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Experimental Design for Time-Series RNA-Seq Analysis of Gene Expression and Alternative Splicing

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Plant Circadian Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2398))

Abstract

RNA-sequencing (RNA-seq) is currently the method of choice for analysis of differential gene expression. To fully exploit the wealth of data generated from genome-wide transcriptomic approaches, the initial design of the experiment is of paramount importance. Biological rhythms in nature are pervasive and are driven by endogenous gene networks collectively known as circadian clocks. Measuring circadian gene expression requires time-course experiments which take into account time-of-day factors influencing variability in expression levels. We describe here an approach for characterizing diurnal changes in expression and alternative splicing for plants undergoing cooling. The method uses inexpensive everyday laboratory equipment and utilizes an RNA-seq application (3D RNA-seq) that can handle complex experimental designs and requires little or no prior bioinformatics expertise.

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References

  1. Harmer SL, Hogenesch JB, Straume M, Chang HS, Han B, Zhu T, Wang X, Kreps JA, Kay SA (2000) Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science 290(5499):2110–2113

    Article  CAS  PubMed  Google Scholar 

  2. Michael TP, McClung CR (2003) Enhancer trapping reveals widespread circadian clock transcriptional control in Arabidopsis. Plant Physiol 132(2):629–639. https://doi.org/10.1104/pp.021006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Covington MF, Maloof JN, Straume M, Kay SA, Harmer SL (2008) Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development. Genome Biol 9(8):R130. https://doi.org/10.1186/gb-2008-9-8-r130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Calixto CPG, Guo W, James AB, Tzioutziou NA, Entizne JC, Panter PE, Knight H, Nimmo HG, Zhang R, Brown JWS (2018) Rapid and dynamic alternative splicing impacts the Arabidopsis cold response transcriptome. Plant Cell 30(7):1424–1444. https://doi.org/10.1105/tpc.18.00177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Breeze E, Harrison E, McHattie S, Hughes L, Hickman R, Hill C, Kiddle S, Kim YS, Penfold CA, Jenkins D, Zhang C, Morris K, Jenner C, Jackson S, Thomas B, Tabrett A, Legaie R, Moore JD, Wild DL, Ott S, Rand D, Beynon J, Denby K, Mead A, Buchanan-Wollaston V (2011) High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation. Plant Cell 23(3):873–894. https://doi.org/10.1105/tpc.111.083345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Carvallo MA, Pino MT, Jeknic Z, Zou C, Doherty CJ, Shiu SH, Chen TH, Thomashow MF (2011) A comparison of the low temperature transcriptomes and CBF regulons of three plant species that differ in freezing tolerance: Solanum commersonii, Solanum tuberosum, and Arabidopsis thaliana. J Exp Bot 62(11):3807–3819. https://doi.org/10.1093/jxb/err066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Leviatan N, Alkan N, Leshkowitz D, Fluhr R (2013) Genome-wide survey of cold stress regulated alternative splicing in Arabidopsis thaliana with tiling microarray. PLoS One 8(6):e66511. https://doi.org/10.1371/journal.pone.0066511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mockler TC, Michael TP, Priest HD, Shen R, Sullivan CM, Givan SA, McEntee C, Kay SA, Chory J (2007) The DIURNAL project: DIURNAL and circadian expression profiling, model-based pattern matching, and promoter analysis. Cold Spring Harb Symp Quant Biol 72:353–363. https://doi.org/10.1101/sqb.2007.72.006

    Article  CAS  PubMed  Google Scholar 

  9. Schaffer R, Landgraf J, Accerbi M, Simon V, Larson M, Wisman E (2001) Microarray analysis of diurnal and circadian-regulated genes in Arabidopsis. Plant Cell 13(1):113–123. https://doi.org/10.1105/tpc.13.1.113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tikkanen M, Gollan PJ, Mekala NR, Isojarvi J, Aro EM (2014) Light-harvesting mutants show differential gene expression upon shift to high light as a consequence of photosynthetic redox and reactive oxygen species metabolism. Philos Trans R Soc Lond Ser B Biol Sci 369(1640):20130229. https://doi.org/10.1098/rstb.2013.0229

