s gene diversity (h) (Nei, 1973) and GST as the coefficient of gene differentiation among the populations was estimated. Further, estimation of gene flow (Nm) from the different populations was done, which was calculated as Nm = (1 − GST)/2GST (Slatkin and Barton, 1989). For the analysis of population structure and identification of admixed individuals, we used a model-based clustering method as implemented in the software program STRUCTURE 2.3.4 (Pritchard et al., 2000). In this model, a number of populations (K) are assumed to be present, each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample were assigned to populations (clusters), or jointly to more populations if their accession indicate that they were admixed. All loci were assumed to be independent, and each K population is assumed to follow HardyWeinberg equilibrium. The posterior probabilities were estimated using the Markov Chain Monte Carlo (MCMC) method. The MCMC chains were run with a 100,000 burn-in period, followed by 100,000 iterations allowing for admixture and correlated allele frequencies.

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

17

Table 1 Names of the Lagenaria siceraria accessions, IC numbers and place of origin used in the present study. S.No*

Sample name/IC number**

Place of origin/Collection

Latitude

Longitude

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

BG2/IC-551046 BG4/IC-418252 BG5/IC-551043 BG6/IC-418253 BG9/IC-551041 BG10/IC-551042 BG11/IC-551045 BG12/IC-449080 BG13/IC-541196 BG14/IC-541197 BG15/IC-541198 BG16/IC-551047 BG17/IC-523660 BG18 BG19 BG20 BG21/IC-470160 BG22/IC-470859 BG23/IC-527921 BG24/IC-438394 BG25/IC-470159 BG26/IC-418245 BG28/IC-551044 BG30 Arka Bahar Pusa Sandesh BG33 BG36 BG37 BG40 BG46 BG48 BG49 BG51 BG53 BG56 BG57 BG58 BG59 PSPL

Faizabad, Uttar Pradesh Faizabad, Uttar Pradesh Moradabad, Uttar Pradesh Faizabad, Uttar Pradesh Moradabad, Uttar Pradesh Moradabad, Uttar Pradesh Uttar Pradesh Kashi Ganga,Varanasi, Uttar Pradesh Pantnagar, Uttarakhand Pantnagar, Uttarakhand Pantlauki 3, Pantnagar, Uttarakhand Faizabad, Uttar Pradesh Varanasi, Uttar Pradesh Warad-Hybrid, Mahyco, Mahyco Vegetable Seeds Ltd, Mumbai VNR Gutka, VNR Seeds Pvt Ltd, Chattisgarh G-2,Amar Seed Company, Khalilabad, Sant Kabir Nagar-U.P. Narendra Dharidar, Faizabad, Uttar Pradesh Pusa Naveen, Faizabad, Uttar Pradesh Narendra-Shishir Faizabad, Uttar Pradesh Pusa samridhi, Punjab Narendra Rashmi, Faizabad, Uttar Pradesh Narendra Jyoti, Faizabad, Uttar Pradesh Uttar Pradesh Meerut, U.P Pure line selection from a local collection(IIHR-20), Karnataka I.A.R.I., Delhi Kohima, Kohima district, Nagaland Dimapur, Dimapur district, Nagaland Agartala, West Tripura district, Tripura Imphal, Imphal East district, Manipur Ranir Bazar, West Tripura district, Tripura North east India Depacherra, South Tripura district, Tripura Amarpur, South Tripura district, Tripura North east India Garobadha, Garo hills district, Meghalaya Garobadha, Garo hills district, Meghalaya Rangpo, East Sikkim district, Sikkim Pakyong, East Sikkim district, Sikkim North east India

26.8 26.8 28.8 26.8 28.8 28.8 28.6 25.3 29.0 29.0 29.0 26.8 25.3 19.1 21.3 26.8 26.8 26.8 26.8 31.2 26.8 26.8 28.7 28.9 15.3 28.5 25.7 25.8 23.8 24.8 23.9 23.5 23.2 23.5 30.3 25.5 25.5 27.2 27.2 22.5

82.1 82.1 78.7 82.1 78.7 78.7 77.2 83.0 79.5 79.5 79.5 82.1 83.0 72.9 81.9 83.1 82.1 82.1 82.1 75.3 82.1 82.1 77.5 77.6 75.7 77.2 94.1 93.8 91.3 93.9 91.4 73.3 91.6 91.7 78.0 90.0 90.0 88.6 88.6 88.4

* **

Masl (meters above sea level) 104 103 197 102 197 197 221 79 235 235 235 102 79 5 279 84 107 107 107 224 107 107 218 222 651 257 1456 228 14 783 36 141 23 43 634 84 84 478 1344 10

Region of India Northern region

Northeast region

Serial number. IC refers to indigenous collections.

