Final SPH -JHS Coverpage 16-2 Jan 2021 single C O N T E N T S JOURNAL OF HORTICULTURAL SCIENCES Volume 16 Issue 2 June 2021 In this Issue i-ii Review Phytoremediation of indoor air pollutants: Harnessing the potential of 131-143 plants beyond aesthetics Shalini Jhanji and U.K.Dhatt Research Articles Response of fruit yield and quality to foliar application of micro-nutrients in 144-151 lemon [Citrus limon (L.) Burm.] cv. Assam lemon Sheikh K.H.A., Singh B., Haokip S.W., Shankar K., Debbarma R. Studies on high density planting and nutrient requirement of banana in 152-163 different states of India Debnath Sanjit Bauri F.K., Swain S., Patel A.N., Patel A.R., Shaikh N.B., Bhalerao V.P., Baruah K., Manju P.R., Suma A., Menon R., Gutam S. and P. Patil Mineral nutrient composition in leaf and root tissues of fifteen polyembryonic 164-176 mango genotypes grown under varying levels of salinity Nimbolkar P.K., Kurian R.M., Varalakshmi L.R., Upreti K.K., Laxman R.H. and D. Kalaivanan Optimization of GA3 concentration for improved bunch and berry quality in 177-184 grape cv. Crimson Seedless (Vitis vinifera L) Satisha J., Kumar Sampath P. and Upreti K.K. RGAP molecular marker for resistance against yellow mosaic disease in 185-192 ridge gourd [Luffa acutangula (L.) Roxb.] Kaur M., Varalakshmi B., Kumar M., Lakshmana Reddy D.C., Mahesha B. and Pitchaimuthu M. Genetic divergence study in bitter gourd (Momordica charantia L.) 193-198 Nithinkumar K.R., Kumar J.S.A., Varalakshmi B, Mushrif S.K., Ramachandra R.K. , Prashanth S.J. Combining ability studies to develop superior hybrids in bell pepper 199-205 (Capsicum annuum var. grossum L.) Varsha V., Smaranika Mishra, Lingaiah H.B., Venugopalan R., Rao K.V. Kattegoudar J. and Madhavi Reddy K. SSR marker development in Abelmoschus esculentus (L.) Moench 206-214 using transcriptome sequencing and genetic diversity studies Gayathri M., Pitchaimuthu M. and K.V. Ravishankar Generation mean analysis of important yield traits in Bitter gourd 215-221 (Momordica charantia) Swamini Bhoi, Varalakshmi B., Rao E.S., Pitchaimuthu M. and Hima Bindu K. Influence of phenophase based irrigation and fertigation schedule on vegetative 222-233 performance of chrysanthemum (Dendranthema grandiflora Tzelev.) var. Marigold Vijayakumar S., Sujatha A. Nair, Nair A.K., Laxman R.H. and Kalaivanan D. Performance evaluation of double type tuberose IIHR-4 (IC-0633777) for 234-240 flower yield, quality and biotic stress response Bharathi T.U., Meenakshi Srinivas, Umamaheswari R. and Sonavane, P. Anti-fungal activity of Trichoderma atroviride against Fusarium oxysporum f. sp. 241-250 Lycopersici causing wilt disease of tomato Yogalakshmi S., Thiruvudainambi S., Kalpana K., Thamizh Vendan R. and Oviya R. Seed transmission of bean common mosaic virus-blackeye cowpea mosaic strain 251-260 (BCMV-BlCM) threaten cowpea seed health in the Ashanti and Brong-Ahafo regions of Ghana Adams F.K., Kumar P.L., Kwoseh C., Ogunsanya P., Akromah R. and Tetteh R. Effect of container size and types on the root phenotypic characters of Capsicum 261-270 Raviteja M.S.V., Laxman R.H., Rashmi K., Kannan S., Namratha M.R. and Madhavi Reddy K. Physio-morphological and mechanical properties of chillies for 271-279 mechanical harvesting Yella Swami C., Senthil Kumaran G., Naik R.K., Reddy B.S. and Rathina Kumari A.C. Assessment of soil and water quality status of rose growing areas of 280-286 Rajasthan and Uttar Pradesh in India Varalakshmi LR., Tejaswini P., Rajendiran S. and K.K. Upreti Qualitative and organoleptic evaluation of immature cashew kernels under storage 287-291 Sharon Jacob and Sobhana A. Physical quality of coffee bean (Coffea arabica L.) as affected by