    Article  CAS  Google Scholar 

  11. Vogel JT, Zarka DG, Van Buskirk HA, Fowler SG, Thomashow MF (2005) Roles of the CBF2 and ZAT12 transcription factors in configuring the low temperature transcriptome of Arabidopsis. Plant J 41(2):195–211. https://doi.org/10.1111/j.1365-313X.2004.02288.x

    Article  CAS  PubMed  Google Scholar 

  12. Windram O, Madhou P, McHattie S, Hill C, Hickman R, Cooke E, Jenkins DJ, Penfold CA, Baxter L, Breeze E, Kiddle SJ, Rhodes J, Atwell S, Kliebenstein DJ, Kim YS, Stegle O, Borgwardt K, Zhang C, Tabrett A, Legaie R, Moore J, Finkenstadt B, Wild DL, Mead A, Rand D, Beynon J, Ott S, Buchanan-Wollaston V, Denby KJ (2012) Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. Plant Cell 24(9):3530–3557. https://doi.org/10.1105/tpc.112.102046

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gehan MA, Park S, Gilmour SJ, An C, Lee CM, Thomashow MF (2015) Natural variation in the C-repeat binding factor cold response pathway correlates with local adaptation of Arabidopsis ecotypes. Plant J 84(4):682–693. https://doi.org/10.1111/tpj.13027

    Article  CAS  PubMed  Google Scholar 

  14. Jia Y, Ding Y, Shi Y, Zhang X, Gong Z, Yang S (2016) The cbfs triple mutants reveal the essential functions of CBFs in cold acclimation and allow the definition of CBF regulons in Arabidopsis. New Phytol 212(2):345–353. https://doi.org/10.1111/nph.14088

    Article  CAS  PubMed  Google Scholar 

  15. Zhao C, Zhang Z, Xie S, Si T, Li Y, Zhu JK (2016) Mutational evidence for the critical role of CBF transcription factors in cold acclimation in Arabidopsis. Plant Physiol 171(4):2744–2759. https://doi.org/10.1104/pp.16.00533

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Stephan-Otto Attolini C, Pena V, Rossell D (2015) Designing alternative splicing RNA-seq studies. Beyond generic guidelines. Bioinformatics 31(22):3631–3637. https://doi.org/10.1093/bioinformatics/btv436

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Calixto CPG, Tzioutziou NA, James AB, Hornyik C, Guo W, Zhang R, Nimmo HG, Brown JWS (2019) Cold-dependent expression and alternative splicing of Arabidopsis long non-coding RNAs. Front Plant Sci 10:235. https://doi.org/10.3389/fpls.2019.00235

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rapazote-Flores P, Bayer M, Milne L, Mayer C-D, Fuller J, Guo W, Hedley PE, Morris J, Halpin C, Kam J, McKim SM, Zwirek M, Casao MC, Barakate A, Schreiber M, Stephen G, Zhang R, Brown JW, Waugh R, Simpson CG (2019) BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq. bioRxiv:638106. https://doi.org/10.1101/638106

  19. Soneson C, Love MI, Robinson MD (2015) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521. https://doi.org/10.12688/f1000research.7563.2

    Article  PubMed  Google Scholar 

  20. Marquez Y, Brown JW, Simpson C, Barta A, Kalyna M (2012) Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res 22(6):1184–1195. https://doi.org/10.1101/gr.134106.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Syed NH, Kalyna M, Marquez Y, Barta A, Brown JW (2012) Alternative splicing in plants—coming of age. Trends Plant Sci 17(10):616–623. https://doi.org/10.1016/j.tplants.2012.06.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, Wong WK, Mockler TC (2010) Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res 20(1):45–58. https://doi.org/10.1101/gr.093302.109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Carvalho RF, Feijao CV, Duque P (2013) On the physiological significance of alternative splicing events in higher plants. Protoplasma 250(3):639–650. https://doi.org/10.1007/s00709-012-0448-9