Five runs of STRUCTURE were performed by setting K from 2 to 10, and an average likelihood value, L (K), across all runs was calculated for each K. The model choice criterion to detect the most probable value of K was K, which is an ad hoc quantity related to the second-order change in the log probability of data with respect to the number of clusters inferred by STRUCTURE (Evanno et al., 2005). 3. Results and discussion 3.1. SSR-enriched library SSR enriched libraries were constructed from the genotype ‘Pusa Santushti’ following a slightly modified protocol of Bloor et al. (2001). Enrichment of the libraries was done by using SSR repeat probes (CA, CT, TG, AG and GA) and from the constructed libraries, 220 clones were selected from the 96-well plates. Sequence analysis of the clones provided 60 sequences that contained inserts in the size range of 200 bp to 1400 bp with an average size of 350 bp. Out of the 60 sequences, 65.31% contained insert of moderate size ranges from 120–400 bp while 22.45% clones had an insert size in between 400–600 bp and 12.24% clones contained inserts of >600 bp. Sequence data of the above mentioned 60 clones were analyzed to reveal 48.33% clones with one or more SSRs regions. Similar

results were reported by He et al. (2003), with 61% of clones containing SSR regions, Gimenes et al. (2007) reported 56% SSRs clones and low enrichment efficiency was shown by Wang et al. (2007) i.e., 43.7% clones with SSRs. 10–31%, clones were found in some other SSR enriched libraries (Ferguson et al., 2004; Moretzsohn et al., 2005). Enrichment effectiveness depends on various factors, comprising the selection of the restriction enzymes used for library construction, the SSR probes used for enrichment, etc., the methodology used in the present study proved to be effective for SSR isolation in bottle gourd. Sixteen per cent of the sequences were redundant. Clones having redundant sequences have been reported in other plant species, e.g., onion (Allium cepa L., 24.3%), groundnut (Arachis hypogaea, up to 67%) (Ferguson et al., 2004; Gimenes et al., 2007; Moretzsohn et al., 2005) and the olive tree (Olea europaea L., 16.6%) (Rallo et al., 2000). The strategy used in the current study seems to be successful to isolate a higher percentage of unique and novel SSRs. The bias detected for certain SSRs may be due to repeated multiple clones which can also be due to the enrichment process, single-strand enriched DNA amplification or growth of bacteria before plating. During the enrichment process the addition of excess adapter, during the initial ligation step produced 7.2% clones that were concatenates, due to the presence of internal RsaI, Sau3A and HaeIII restriction sites in them. Another type of concatenation may be

18

Table 2 The bottle gourd (Lagenaria siceraria) forward and reverse SSR/genic primer sequences developed by using SSR-enriched genomic DNA library of ‘Pusa Santusthi’. Sequence (5 –3 ) F

Sequence (5 –3 ) R

Microsatellite with putative function

Accession number or GI number of homolog

BAT2 BAT3 BAT7 BAT14 BAT32A BAT32B BAT36 B3 B9 B11 B30 B41 B50 B51 B58 B61 B84 B85 B101 B102a B102b B113a B113b B114 B116 B124 B125 B126a B126b B126c

CTTCGTGGAGGGCTAATGG GCTCTGATGAATCCCCTAATGA GCTGACATACAAAGGCGCAC TGTGTGAAATTGTTATCCGCTC CTTCGACACTTCCCGTTCAT CCGTTCATCACTCACACTCAAT CTTACTGGCTTCCCTACCACCT GCGTTAGAGCATTGAGAGGAC GCGACCGATTTTACGAACTTTA CCAGGATAAAGTACCCAACCC GTCAAAACATCCATACCCATGA GCGTGGACTAACAACCGTCTAA TGAAAATGCTATGAGGTGTTCG GTTATCCGCTCACAAATTCCAC TTCACACCAAGTATCGCATTTC GTCAGTCGATTTCTCTCCCATC AATTCGATTCTCTTGCTTACG CAAATATCACTTCGGAGCACAA GACATGCCAAATAGGGTCAAAT AGGCACTATTGACATCCATTTG CTGCCACTTGAGAAATATGCC GATGGTGGAGACTAAAGGAGAG GGACTACCGAAGGGAATGAGAT TGGACTACCTACCCGAAAGAGA AAAGCTATGGAACTTGGGTGTG GGGCTAGGGGTAAACGTATCTC GGGCTAGGGGTAAACGTATCTC TAATAGTTGGCCCCTCTTTCCT GCTTCTGGCGACTAGCTTCTAC TAATAGTTGGCCCCTCTTTCCT

AAGAATGCAAAGGAGACCAGAA GTATTGCCCGAACAGATAAAGC GGCCAGAGACCCCAAGC CATGTTCTTTCCTGCGTTATCC CTTGTGGTGGTTTAGCTTGTTG GCGACCGATTTTACGAACTTTA CAGGACTTTGTGTTCTGTGCAT TGGGGTGGGCTTACTACTTAGA CAACAAGCTAAACCACCACAAG TTGATGCCCTAAAACTCGTAGA GGTCTAAACAGCAAGGCAAAGT CATCGGCTAAATGTTCGATTTG AGGCACAAGGAAAGAGCAATAG CACGACAAGTTTCCCGACTG TCGTAACAAGGTTTCCGTAGGT GTCTGCATCTGTGAACCCATTA TTTCTCCTCTAATCCACCATTT ACCCATTACACATCTCCCACTT CGCGTGGACTACCAAATTAAG GATGAGGCATTGTTGAGATGAG CAACTTATTGTGAATGTGAGGGAC TGGACTACCCCACTAACTTCTTA AACAAGTAAAACGCATGGACCT TGTATGGATGGTATGAACTGCC CGTGGACTAACTTGCTCTGCTA CGTGGACTACCAAACCACAAT CGTGGACTACCAAACCACAAT GGTAGAAGCTAGTCGCCAGAAG CCTGTCCTCCCTTTCTATTCCT TGACACTCTCGCCTCATACG

