Molecular Biology Reports

, Volume 46, Issue 6, pp 6421–6434 | Cite as

Functional annotation of mulberry (Morus spp.) transcriptome, differential expression of genes related to growth and identification of putative genic SSRs, SNPs and InDels

  • M. S. Rukmangada
  • R. Sumathy
  • Vorkady Girish NaikEmail author
Original Article


Growth is a complex trait associated with mulberry leaf yield and controlled by several genes. In this study, we have explored the molecular basis underlying growth using Transcriptome profiling of contrasting genotypes. A total of 66.6 Mbp of primary transcriptomes from high growth (HGG)—Jalalgarah-3 and M. laevigata (H) and, low growth genotypes (LGG)—Harmutty and Vadagaraparai-2; resulting in 24210, 27998, 28085 and 28764 final transcripts respectively. Out of the 34096 pooled transcripts, 20249 transcripts matched with at least one sequence of the non-redundant database. Functional annotation resulted in the categorization of 18970 transcripts into 3 gene ontology (GO) terms and 7440 were assigned to 23 Kyoto encyclopaedia of genes and genomes (KEGG) pathway. Based on the differentially expressed genes and gene enrichment analysis, over expression of photosynthetic related transcripts in HGG and defence related transcripts in LGG were noted. Simple sequence repeats were mined from unique transcripts and the most abundant motifs were tri- (1883) followed by di- (1710), tetra- (192), penta- (68) and hexa- (40) repeats. Further, a total of 390897 high quality SNPs and 8081 InDels were identified by mapping onto Morus notabilis reference genome. The study provides an insight into the expression of genes involved in growth and further research on utilization in gentic improvement of the crop.


Mulberry growth Transcriptome profiling Differentially expressed genes GO/KEGG pathways Simple sequence repeats Single nucleotide polymorphisms 



High growth genotypes


Low growth genotypes


Differentially expressed genes


Simple sequence repeats


Single nucleotide polymorphisms




Gene ontology


Kyoto encyclopaedia of genes and genomes


Shoot fresh biomass


Shoot dry biomass


Photosynthetic rate


Stomatal conductance


Transpiration rate



The authors gratefully acknowledge the Director, CSRTI, Mysuru for providing an opportunity to undertake the research work.

