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Inferring Hypotheses on Functional Relationships of Genes: Analysis of the Arabidopsis thaliana Subtilase Gene Family

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  • Carsten Rautengarten
  • Dirk Steinhauser
  • Dirk Büssis
  • Annick Stintzi
  • Andreas Schaller
  • Joachim Kopka
  • Thomas Altmann

Abstract

The gene family of subtilisin-like serine proteases (subtilases) in Arabidopsis thaliana comprises 56 members, divided into six distinct subfamilies. Whereas the members of five subfamilies are similar to pyrolysins, two genes share stronger similarity to animal kexins. Mutant screens confirmed 144 T-DNA insertion lines with knockouts for 55 out of the 56 subtilases. Apart from SDD1, none of the confirmed homozygous mutants revealed any obvious visible phenotypic alteration during growth under standard conditions. Apart from this specific case, forward genetics gave us no hints about the function of the individual 54 non-characterized subtilase genes. Therefore, the main objective of our work was to overcome the shortcomings of the forward genetic approach and to infer alternative experimental approaches by using an integrative bioinformatics and biological approach. Computational analyses based on transcriptional co-expression and co-response pattern revealed at least two expression networks, suggesting that functional redundancy may exist among subtilases with limited similarity. Furthermore, two hubs were identified, which may be involved in signalling or may represent higher-order regulatory factors involved in responses to environmental cues. A particular enrichment of co-regulated genes with metabolic functions was observed for four subtilases possibly representing late responsive elements of environmental stress. The kexin homologs show stronger associations with genes of transcriptional regulation context. Based on the analyses presented here and in accordance with previously characterized subtilases, we propose three main functions of subtilases: involvement in (i) control of development, (ii) protein turnover, and (iii) action as downstream components of signalling cascades. Supplemental material is available in the Plant Subtilase Database (PSDB) (http://csbdb.mpimp-golm.mpg.de/psdb.html) , as well as from the CSB.DB (http://csbdb.mpimp-golm.mpg.de).: The first complete plant genome sequence was available for Arabidopsis thaliana, a common weed. The number of genes in the Arabidopsis genome is estimated to be around 25,000. The functions of most of these gene are, however, still unknown. Many genes are grouped into gene families due to conserved sequences and predicted protein structures. In this article, the large subtilisin-like serine protease (subtilase) family of Arabidopsis is analysed. Although 56 subtilase genes have been identified in Arabidopsis, the function of only two subtilases is known. Analysis of mutants has revealed no further hints about the function of the other 54 subtilases. Here the authors present a novel approach to infer hypotheses about functions of the subtilase genes using computational analysis. Based on the analyses presented here and in accordance with previously characterized subtilases, they propose three main functions of subtilases: involvement in (i) control of development, (ii) protein degradation, and (iii) signalling. The results presented can be used to direct further analysis to elucidate functions of subtilases in plants.

Suggested Citation

  • Carsten Rautengarten & Dirk Steinhauser & Dirk Büssis & Annick Stintzi & Andreas Schaller & Joachim Kopka & Thomas Altmann, 2005. "Inferring Hypotheses on Functional Relationships of Genes: Analysis of the Arabidopsis thaliana Subtilase Gene Family," PLOS Computational Biology, Public Library of Science, vol. 1(4), pages 1-1, September.
  • Handle: RePEc:plo:pcbi00:0010040
    DOI: 10.1371/journal.pcbi.0010040
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    1. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
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