No hits No hits No hits No hits No hits No hits No hits 26S ribosomal RNA gene, complete sequence NOD26-like membrane integral protein gene Resistance gene analog (RGA) partial sequence Resistance gene analog (RGA) partial sequence Resistance gene analog (RGA) partial sequence chloroplast genome, gene = “psbD” Resistance gene analog (RGA) partial sequence 17S rRNA, 5.8S rRNA, and 25S rRNA gene region C-repeat/DRE binding factor 1 (cbf1) mRNA, complete cds Resistance gene analog (RGA) partial sequence large subunit GTPase 1 homolog of gene mitochondrion nad1 gene Prv splice variant B (Prv) gene Prv splice variant B (Prv) gene Resistance gene analog (RGA) partial sequence Resistance gene analog (RGA) partial sequence ycf15-like gene tRNA-Arg (trnR) gene Sat gene for serine acetyltransferase Sat gene for serine acetyltransferase mitochondrion genome region mitochondrion genome region mitochondrion genome region

– – – – – – – AF479108.1 GQ487333.1 KC107185.1 KC107185.1 KC107185.1 JF412791.1 KC107185.1 M36377.1 DQ776899.1 KC107185.1 XM 004155980.1 GQ856147.1 JX295634.2 JX295634.1 KC107185.2 KC107185.1 DQ865976.1 JF412791.1 AB006530.1 AB006530.1 JX295634.1 GQ856147.2 GQ856147.1

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20 19 18 21 22 23 24 26 25 27 29 28 30

Name

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

19

Fig. 1. Agarose gel revealed size variant alleles obtained using microsatellite markers B-9 (a) and B-3 (b) in 40 Lagenaria siceraria accessions.

produced during the PCR step of the procedure (Koblizkova et al., 1998). Such chimeras usually remain hidden and may affect the failure of amplification of genomic DNA by using such primer pairs. 3.2. Occurrence and features of SSRs Sequence analysis of 60 clones showed the presence of one or more SSRs with different proportion of SSRs repeats in 37 clones having total 52 SSR repeats with different type, repeat and size (Table 3, Fig. 2). Among these 52 SSR repeats, 86.53% of the SSRs identified were perfect and 13.46% were compound repeats. In terms of the repeat motifs, the CT accounted for 23.07%, after that TGG with 9.61%, followed by GA, GTC, CTT with 7.69% and AG, TC, TG, GC, GTTTT, GAA repeats with 3.85%. The use of CT, GA, GTC, CTT, AG, TC, TG, GC, GTTTT, GAA filters might increase the efficiency of retrieving perfect repeat motifs.

There are various repeats like AC, CCA, TTG etc. having only 1.92% occurrence in the libraries. However the present study reported around 13.46% of compound SSRs which were retrieved by using a mixture of different SSR oligos. The maximum repeat number of dinucleotide motifs, TC and GA were 28 and 26 units, respectively; overall 3 to 28 repeat motif numbers are found in the sequences. Some sequences show variation in the number of SSR. In addition to TC and GA repeats, several other SSRs containing the repeat motifs (AAG)n , (TGG)n , (CCA)n , (TTG)n , (GAA)n , (CTT)n , (TCT)n , (GTC)n , (GTG)n , (TCGG)n , (ACAT)n and (AATT)n and (GTGTTT)n with 3–11 repeat numbers were also found. Maximum numbers of the clones comprising repeats were not completely complementary sequences to the oligo nucleotide probes used in the study. In the reported study, only 100 clones sequenced were selected randomly from the set of 220 clones, possibly sequencing of more number of clones can produce more number of SSR repeat motifs.

3.3. Partial Sequences Description

Fig. 2. Proportion of mono, di, tri, tetra and penta SSR repeats in SSR containing sequences.

In the present study, we have reported 49 sequences (Table 5), maximum amidst them are partial sequences. In order to find the function of these sequences we performed BLAST, which identified the sequences. From these sequences, we found various microsatellites fragments, some partial coding sequences of gene like clp protease proteolytic subunit protein (clpP) gene, cytochrome b6 (petB) gene, tRNA-Arg (trnR) gene, NADH dehydrogenase subunit I (nad1) gene, ribosomal protein L5 gene, cytochrome c oxidase subunit 1 gene, psbD protein (psbD) gene, C-repeat/DRE binding factor 1 (CBFI) gene and complete sequence of ycf15-likegene, two

20

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

Table 3 Table showing SSR number and type in the different clones isolated by using SSR-enriched genomic DNA library.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 *

Lagenaria siceraria Gene ID

SSR No.