harvesting and 292-300 drying methods Chala T., Lamessa K. and Jalata Z Vegetative vigour, yield and field tolerance to leaf rust in four F1 hybrids of 301-308 coffee (Coffea arabica L.) in India Divya K. Das, Shivanna M.B. and Prakash N.S. Limonene extraction from the zest of Citrus sinensis, Citrus limon, Vitis vinifera 309-314 and evaluation of its antimicrobial activity Wani A.K., Singh R., Mir T.G. and Akhtar N. Event Report 315-318 National Horticultural Fair 2021 - A Success Story Dhananjaya M.V., Upreti K.K. and Dinesh M.R. Subject index 319-321 Author index 322-323 J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 This is an open access article d istributed under the terms of Creative Commons Attribution-NonCommer cial-ShareAl ike 4.0 International License, which permits unrestricted non-commercial use, d istribution, and reproduction in any med ium, provide d the original author and source are credited. Original Research Paper Okra [Abelmoschus esculentus (L.) Moench] also known as bhendi or lady’s finger is an important vegetable crop in India, West Africa, South Africa, Brazil, USA and Turkey. It belongs to the family Malvaceae a nd is mainly gr own in tropics and subtropics of the world (Priyavathi et al. 2018). The total okra production in the world was found to be 9.8 Million-ton pods with an area of around 2.0 million ha and in India it is 6.1 Million ton with an area of around 5.14 lakhs ha followed by Nigeria (FAOSTAT, 2018). The chromosome number is reported variously for this species as 2n=130 and also 2n=72, invariably the chromosome number was found to be 2n=130 with the genome size of 1.6 Gb (Joshi and Hardas 1956). It was reported that there are two kinds of A. esculentus L. as diploid 2n=60-70 and as a tetraploids 2n=120- 130, this could be due to ir r egula rities in the chromosome movement during the cell division of mitotic phase (Nwangburuka et al. 2011). Further this polyploidy level was assessed through the chloroplast DNA (cpDNA) intronic spacer and revealed that A. esculentus are the closest relatives of two wild species that is A. ficulneus and A. moschatus (Ramya and Bhat 2012). Molecular markers have paved way for the assessment of genetic variations and genetic relationships among and within the species (Chakravarthi and Naravaneni 2006; Yuan et al. 2014, 2015). Molecular marker techniques like RFLP, RAPD, AFLP and SSR are SSR marker development in Abelmoschus esculentus (L.) Moench using transcriptome sequencing and genetic diversity studies Gayathri M.1,3, Pitchaimuthu M.2 and Ravishankar K.V.1* 1Division of Basic Sciences, 2Division of Vegetable Crops, ICAR-Indian Institute of Horticultural Research, Hessaraghatta Lake Post Bangalore - 560089, India 3Department of Biotechnology, Centre for Post-graduate studies, Jain University, Bangalore, India *Corresponding author email : kv_ravishankar@yahoo.co.in, ravishankar.kv@icar.gov.in ABSTRACT Okra [Abelmoschus esculentus (L.) Moench] also known as bhindi or lady’s finger is an important vegetable crop in India, West Africa, South Africa, Brazil, USA and Turkey. It belongs to the family Malvaceae. Okra is mainly grown in tropics and subtropics of the world. The studies regarding the molecular marker development are very limited; still there is no SSR marker development from comprehensive transcriptome data in this crop. This study presents the first comprehensive transcriptome data, using RNA from different parts of okra such as root, stem, leaf, bud, flower, different stages of developing pod and from twenty days old plantlets of heat, drought and salt stressed. A total of 10,492 SSRs were identified in this study. Among these tri repeats (2112) were found to