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Global transcriptome analysis identifies shade avoidance-related genes regulated by BBX24 in Arabidopsis thaliana

ABSTRACT: In seedlings, the induction of shade avoidance syndrome (SAS) involves a rapid up-regulation for known shade marker genes and subsequently activates an interacting network of various hormones that will eventually lead to cell elongation. We found that the B-box protein AtBBX24 have positive effects on the SAS (positive regulators). Global expression analysis of col and bbx24 seedlings reveals that a large number of genes involved in hormonal signaling pathways are positively regulated by BBX24 in response to simulated shade.

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE64755 | GEO | 2015/01/08

SECONDARY ACCESSION(S): PRJNA271800

REPOSITORIES: GEO

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Global transcriptome analysis identifies shade avoidance-related genes regulated by BBX24 in Arabidopsis thaliana

Project description:In seedlings, the induction of shade avoidance syndrome (SAS) involves a rapid up-regulation for known shade marker genes and subsequently activates an interacting network of various hormones that will eventually lead to cell elongation. We found that the B-box protein AtBBX24 have positive effects on the SAS (positive regulators). Global expression analysis of col and bbx24 seedlings reveals that a large number of genes involved in hormonal signaling pathways are positively regulated by BBX24 in response to simulated shade. The microarray experiment consisted in a factorial design comprising two genotypes (col and bbx24-1) and two light treatments (white light and simulated shade). RNA material was extracted from 7-day-old seedlings. Two replicates per treatment were performed using Affymetrix Arabidopsis ATH1 GeneChips. RNA was prepared, labeled and hybridized in accordance with the manufacturerM-bM-^@M-^Ys instructions. Data were normalized and genes with M-bM-^@M-^XabsentM-bM-^@M-^Y calls and a signal of <50 units in all replicate experiments were filtered out. Significantly differentially expressed genes were identified by performing profile analysis using Significance Analysis of Microarrays (SAM; Tusher et al., 2001).

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