SSR type

SSR repeat

KC479068.1 KC422264.1 KC422264.1 KC422264.1 KC422263.1 KC422263.1 KC422262.1 KC422261.1 KC422260.1 KC417456.1 KC417454.1 KC417453.1 KC417452.1 KC171971.1 KC171971.1 KC171969.1 KC171967.1 KC171964.1 KC248066.1 KC248066.1 KC248065.1 KC248064.1 KC248063.1 KC248062.1 KC248062.1 KC248062.1 KC248062.1 KC248062.1 KC248061.1 KC248060.1 KC215419.1 KC215416.1 KJ408783.1 KJ408784.1 KJ374730.1 KJ408785.1 KJ408785.1 Total SSR repeats

1 1 2 3 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 3 4 5 1 1 1 1 1 1 1 1 1 52

p2 p3 p3 p3 p3 p3 p2 c p2 p2 p2 c c c p2 p2 p3 p2 p2 p3 p2 p2 p2 c p2 p2 p3 c* p2 p2 p2 c p3 p4 p4 p5 p5

(GC)8 (GTG)5 (TGG)10 (TGG)10 (GTC)6 (GTC)6 (AG)11 (AATT)3t(AAG)10 (GA)9 (AC)12 (AG)17 (TG)8c(GT)6 (TG)8(AG)15 (TG)8c(GT)6 (GC)8 (TC)15 (CCA)4 (GT)12 (CT)15 (GAA)4 (CT)6 (CT)19 (GA)6 (AG)19atttgc(CTT)11 (TC)8 (CT)8 (CTT)5 (CT)6(TCT)5* (GA)17 (CT)20 (GA)26 (TC)8tatatcattgaaagatatt(TC)28 (TTG)4 (ACAT)3 (TCGG)3 (GTTTT)3 (GTTTT)3

c: Compound repeat, p: Perfect repeat.

resistance protein fragments – NBS-LRR-like protein gene and sk20 resistance-like protein gene, some partial ribosomal RNA genes – 18S, 25S, 26S, 5.8S and complete gene of 5S ribosomal RNA, three AFLP and two RAPD fragments and one uncharacterized region. These sequences can be used in future analysis of these genes by utilizing the primers and performing other downstream analysis for gene characterization.

3.4. Marker development From a total of 100 sequenced clones 40 sequences were used to design primers, out of which 30 primers produced clear amplicons. SSR repeats (16.67%) produced were higher than several reported studies; A. hypogaea (10.5%; Moretzsohn et al., 2005), while lesser than some other reports 21.3%, Ferguson et al. (2004). Only seven

Table 4 Various genetic diversity parameters like number of alleles, gene diversity, Shannon’s information index, gene flow, etc. in 40 bottle gourd accessions using the seven developed novel SSR markers are mentioned. Locus

na

ne

I

Obs Hom

Obs Het

Exp Hom

Exp Het

h

Ave.Het

Nm

B3-01 B9-02 B30-03 B41-04 B102A-05 B113-06 B126-07 Mean St. Dev

2 2 2 2 2 2 2 2 0

1.90 1.83 1.54 1.38 1.96 1.95 1.88 1.78 0.23

0.67 0.65 0.53 0.45 0.68 0.68 0.66 0.62 0.09

0.23 0.65 0.75 0.67 1.00 1.00 0.87 0.74 0.27

0.78 0.35 0.25 0.33 0.00 0.00 0.13 0.26 0.27

0.52 0.54 0.65 0.72 0.50 0.51 0.53 0.57 0.08

0.47 0.46 0.35 0.28 0.50 0.49 0.47 0.43 0.08

0.48 0.46 0.35 0.28 0.49 0.49 0.47 0.43 0.08

0.48 0.42 0.35 0.25 0.44 0.41 0.45 0.40 0.08

85.90 6.71 10.00 14.40 1.83 1.09 3.63 3.46

na = Observed number of alleles ne = Effective number of alleles I = Shannon’s Information index Obs Hom = Observed Homozygosty Obs Het = Observed Heterozygosity Exp Hom = Expected Homozygosty Exp Het = Expected Heterozygosity h = Nei’s gene diversity Ave.Het = Average Heterozygosity Nm = Gene flow estimated from GST

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

21

Table 5 Results of homology search (BlastN) of bottle gourd with known feature from other plant species as in GenBank accessions. Bottle gourd accession and GI number

Bottle gourd clone size (bp)

Plant species and description

Accession

E value

Ident (%)

KC479072.1 GI:480359297 KC479071.1 GI:480359296 KC479070.1 GI:480359295 KC479069.1 GI:480359294 KC479068.1 GI:480359293 KC422264.1 GI:449084443 KC422263.1 GI:449084442 KC422262.1 GI:449084441 KC422261.1 GI:449084440 KC422260.1 GI:449084439 KC417456.1 GI:449084438 KC417455.1 GI:449084437 KC417454.1 GI:449084436 KC417453.1 GI:449084435 KC417452.1 GI:449084434 KC171972.1 GI:443682237 KC171971.1 GI:443682236 KC171970.1 GI:443682235 KC171969.1 GI:443682234 KC171968.1 GI:443682232

588 490 235 211 420 231 712 415 496 280 350 236 297 224 578 243 212 210 300 255

EU371542.1 AJ516005.1 FN552475.1 FN552478.1 FN552470.1 EF117762.1 EF117749.1 AY281860.2 AY583773.1 FR754321.1 AM085801.1 AJ507492.1 AJ968936.1 AB292165.1 AJ831399.1 XM 004162750.1 AB292165.1 FN552504.1 AJ420893.1 DQ409242.1

0 0 4.00E-114 3.00E-102 0 2.00E-105 0 0 0 1.00E-139 1.00E-176 9.00E-116 7.00E-149 3.00E-109 0 1.00E-119 9.00E-103 1.00E-101 2.00E-150 4.00E-126

