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High Impact Gene Discovery: Simple Strand-Specific mRNA Library Construction and Differential Regulatory Analysis Based on Gene Co-Expression Network

  • Yasunori Ichihashi
  • Atsushi Fukushima
  • Arisa Shibata
  • Ken Shirasu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1830)

Abstract

Plant transcription factors have potential to behave as hubs in gene regulatory networks through altering the expression of many downstream genes, and identification of such hub transcription factors strongly enhances our understating of biological processes. Transcriptome analysis has become a staple of gene expression analyses. In addition to current advances in Next Generation Sequencing (NGS) technology, various methods for mRNA library construction and downstream data analyses have been enthusiastically developed. Here, we describe Breath Adapter Directional sequencing (BrAD-seq), a simple strand-specific mRNA library preparation for the Illumina platform, allowing easy scaling of transcriptome experiments with low reagent and labor costs. This protocol includes our recent modifications and the detailed practical procedure for BrAD-seq. Because extracting biological meanings from large-scale transcriptome data presents a significant challenge, we also describe a new analytical method that goes beyond differential expression. Differential regulatory analysis (DRA) is based on a gene co-expression network to address which regulatory factor or factors have the ability to predict the abundance of differentially expressed genes between two groups or conditions. This protocol provides a ready-to-use informatics pipeline from raw sequence data to DRA for plant transcriptome datasets.

Key words

Bioinformatics Breath adapter directional sequencing Differential regulatory analysis Network analysis RNA-seq Transcriptome 

Abbreviations

ABR

AMPure XP bead resuspension buffer

BrAD

Breath adapter directional sequencing

DEGs

Differentially expressed genes

DGE

Digital gene expression

DRA

Differential regulatory analysis

LBB

Lysis/binding buffer

LSB

Low-salt buffer

NGS

Next generation sequencing

SHO

Shotgun

WBA

Washing buffer A

WBB

Washing buffer B

Notes

Acknowledgment

This work was supported by PRESTO, Japan Science and Technology Agency (JPMJPR15Q2) to YI and JSPS KAKENHI Grant Number 17 K07663 to AF, 15H05959 and 17H06172 to KS.

Supplementary material

435160_1_En_11_MOESM1_ESM.xlsx (40 kb)
Supplementary File 1: (XLSX 40 kb)

References

  1. 1.
    Ramirez SR, Basu C (2009) Comparative analyses of plant transcription factor databases. Curr Genomics 10(1):10–17.  https://doi.org/10.2174/138920209787581253 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12(2):87–98.  https://doi.org/10.1038/nrg2934 CrossRefPubMedGoogle Scholar
  3. 3.
    Mader U, Nicolas P, Richard H, Bessieres P, Aymerich S (2011) Comprehensive identification and quantification of microbial transcriptomes by genome-wide unbiased methods. Curr Opin Biotechnol 22(1):32–41.  https://doi.org/10.1016/j.copbio.2010.10.003 CrossRefPubMedGoogle Scholar
  4. 4.
    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18(9):1509–1517.  https://doi.org/10.1101/gr.079558.108 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63.  https://doi.org/10.1038/nrg2484 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Townsley BT, Covington MF, Ichihashi Y, Zumstein K, Sinha NR (2015) BrAD-seq: breath adapter directional sequencing: a streamlined, ultra-simple and fast library preparation protocol for strand specific mRNA library construction. Front Plant Sci 6:366.  https://doi.org/10.3389/fpls.2015.00366 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    von Hippel PH, Johnson NP, Marcus AH (2013) Fifty years of DNA "breathing": reflections on old and new approaches. Biopolymers 99(12):923–954.  https://doi.org/10.1002/bip.22347 CrossRefGoogle Scholar
  8. 8.
    Hudson NJ, Dalrymple BP, Reverter A (2012) Beyond differential expression: the quest for causal mutations and effector molecules. BMC Genomics 13:356.  https://doi.org/10.1186/1471-2164-13-356 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    de la Fuente A (2010) From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. Trends Genet 26(7):326–333.  https://doi.org/10.1016/j.tig.2010.05.001 CrossRefPubMedGoogle Scholar
  10. 10.
    Fukushima A (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518(1):209–214.  https://doi.org/10.1016/j.gene.2012.11.028 CrossRefPubMedGoogle Scholar
  11. 11.
    Fukushima A, Nishizawa T, Hayakumo M, Hikosaka S, Saito K, Goto E, Kusano M (2012) Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression approaches. Plant Physiol 158(4):1487–1502.  https://doi.org/10.1104/pp.111.188367 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ichihashi Y, Aguilar-Martinez JA, Farhi M, Chitwood DH, Kumar R, Millon LV, Peng J, Maloof JN, Sinha NR (2014) Evolutionary developmental transcriptomics reveals a gene network module regulating interspecific diversity in plant leaf shape. Proc Natl Acad Sci U S A 111(25):E2616–E2621.  https://doi.org/10.1073/pnas.1402835111 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Sinha NR, Rowland SD, Ichihashi Y (2016) Using gene networks in EvoDevo analyses. Curr Opin Plant Biol 33:133–139.  https://doi.org/10.1016/j.pbi.2016.06.016 CrossRefPubMedGoogle Scholar
  14. 14.
    Reverter A, Hudson NJ, Nagaraj SH, Perez-Enciso M, Dalrymple BP (2010) Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics 26(7):896–904.  https://doi.org/10.1093/bioinformatics/btq051 CrossRefPubMedGoogle Scholar
  15. 15.
    Deng SP, Zhu L, Huang DS (2015) Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics 16(Suppl 3):S4.  https://doi.org/10.1186/1471-2164-16-S3-S4 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Li J, Li YX, Li YY (2016) Differential regulatory analysis based on coexpression network in cancer research. Biomed Res Int 2016:4241293.  https://doi.org/10.1155/2016/4241293 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Xu F, Yang J, Chen J, Wu Q, Gong W, Zhang J, Shao W, Mu J, Yang D, Yang Y, Li Z, Xie P (2015) Differential co-expression and regulation analyses reveal different mechanisms underlying major depressive disorder and subsyndromal symptomatic depression. BMC Bioinformatics 16:112.  https://doi.org/10.1186/s12859-015-0543-y CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Jiang Z, Dong X, Li ZG, He F, Zhang Z (2016) Differential coexpression analysis reveals extensive rewiring of arabidopsis gene coexpression in response to pseudomonas syringae infection. Sci Rep 6:35064.  https://doi.org/10.1038/srep35064 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Fukushima A, Kanaya S, Nishida K (2014) Integrated network analysis and effective tools in plant systems biology. Front Plant Sci 5:598.  https://doi.org/10.3389/fpls.2014.00598 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Fukushima A, Kusano M (2014) A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches. J Exp Bot 65(19):5619–5630.  https://doi.org/10.1093/jxb/eru322 CrossRefPubMedGoogle Scholar
  21. 21.
    Hudson NJ, Reverter A, Dalrymple BP (2009) A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput Biol 5(5):e1000382.  https://doi.org/10.1371/journal.pcbi.1000382 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yasunori Ichihashi
    • 1
    • 2
    • 3
  • Atsushi Fukushima
    • 1
  • Arisa Shibata
    • 1
  • Ken Shirasu
    • 1
    • 4
  1. 1.RIKEN Center for Sustainable Resource ScienceYokohamaJapan
  2. 2.JST, PRESTOKawaguchiJapan
  3. 3.RIKEN BioResource Research CenterTsukubaJapan
  4. 4.Graduate School of ScienceThe University of TokyoTokyoJapan

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