Author contribution

VGN provided the experimental materials. MSR designed the workflow and conducted the experiment. RS and MSR performed the bioinformatics analysis and drafted the manuscript. VGN guided the work, critically evaluated data interpretation and revised the manuscript. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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  1. 1.
    Rahmathulla VK (2012) Management of climatic factors for successful silkworm (Bombyx mori L.) crop and higher silk production. A review. Psyche. CrossRefGoogle Scholar
  2. 2.
    Sánchez MD (2002) Mulberry: an exceptional forage available almost worldwide. World Anim Rev 93:1–21Google Scholar
  3. 3.
    Bown D (2003) The Royal Horticultural Society encyclopedia of herbs and their uses. Dorling Kindersley Limited, London, p 448Google Scholar
  4. 4.
    Chen PN, Chu SC, Chiou HL et al (2006) Mulberry anthocyanins, cyanidin 3-rutinoside and cyanidin 3-glucoside, exhibited an inhibitory effect on the migration and invasion of a human lung cancer cell line. Cancer Lett 235:248–259CrossRefGoogle Scholar
  5. 5.
    Konno K, Ono H, Nakamura M et al (2006) Mulberry latex rich in antidiabetic sugar-mimic alkaloids forces dieting on caterpillars. Proc Natl Acad Sci 103:1337–1341CrossRefGoogle Scholar
  6. 6.
    Machii H, Koyama A (1917) Mulberry breeding, cultivation and utilization in Japan. Anim Prod Health Paper 147:63Google Scholar
  7. 7.
    De Souza MM, Bittar M, Cechinel-Filho V et al (2000) Antinociceptive properties of morusin, a prenylflavonoid isolated from Morus nigra root bark. Zeitschrift für Naturforschung C 55:256–260CrossRefGoogle Scholar
  8. 8.
    Gerasopoulos D, Stavroulakis G (1997) Quality characteristics of four mulberry (Morus sp) cultivars in the area of Chania, Greece. J Sci Food Agric 73:261–264CrossRefGoogle Scholar
  9. 9.
    Rukmangada MS, Ramasamy S, Sivaprasad V, Varkody GN (2018) Growth performance in contrasting sets of mulberry (Morus Spp.) genotypes explained by logistic and linear regression models using morphological and gas exchange parameters. Sci Hortic 235:53–61CrossRefGoogle Scholar
  10. 10.
    Rukmangada MS, Sumathy R, Sivaprasad V, Naik VG (2018) Genome-wide identification and characterization of growth-regulating factors in mulberry (Morus spp.). Trees 32:1695–1705CrossRefGoogle Scholar
  11. 11.
    Hollender CA, Dardick C (2015) Molecular basis of angiosperm tree. New Phytol 206:541–556CrossRefGoogle Scholar
  12. 12.
    González-Plaza JJ, Ortiz-Martín I, Muñoz-Mérida A et al (2016) Transcriptomic analysis using olive varieties and breeding progenies identifies candidate genes involved in plant architecture. Front Plant Sci 7:1–21CrossRefGoogle Scholar
  13. 13.
    Morozova O, Hirst M, Marra MA (2009) Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet 10:135–151CrossRefGoogle Scholar
  14. 14.
    Wall PK, Leebens-Mack J, Chanderbali AS et al (2009) Comparison of next generation sequencing technologies for transcriptome characterization. BMC Genomics 10:347CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Krost C, Petersen R, Schmidt ER (2012) The transcriptomes of columnar and standard type apple trees (Malus x domestica)—a comparative study. Gene 498:223–230CrossRefGoogle Scholar
  16. 16.
    Alagna F, D’Agostino N, Torchia L et al (2009) Comparative 454 pyrosequencing of transcripts from two olive genotypes during fruit development. BMC Genomics 10:399CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Zhang J, Wu K, Zeng S et al (2013) Transcriptome analysis of Cymbidium sinense and its application to the identification of genes associated with floral development. BMC Genomics 14:1–17CrossRefGoogle Scholar
  18. 18.
    Du W, Ban Y, Nie H et al (2016) A comparative transcriptome analysis leads to new insights into the molecular events governing root formation in mulberry softwood cuttings. Plant Mol Biol Rep 34:365–373CrossRefGoogle Scholar
  19. 19.
    Wang W, Wang Y, Zhang Q et al (2009) Global characterization of Artemisia annua glandular trichome transcriptome using 454 pyrosequencing. BMC Genomics 10:465CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wang XW, Luan JB, Li JM et al (2010) De novo characterization of a whitefly transcriptome and analysis of its gene expression during development. BMC Genomics 11:400CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Wang Z, Fang B, Chen J et al (2010) De novo assembly and characterization of root transcriptome using Illumina paired-end sequencing and development of cSSR markers in sweetpotato (Ipomoea batatas). BMC Genomics 11:1–14CrossRefGoogle Scholar
  22. 22.