100 94 99 100 99 98 100 100 96 100 100 100 100 100 100 100 100 100 100 100

KC171967.1 GI:443682230 KC171966.1 GI:443682229 KC171965.1 GI:443682228 KC171964.1 GI:443682227 KC248066.1 GI:443254209 KC248065.1 GI:443254191 KC248064.1 GI:443254173 KC248063.1 GI:443254153 KC248062.1 GI:443254141 KC248061.1 GI:443254127 KC248060.1 GI:443254110 KC248059.1 GI:443254097 KC215419.1 GI:443254084 KC215418.1 GI:443254036 KC215416.1 GI:443253988 KC020485.1 GI:425862540

210 408 433 205 448 280 356 357 867 297 216 242 356 219 251 399

KC171974.1 AY337867.1 AF017158.1 AB608381.1 DQ983585.1 DQ983587.1 DQ983589.1 DQ983591.1 DQ983593.1 AJ271637.1 AJ420893.1 HE579139.1 AF547655.2 AY970316.1 AJ420892.1 DQ776899.1

1.00E-101 0 0 2.00E-93 0 1.00E-139 0 0 0 6.00E-137 3.00E-89 9.00E-97 3.00E-128 1.00E-106 6.00E-124 0

100 100 100 100 99 100 100 100 100 98 98 95 91 100 100 100

KC020484.1 GI:425862538 KC020483.1 GI:425862536 KC020482.1 GI:425862534 KC020481.1 GI:425862532 KC020480.1 GI:425862531

910 442 399 259 121

JF412791.1 XM 004173252.1 GQ856147.1 GQ856147.1 JX459587.1

0 0 0 3.00E-128 2.00E-53

100 100 100 100 100

KC020479.1 GI:425862530 KC020478.1 GI:425862529 JX996128.1 GI:417355757 JX996127.1 GI:417355755 JX996126.1 GI:417355745 JX996125.1 GI:417355726

1,400 1,260 537 258 222 361

AF534782.1 AF479108.1 DQ865976.1 JF412791.1 JF412791.1 GQ997058.1

0 0 6.00E-175 5.00E-106 3.00E-77 2.00E-72

100 100 86 94 88 83

JX996124.1 GI:417355701

609

M36377.1

0

100

JX996123.1 GI:417355696

327

Cucumis melo, RAPD marker Camellia sinensis, RAPD marker Pennisetum glaucum, AFLP fragment Pennisetum glaucum, AFLP fragment Pennisetum glaucum, AFLP fragment Panax ginseng, microsatellite sequence Zingiber officinale, microsatellite sequence Avicennia alba, microsatellite sequence Antirhea borbonica, microsatellite sequence Citrus reshni, microsatellite DNA Anacardium occidentale, microsatellite DNA Bambusa bambos, microsatellite DNA Tectona grandis, microsatellite DNA Durio zibethinus, microsatellite Santalum austrocaledonicum, microsatellite DNA Cucumis sativus, uncharacterized mRNA Durio zibethinus, microsatellite locus Pennisetum glaucum, AFLP fragment Entandrophragma cylindricum, microsatellite DNA Desideria himalayensis, chalcone synthase (Chs) partial gene Eleusine coracana, chlorophyllase 3 (Chl3) partial gene Oryza sativa, NBS-LRR-like partial gene Cucurbita maxima, 25S ribosomal RNA gene Raphanus sativus DNA, microsatellite locus Cucumis melo, microsatellite sequence Cucumis melo, microsatellite sequence Cucumis melo, microsatellite sequence Cucumis melo, microsatellite sequence Cucumis melo, microsatellite sequence Elaeis guineensis, microsatellite DNA Entandrophragma cylindricum, microsatellite DNA Sesamum indicum, microsatellite DNA Posidonia oceanica, microsatellite Oryza sativa, sk20 resistance protein-like partial gene Entandrophragma cylindricum, microsatellite DNA Cucumis sativus, C-repeat/DRE binding factor 1 (cbf1) mRNA Cucumis melo, chloroplast genome Cucumis sativus, cytochrome c oxidase subunit 1-like Citrullus lanatus, mitochondrion genome Citrullus lanatus, mitochondrion genome Coffea arabica, 4.5S ribosomal RNA, 5S ribosomal RNA, and tRNA-Arg genes Cephalopentandra ecirrhosa, 18S ribosomal RNA gene Cucurbita pepo, 26S ribosomal RNA gene Cucumis sativus, chloroplast genome Cucumis melo, chloroplast genome Cucumis melo, chloroplast genome Aucuba japonica, clp protease proteolytic subunit protein (clpP) partial gene Melon, 17S rRNA, 5.8S rRNA, and 25S rRNA partial gene region Gossypium barbadense, chloroplast DNA

AP009123.1

6.00E-162

(23.33%) primers produced polymorphic bands, that is relatively lower than the Moretzsohn et al. (2005) and Ferguson et al. (2004) reporting 81.6% and 84.9%, respectively. 63.3% markers were gene based; amplifying regions—NOD26-like membrane integral protein, large subunit GTPase homolog, C-repeat/DRE binding factor 1 (cbf1) mRNA, 17S rRNA, 5.8S rRNA, and 25S rRNA gene region, resistance gene analog (RGA) partial sequences, 26S ribosomal RNA gene, Sat gene for serine acetyltransferase, tRNA-Arg (trainer) gene, ycf15-like gene, Prv splice variant B (Prv) gene and whereas 13.34% amplified genomic regions from chloroplast and mitochondria (Table 2). Among the 30 SSR markers, 20 SSRs markers fell within genes, whereas three primers were of non-genic regions and seven showed no hits either with genic or non-genic region of the genome (Table 2). For many of the sequences, primer designing was not possible as in many cases SSRs repeats were too near the start or end of the insert. This may be due to differences in the