    Article  CAS  PubMed  Google Scholar 

  24. Drechsel G, Kahles A, Kesarwani AK, Stauffer E, Behr J, Drewe P, Ratsch G, Wachter A (2013) Nonsense-mediated decay of alternative precursor mRNA splicing variants is a major determinant of the Arabidopsis steady state transcriptome. Plant Cell 25(10):3726–3742. https://doi.org/10.1105/tpc.113.115485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chamala S, Feng G, Chavarro C, Barbazuk WB (2015) Genome-wide identification of evolutionarily conserved alternative splicing events in flowering plants. Front Bioeng Biotechnol 3:33. https://doi.org/10.3389/fbioe.2015.00033

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mastrangelo AM, Marone D, Laido G, De Leonardis AM, De Vita P (2012) Alternative splicing: enhancing ability to cope with stress via transcriptome plasticity. Plant Sci 185–186:40–49. https://doi.org/10.1016/j.plantsci.2011.09.006

    Article  CAS  PubMed  Google Scholar 

  27. Schurch NJ, Schofield P, Gierlinski M, Cole C, Sherstnev A, Singh V, Wrobel N, Gharbi K, Simpson GG, Owen-Hughes T, Blaxter M, Barton GJ (2016) Erratum: how many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22(10):1641. https://doi.org/10.1261/rna.058339.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hayer KE, Pizarro A, Lahens NF, Hogenesch JB, Grant GR (2015) Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data. Bioinformatics 31(24):3938–3945. https://doi.org/10.1093/bioinformatics/btv488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kanitz A, Gypas F, Gruber AJ, Gruber AR, Martin G, Zavolan M (2015) Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data. Genome Biol 16:150. https://doi.org/10.1186/s13059-015-0702-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33(3):290–295. https://doi.org/10.1038/nbt.3122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Steijger T, Abril JF, Engstrom PG, Kokocinski F, Consortium R, Hubbard TJ, Guigo R, Harrow J, Bertone P (2013) Assessment of transcript reconstruction methods for RNA-seq. Nat Methods 10(12):1177–1184. https://doi.org/10.1038/nmeth.2714

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME (2016) RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Research 5:1408. https://doi.org/10.12688/f1000research.9005.3

  33. Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15(2):R29. https://doi.org/10.1186/gb-2014-15-2-r29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gloss BS, Signal B, Cheetham SW, Gruhl F, Kaczorowski DC, Perkins AC, Dinger ME (2017) High resolution temporal transcriptomics of mouse embryoid body development reveals complex expression dynamics of coding and noncoding loci. Sci Rep 7(1):6731. https://doi.org/10.1038/s41598-017-06110-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guo W, Tzioutziou N, Stephen G, Milne I, Calixto C, Waugh R, Brown JWS, Zhang R (2019) 3D RNA-seq—a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists. bioRxiv:656686. https://doi.org/10.1101/656686

  36. Arteca RN, Arteca JM (2000) A novel method for growing Arabidopsis thaliana plants hydroponically. Physiol Plantarum 108(2):188–193. https://doi.org/10.1034/j.1399-3054.2000.108002188x./

    Article  CAS  Google Scholar 

  37. Koressaar T, Lepamets M, Kaplinski L, Raime K, Andreson R, Remm M (2018) Primer3_masker: integrating masking of template sequence with primer design software. Bioinformatics 34(11):1937–1938. https://doi.org/10.1093/bioinformatics/bty036

    Article  CAS  PubMed  Google Scholar 

  38. Koressaar T, Remm M (2007) Enhancements and modifications of primer design program Primer3. Bioinformatics 23(10):1289–1291. https://doi.org/10.1093/bioinformatics/btm091

    Article  CAS  PubMed  Google Scholar 

  39. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3—new capabilities and interfaces. Nucleic Acids Res 40(15):e115. https://doi.org/10.1093/nar/gks596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thornton B, Basu C (2015) Rapid and simple method of qPCR primer design. Methods Mol Biol 1275:173–179. https://doi.org/10.1007/978-1-4939-2365-6_13

    Article  CAS  PubMed  Google Scholar 

  41. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5):1792–1797. https://doi.org/10.1093/nar/gkh340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Notredame C, Higgins DG, Heringa J (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1):205–217. https://doi.org/10.1006/jmbi.2000.4042

