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Mining the genome of Arabidopsis thaliana as a basis for the identification of novel bioactive peptides involved in oxidative stress tolerance

ABSTRACT: Although evidence accumulates on the role of plant peptides in the response to external conditions, the number of peptide-encoding genes in the genome is still underestimated. In order to identify novel oxidative stress-induced peptides in Arabidopsis thaliana a tiling array analysis (GeneChip® Arabidopsis Tiling 1.0R Arrays ) was performed on mRNA extracted from leaves treated with paraquat (PQ), an reactive oxygen species inducer and water (control), resulting in the identification of 92,844 and 86,272 transcriptionally active regions (TARs), respectively. Normalized log signals were obtained using the Affymetrix Tiling Analysis Software - Version 1.1, Build 2. ON and OFF probes were selected using a treshold, based on positive controls. Next, groups of 4-13 successive ON probes were combined into short TARs and a selection was made of TARs having an average signal intensity at least 2.6-fold higher after PQ treatment compared to the control treatment, resulting in 176 TARS induced by PQ.

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE49001 | GEO | 2013/08/31

SECONDARY ACCESSION(S): PRJNA212555

REPOSITORIES: GEO

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Project description:Experiments conducted on this tiling array are used to (1) validate the frozen gene sets of the current genome annotation, (2) improve the predicted gene structures by empirically determining UTRs and intron-exon boundaries, identifying missing upstream, internal, and downstream exons and alternative transcripts, (3) propose gene structure models in transcribed regions containing no predicted genes and (4) delineate transcriptionally active regions of the genome from intergenic, intronic and genic regions. Signal to background ratios were determined by first calling probes that fluoresced at intensities greater than 99% of the random probes’ signal intensities; therefore only 1% of fluorescing experimental probes should be false positives. We conducted two-color competitive hybridizations that measure differential expression from three replicates, each using RNA from independent biological extractions. Transcriptional active regions (TARs) were defined by stringing together overlapping probes showing fluorescence above a 1% false positive rate (FPR). Positive probes were joined into a TAR if they were adjacent (maxgap=0, no intermittent non-positive probe) and a TAR’s length had to be at least 45 bp (minrun=45, mid-point first positive probe to mid-point last positive probe, resulting in at least 3 adjacent positive probes for a TAR). Transcriptional active regions (TARs) were defined by stringing together overlapping probes showing fluorescence above a 1% false positive rate (FPR). The data analysis to measure differential expression of genes and of unannotated TARs was performed using the statistical software package R and Bioconductor with additions and modifications. The signal distributions across chips, samples and replicates were adjusted to be equal according to the mean fluorescence of the random probes on each array. All probes including random probes were quantile-normalized across replicates. Expression-level scores were assigned for each predicted gene based on the median log2 fluorescence over background intensity of probes falling within the exon boundaries.

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Mining the genome of Arabidopsis thaliana as a basis for the identification of novel bioactive peptides involved in oxidative stress tolerance

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