99

methodology, size range of inserts, the restriction enzyme used in the construction of genomic DNA libraries (Gupta and Varshney, 2000). 3.5. SSR polymorphism In order to evaluate the potential of the developed SSR markers to measure the variability in bottle gourd accession, the 40 accession were profiled with the 30 primer pairs. The alleles identified by the seven novel polymorphic marker pairs varied from 2 to 5 with an average of 2.85 alleles per locus. The PIC values for these polymorphic markers varied from 0.45 (B 113) to 0.6 (B 30) with a mean of 0.27. The polymorphic marker B-9 harboring the trinucleotide SSRs repeat TTG4 had four alleles with PIC value 0.13 while B-30 harboring tetra repeat ACAT3 had two alleles with a PIC value of 0.06. Various parameters like number of alleles, gene

22

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

Fig. 3. Dendrodram of the 40 Lagenaria siceraria germplasm based on SSR markers constructed using UPGMA.

diversity, Shannon’s information index, gene flow etc of each of these seven polymorhic markers and of both populations are shown in Table 4a and b respectively.

3.6. Genetic diversity analysis Polymorphic markers were utilized for diversity analysis, based on the distinctive DNA marker profiles of all the accession of bottle gourd used in the study. Data were subjected to NTSYS-PC 2.01 software for deriving the UPGMA dendrogram based on the Jaccard’s similarity coefficient. Dendrogram classified the 40 accession in two groups—group I with 32 accessions and group II with eight accessions (Fig. 3). Each of these groups had further sub clusters, but none had significant geographical concordance. This may be due to the cross pollinated nature of bottle gourd and also

corroborates the genetic structure found in these accessions by Bayesian analyses in 3.2. The obtained similarity matrix of Jaccard’s coefficient (GS) ranged from 0.12 to 0.92, with a mean of 0.56 revealing a moderate level of genetic diversity (GD) within these 40 accession. The minimum similarity value, 0.12, suggested high divergence between BG22 and BG16 and the maximum similarity value 0.92 was scored between Arka Bahar and BG46 indicating that both cultivars were most similar. The diverse accession can be used as prospective parents to generate hybrids with vigor as well as for generating the mapping population(s) for linkage studies. The UPGMA based dendrogram does not show correlation with the geographical locations, possibly due to cross pollinated nature of bottle gourd. Selection and exact utilization of diverse accession in breeding programs is required to improve the breeding populations in the future (Krishna et al., 2004).

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

23

Fig. 4. Clusters inferred with the STRUCTURE program for Lagenaria siceraria germplasm at K values from 2 to 5. Colors indicate individual estimated membership fraction. 1 is north and 2 is north eastern population of India.

Inter-population analysis between North Indian and Northeast Indian population revealed that observed number of alleles and expected number of alleles were 1.9 and 1.52 respectively. Nei’s Gene Diversity which is equivalent to the average heterozygosity was 0.43 ± 0.08 and Shannon’s Information Index was 0.62 ± 0.09. These results indicate that populations harbor a moderate inter population diversity, thereby, enhancing their chances to survive under variable climatic conditions. High gene flow value was obtained, i.e., 3.46 for the above two populations. This gene flow value is probably due to the cross-pollinated nature of the crop and no impediment on genetic exchange within India. Nei’s genetic distance between the populations of North India and Northeast India was 0.1, which indicates that both Northeast Indian population and North Indian population are genetically similar to each other and there is a high rate of movement of accession within India that may be responsible for the low genetic distance between the two populations. 3.7. Population structure analyses When performing STRUCTURE on the dataset, a maximum value of the rate of change in the log probability of data using the Evanno

method (Evanno et al., 2005) was observed at K9, although the value of delta K(3.84) was quite low, meaning that this structuring of populations was not strong. The value of Fst obtained was 0.067, which also explains that there is only a moderate genetic differentiation between the populations. The second higher value of delta K(3.35) was observed at K5, which provided a better picture of the groupings. The same situation was also remarked when populations were identified by means of their geographical origin, using the LOCPRIOR option in STRUCTURE (Fig. 4). In most cases, individuals from a population were admixed with the other entire populations, which was as expected due to the cross-pollinated nature of the crop and also corroborates with the diversity parameters outlined in this analysis. Similar clustering was observed in the UPGMA method as well, reiterating the fact that in bottle gourd geographically the gene flow is not restricted. Although the morphology of the Northeast Indian population is different (data not shown) and the accession show adaptation to local environmental conditions there are no barriers to movement that prevent the exchange of genes with North Indian population. In the Bayesian assignment tests five genetic clusters with substantial admixture were identified and could not clearly differentiate previously identified geographical boundaries, corroborating admixture as a result

