4.6 Article

Evaluation of Reference Genes for RT qPCR Analyses of Structure-Specific and Hormone Regulated Gene Expression in Physcomitrella patens Gametophytes

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PLOS ONE
卷 8, 期 8, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0070998

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资金

  1. FORMAS grant (The Swedish Research Council Formas) [229-2008-100]
  2. DFG (Deutsche Forschungsgemeinschaft)
  3. FAU (Friedrich-Alexander-Universitat Erlangen-Nurnberg)

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The use of the moss Physcomitrella patens as a model system to study plant development and physiology is rapidly expanding. The strategic position of P. patens within the green lineage between algae and vascular plants, the high efficiency with which transgenes are incorporated by homologous recombination, advantages associated with the haploid gametophyte representing the dominant phase of the P. patens life cycle, the simple structure of protonemata, leafy shoots and rhizoids that constitute the haploid gametophyte, as well as a readily accessible high-quality genome sequence make this moss a very attractive experimental system. The investigation of the genetic and hormonal control of P. patens development heavily depends on the analysis of gene expression patterns by real time quantitative PCR (RT qPCR). This technique requires well characterized sets of reference genes, which display minimal expression level variations under all analyzed conditions, for data normalization. Sets of suitable reference genes have been described for most widely used model systems including e. g. Arabidopsis thaliana, but not for P. patens. Here, we present a RT qPCR based comparison of transcript levels of 12 selected candidate reference genes in a range of gametophytic P. patens structures at different developmental stages, and in P. patens protonemata treated with hormones or hormone transport inhibitors. Analysis of these RT qPCR data using GeNorm and NormFinder software resulted in the identification of sets of P. patens reference genes suitable for gene expression analysis under all tested conditions, and suggested that the two best reference genes are sufficient for effective data normalization under each of these conditions.

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