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      Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions

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          Abstract

          The turnover of brain proteins is critical for organism survival, and its perturbations are linked to pathology. Nevertheless, protein lifetimes have been difficult to obtain in vivo. They are readily measured in vitro by feeding cells with isotopically labeled amino acids, followed by mass spectrometry analyses. In vivo proteins are generated from at least two sources: labeled amino acids from the diet, and non-labeled amino acids from the degradation of pre-existing proteins. This renders measurements difficult. Here we solved this problem rigorously with a workflow that combines mouse in vivo isotopic labeling, mass spectrometry, and mathematical modeling. We also established several independent approaches to test and validate the results. This enabled us to measure the accurate lifetimes of ~3500 brain proteins. The high precision of our data provided a large set of biologically significant observations, including pathway-, organelle-, organ-, or cell-specific effects, along with a comprehensive catalog of extremely long-lived proteins (ELLPs).

          Abstract

          Measuring precise protein turnover rates in animals is technically challenging at the proteomic level. Here, Fornasiero and colleagues use isotopic labeling with mass spectrometry and mathematical modeling to accurately determine protein lifetimes in the mouse brain

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          The Universal Protein Resource (UniProt)

          The Universal Protein Resource (UniProt) provides a stable, comprehensive, freely accessible, central resource on protein sequences and functional annotation. The UniProt Consortium is a collaboration between the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt website, and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of four major components, each optimized for different uses: the UniProt Knowledgebase, the UniProt Reference Clusters, the UniProt Archive and the UniProt Metagenomic and Environmental Sequences database. UniProt is updated and distributed every three weeks, and can be accessed online for searches or download at http://www.uniprot.org.
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            Recovery of learning and memory is associated with chromatin remodelling.

            Neurodegenerative diseases of the central nervous system are often associated with impaired learning and memory, eventually leading to dementia. An important aspect in pre-clinical research is the exploration of strategies to re-establish learning ability and access to long-term memories. By using a mouse model that allows temporally and spatially restricted induction of neuronal loss, we show here that environmental enrichment reinstated learning behaviour and re-established access to long-term memories after significant brain atrophy and neuronal loss had already occurred. Environmental enrichment correlated with chromatin modifications (increased histone-tail acetylation). Moreover, increased histone acetylation by inhibitors of histone deacetylases induced sprouting of dendrites, an increased number of synapses, and reinstated learning behaviour and access to long-term memories. These data suggest that inhibition of histone deacetylases might be a suitable therapeutic avenue for neurodegenerative diseases associated with learning and memory impairment, and raises the possibility of recovery of long-term memories in patients with dementia.
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              A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards*

              Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a “proteomic ruler” because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset—even in retrospective analysis—and we demonstrate its usefulness with a series of mouse organ proteomes.
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                Author and article information

                Contributors
                efornas@gwdg.de
                henning.urlaub@med.uni-goettingen.de
                srizzol@gwdg.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 October 2018
                12 October 2018
                2018
                : 9
                : 4230
                Affiliations
                [1 ]ISNI 0000 0001 0482 5331, GRID grid.411984.1, Department of Neuro- and Sensory Physiology, , University Medical Center Göttingen, Cluster of Excellence Nanoscale Microscopy and Molecular Physiology of the Brain, ; 37073 Göttingen, Germany
                [2 ]ISNI 0000 0001 0482 5331, GRID grid.411984.1, Department of Clinical Chemistry, , University Medical Center Göttingen, ; 37077 Göttingen, Germany
                [3 ]ISNI 0000 0001 2104 4211, GRID grid.418140.8, Bioanalytical Mass Spectrometry Group, , Max Planck Institute of Biophysical Chemistry, ; 37077 Göttingen, Germany
                [4 ]Center for Biostructural Imaging of Neurodegeneration (BIN), 37075 Göttingen, Germany
                [5 ]ISNI 0000 0001 2104 4211, GRID grid.418140.8, Genes and Behavior Department, , Max Planck Institute of Biophysical Chemistry, ; 37073 Göttingen, Germany
                [6 ]ISNI 0000 0001 2364 4210, GRID grid.7450.6, Department of Plant Biochemistry, Albrecht-von-Haller-Institute, , Georg-August-University, ; 37073 Göttingen, Germany
                [7 ]ISNI 0000 0004 0438 0426, GRID grid.424247.3, Laboratory of Epigenetics in Neurodegenerative Diseases, , German Center for Neurodegenerative Diseases (DZNE), ; 37075 Göttingen, Germany
                [8 ]German Center for Neurodegenerative Disease (DZNE), 81377 Munich, Germany
                [9 ]ISNI 0000 0001 2104 4211, GRID grid.418140.8, Department of Cellular Logistics, , Max Planck Institute for Biophysical Chemistry, ; 37077 Göttingen, Germany
                [10 ]ISNI 0000 0004 0438 0426, GRID grid.424247.3, Laboratory of Computational Systems Biology, , German Center for Neurodegenerative Diseases (DZNE), ; 37075 Göttingen, Germany
                [11 ]ISNI 0000 0001 0482 5331, GRID grid.411984.1, Department of Psychiatry and Psychotherapy, , University Medical Center Göttingen, ; 37075 Göttingen, Germany
                [12 ]ISNI 0000 0001 0482 5331, GRID grid.411984.1, Department of Cellular Biochemistry, , University Medical Center Göttingen, ; 37077 Göttingen, Germany
                [13 ]ISNI 0000 0001 2104 4211, GRID grid.418140.8, Max Planck Institute for Biophysical Chemistry, ; Göttingen, 37077 Germany
                [14 ]ISNI 0000 0001 2180 3484, GRID grid.13648.38, Institute of Medical Systems Biology, Center for Molecular Neurobiology (ZMNH), , University Medical Center Hamburg-Eppendorf (UKE), ; 20246 Hamburg, Germany
                [15 ]ISNI 0000 0004 0438 0426, GRID grid.424247.3, German Center for Neurodegenerative Diseases (DZNE), ; 72076 Tübingen, Germany
                [16 ]GRID grid.452617.3, Munich Cluster for Systems Neurology (SyNergy), ; 81377 Munich, Germany
                [17 ]ISNI 0000000123222966, GRID grid.6936.a, Institute of Neuronal Cell Biology, , Technical University Munich, ; 80805 Munich, Germany
                Author information
                http://orcid.org/0000-0001-7643-4962
                http://orcid.org/0000-0002-4968-9713
                http://orcid.org/0000-0002-2289-0652
                http://orcid.org/0000-0002-9329-8912
                http://orcid.org/0000-0001-5661-5272
                http://orcid.org/0000-0002-9888-7003
                http://orcid.org/0000-0003-4366-5662
                Article
                6519
                10.1038/s41467-018-06519-0
                6185916
                30315172
                b50af356-cf91-4cae-9828-3ebf70d071e2
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 August 2018
                : 7 September 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000781, EC | European Research Council (ERC);
                Award ID: ERC-2013-CoG
                Award Recipient :
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