be predominant followed by di (1285), tetra (149), penta (24) and hexa repeats. Thirty-four SSRs were standardized for PCR and screened in 36 okra genotypes and accessions. Among these, 18 SSR primers were found to be highly polymorphic with the PIC values more than 0.5. And the overall results of analysis showed that expected heterozygosity ranged from 0.125 to 0.971 with a mean of 0.593; the values for observed heterozygosity ranged from 0.000 to 0.839 with the mean of 0.203; the number of allele per locus ranged from 1 to 30 and the polymorphic information content (PIC) ranged from 0.119 to 0.955 with the mean value of 0.554. The genic SSR markers developed will help in germplasm characterization mapping, genetic diversity studies, molecular assisted breeding and also in gene discovery. Key words: Abelmoschus esculentus, microsatellite markers, next generation sequencing, RNA sequencing and transcriptome INTRODUCTION 207 SSR marker development in Abelmoschus esculentus (L.) J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 widely used for genetic characterization and crop improvement (Sawadogo et al. 2009). Especially in the less researched species, transcriptome analysis plays a vital role for the development of molecular markers (Strickler et al. 2012). Recently, the first report on genomic SSR marker in okra were developed using Next-Generation Sequencing technology (NGS) which wa s used for the a ssessment of genetic r ela tedness a nd cr oss species tr a nsfer a bility (Ravishankar et al. 2018). SSR markers play a key role in many applications of plant genetics and breeding due to its codominant inheritance, multi- allelic nature, high reproducibility and good genome coverage (Bertini et al. 2006). There are some studies reported on SSR developed using transcriptome through NGS in okra (Schafleitner et al. 2013; Zhang et al. 2017) and transcriptome data on M. balbisiana and M. acuminate ssp. using illumina GA II X technology (Ravishankar et al. 2015). With the advent of sequencing technology, RNA sequencing has become an efficient and convenient technique for the SSR detection (Ronoh et al. 2018; Xu et al. 2017). However, these studies used transcriptome from one or very few tissues, which may not completely cover genic SSRs in the okra genome. Keeping this in view in this study, we present the first comprehensive characterization of combined okra transcriptome from root, stem, leaf, bud and flower, different stages of developing pods and from the abiotic stressed plantlets (drought, heat and salt). Here we also report SSR markers which would greatly help in mapping genes and linkage map development. MATERIALS AND METHODS Plant material and DNA isolation Thirty-six okra genotypes including, a few varieties from germplasm collection were used in this study (Table 1). Young leaves were collected from the okra plants which were maintained at Indian Council Of Agricultural Research- Indian Institute of Horticultural Research Bengaluru India (ICAR-IIHR), and the total genomic DNA was isolated by using the modified CTAB method (Ravishankar et al. 2000) with the repetition of chloroform: isoamylalcohol (24:1) for three to five times till the mucilage was removed. Finally. sDNA concentration was determined using Nano drop (NABI micro digita l) by taking the absorption at 260 and 280nm. RNA isolation and Sequencing For the transcriptome sequencing we isolated RNA from tissues of root, stem, leaf, bud, flower, different stages of developing pod and from twenty days old seedlings were stressed for heat (400C for 4h), salt (200mM NaCl) for two days and drought (five days of dehydration) of accession IIHR-299 using by Trizol method where 30 mg