doi: 10.15389/agrobiology.2015.3.278eng

UDC 633.358:577.212.3:577.218

Supported by Russian Science Foundation (grant № 14-24-00135).

«NEXT GENERATION SEQUENCING» FOR STUDYING
TRANSCRIPTOME PROFILES OF TISSUES AND ORGANS
OF GARDEN PEA (Pisum sativum L.) (review)

V.A. Zhukov, O.A. Kulaeva, A.I. Zhernakov, I.A. Tikhonovich

All-Russian Research Institute for Agricultural Microbiology, Federal Agency of Scientific Organizations, 3, sh. Podbel’skogo, St. Petersburg, 196608 Russia,
e-mail: zhukoff01@yahoo.com

Received February 2, 2015

 

The term «Next Generation Sequencing» refers to modern technologies that help to obtain information about the nucleotide composition of tens and hundreds of millions of sequences in one experiment. NGS technologies are used to solve a wide range of problems (genome sequencing, gene expression assays, development of molecular markers, metagenomic studies of microbial communities, epigenetic studies etc.). One of the major applications of the NGS methods is concerned with analysis of gene expression by sequencing of transcriptome (the whole set of transcribed RNA). The review considers the approaches used for total gene expression analysis by «Next Generation Sequencing» — RNAseq (RNA sequencing) and its modification MACE (Massive Analysis of cDNA Ends). In this modification, developed by GenXPro GmbH (Frankfurt am Main, Germany), for each cDNA molecule only a 100-500 bp fragment (which is adjacent to the 3´-end of the transcript or, in another version, to its 5´-end) is subjected to sequencing; thus, the resolution of the method is increased by several times. In this way, MACE can capture the transcripts with low expression level, which correspond to the key regulatory genes forming the basis of biological processes. Also the review describes functional analysis of RNA sequencing, including the identification of biological patterns based on the detection of differentially expressed genes. An important step of this work is a hierarchical clustering of detected transcripts in accordance with the principles of gene ontology. The genes and gene products interact with each other to form a structured regulatory network, but the identification and analysis of regulatory networks is a complex task that requires the development of mathematical methods and the accumulation of data on gene expression, localization of gene products and their functional annotation. The review presents case studies of transcriptional profiles of the tissues and organs of pea (Pisum sativum L.), including those using the MACE technique. Thus, the use of NGS for gene expression studies is, at the moment, the optimal approach for studying the transcriptional profiles of any objects. The combination of NGS and potential of modern computational biology opens up new opportunities for studying the transcriptomes, including those of non-model species, that ensures progressive advance in many areas of biological science.

Keywords: plant genetics, «Next Generation Sequencing», RNA sequencing, gene expression, garden pea.

 

Full article (Rus)

Full text (Eng)

 

