Literature DB >> 20833726

Regulatory network identification by genetical genomics: signaling downstream of the Arabidopsis receptor-like kinase ERECTA.

Inez R Terpstra1, L Basten Snoek, Joost J B Keurentjes, Anton J M Peeters, Guido van den Ackerveken.   

Abstract

Gene expression differences between individuals within a species can be largely explained by differences in genetic background. The effect of genetic variants (alleles) of genes on expression can be studied in a multifactorial way by the application of genetical genomics or expression quantitative trait locus mapping. In this paper, we present a strategy to construct regulatory networks by the application of genetical genomics in combination with transcript profiling of mutants that are disrupted in single genes. We describe the network identification downstream of the receptor-like kinase ERECTA in Arabidopsis (Arabidopsis thaliana). Extending genetical genomics on the Landsberg erecta/Cape Verde Islands (Ler/Cvi) recombinant inbred population with expression profiling of monogenic mutants enabled the identification of regulatory networks in the so far elusive ERECTA signal transduction cascade. We provide evidence that ERECTA is the causal gene for the major hotspot for transcript regulation in the Arabidopsis Ler/Cvi recombinant inbred population. We further propose additional genetic variation between Ler and Cvi in loci of the signaling pathway downstream of ERECTA and suggest candidate genes underlying these loci. Integration of publicly available microarray expression data of other monogenic mutants allowed us to link ERECTA to a downstream mitogen-activated protein kinase signaling cascade. Our study shows that microarray data of monogenic mutants can be effectively used in combination with genetical genomics data to enhance the identification of genetic regulatory networks.

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Year:  2010        PMID: 20833726      PMCID: PMC2971588          DOI: 10.1104/pp.110.159996

Source DB:  PubMed          Journal:  Plant Physiol        ISSN: 0032-0889            Impact factor:   8.340


  49 in total

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