of the sample were ground into fine powder using liquid nitrogen and 1ml of Trizol (TAKARA BIO INC. Japan) was added to it and centr ifuged a t 12, 500 r pm for 20 min, to the supernatant equal amount of chloroform was added and centrifuged at 12,500 rpm for 15 min and equal amount of isopropanol was added to the supernatant a nd precipita ted at -80 0C for 1hr followed by centrifugation at 12,500 rpm for 15 min and the pellet was washed using 75% ethanol and dried pellet was dissolved using DEPC water and the RNA integrity was examined by gel electrophoresis. RNA purity was examined using Nano drop (NABI micro digital) and the equal amount of RNA from each samples were pooled and sent for RNA sequencing. Sequencing, quality control and de novo assembly RNAseq was done at Sandoor Speciality Diagnostics Pvt. Ltd. Hyderabad facility using Illumina Hiseq Table 1. Genotypes and the accessions used in the study Genotypes /Accessions 1. Pule Vimukha 2. Azad bendi 3. Punjab 7 4. Kashi Kranthi 5. Varsha Upahar 6. Parbhani Kranthi 7. Kashi Leela 8. Pusa Sawani 9. Shakthi 10. Punjab Padmini 11. Azad Bendi 3 12. Kashi Vibhuthi 13. IC-0600808 14. IC-0602363 15. IC-0128888 16. IC-0282274 17. IC-0469655 18. IC-0043752 19. IC-0282266 20. IC-0128903 21. IC-0128885 22. IC-0085595 23. IC-0397980 24. IC-0282296 25. IC-0282232 26. IC-0128891 27. IC-0069242 28. IC-0433743 29. IC-0069302 30. IC-0433628 31. IC-0043750 32. IC-0600832 33. IC-0397271 34. IC-0560493 35. IC-0282233 36. IC-0600256 208 Gayathri et al platform following manufactures instructions. Paired end cDNA library are from the pooled sample (root, stem, leaf, bud, flower, different parts of developing pods, drought stress, heat stress and salt stress plantlets) to get comprehensive okra transcriptome. Then Quality control were carried out to filter out the adaptors low quality reads >20% of bases and the unknown nucleotides with >5% reads. The clean reads was used for calculating the proportion of nucleotides with quality value larger than 20 (Q20). De-novo assembly was done using Trinity software assembly with the default parameters for generating contigs and transcripts (Grabherr et al. 2011). The NGS data was submitted to NCBI (SRR 13451946). Mining of SSR primer and designing The assembled unigenes were further examined for the presence of microsatellites using MISA software (Suping et al. 2013). A total of 10492 SSR Primers were identified and 2532 SSR primers were designed using Primer 3.0 software (Untergasser et al. 2012). A total of 51 SSR primers were randomly selected and these were used for PCR standa r diza tion a nd amplification of 36 okra genotypes. PCR conditions and genotyping For the amplification of mined SSRs, fluorescent based M13 tailed PCR assay was performed (Oetting et al. 1995). And all the primers at 5’ end were labelled with standard M13 tail (Schuelke 2000). A total of 51 SSR markers were initially synthesised and screened with pooled okra DNA. Further the primers which amplified, clear bands were screened over 36 okra genotypes. The PCR conditions employed are as follows initial denaturation at 94oC for 3 min, followed by 35 cycles of dena tur a tion, a nnea ling a nd polymerization steps (94oC for 30s, 50-60oC and 72oC for 1 min) and a final extension of 72oC for 8 min. PCR amplification was carried out in 20 μL volume containing 75-100 ng of okra DNA, 2 μL of 10x Taq Buffer (Tris pH with 15mM MgCl2), 1.5 μL of MgCl2 (25mM of MgCl2), 0.5μL of dNTPs (10mM), 0.5 μL of forward primer M13 tail (5 pM), 1 μL of reverse primer M13 tail (5 pM), 0.5 μL of probes FAM,VIC, NED and PET (5 pM), 0.2 μL of Taq polymerase (5 units per μL) (Genei. Pvt. Ltd Bengaluru) and 9.8 μL of nuclease