    Article  CAS  PubMed  Google Scholar 

  43. Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30(4):772–780. https://doi.org/10.1093/molbev/mst010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. James AB, Monreal JA, Nimmo GA, Kelly CL, Herzyk P, Jenkins GI, Nimmo HG (2008) The circadian clock in Arabidopsis roots is a simplified slave version of the clock in shoots. Science 322(5909):1832–1835. https://doi.org/10.1126/science.1161403

    Article  CAS  PubMed  Google Scholar 

  45. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3. https://doi.org/10.1186/1471-2199-7-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chang Z, Wang Z, Li G (2014) The impacts of read length and transcriptome complexity for de novo assembly: a simulation study. PLoS One 9(4):e94825. https://doi.org/10.1371/journal.pone.0094825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hara Y, Tatsumi K, Yoshida M, Kajikawa E, Kiyonari H, Kuraku S (2015) Optimizing and benchmarking de novo transcriptome sequencing: from library preparation to assembly evaluation. BMC Genomics 16:977. https://doi.org/10.1186/s12864-015-2007-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14(4):417–419. https://doi.org/10.1038/nmeth.4197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang R, Calixto CPG, Marquez Y, Venhuizen P, Tzioutziou NA, Guo W, Spensley M, Entizne JC, Lewandowska D, Ten Have S, Frei Dit Frey N, Hirt H, James AB, Nimmo HG, Barta A, Kalyna M, Brown JWS (2017) A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing. Nucleic Acids Res 45(9):5061–5073. https://doi.org/10.1093/nar/gkx267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. James AB, Syed NH, Bordage S, Marshall J, Nimmo GA, Jenkins GI, Herzyk P, Brown JW, Nimmo HG (2012) Alternative splicing mediates responses of the Arabidopsis circadian clock to temperature changes. Plant Cell 24(3):961–981. https://doi.org/10.1105/tpc.111.093948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25(4):402–408. https://doi.org/10.1006/meth.2001.1262

    Article  CAS  PubMed  Google Scholar 

  52. Simpson CG, Fuller J, Maronova M, Kalyna M, Davidson D, McNicol J, Barta A, Brown JW (2008) Monitoring changes in alternative precursor messenger RNA splicing in multiple gene transcripts. Plant J 53(6):1035–1048. https://doi.org/10.1111/j.1365-313X.2007.03392.x

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [BB/P009751/1 to JWSB; BB/K006835/1 to HGN] and the Scottish Government Rural and Environment Science and Analytical Services division (RESAS) [to JWSB and RZ].

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Authors and Affiliations

  1. Division of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK

    Nikoleta A. Tzioutziou, Wenbin Guo, Cristiane P. G. Calixto & John W. S. Brown

  2. Institute of Molecular, Cell, and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Allan B. James & Hugh G. Nimmo

  3. Information and Computational Sciences, The James Hutton Institute, Dundee, UK

    Runxuan Zhang

  4. Cell and Molecular Sciences, The James Hutton Institute, Dundee, UK

    John W. S. Brown

Authors
  1. Nikoleta A. Tzioutziou
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  2. Allan B. James
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  3. Wenbin Guo
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  4. Cristiane P. G. Calixto
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  5. Runxuan Zhang
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  6. Hugh G. Nimmo
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  7. John W. S. Brown
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Correspondence to John W. S. Brown .

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Editors and Affiliations

  1. Department of Biology, University of Bielefeld, Bielefeld, Nordrhein-Westfalen, Germany

    Dorothee Staiger

  2. Department of Biology, University of York, Heslington, UK

    Seth Davis

  3. Department of Biology, University of York, Heslington, UK

    Amanda Melaragno Davis

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Tzioutziou, N.A. et al. (2022). Experimental Design for Time-Series RNA-Seq Analysis of Gene Expression and Alternative Splicing. In: Staiger, D., Davis, S., Davis, A.M. (eds) Plant Circadian Networks. Methods in Molecular Biology, vol 2398. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1912-4_14

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  • DOI: https://doi.org/10.1007/978-1-0716-1912-4_14

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