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  • 1
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    Proteomics. Tissue-based map of the human proteome (2015)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2015-01-24
    Description: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Uhlen, Mathias -- Fagerberg, Linn -- Hallstrom, Bjorn M -- Lindskog, Cecilia -- Oksvold, Per -- Mardinoglu, Adil -- Sivertsson, Asa -- Kampf, Caroline -- Sjostedt, Evelina -- Asplund, Anna -- Olsson, IngMarie -- Edlund, Karolina -- Lundberg, Emma -- Navani, Sanjay -- Szigyarto, Cristina Al-Khalili -- Odeberg, Jacob -- Djureinovic, Dijana -- Takanen, Jenny Ottosson -- Hober, Sophia -- Alm, Tove -- Edqvist, Per-Henrik -- Berling, Holger -- Tegel, Hanna -- Mulder, Jan -- Rockberg, Johan -- Nilsson, Peter -- Schwenk, Jochen M -- Hamsten, Marica -- von Feilitzen, Kalle -- Forsberg, Mattias -- Persson, Lukas -- Johansson, Fredric -- Zwahlen, Martin -- von Heijne, Gunnar -- Nielsen, Jens -- Ponten, Fredrik -- New York, N.Y. -- Science. 2015 Jan 23;347(6220):1260419. doi: 10.1126/science.1260419.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Horsholm, Denmark. mathias.uhlen@scilifelab.se. ; Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. ; Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. ; Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden. ; Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. ; Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden. ; Leibniz Research Centre for Working Environment and Human Factors (IfADo) at Dortmund TU, D-44139 Dortmund, Germany. ; Lab Surgpath, Mumbai, India. ; Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. ; Science for Life Laboratory, Department of Neuroscience, Karolinska Institute, SE-171 77 Stockholm, Sweden. ; Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden. ; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Horsholm, Denmark. Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25613900" target="_blank"〉PubMed〈/a〉
    Keywords: Alternative Splicing ; Cell Line ; *Databases, Protein ; Female ; Genes ; Genetic Code ; Humans ; Internet ; Male ; Membrane Proteins/genetics/metabolism ; Mitochondrial Proteins/genetics/metabolism ; Neoplasms/genetics/metabolism ; Protein Array Analysis ; Protein Isoforms/genetics/metabolism ; Proteome/genetics/*metabolism ; Tissue Distribution ; Transcription, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 2
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    Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 3
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    Logical transformation of genome-scale metabolic models for gene level applications and analysis (2015)
    Oxford University Press
    Details
    Publication Date: 2015-07-09
    Description: Motivation: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of gene–reaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. Results: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene–reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene–reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively. Availability and implementation: Source code implemented in MATLAB, RAVEN toolbox and COBRA toolbox, is public available at https://sourceforge.net/projects/logictransformationofmodel . Contact: nielsenj@chalmers.se or qhua@ecust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics Online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 4
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    A pathology atlas of the human cancer transcriptome (2017)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2017-08-18
    Description: Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database ( www.proteinatlas.org/pathology ) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.
    Keywords: Genetics, Medicine, Diseases, Online Only
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 5
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    The human secretome (2019)
    The American Association for the Advancement of Science (AAAS)
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    Publication Date: 2019
    Description: 〈p〉The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry–based proteomics and antibody-based immunoassays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.〈/p〉
    Print ISSN: 1945-0877
    Electronic ISSN: 1937-9145
    Topics: Biology , Medicine
    Published by The American Association for the Advancement of Science (AAAS)
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  • 6
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    A genome-wide transcriptomic analysis of protein-coding genes in human blood cells (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Description: 〈p〉Blood is the predominant source for molecular analyses in humans, both in clinical and research settings. It is the target for many therapeutic strategies, emphasizing the need for comprehensive molecular maps of the cells constituting human blood. In this study, we performed a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations to characterize the expression levels of each individual gene across the blood cell types. All data are presented in an interactive, open-access Blood Atlas as part of the Human Protein Atlas and are integrated with expression profiles across all major tissues to provide spatial classification of all protein-coding genes. This allows for a genome-wide exploration of the expression profiles across human immune cell populations and all major human tissues and organs.〈/p〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 7
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    A subcellular map of the human proteome (2017)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2017-05-26
    Description: Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
    Keywords: Biochemistry, Online Only
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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