free water. All the PCR reactions were carried out using Bio-RAD Thermal cycler (Bio-RAD, US). The amplified PCR products were separated on ABI3730 Genetic Analyzer (Applied Biosystem, USA), at M/S Eurofins facility Bengaluru. The obtained data were further analysed using Peak Scanner software (Applied Biosystems, USA) for determining the exact fragment size in base pair. Statistical analysis The fragment size in base pair of the PCR products were used for calculating the expected heterozygosity (He), observed heterozygosity (Ho), polymorphic information content (PIC) and number of alleles per locus employing Cervus 3.0 software (Kalinowski et al. 2007). And the dendrogram analysis was performed using neighbour-joining method (NJ) employing Da r win Softwar e (Per rier et al. 2003; http:// darwin.cirad.fr/darwin). RESULTS Sequencing and de novo assembly A total of 3.8Gb raw data were obtained using Illumina-Hiseq platform from comprehensive okra transcriptome analysis (root, stem, leaf, bud, flower, different parts of developing pods, drought stress, heat stress and salt stress plantlets). Quality control analysis was performed in order to filter out the reads containing adaptor s, low qua lity reads and the unknown nucleotides. The total number of generated transcripts was 112597 with maximum transcripts length of 20701bp and minimum transcripts length of 201bp and total length of transcripts generated was 72314062bp. The size distributions of the transcripts are given in the Table 2. And the sequencing analysis of GC content was found to be 47.1% and AT content as 53.9%. Table 2. De novo assembly statistics Transcriptome assembly Transcripts Generated : 112597 Maximum Transcript Length : 20701bp Minimum Transcript Length : 201bp Average Transcript Length : 642.2bp Total Transcripts Length : 72314062bp Transcripts > 100 bp : 112597 Transcripts > 500 bp : 47905 Transcripts > 1 Kbp : 21670 Transcripts > 10 Kbp : 722369251 Number of reads used Total number of reads : 24885138 Percentage of reads used : 89.9% J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 209 Assembly statistics and designing primers All the obta ined unigenes wer e scr eened for identification of SSRs using MISA software. The total number of examined sequence was 112597 with a total of 72314062bp and the total number of identified SSRs was found to be 10492 and the number of SSR designed using Primer 3.0 software were 2532 (Untergasser et al. 2012). Number of SSR containing sequence were 9849 and the number of sequence containing more than one SSR is 568 and the number of SSR present in compound formation is found to be 783 (Table 3). Further the identified SSRs were screened for di, tri, tetra, penta and hexa nucleotide repeat motifs (a total of 1285 di-repeats, 2112 tri- repeats, 149 tetra-repeats, 24 penta-repeats, 9 hexa- repeats and 783 complex repeats were observed). Tri- repeats were found to be more predominant class of microsatellite than any other classes like di, tetra, penta and hex- repeats (Fig 1). The kind of repeats observed in high frequency among tri was found to be (AGT)10 and (CCA)10 and di-repeats as (TA)22 and tetra as (TTTC)18 and among hexa all repeats were found to present once. Table 3. Assembly statistics of SSRs Total number of sequences examined 112597 Total size of examined sequences (bp) 72314062 Total number of identified SSRs 10492 Number of SSR containing sequences 9849 Number of sequences containing more than 1 SSR 568 Number of SSRs present in compound formation 783 Fig. 1. Distribution to different repeat type classes of SSR repeats Genetic analysis The allelic data regarding the expected heterozygosity, observed heterozygosity and