24

Bhawna et al. / Scientia Horticulturae 191 (2015) 15–24

of movement of accession within India. Specifically, there is little geographic structure and high levels of admixture. These findings have important implications for the management of L. siceraria populations in India, especially for genetic resources conservation, and more broadly, for studies that aim to understand the evolutionary dynamics of the historical domesticated crop. Moderate genetic differentiation between the two distant populations was observed, hence, to enhance the genetic base of Indian cultivars, there is a need to introduce elite accession and cultivars from Africa, the centre origin of the crop. 4. Conclusions The outcome of this study highlights an efficient and reliable method of obtaining microsatellites markers from the cultivated bottle gourd. It is imperative to isolate and characterize further DNA markers for more productive genomic studies, such as markerassisted selection and genetic mapping in this medicinally important vegetable crop. SSR enriched library construction and sequencing yielded some genome sequences and a total of 30 functional SSRs in the bottle gourd genome, among them seven are novel set of SSR markers that produce reasonable level of polymorphism in bottle gourd. These markers would be very beneficial for phylogenetic relationships, diversity and population genetic structure analysis, in addition to marker assisted selection for breeding improved varieties of bottle gourd, that further enhance cultivation. The gene sequences can be utilized for further gene characterizations and expression studies in bottle gourd and related cucurbit species. We found moderate population genetic structure and high levels of gene flow in the bottle gourd accession studied. This observation asserts that admixture frequently occurs in the bottle gourd as it is cross pollinated. It is also conceivable that this observation for L. siceraria may simply reflect a species with high levels of gene flow. Acknowledgments The authors express their sincere thanks to Head, DGR and Director, NBPGR for sparing facilities for conducting the experiments. The first author thanks UGC for fellowship during the course of this study. References Badmanaban, R., Patel, C.N., Daniel, P.S., Kamal, M., 2009. Pharmacognostical studies on Lagenaria siceraria stand leaves. Int. J. Chem. Sci. 7 (4), 2259–2264. Bhawna, Abdin, M.Z., Arya, L., Dipnarayan, S., Sureja, A.K., Chitra, P., Verma, M., 2014. Population structure and genetic diversity in bottle gourd [Lagenaria siceraria (Mol.) Standl.] accession from India assessed by ISSR markers. Plant Syst. Evol. 300, 767–773. Bhawna, Abdin, M.Z., Arya, L., Verma, M., 2015. Transferability of cucumber microsatellite markers used for phylogenetic analysis and population structure study in bottle gourd (Lagenaria siceraria (Mol.) Standl.). Appl. Biochem. Biotechnol. 175, 2206–2223. Bloor, P.A., Barker, F.S., Watts, P.C., Noyes, H.A., Kemp, S.J., 2001. Microsatellite libraries by enrichment 2001, 1–14, Website http://www.genomics.liv. ac.uk/animal/research/MicrosatelliteEnrichment.pdf. Chen, C., Zhou, P., Choi, Y., Huang, S., Gmitter, F., 2006. Mining and characterizing microsatellites from citrus ESTs. Theory Appl. Genet. 112, 1248–1257. Chimonyo, V.G.P., Modi, A.T., 2013. Seed performance of selected bottle gourd (Lagenaria siceraria (Molina) Standl.). Am. J. Exp. Agric. 3 (4), 740–766. Decker-Walters, D., Staub, J., Lopez-Sese, A., Nakata, E., 2001. Diversity in landraces and cultivars of bottle gourd (Lagenaria siceraria; Cucurbitaceae) as assessed by random amplified polymorphic DNA. Genet. Resour. Crop Evol. 48, 369–380. Erickson, D.L., Smith, B.D., Clarke, A.C., Sandweiss, D.H., Tuross, N., 2005. An Asian origin for a 10,000-year-old domesticated plant in the Americas. Proc. Natl. Acad. Sci. U. S. A. 102, 18315–18320. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. Fard, M.H., Bodhankar, S.L., Dikshit, M., 2008. Cardioprotective activity of fruit of Lagenaria siceraria (Molina) Standley on doxorubicin induced cardiotoxity in rats. Int. J. Pharma. 4 (6), 466–471.

