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      Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics

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          Abstract

          Gene networks are an efficient route for associating candidate genes with biological processes. Here, networks are used to discover more than 15 new genes for ribosomal subunit maturation, rRNA processing, and ribosomal export from the nucleus.

          Abstract

          Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly, modification, and trafficking of ribosome components through multiple cellular compartments. Despite intensive study, this pathway likely involves many additional genes. Here, we employ network-guided genetics—an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies—to computationally identify additional ribosomal biogenesis genes. We experimentally evaluated >100 candidate yeast genes in a battery of assays, confirming involvement of at least 15 new genes, including previously uncharacterized genes ( YDL063C, YIL091C, YOR287C, YOR006C/TSR3, YOL022C/TSR4). We associate the new genes with specific aspects of ribosomal subunit maturation, ribosomal particle association, and ribosomal subunit nuclear export, and we identify genes specifically required for the processing of 5S, 7S, 20S, 27S, and 35S rRNAs. These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes—most with human orthologs—to the biogenesis pathway, significantly extending our understanding of a universally conserved eukaryotic process.

          Author Summary

          Ribosomes are the extremely complex cellular machines responsible for constructing new proteins. In eukaryotic cells, such as yeast, each ribosome contains more than 80 protein or RNA components. These complex machines must themselves be assembled by an even more complex machinery spanning multiple cellular compartments and involving perhaps 200 components in an ordered series of processing events, resulting in delivery of the two halves of the mature ribosome, the 40S and 60S components, to the cytoplasm. The ribosome biogenesis machinery has been only partially characterized, and many lines of evidence suggest that there are additional components that are still unknown. We employed an emerging computational technique called network-guided genetics to identify new candidate genes for this pathway. We then tested the candidates in a battery of experimental assays to determine what roles the genes might play in the biogenesis of ribosomes. This approach proved an efficient route to the discovery of new genes involved in ribosome biogenesis, significantly extending our understanding of a universally conserved eukaryotic process.

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              Network-based prediction of protein function

              Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                October 2009
                October 2009
                6 October 2009
                : 7
                : 10
                : e1000213
                Affiliations
                [1 ]Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin, Texas, United States of America
                [2 ]Department of Biotechnology, College of Life science and Biotechnology, Yonsei University, 134 Shinchon-dong, Seodaemun-ku, Seoul 120-749, South Korea
                [3 ]Section of Molecular Genetics and Microbiology, Institute for Cellular and Molecular Biology, University of Texas, Austin, Texas, United States of America
                [4 ]Department of Chemistry and Biochemistry, University of Texas, Austin, Texas, United States of America
                University of California, Berkeley, United States of America
                Author notes

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: ZL AWJ EMM. Performed the experiments: ZL. Analyzed the data: ZL IL EM AWJ EMM. Contributed reagents/materials/analysis tools: ZL NJH IL EM. Wrote the paper: ZL AWJ EMM.

                Article
                08-PLBI-RA-3400R3
                10.1371/journal.pbio.1000213
                2749941
                19806183
                2ac4c8f9-a7f3-4cf5-b01b-adb4b24befc4
                Li 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
                : 15 August 2008
                : 24 August 2009
                Page count
                Pages: 17
                Categories
                Research Article
                Biochemistry/Bioinformatics
                Biochemistry/Macromolecular Assemblies and Machines
                Biochemistry/Transcription and Translation
                Cell Biology/Microbial Growth and Development
                Computational Biology/Genomics
                Computational Biology/Molecular Genetics
                Computational Biology/Systems Biology
                Genetics and Genomics/Bioinformatics
                Genetics and Genomics/Functional Genomics
                Genetics and Genomics/Gene Function
                Molecular Biology/Bioinformatics
                Molecular Biology/Nucleolus and Nuclear Bodies
                Molecular Biology/RNA-Protein Interactions
                Molecular Biology/Translation Mechanisms

                Life sciences
                Life sciences

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