number of alleles per locus were examined using Cervus 3.0 software. And the values for expected heterozygosity ranged from 0.125 to 0.971 with the mean value of 0.593; the values for observed heterozygosity ranged from 0.000 to 0.839 with a mean value of 0.203; the number of a llele per locus r a nged fr om 1 to 30 a nd the polymorphic information content (PIC) ranged from 0.119 to 0.955 with the mean PIC value of 0.554 and PI (Probability of identity) values ranged from 0.0036 to 1.0000 with a mean value of 0.263 (Table 4). Dendrogram analysis showed that the genotypes used in the study were classified into three major clusters (Fig 2). Fig. 2. Dendrogram analysis showing the genetic relationship among Abelmoschus esculentus L. accessions using transcriptome SSR marker data SSR marker development in Abelmoschus esculentus (L.) J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 210 Table 4. Genetic analysis of microsatellite loci using 34 SSRs Sl. Primer Primers Tm Allele No. of Ho He PIC PINo. name size Allele/locus value 1 IIHR-2434 F: AGCTTCCGTATATTTTGGATT R: CCAAACTATCCAACTATGCTT 55 160 18 0.156 0.884 0.861 0.0265 2 IIHR-1877 F: TGAGATTCGTTTGATCGTTTA R: CTTTGGGTCAAAGCTGTC 55 151 4 0.200 0.444 0.408 0.3464 3 IIHR-817 F: TAAATATGCTTCTCAGGCATT R: CGTCTTGTTACGATTTATATGC 55 163 1 0.000 0.324 0.307 1.0000 4 IIHR-518 F: TCCCTCGTACTAGATCATTCA R: GTAACAAGGATGAGCAAAAGA 55 150 5 0.143 0.508 0.457 0.2935 5 IIHR-205 F: GGGAAGATTTTGCTAAACTTATT R: CCAATAGGATGTCTCAGTCAA 57 151 5 0.200 0.414 0.386 0.3727 6 IIHR-91 F: TGATCTTCGATTCATCCTTAT R: AGAATGGCAGCGCCAAAAG 55 151 3 0.030 0.287 0.250 0.5472 7 IIHR-30 F: TAAAATTTTCCCATCAATCC R: GGTGTTTGTTTTGTGGTGATA 60 172 4 0.094 0.424 0.371 0.3861 8 IIHR-18 F: TCTCTTTAAAATCACCGCTAA R: TTTAGCAAGGAAGGGAGAA 57 152 19 0.152 0.908 0.887 0.0187 9 IIHR-353 F: TAAAAATCAGAGCCTTCCTTT R: CAGATTTCTGAGAGCAAAGAG 55 174 6 0.457 0.701 0.644 0.1429 10 IIHR-343 F: GATATGGGATGGTTGAAATC R: GAGAAAACCAACGGATGAT 57 152 5 0.171 0.472 0.439 0.3122 11 IIHR-328 F: TAGGAAAACTACAGCAAGGATT R: GGACTTGGTTCTGCAATCT 60 150 3 0.000 0.125 0.119 0.7731 12 IIHR-319 F: GCACTTGATATTGCATTACATT R: CCAAATCATTATCAGGGAGT 55 150 3 0.156 0.347 0.311 0.4641 13 IIHR-303 F: TAGGAGGACAATCACAGAAAA R: GGTAACCCAAGTGTTGTTCTT 57 151 22 0.176 0.921 0.902 0.0139 14 IIHR-277 F: GCTCAAGTAAGCATTAAAACAG R: GTCGTGCAAAACTTGTCTAAG 55 162 11 0.000 0.869 0.839 0.0370 15 IIHR-267 F: TAAGGAGTCCAAACTCCAACT R: TGGTTGTTTAGGTTCCAATTT 55 160 11 0.313 0.760 0.724 0.0881 16 IIHR-254 F: TGTCTGTAGTCTCGCAACTTT R: ATACATTGACGGTACAAGTGG 57 152 13 0.794 0.679 0.620 0.1590 17 IIHR-244 F: TGGGGCCTAAGTAAATACAAT R: AAAGTTAGTTCAATGCAGTTTTC 57 180 11 0.030 0.759 0.732 0.0792 18 IIHR-221 F: ACAGGTCCATAAATGCTATGA R: CCCTAATATTATTGTTTTTACCC 58 161 7 0.000 0.673 0.605 0.1713 19 IIHR-195 F: TCACTTAACCCCATGAAAAAT R: GTTTCTGAGAATCCTTGCTG 55 158 28 0.324 0.957 0.940 0.0060 20 IIHR-165 F: GGATGACCAAAACGAAGTG R: CTGTCATTTTCTTTCCTTCTG 57 151 2 0.000 0.507 0.375 0.3752 21 IIHR-154 F: CGCCGTAGTACCTCAATCTT R: GCAATTAACGGTGACGAC 55 153 30 0.333 0.971 0.955 0.0036 22 IIHR-99 F: TGAAAAGAACATGAAAGCCTA R: CCTTCCTTCCTAGTCATCATC 57 160 15 0.156 0.742 0.707 0.0958 23 IIHR-94 F: TATATTTGCAGCATTTGTCTGT R: AACAGTCGGTACTTAGACAGC 57 151 18 0.545 0.891 0.870 0.0228 24 IIHR-68 F: GAACTTTTGGAATTTGTGTCA R: TTCTTGGAGTAGGAGCTTGAT 60 153 11 0.061 0.822 0.791 0.0543 25 IIHR-50 F: GTTCAGGATCAGAGTCGAG R: GCGGCCTCAATATTCACT 55 150 8 0.032 0.589 0.544 0.2118 26 IIHR-36 F: GGGACAGAGTTGAAAATGAC R: GGATCAGGAATGTTATCGACT 55 150 7 0.065 0.396 0.377 0.3856 27 IIHR-27 F: GGAACTCCGGTGGAGAAG R: AAGCTTTATCTCAAAAATCC 57 150 7 0.188 0.624 0.589 0.1738 Gayathri et al J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 211 28 IIHR-11 F: TGGAAGAGAAGAAGAACAACA