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      High resolution transcriptome maps for wild-type and nonsense-mediated decay-defective Caenorhabditis elegans

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          Abstract

          The high-resolution transcriptome of wild-type and nonsense-mediated decay (NMD) defective C. elegans during development reveals insights into the NMD pathway and it’s role in development.

          Abstract

          Background

          While many genome sequences are complete, transcriptomes are less well characterized. We used both genome-scale tiling arrays and massively parallel sequencing to map the Caenorhabditis elegans transcriptome across development. We utilized this framework to identify transcriptome changes in animals lacking the nonsense-mediated decay (NMD) pathway.

          Results

          We find that while the majority of detectable transcripts map to known gene structures, >5% of transcribed regions fall outside current gene annotations. We show that >40% of these are novel exons. Using both technologies to assess isoform complexity, we estimate that >17% of genes change isoform across development. Next we examined how the transcriptome is perturbed in animals lacking NMD. NMD prevents expression of truncated proteins by degrading transcripts containing premature termination codons. We find that approximately 20% of genes produce transcripts that appear to be NMD targets. While most of these arise from splicing errors, NMD targets are enriched for transcripts containing open reading frames upstream of the predicted translational start (uORFs). We identify a relationship between the Kozak consensus surrounding the true start codon and the degree to which uORF-containing transcripts are targeted by NMD and speculate that translational efficiency may be coupled to transcript turnover via the NMD pathway for some transcripts.

          Conclusions

          We generated a high-resolution transcriptome map for C. elegans and used it to identify endogenous targets of NMD. We find that these transcripts arise principally through splicing errors, strengthening the prevailing view that splicing and NMD are highly interlinked processes.

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          Most cited references50

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          Genome sequence of the nematode C. elegans: a platform for investigating biology.

          (1999)
          The 97-megabase genomic sequence of the nematode Caenorhabditis elegans reveals over 19,000 genes. More than 40 percent of the predicted protein products find significant matches in other organisms. There is a variety of repeated sequences, both local and dispersed. The distinctive distribution of some repeats and highly conserved genes provides evidence for a regional organization of the chromosomes.
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            Functional discovery via a compendium of expression profiles.

            Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.
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              Causal protein-signaling networks derived from multiparameter single-cell data.

              Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2009
                24 September 2009
                : 10
                : 9
                : R101
                Affiliations
                [1 ]Donnelly CCBR, College Street, University of Toronto, Toronto, M5S 3E1, Canada
                [2 ]The Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
                [3 ]Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3DY, UK
                [4 ]Affymetrix, Inc., Central Expressway, Santa Clara, CA 95051, USA
                [5 ]Helicos Biosciences Corporation, Cambridge, MA 02139, USA
                [6 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY 11724, USA
                Article
                gb-2009-10-9-r101
                10.1186/gb-2009-10-9-r101
                2768976
                19778439
                d1f5dcfa-874f-489e-a2a3-c8162f288ce5
                Copyright © 2009 Ramani et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 5 June 2009
                : 11 August 2009
                : 24 September 2009
                Categories
                Research

                Genetics
                Genetics

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