    Zhao S, Tuan PA, Li X et al (2013) Identification of phenylpropanoid biosynthetic genes and phenylpropanoid accumulation by transcriptome analysis of Lycium chinense. BMC Genomics 14:802CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Sun Y, Fan XY, Cao DM et al (2010) Integration of brassinosteroid signal transduction with the transcription network for plant growth regulation in Arabidopsis. Dev Cell 19:765–777CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Garg R, Patel RK, Tyagi AK, Jain M (2011) De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res 18:53–63CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Thangavelu K, Mukherjee P, Tikader A et al (1997) Catalogue on mulberry (Morus spp.) germplasm. Silkworm and Mulberry Germplasm Station, HosurGoogle Scholar
  26. 26.
    Thangavelu K, Tikader A, Ramesh SR et al (2000) Catalogue on mulberry (Morus spp.) germplasm. Silkworm and Mulberry Germplasm Station, HosurGoogle Scholar
  27. 27.
    Tikader A, Chandrasekhar M, Borpuzari MM et al (2006) Catalogue on mulberry (Morus Spp.) germplasm. Silkworm and Mulberry Germplasm Station, HosurGoogle Scholar
  28. 28.
    Pinto MV, Poornima HS, Rukmangada MS et al (2018) Association mapping of quantitative resistance to charcoal root rot in mulberry germplasm. PLoS ONE 13:1–25Google Scholar
  29. 29.
    Minamizawa K (1997) Moriculture science of mulberry cultivation. Oxford & IBH Publishing Co Pvt, LtdGoogle Scholar
  30. 30.
    Patel RK, Jain M (2012) NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE 7:e30619CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bankar KG, Todur VN, Shukla RN, Vasudevan M (2015) Ameliorated de novo transcriptome assembly using illumina paired end sequence data with trinity assembler. Genomics Data 5:352–359CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659CrossRefGoogle Scholar
  33. 33.
    Conesa A, Gotz S, Garcia-Gomez JM et al (2005) Blast2GO: a universal tool for anotation, visualization and analysis in functional genomics research. Bioinformatics 21:3674–3676CrossRefGoogle Scholar
  34. 34.
    Ye J, Fang L, Zheng H et al (2006) WEGO: a web tool for plotting GO annotations. Nucleic Acids Res 34:293–297CrossRefGoogle Scholar
  35. 35.
    Moriya Y, Itoh M, Okuda S et al (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:182–185CrossRefGoogle Scholar
  36. 36.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Beissbarth T, Speed TP (2004) GOstat: find statistically overrepresented gene ontologies within a group of genes. Bioinformatics 20:1464–1465CrossRefGoogle Scholar
  39. 39.
    Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16:284–287CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    de Hoon MJL, Imoto S, Nolan J, Miyano S (2004) Open source clustering software. Bioinformatics 20:1453–1454CrossRefGoogle Scholar
  41. 41.
    Saldanha AJ, Urbach JM, Wu G et al (2004) Java treeview–extensible visualization of microarray data. Bioinformatics 20:3246–3248CrossRefGoogle Scholar
  42. 42.
    Ramesh Krishnan R, Sumathy R, Bindroo BB, Girish Naik V (2014) MulSatDB: a first online database for mulberry microsatellites. Trees 28:1793–1799CrossRefGoogle Scholar
  43. 43.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Danecek P, Auton A, Abecasis G et al (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Cingolani P, Platts A, Wang LL et al (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: sNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6:80–92CrossRefGoogle Scholar
  46. 46.
    Zenoni S, Ferrarini A, Giacomelli E et al (2010) Characterization of transcriptional complexity during berry development in Vitis vinifera using RNA-Seq. Plant Physiol 152:1787–1795CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Dai F, Tang C, Wang Z et al (2015) De novo assembly, gene annotation, and marker development of mulberry (Morus atropurpurea) transcriptome. Tree Genet Genomes 11:26CrossRefGoogle Scholar
  48. 48.
    Saeed B, Baranwal VK, Khurana P (2016) Comparative transcriptomics and comprehensive marker resource development in mulberry. BMC Genomics 17:98CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Liu Y, Zhou J, White KP (2014) RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30:301–304CrossRefGoogle Scholar
  50. 50.
    Gharat SA, Parmar S, Tambat S et al (2016) Transcriptome analysis of the response to NaCl in Suaeda maritima provides an insight into salt tolerance mechanisms in halophytes. PLoS ONE 11:1–35CrossRefGoogle Scholar
  51. 51.