R: TTCACGATGAACTGACC 55 151 6 0.645 0.556 0.478 0.2741 29 IIHR-02 F: AACAACAACAACAACAGTCG R: CATAAAAAGTGTTTGCGTCTC 55 158 18 0.147 0.879 0.855 0.0295 30 IIHR-1463 F: TGACGATCTTCACAGGCTAGTA R: AAGTGAACCAGGTAGCATGT 57 153 4 0.219 0.584 0.521 0.2342 31 IIHR-1506 F: TTGAAACTCCCACTATCAAAA R: TAATTATGGAGGTGGAGGTG 55 150 4 0.839 0.543 0.447 0.3042 32 IIHR-1896 F: CAATGCCAGATTTCTTTGTAG R: TTCCTTGCTTTAGTTTTCCTT 55 163 3 0.029 0.140 0.132 0.7494 33 IIHR-1835 F: CCATTATATCTTATCCGTTCG R: CATACACGTCAAAAACATCAA 55 214 5 0.286 0.505 0.467 0.2825 34 IIHR-1680 F: GGTGGCAACATTATCCAT R: GGAGGTGGCTATAACAGAAAT 55 168 3 0.031 0.294 0.256 0.5386 Mean 9.412 0.2037 0.5933 0.5546 0.2639 DISCUSSION Okra is an important vegetable crop in India, Africa and other Asian countries and is considered as a minor crop at the genome studies until recently, very little attention was paid towards its genetic improvement and generation of genomic resources. The studies regarding the molecular marker development are very limited, and there is a still no compr ehensive transcriptome data for this crop. This study presents the first comprehensive transcriptome data from different parts of okra such as from root, stem, leaf, bud, flower, different stages of developing pod and from twenty days old plantlets of heat, drought and salt stressed by RNA sequencing. RNA sequencing is considered as an effective way for obtaining the gene sequences of a non-model crop (Strickler et al. 2012) and for developing the SSR markers (Zhang et al., 2010; Guo et al., 2016& Ravishankar et al. 2015). The application and development of molecular marker technology as it detects the genetic differences at the DNA level and is commonly used in the evaluation of genetic diversity and mapping (Yoder et al. 2018; Niemandt et al. 2018 & Pan et al. 2017). SSRs are considered as an important marker for application in plant genetics and breeding studies, because of its high reproducibility, codominant, multi allelic nature and good genome coverage. Genic SSRs developed from transcriptome data are highly useful as they reflect functional variability. On an average 112,597 unigenes were with an maximum length of 20,701 were obtained through comprehensive okra transcriptome which was little lesser than a study on combined lea f and pod transcriptome of okra which yielded a total of 150,000 unigenes (Schafleitner et al. 2013) and higher than studies on okra by NGS using RNA sequencing from the lea f sa mples which yielded a total 66, 382 assembled unigenes (Priyavathi et al. 2018); 94,769 unigenes with an length of 1921bp were obtained through NGS of transcriptome sequencing in okra to drought stress (Shi et al. 2020) and 293971 unigenes with okra transcriptome sequencing of five organs (roots, stem, leaves, flower and fruits) (Zhang et al. 2018). In our present study though a large number of unigenes have been produced compound than a study by (Schafleitner et al. 2013) and higher than the other studies which indicates that the sequencing depth was not sufficient to represent the whole transcriptome. Deeper and increased sequencing would have reduced the redundancy of unigenes annotation. However, r edunda ncy a t cer tain level is a lso due to the allopolyploid of Abelmoschus, where the transcripts from different genomes with slightly differ ent sequences a r e pr esent in the tr a nscr iptome (Schafleitner et al. 2013). The number of SSRs identified in this study are 10, 492 which is high compared to earlier studies on Abelmoschus esculentus 9,574 (Priyavathi et al. 2018) and Clerodendrum trichotomum which is 6,444 (Chen et al. 2019) and among the mined SSRs the tri repeats (2112) are more predominant followed