REFERENCES

  1. Ronaghi M. Pyrosequencing sheds light on DNA sequencing. Genome Research, 2001, 11: 3-11 CrossRef 
  2. Mardis E.R. Next-generation DNA sequencing methods. Annu. Rev. Genomics Hum. Genet., 2008, 9: 387-402 CrossRef
  3. Pandey V., Nutter R.C., Prediger E. Applied biosystems SOLiD™ system: ligation-based sequencing. In: Next-generation genome sequencing: towards personalized medicine. M. Janitz (ed.). WileyVCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2008: 29-41 (ISBN: 978-3-527-32090-5).
  4. Rusk N. Torrents of sequence. Nat. Methods, 2011, 8(1): 44 CrossRef
  5. Metzker M.L. Sequencing technologies — the next generation. Nat. Rev. Genet., 2010, 11(1): 31-46 CrossRef
  6. Shendure J., Ji H. Next-generation DNA sequencing. Nat. Biotechnol., 2008, 26(10): 1135-1145 CrossRef
  7. Knief C. Analysis of plant microbe interactions in the era of next generation sequencing technologies. Front. Plant Sci., 2014, 5: 216 CrossRef
  8. Wang Z., Gerstein M., Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 2009, 10(1): 57-63 CrossRef
  9. Ozsolak F., Milos P.M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet., 2011, 12(2): 87-98 CrossRef
  10. 't Hoen P.A., Ariyurek Y., Thygesen H.H., Vreugdenhil E., Vossen R.H., de Menezes R.X., Boer J.M., van Ommen G.J., den Dunnen J.T. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucl. Acids Res., 2008, 36(21): e141 CrossRef
  11. Marioni J.C., Mason C.E., Mane S.M., Stephens M., Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 2008, 18(9): 1509-1517 CrossRef
  12. Wilhelm B.T., Marguerat S., Watt S., Schubert F., Wood V., Goodhead I., Penkett C.J., Rogers J., Bahler J. Dynamic repertoire of an eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature, 2008, 453(7199): 1239-1243 CrossRef
  13. Wang E.T., Sandberg R., Luo S., Khrebtukova I., Zhang L., Mayr C., Kingsmore S.F., Schroth G.P., Burge C.B. Alternative isoform regulation in human tissue transcriptomes. Nature, 2008, 456(7221): 470-476 CrossRef
  14. Wang X., Sun Q., McGrath S.D., Mardis E.R., Soloway P.D., Clark A.G. Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain. PLoS ONE, 2008, 3(12): e3839 CrossRef
  15. Wahlstedt H., Daniel C., Enstero M., Ohman M. Large-scale mRNA sequencing determines global regulation of RNA editing during brain development. Genome Res., 2009, 19(6): 978-986 CrossRef
  16. Mardis E.R. A decade's perspective on DNA sequencing technology. Nature, 2011, 470(7333): 198-203 CrossRef
  17. Nakamura K., Oshima T., Morimoto T., Ikeda S., Yoshikawa H., Shiwa Y., Ishikawa S., Linak M.C., Hirai A., Takahashi H., Altaf-Ul-Amin M., Ogasawara N., Kanaya S. Sequence-specific error profile of Illumina sequencers. Nucl. Acids Res., 2011, 39(13): e90 CrossRef
  18. Quail M.A., Smith M., Coupland P., Otto T.D., Harris S.R., Connor T.R., Bertoni A., Swerdlow H.P., Gu Y. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics, 2012, 13: 341 CrossRef
  19. Garg R., Patel R.K., Jhanwar S., Priya P., Bhattacharjee A., Yadav G., Bhatia S., Chattopadhyay D., Tyagi A.K., Jain M. Gene discovery and tissue-specific transcriptome analysis in chickpea with massively parallel pyrosequencing and web resource development. Plant Physiol., 2011, 156(4): 1661-1678 CrossRef
  20. Jain M. Next-generation sequencing technologies for gene expression profiling in plants. Brief. Funct. Genomics, 2012, 11(1): 63-70 CrossRef
  21. Trapnell C., Salzberg S.L. How to map billions of short reads onto genomes. Nat. Biotechnol., 2009, 27(5): 455-457 CrossRef
  22. Mortazavi A., Williams B.A., McCue K., Schaeffer L., Wold B. Mapping and quantifying mammalian transcriptomes by RNAseq. Nat. Methods, 2008, 5(7): 621-628 CrossRef
  23. Wagner G.P., Kin K., Lynch V.J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci., 2012, 131(4): 281-285 CrossRef
  24. Langmead B., Salzberg S. Fast gapped-read alignment with Bowtie 2. Nat. Methods., 2012, 9(4): 357-359 CrossRef
  25. Robinson M.D., McCarthy D.J., Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140 CrossRef
  26. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., Rubin G.M., Sherlock G. Gene ontology: tool for the unification of biology. Nat. Genet., 2000, 25(1): 25-29 CrossRef
  27. Gene Ontology Consortium. The Gene Ontology in 2010: extensions and refinements. Nucl. Acids Res., 2010, 38(Suppl. 1): D331- D335 CrossRef
  28. Blake J.A. Ten quick tips for using the gene ontology. PLoS Comput. Biol., 2013, 9(11): e1003343 CrossRef
  29. Du Z., Zhou X., Ling Y., Zhang Z., Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucl. Acids Res., 2010, 38(Suppl. 2): W64-W70 CrossRef
  30. Conesa A., Götz S., García-Gómez J.M., Terol J., Talón M., Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 2005, 21(18): 3674-3676 CrossRef
  31. Usadel B., Nagel A., Thimm O., Redestig H., Blaesing O.E., Palacios-Rojas N., Selbig J., Hannemann J., Piques M.C., Steinhauser D., Scheible W.-R., Gibon Y., Morcuende R., Weicht D., Meyer S., Stitt M. Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol., 2005, 138(3): 1195-1204 CrossRef