    Yong HY, Zou Z, Kok EP et al (2014) Comparative transcriptome analysis of leaves and roots in response to sudden increase in salinity in Brassica napus by RNA-seq. Biomed Res Int 2014:467395CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Yamamoto N, Takano T, Tanaka K et al (2015) Comprehensive analysis of transcriptome response to salinity stress in the halophytic turf grass Sporobolus virginicus. Front Plant Sci 6:241CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Wei W, Qi X, Wang L et al (2011) Characterization of the sesame (Sesamum indicum L.) global transcriptome using Illumina paired-end sequencing and development of EST-SSR markers. BMC Genomics 12(1):451CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Huang R, Wallqvist A, Covell DG (2006) Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute’s anticancer screen. Genomics 87:315–328CrossRefGoogle Scholar
  55. 55.
    Wang S, Wang X, He Q et al (2012) Transcriptome analysis of the roots at early and late seedling stages using Illumina paired-end sequencing and development of EST-SSR markers in radish. Plant Cell Rep 31:1437–1447CrossRefGoogle Scholar
  56. 56.
    Hiz MC, Canher B, Niron H, Turet M (2014) Transcriptome analysis of salt tolerant common bean (Phaseolus vulgaris L.) under saline conditions. PLoS ONE 9(3):e92598CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Tao X, Gu YH, Wang HY et al (2012) Digital gene expression analysis based on integrated De Novo transcriptome assembly of sweet potato [Ipomoea batatas (L.) Lam.]. PLoS ONE 7(4):e36234CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Lal S, Ravi V, Khurana JP, Khurana P (2009) Repertoire of leaf expressed sequence tags (ESTs) and partial characterization of stress-related and membrane transporter genes from mulberry (Morus indica L.). Tree Genet Genomes 5:359–374CrossRefGoogle Scholar
  59. 59.
    Lambers H, Poorter H (1992) Inherent variation in growth rate between higher plants: a search for ecological causes and consequences. Adv Ecol Res 23:187–261CrossRefGoogle Scholar
  60. 60.
    Le DT, Nishiyama R, Watanabe Y et al (2012) Differential gene expression in soybean leaf tissues at late developmental stages under drought stress revealed by genome-wide transcriptome analysis. PLoS ONE 7:e49522CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Matsui A, Ishida J, Morosawa T et al (2008) Arabidopsis transcriptome analysis under drought, cold, high-salinity and ABA treatment conditions using a tiling array. Plant Cell Physiol 49:1135–1149CrossRefGoogle Scholar
  62. 62.
    Ma S, Gong Q, Bohnert HJ (2006) Dissecting salt stress pathways. J Exp Bot 57:1097–1107CrossRefGoogle Scholar
  63. 63.
    Coley PD (1986) Costs and benefits of defense by tannins in a neotropical tree. Oecologia 70:238–241CrossRefGoogle Scholar
  64. 64.
    PG Waterman (1989) Herbivory and secondary compond in rain-forest plants. Trop Rain For Ecosyst 513–536Google Scholar
  65. 65.
    Shaw BP, Sahu SK, Mishra RK (2004) Heavy metal induced oxidative damage in terrestrial plants. Heavy metal stress in plants. Springer, Berlin, pp 84–126CrossRefGoogle Scholar
  66. 66.
    Wasternack C (2007) Jasmonates: an update on biosynthesis, signal transduction and action in plant stress response, growth and development. Ann Bot 100:681–697CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Sun W, Xu X, Zhu H et al (2010) Comparative transcriptomic profiling of a salt-tolerant wild tomato species and a salt-sensitive tomato cultivar. Plant Cell Physiol 51:997–1006CrossRefGoogle Scholar
  68. 68.
    Liu Q, Kasuga M, Sakuma Y et al (1998) Two transcription factors, DREB1 and DREB2, with an EREBP/AP2 DNA binding domain separate two cellular signal transduction pathways in drought- and low-temperature-responsive gene gxpression, respectively, in Arabidopsis. Plant Cell 10:1391CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Mathithumilan B, Kadam NN, Biradar J et al (2013) Development and characterization of microsatellite markers for Morus spp. and assessment of their transferability to other closely related species. BMC Plant Biol 13(1):194CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Varshney RK, Graner A, Sorrells ME (2005) Genic microsatellite markers in plants: features and applications. Trends Biotechnol 23:48–55CrossRefGoogle Scholar
  71. 71.
    Varshney RK (2010) Gene-based marker systems in plants: high throughput approaches for marker discovery and genotyping. Molecular techniques in crop improvement. Springer, Netherlands, pp 119–142CrossRefGoogle Scholar
  72. 72.
    Jones ES, Sullivan H, Bhattramakki D, Smith JSC (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet 115:361–371CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Molecular Biology Laboratory – 1Central Sericultural Research and Training InstituteMysuruIndia
  2. 2.Bioinformatics Centre, Central Sericultural Research and Training InstituteMysuruIndia
  3. 3.Regional Sericultural Research StationCentral Silk Board, Ministry of Textiles - Govt. of IndiaChamarajanagaraIndia

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