IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0203266.html
   My bibliography  Save this article

Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress

Author

Listed:
  • Rinku Sharma
  • Garima Singh
  • Sudeepto Bhattacharya
  • Ashutosh Singh

Abstract

Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as—‘photosynthesis’, ‘respiratory burst’, ‘response to hormone’, ‘signal transduction’, ‘metabolic process’, ‘response to water deprivation’, which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.

Suggested Citation

  • Rinku Sharma & Garima Singh & Sudeepto Bhattacharya & Ashutosh Singh, 2018. "Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0203266
    DOI: 10.1371/journal.pone.0203266
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203266
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0203266&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0203266?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peter Langfelder & Rui Luo & Michael C Oldham & Steve Horvath, 2011. "Is My Network Module Preserved and Reproducible?," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-29, January.
    2. Zhijin Wu & Rafael Irizarry & Robert Gentleman & Francisco Martinez Murillo & Forrest Spencer, 2004. "A Model Based Background Adjustment for Oligonucleotide Expression Arrays," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1001, Berkeley Electronic Press.
    3. Zhijin Wu & Rafael A. Irizarry & Robert Gentleman & Francisco Martinez-Murillo & Forrest Spencer, 2004. "A Model-Based Background Adjustment for Oligonucleotide Expression Arrays," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 909-917, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    2. Nan Li & Matthew N. McCall & Zhijin Wu, 2017. "Establishing Informative Prior for Gene Expression Variance from Public Databases," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 160-177, June.
    3. Sigrun Helga Lund & Daniel Fannar Gudbjartsson & Thorunn Rafnar & Asgeir Sigurdsson & Sigurjon Axel Gudjonsson & Julius Gudmundsson & Kari Stefansson & Gunnar Stefansson, 2014. "A Method for Detecting Long Non-Coding RNAs with Tiled RNA Expression Microarrays," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    4. Krishanpal Anamika & Àkos Gyenis & Laetitia Poidevin & Olivier Poch & Làszlò Tora, 2012. "RNA Polymerase II Pausing Downstream of Core Histone Genes Is Different from Genes Producing Polyadenylated Transcripts," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    5. Lei Zhang & Linlin Wang & Pu Tian & Suyan Tian, 2016. "Identification of Genes Discriminating Multiple Sclerosis Patients from Controls by Adapting a Pathway Analysis Method," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    6. Upton Graham J. G. & Harrison Andrew P, 2010. "The Detection of Blur in Affymetrix GeneChips," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-19, October.
    7. Ryan Abo & Gregory D Jenkins & Liewei Wang & Brooke L Fridley, 2012. "Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-11, August.
    8. Jeremiah J Faith & Boris Hayete & Joshua T Thaden & Ilaria Mogno & Jamey Wierzbowski & Guillaume Cottarel & Simon Kasif & James J Collins & Timothy S Gardner, 2007. "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-13, January.
    9. Chalise, Prabhakar & Fridley, Brooke L., 2012. "Comparison of penalty functions for sparse canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 245-254.
    10. Marot Guillemette & Mayer Claus-Dieter, 2009. "Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-35, January.
    11. Wei-Chung Cheng & Cheng-Wei Chang & Chaang-Ray Chen & Min-Lung Tsai & Wun-Yi Shu & Chia-Yang Li & Ian C Hsu, 2011. "Identification of Reference Genes across Physiological States for qRT-PCR through Microarray Meta-Analysis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-8, February.
    12. Parker Hilary S. & Leek Jeffrey T., 2012. "The practical effect of batch on genomic prediction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-22, April.
    13. Suyan Tian & James G Krueger & Katherine Li & Ali Jabbari & Carrie Brodmerkel & Michelle A Lowes & Mayte Suárez-Fariñas, 2012. "Meta-Analysis Derived (MAD) Transcriptome of Psoriasis Defines the “Core” Pathogenesis of Disease," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-15, September.
    14. Akul Singhania & Hitasha Rupani & Nivenka Jayasekera & Simon Lumb & Paul Hales & Neil Gozzard & Donna E Davies & Christopher H Woelk & Peter H Howarth, 2017. "Altered Epithelial Gene Expression in Peripheral Airways of Severe Asthma," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-16, January.
    15. Russell D J Huby & Philip Glaves & Richard Jackson, 2014. "The Incidence of Sexually Dimorphic Gene Expression Varies Greatly between Tissues in the Rat," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-19, December.
    16. Erick da Conceição Amorim & Vinícius Diniz Mayrink, 2020. "Clustering non-linear interactions in factor analysis," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 329-352, December.
    17. Huixia Wang & Xuming He, 2008. "An Enhanced Quantile Approach for Assessing Differential Gene Expressions," Biometrics, The International Biometric Society, vol. 64(2), pages 449-457, June.
    18. Shinya Tasaki & Jishu Xu & Denis R. Avey & Lynnaun Johnson & Vladislav A. Petyuk & Robert J. Dawe & David A. Bennett & Yanling Wang & Chris Gaiteri, 2022. "Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    19. Peter Langfelder & Paul S Mischel & Steve Horvath, 2013. "When Is Hub Gene Selection Better than Standard Meta-Analysis?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    20. Jenna Lihavainen & Jan Šimura & Pushan Bag & Nazeer Fataftah & Kathryn Megan Robinson & Nicolas Delhomme & Ondřej Novák & Karin Ljung & Stefan Jansson, 2023. "Salicylic acid metabolism and signalling coordinate senescence initiation in aspen in nature," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0203266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.