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Prediction of new abiotic stress genes in Arabidopsis thaliana and Oryza sativa according to enumeration-based statistical analysis

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Abstract

Plants undergo an extensive change in gene regulation during abiotic stress. It is of great agricultural importance to know which genes are affected during stress response. The genome sequence of a number of plant species has been determined, among them Arabidopsis and Oryza sativa, whose genome has been annotated most completely as of yet, and are well-known organisms widely used as experimental systems. This paper applies a statistical algorithm for predicting new stress-induced motifs and genes by analyzing promoter sets co-regulated by abiotic stress in the previously mentioned two species. After identifying characteristic putative regulatory motif sequence pairs (dyads) in the promoters of 125 stress-regulated Arabidopsis genes and 87 O. sativa genes, these dyads were used to screen the entire Arabidopsis and O. sativa promoteromes to find related stress-induced genes whose promoters contained a large number of these dyads found by our algorithm. We were able to predict a number of putative dyads, characteristic of a large number of stress-regulated genes, some of them newly discovered by our algorithm and serve as putative transcription factor binding sites. Our new motif prediction algorithm comes complete with a stand-alone program. This algorithm may be used in motif discovery in the future in other species. The more than 1,200 Arabidopsis and 1,700 Orzya sativa genes found by our algorithm are good candidates for further experimental studies in abiotic stress.

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Abbreviations

ABA:

Abscisic acid

AUC:

Area under curve

PLACE:

Plant cis-acting regulatory DNA elements

REP:

Regulatory element pair

rev comp:

Reverse complement

ROC:

Receiver operating characteristic

TC:

Tentative consensus

TFBS:

Transcription factor binding site

TIGR:

The institute for genome research

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Acknowledgments

The first author would hereby like to mention that the underlying idea of the analysis of the occurence distributions of putative transcription factor binding site dyads originated from his college thesis work which was done at the Agricultural Biotechnology Center in Gödöllő, Hungary. This work was funded by the OTKA T046495 grant given by the Hungarian National Science foundation and the Bio-140-KPI given by the National Office for Research and Technology (NKTH) as well as grant number 4-065-2004. We would also like to thank William Gruissem for supplying us data from the Genevestigator Arabidopsis database. The authors would like to thank Maria Sečenji and Krisztina Talpas for their assistance in setting up and helping in greenhouse experiments and QT-PCR work.

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Authors and Affiliations

  1. Biological Research Center, Institute of Plant Biology, Hungarian Academy of Sciences, P.O. BOX 521, Temesvári Krt. 62, 6701, Szeged, Hungary

    Mátyás Cserháti, Zoltán Turóczy, Zoltán Zombori, Dénes Dudits & János Györgyey

  2. Institute of Physiology, Semmelweis University, 1094 Budapest, 37-47, Tűzoltó u, Hungary

    Miklós Cserző

  3. ICGEB, Padriciano 99, 34012, Trieste, Italy

    Sándor Pongor

Authors
  1. Mátyás Cserháti
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  2. Zoltán Turóczy
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  3. Zoltán Zombori
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  4. Miklós Cserző
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  5. Dénes Dudits
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  6. Sándor Pongor
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  7. János Györgyey
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Corresponding author

Correspondence to Mátyás Cserháti.

Additional information

Communicated by Y. Van de Peer.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online resource 1 –Excel data file for Arabidopsis (SupplementaryArabidopsisData.xls) This file contains all the data produced by our analysis for Arabidopsis. This includes information on the genes whose promoters were used in the analysis, as well as the distribution of the TRANSFAC and PLACE motifs, the dyads found in the stress learning set with a minimum occurrence of 5, and a minimum score of 0.6, as well as the 81 optimum dyads. Furthermore, data from the ROC analysis, the promoterome search, and the regulatory network analysis is also included.

Online resource 2 –Excel data file for Oryza sativa (SupplementaryRiceData.xls) This file contains all the data produced by our analysis for Oryza sativa. This includes information on the genes whose promoters were used in the stress and non-stress promoter sets, the dyads found in the stress learning set with a minimum occurrence of 5, and a minimum score of 0.5, as well as the 38 optimum dyads. Furthermore, data from the ROC analysis, the promoterome search, and the GO term analysis is also included.

438_2011_605_MOESM1_ESM.tif

Supplemental Figure 1-The optimum AUC score was calculated in Arabidopsis using the top 81 dyads found in the two learning promoter sets. These dyads were found back in the tuning promoter set according to a number of parameters (minimum dyad score, spacer wobbling, minimum occurrence of dyad in stress learning set). In Arabidopsis, this parameter combination is 0.9, 2 bp, and a minimum occurrence of 14. The different colors denote different AUC value ranges. (TIFF 168 kb)

438_2011_605_MOESM2_ESM.tif

Supplemental Figure 2-The optimum AUC score was calculated in Oryza sativa using the top 38 dyads found in the two learning promoter sets. These dyads were found back in the tuning promoter set according to a number of parameters (minimum dyad score, spacer wobbling, minimum occurrence of dyad in stress learning set). In Oryza sativa, this parameter combination is 0.89, 0 bp, and a minimum occurrence of 9. (TIFF 126 kb)

Supplementary material 3 (XLS 2916 kb)

Supplementary material 4 (XLS 7881 kb)

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Cserháti, M., Turóczy, Z., Zombori, Z. et al. Prediction of new abiotic stress genes in Arabidopsis thaliana and Oryza sativa according to enumeration-based statistical analysis. Mol Genet Genomics 285, 375–391 (2011). https://doi.org/10.1007/s00438-011-0605-4

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