  32. Croft D., Mundo A.F., Haw R., Milacic M., Weiser J., Wu G., Caudy M., Garapati P., Gillespie M., Kamdar M.R., Jassal B., Jupe S., Matthews L., May B., Palatnik S., Rothfels K., Shamovsky V., Song H., Williams M., Birney E., Hermjakob H., Stein L., D’Eustachio P. The Reactome pathway knowledgebase. Nucl. Acids Res., 2014, 42(D 1): D472-D477 CrossRef
  33. Wang M., Verdier J., Benedito V.A., Tang Y., Murray J.D., Ge Y., Becker J.D., Carvalho H., Rogers C., Udvardi M., He J. LegumeGRN: a gene regulatory network prediction server for functional and comparative studies. PLoS ONE, 2013, 8(7): e67434 CrossRef
  34. He J., Benedito V.A., Wang M., Murray J.D., Zhao P.X., Tang Y., Udvardi M.K. The Medicago truncatula gene expression atlas web server. BMC Bioinformatics, 2009, 10: 441 CrossRef
  35. Libault M., Farmer A., Joshi T., Takahashi K., Langley R.J., Franklin L.D., He J., Xu D., May G., Stacey G. An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. Plant J., 2010, 63(1): 86-99 CrossRef
  36. Severin A.J., Woody J.L., Bolon Y.T., Joseph B., Diers B.W., Farmer A.D., Muehlbauer G.J., Nelson R.T., Grant D., Specht J.E., Graham M.A., Cannon S.B., May G.D., Vance C.P., Shoemaker R.C. RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biol., 2010, 10: 160 CrossRef
  37. Verdier J., Torres-Jerez I., Wang M., Andriankaja A., Allen S.N., He J., Tang Y., Murray J.D., Udvardi M.K. Establishment of the Lotus japonicus Gene Expression Atlas (LjGEA) and its use to explore legume seed maturation. Plant J., 2013, 74(2): 351-362 CrossRef
  38. Food and agriculture organization corporate statistical database. FAOSTAT, 2014 (http://faostat.fao.org).
  39. Journet E.P., van Tuinen D., Gouzy J., Crespeau H., Carreau V., Farmer M.J., Niebel A., Schiex T., Jaillon O., Chatagnier O., Godiard L., Micheli F., Kahn D., Gianinazzi-Pearson V., Gamas P. Exploring root symbiotic programs in the model legume Medicago truncatula using EST analysis. Nucl. Acids Res., 2002, 30(24): 5579-5592 CrossRef
  40. Franssen S.U., Shrestha R.P., Bräutigam A., Bornberg-Bauer E., Weber A.P.M. Comprehensive transcriptome analysis of the highly complex Pisum sativum genome using next generation sequencing. BMC Genomics, 2011, 12: 227 CrossRef
  41. Kaur S., Pembleton L.W., Cogan N.O., Savin K.W., Leonforte T., Paull J., Materne M., Forster J.W. Transcriptome sequencing of field pea and faba bean for discovery and validation of SSR genetic markers. BMC Genomics, 2012, 13: 104 CrossRef
  42. Duarte J., Rivière N., Baranger A., Aubert G., Burstin J., Cornet L., Lavaud C., Lejeune-Hénaut I., Martinant J.P., Pichon J.P., Pilet-Nayel M.L., Boutet G. Transcriptome sequencing for high throughput SNP development and genetic mapping in Pea. BMC Genomics, 2014, 15: 126 CrossRef
  43. Zhukov V.A., Zhernakov A.I., Ershov N.I., Shtratnikova V.A., Pekov Yu.A., Malakho S.G., Borisov A.Yu., Tikhonovich I.A. Tezisy dokladov VI s"ezda Vavilovskogo obshchestva genetikov i selektsionerov (VOGiS) i assotsiirovannykh geneticheskikh simpoziumov [Proc. VI Congress of Vavilov Society of Geneticists and Breeders and associated genetic Symposia]. Rostov-na-Donu, 2014: 72.
  44. Grabherr M.G., Haas B.J., Yassour M., Levin J.Z., Thompson D.A., Amit I., Adiconis X., Fan L., Raychowdhury R., Zeng Q., Chen Z., Mauceli E., Hacohen N., Gnirke A., Rhind N., di Palma F., Birren B.W., Nusbaum C., Lindblad-Toh K., Friedman N., Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol., 2011, 29(7): 644-652 CrossRef
  45. Kulaeva O.A., Tsyganov V.E. Tezisy dokladov VI s"ezda Vavilovskogo obshchestva genetikov i selektsionerov (VOGiS) i assotsiirovannykh geneticheskikh simpoziumov [Proc. VI Congress of Vavilov Society of Geneticist and Breeders and associated genetic Symposia]. Rostov-na-Donu, 2014: 194.
  46. Wong C.E., Bhalla P.L., Ottenhof H., Singh M.B. Transcriptional profiling of the pea shoot apical meristem reveals processes underlying its function and maintenance. BMC Plant Biol., 2008, 8: 73 CrossRef
  47. Liang D., Wong C.E., Singh M.B., Beveridge C.A., Phipson B., Smyth G.K., Bhalla P.L. Molecular dissection of the pea shoot apical meristem. J. Exp. Bot., 2009, 60(14): 4201-4213 CrossRef
  48. Fondevilla S., Küster H., Krajinski F., Cubero J.I., Rubiales D. Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology. BMC Genomics, 2011, 12: 28 CrossRef
  49. Fragkostefanakis S., Simm S., Paul P., Bublak D., Scharf K.D., Schleiff E. Chaperone network composition in Solanum lycopersicum explored by transcriptome profiling and microarray meta-analysis. Plant Cell Environ., 2015, 38(4): 693-709 CrossRef
  50. Zajac B.K., Amendt J., Horres R., Verhoff M.A., Zehner R. De novo transcriptome analysis and highly sensitive digital gene expression profiling of Calliphora vicina (Diptera: Calliphoridae) pupae using MACE (Massive Analysis of cDNA Ends). Forensic Sci. Int. Genet., 2015, 15: 137-146 CrossRef

back