Ferguson, M.E., Burow, M.D., Schultz, S.R., Bramel, P.J., Paterson, A.H., Kres-ovich, S., Mitchell, S., 2004. Microsatellite identification and characterization in peanut (A. hypogaea L.). Theory Appl. Genet. 108, 1064–1070. Fraser, L.G., Harvey, C.F., Crowhurst, R.N., de Silva, H.N., 2004. EST-derived microsatellites from Actinidia species and their potential for mapping. Theory Appl. Genet. 108, 1010–1016. Given, D.R., 1987. What the conservationist requires of ex situ collections. In: Branwell, D., Hamann, O., Heywood, V., Synge, H. (Eds.), Botanic Gardens And The World Conservation Strategy. Academic Press, London, pp. 103–116. Gimenes, M.A., Hosino, A.A., Barbosa, A.V.G., Palmieri, D.A., Lopes, C.R., 2007. Characterization and transferability of microsatellite markers of cultivated peanut (Rachis hypogaea). BMC Plant Biol. 7, 9. Gupta, P.K., Varshney, R.K., 2000. The development and use of microsatellites markers for genetic analysis and plant breeding with emphasis on bread wheat. Euphytica 113, 63–185. Han, J.S., Park, S., Shigaki, T., Hirschi, K.D., Kim, C.K., 2009. Improved watermelon quality using bottle gourd rootstock expressing a Ca2+ /H+ antiporter. Mol. Breed. 24, 201–211. He, G., Meng, R., Newman, M., Gao, G.M., Pittman, R.N., Prakash, C.S., 2003. Microsetellites as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 3, 3. Jiao, Y., Jia, H., Li, X., Chai, M., Jia, H., 2012. Development of simple sequence repeat (SSR) markers from a genome survey of Chinese bayberry (Myrica rubra). BMC Genomics 13, 201. Khayer, U., Akhter, S., Mondal, R.K., 2011. Comparative economics of bean and bottle gourd production in some selected areas of Bangladesh. Dev. Ctry. Stud. 1 (2), 34–40. Koblizkova, A., Dolezel, J., Macas, J., 1998. Subtraction with 3 modified oligonucleotides eliminates amplification artifacts in DNA libraries enriched for microsatellites. BioTechniques 25, 32–38. Krishna, G.K., Zhang, J., Burow, M., Pittman, R.N., Delikostadinov, S.G., Lu, Y., Puppala, N., 2004. Genetic diversity analysis in valencia peanut (Arachis hypogaea L.) using microsatellite markers. Cell. Mol. Biol. Lett. 9, 685–697. Moretzsohn, M.C., Leoi, L., Proite, K., Guimaraes, P.M., Leal-Bertioli, S.C.M., Gimenes, M.A., Martin, W.S., Valls, J.F.M., Grattapaglia, D., Bertioli, D., 2005. A microsatellite-based, gene-rich linkage map for the AA genome of Arachis (Fabaceae). Theory Appl. Genet. 111, 1060–1071. Nainwal, P., Damija, K., Tripathi, S., 2011. Study of antihyperlipidemic effect of the juice of the fresh fruits of Lageraria siceraria. Int. J. Pharm. Pharm. Sci. 3 (1), 88–90. Nei, M., 1973. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. U. S. A. 70, 3321–3323. Pritchard, J., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Rallo, P., Dorado, G., Martin, A., 2000. Development of simple sequence repeats (SSRs) in olive tree (Olea europaea L). Theory Appl. Genet. 101, 984–989. Rahman, A.S.H., 2003. Bottle gourd (Lagenaria siceraria): a vegetable for good health. Nat. Prod. Radiance 2 (5), 249–250. Saghai-Maroof, M.A., Soliman, K.M., Jorgensen, R.A., Allard, R.W., 1984. Ribosomal DNA spacer-length polymorphisms in barley: mendelian inheritance, chromosomal location, and population dynamics. Proc. Natl. Acad. Sci. 81, 8014–8018. Sarao, N.K., Pathak, M., Kaur, N., Kaur, K., 2014. Microsatellite-based DNA fingerprinting and genetic diversity of bottle gourd genotypes. Plant Genet. Resour.: Characterisation Util. 12 (1), 156–159. Srivastava, D., Khan, N.A., Shamim, M., Yadav, P., Pandey, P., Singh, K.N., 2014. Assessment of the genetic diversity in bottle gourd (Lagenaria siceraria [Molina] Standl.) genotypes using SDS-PAGE and RAPD markers. Nat. Acad. Sci. Lett. 37 (2), 155–161. Shah, B.N., Seth, A.K., 2010. Pharmocognostic studies of Lagenaria siceraria (Molina) Standley. Int. J. Pharm. Sci. 58, 197–202. Saha, P.U., Mazumder, U.K., Halder, P.K., Gupta, M., Sen, S.K., Islam, A., 2011. Antioxidant and hepatoprotective activity of Lagenaria sicerania aerial parts. Pharmacogn. J. 3, 67–74. Slatkin, M., Barton, N.H., 1989. A comparison of three indirect methods for estimating the average level of gene flow. Evolution 43, 1349–1368. Upaganlawar, A., Balaraman, R., 2010. Protective effects of Lagenaria siceraria (Molina) fruit juice in isoproterenol induced myocardial infarction. Int. J. Pharmacol. 6 (5), 645–651. Wang, C.T., Yang, X.D., Chen, D.X., Yu, S.L., Liu, G.Z., Tang, Y.Y., Xu, J.Z., 2007. Isolation of simple sequence repeats from groundnut. Electron. J. Biotechnol. 10, 473–480. Weir, B.S., 1990. Genetic Data Analysis. Sinauer Associates, Sunderland, Massachusetts. Watanabe, K.N., Hirano, R., Ishii, H., Oo, T.H., Gilani, S.A., Kikuchi, A., 2011. Propagation management methods have altered the genetic variability of two traditional mango varieties in Myanmar, as revealed by SSR. Plant Genet. Resour. 9, 404–410. Xu, S.X., Liu, J., Liu, G.S., 2004. The use of SSRs for predicting the hybrid yield and yield heterosis in 15 key inbred lines of Chinese maize. Hereditas 141 (3), 207–215. Xu, P., Wu, X., Luo, J., Wang, B., Liu, Y., Ehlers, J.D., Wang, S., Lu, Z., Li, G., 2011. Partial sequencing of the bottle gourd genome reveals markers useful for phylogenetic analysis and breeding. BMC Genomics 12, 467. F.C. Yeh, R.C. Yang, T. Boyle, POPGENE Version 1.31. Microsoft Window-based Freeware for Population Genetic Analysis, 1999.