by di (1285), tetra (149), penta (24) and hexa (9) this pattern is similar in earlier studies in Abelmoschus esculentus (Shi et al. 2020; Priyavathi et al. 2018 & Schafleitner et al. 2013). The frequency of tri repeats are higher in transcriptome sequencing (Schafleitner et al. 2013) because of the shortening or the extension of amino acid in proteins may not cause much alteration in SSR marker development in Abelmoschus esculentus (L.) J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 212 functions and other type of repeats cause frame shift mutation. But the mechanisms behind the evolution and origin of microsatellite repeats are not very clear. So, the relative dominant occurrence of repeats motifs may be due to their evolution through various selection pressures. It was assumed that replication slippage and unequal crossing over are the few common mutation mechanisms which might come addition or removal of motifs leading to the variation in length (Buschiazzo and Gemmell 2006; Sonah et al. 2011). In the present study, we successfully identified 10,492 SSRs and 34 SSRs were standardized for PCR and screened over 36 okra genotypes and accessions. Among these 18 SSR primers were found to be highly polymorphic with the PIC values more than 0.5. And the overall statistical analysis revealed that expected heterozygosity ranged from 0.125 to 0.971 with the mean 0.593; the values for observed heterozygosity ranged from 0.000 to 0.839 with the mean of 0.203; the number of allele per locus ranged from 1 to 30 and the Polymorphic Information Content (PIC) ranged from 0.119 to 0.955 with the mean value of 0.554. Similar kind of work were performed on okra reported Polymorphic information content (PIC) across all 50 loci values ranged from 0.000 to 0.865 with a mean value of 0.519. The observed and expected heterozygosity ranged from 0.000 to 0.750, and 0.000 to 0.972, respectively. Alleles per locus ranged from 1 to 27 (Ravishankar et al. 2018). A study for the development and characterization of SSR in cotton with the mean PIC of 0.65 (John et al., 2012) and a genetic diversity in cotton with the mean PIC of 0.8 (Muhammad et al. 2013). Dendrogram analysis depicted that all the genotypes used in the study were classified into three major clusters with most of the accessions grouped to cluster I, and the Cluster II & III with the mixture of genotypes and accessions. The SSR markers developed here will help in genetic diversity studies, mapping, marker assisted breeding and helpful in gene discovery. Being gene based SSR markers, these markers would be great help in tagging genes for various traits. ACKNOWLEDGEMENTS We thank the RKVY (Rashtriya Krishi Vikas Yojana) for the financial assistance. Bertini, C. D., Schuster, I., Sediyama, T., Barros, E. G. and Moreira, M. A. 2006. Characterization and genetic diversity analysis of cotton cultivars using microsatellites. Genet. Mol. Biol, 29: 321–329. Buschiazzo, E. and Gemmell, N. 2006. The rise fall and renaissance of microsatellites in eukaryotic genomes. BioEssays, 28: 1040– 1050. Chakravarthi, B. K. and Naravaneni, R. 2006. 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Development of Juglans Regia SSR Markers by Data Mining of the EST Database. Plant Mol. Biol. Rep, 28(4): 646–653. Zhang, S., Qiu, S., Zheng, Y., Zhang, S., Wu, S., He, Y. a nd Zheng, K. 2017. T he pur ple transcriptome as a source for gene sequence infor mation. J. Nucl. Agric. Sci, 31(4): 643-663. (Received on 24.08.2021, Revised on 27.11.2021 and Accepted on 18.01.2022) Gayathri et al J. Hortl. Sci. Vol. 16(2) : 206-214, 2021 00 Contents.pdf 09 Ravishankar.pdf 19 Lamesssa.pdf 20 Divya.pdf 21 Wani.pdf 23 Index and Last Pages.pdf