Proteomic and phosphoproteomic comparison of human ES and iPS cells
TL;DR: Deep proteomic coverage of four human embryonic stem cell and four induced pluripotent stem cell lines in biological triplicate is reported and the Stem Cell–Omics Repository is introduced, a resource to collate and display quantitative information across multiple planes of measurement, including mRNA, protein and post-translational modifications.
Abstract: Combining high-mass-accuracy mass spectrometry, isobaric tagging and software for multiplexed, large-scale protein quantification, we report deep proteomic coverage of four human embryonic stem cell and four induced pluripotent stem cell lines in biological triplicate. This 24-sample comparison resulted in a very large set of identified proteins and phosphorylation sites in pluripotent cells. The statistical analysis afforded by our approach revealed subtle but reproducible differences in protein expression and protein phosphorylation between embryonic stem cells and induced pluripotent cells. Merging these results with RNA-seq analysis data, we found functionally related differences across each tier of regulation. We also introduce the Stem Cell-Omics Repository (SCOR), a resource to collate and display quantitative information across multiple planes of measurement, including mRNA, protein and post-translational modifications.
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01 Jan 2000
3,536 citations
TL;DR: The Comet search engine is introduced, open source, freely available, and based on one of the original sequence database search tools that has been widely used for many years.
Abstract: Proteomics research routinely involves identifying peptides and proteins via MS/MS sequence database search. Thus the database search engine is an integral tool in many proteomics research groups. Here, we introduce the Comet search engine to the existing landscape of commercial and open-source database search tools. Comet is open source, freely available, and based on one of the original sequence database search tools that has been widely used for many years.
1,143 citations
Cites methods from "Proteomic and phosphoproteomic comp..."
...Two LC-MS/MS files of human embryonic stem cells were analyzed, both of which were obtained from the Stem Cell Omics Repository [15]....
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TL;DR: EBSeq is developed, using the merits of empirical Bayesian methods, for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions and proves to be a robust approach for identifying De genes.
Abstract: Motivation: Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others. Results: Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes. Availability and implementation: An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/ � kendzior/EBSEQ/.
1,048 citations
TL;DR: PINK1 phosphorylates ubiquitin, which then binds to Parkin and activates its E3 ligase activity, leading to induction of selective autophagy of damaged mitochondria.
Abstract: PINK1 kinase activates the E3 ubiquitin ligase Parkin to induce selective autophagy of damaged mitochondria. However, it has been unclear how PINK1 activates and recruits Parkin to mitochondria. Although PINK1 phosphorylates Parkin, other PINK1 substrates appear to activate Parkin, as the mutation of all serine and threonine residues conserved between Drosophila and human, including Parkin S65, did not wholly impair Parkin translocation to mitochondria. Using mass spectrometry, we discovered that endogenous PINK1 phosphorylated ubiquitin at serine 65, homologous to the site phosphorylated by PINK1 in Parkin’s ubiquitin-like domain. Recombinant TcPINK1 directly phosphorylated ubiquitin and phospho-ubiquitin activated Parkin E3 ubiquitin ligase activity in cell-free assays. In cells, the phosphomimetic ubiquitin mutant S65D bound and activated Parkin. Furthermore, expression of ubiquitin S65A, a mutant that cannot be phosphorylated by PINK1, inhibited Parkin translocation to damaged mitochondria. These results explain a feed-forward mechanism of PINK1-mediated initiation of Parkin E3 ligase activity.
968 citations
Cites background from "Proteomic and phosphoproteomic comp..."
...…et al., 2009; Shiromizu et al., 2013), T12 (Lee et al., 2009), Y59 (Rikova et al., 2007; Bodenmiller et al., 2008; Moritz et al., 2010; Gu et al., 2011), and S57 (Villén et al., 2007; Malik et al., 2009; Bennetzen et al., 2010; Phanstiel et al., 2011), although the kinases involved remain unknown....
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TL;DR: MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis, and to highlight recent applications.
Abstract: Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.
645 citations
References
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TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
83,420 citations
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
Abstract: Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).
24,024 citations
TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.
20,335 citations
TL;DR: It is demonstrated that iPS cells can be generated from adult human fibroblasts with the same four factors: Oct3/4, Sox2, Klf4, and c-Myc.
18,175 citations