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      Proteome-Wide Search Reveals Unexpected RNA-Binding Proteins in Saccharomyces cerevisiae

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          Abstract

          The vast landscape of RNA-protein interactions at the heart of post-transcriptional regulation remains largely unexplored. Indeed it is likely that, even in yeast, a substantial fraction of the regulatory RNA-binding proteins (RBPs) remain to be discovered. Systematic experimental methods can play a key role in discovering these RBPs - most of the known yeast RBPs lack RNA-binding domains that might enable this activity to be predicted. We describe here a proteome-wide approach to identify RNA-protein interactions based on in vitro binding of RNA samples to yeast protein microarrays that represent over 80% of the yeast proteome. We used this procedure to screen for novel RBPs and RNA-protein interactions. A complementary mass spectrometry technique also identified proteins that associate with yeast mRNAs. Both the protein microarray and mass spectrometry methods successfully identify previously annotated RBPs, suggesting that other proteins identified in these assays might be novel RBPs. Of 35 putative novel RBPs identified by either or both of these methods, 12, including 75% of the eight most highly-ranked candidates, reproducibly associated with specific cellular RNAs. Surprisingly, most of the 12 newly discovered RBPs were enzymes. Functional characteristics of the RNA targets of some of the novel RBPs suggest coordinated post-transcriptional regulation of subunits of protein complexes and a possible link between mRNA trafficking and vesicle transport. Our results suggest that many more RBPs still remain to be identified and provide a set of candidates for further investigation.

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          Most cited references36

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          Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

          We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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            Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

            A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.
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              RNA regulons: coordination of post-transcriptional events.

              Jack Keene (2007)
              Recent findings demonstrate that multiple mRNAs are co-regulated by one or more sequence-specific RNA-binding proteins that orchestrate their splicing, export, stability, localization and translation. These and other observations have given rise to a model in which mRNAs that encode functionally related proteins are coordinately regulated during cell growth and differentiation as post-transcriptional RNA operons or regulons, through a ribonucleoprotein-driven mechanism. Here I describe several recently discovered examples of RNA operons in budding yeast, fruitfly and mammalian cells, and their potential importance in processes such as immune response, oxidative metabolism, stress response, circadian rhythms and disease. I close by considering the evolutionary wiring and rewiring of these combinatorial post-transcriptional gene-expression networks.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                10 September 2010
                : 5
                : 9
                : e12671
                Affiliations
                [1 ]Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America
                [2 ]Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, United States of America
                [3 ]Department of Statistics, Stanford University, Stanford, California, United States of America
                Texas A&M University, United States of America
                Author notes

                Conceived and designed the experiments: NGT DMK POB. Performed the experiments: NGT DMK. Analyzed the data: NGT DMK. Contributed reagents/materials/analysis tools: NGT DMK JS. Wrote the paper: NGT DMK POB.

                Article
                10-PONE-RA-20787R1
                10.1371/journal.pone.0012671
                2937035
                20844764
                220107a8-2679-4251-9519-7775f32492c9
                Tsvetanova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 2 July 2010
                : 17 August 2010
                Page count
                Pages: 12
                Categories
                Research Article
                Cell Biology/Gene Expression
                Genetics and Genomics/Gene Expression
                Molecular Biology/Post-Translational Regulation of Gene Expression

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                Uncategorized

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