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      Region and cell-type resolved quantitative proteomic map of the human heart

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          Abstract

          The heart is a central human organ and its diseases are the leading cause of death worldwide, but an in-depth knowledge of the identity and quantity of its constituent proteins is still lacking. Here, we determine the healthy human heart proteome by measuring 16 anatomical regions and three major cardiac cell types by high-resolution mass spectrometry-based proteomics. From low microgram sample amounts, we quantify over 10,700 proteins in this high dynamic range tissue. We combine copy numbers per cell with protein organellar assignments to build a model of the heart proteome at the subcellular level. Analysis of cardiac fibroblasts identifies cellular receptors as potential cell surface markers. Application of our heart map to atrial fibrillation reveals individually distinct mitochondrial dysfunctions. The heart map is available at maxqb.biochem.mpg.de as a resource for future analyses of normal heart function and disease.

          Abstract

          The human heart is composed of distinct regions and cell types, but relatively little is known about their specific protein composition. Here, the authors present a region- and cell type-specific proteomic map of the healthy human heart, revealing functional differences and potential cell type markers.

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          Most cited references46

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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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            Reprogramming of human fibroblasts toward a cardiac fate.

            Reprogramming of mouse fibroblasts toward a myocardial cell fate by forced expression of cardiac transcription factors or microRNAs has recently been demonstrated. The potential clinical applicability of these findings is based on the minimal regenerative potential of the adult human heart and the limited availability of human heart tissue. An initial but mandatory step toward clinical application of this approach is to establish conditions for conversion of adult human fibroblasts to a cardiac phenotype. Toward this goal, we sought to determine the optimal combination of factors necessary and sufficient for direct myocardial reprogramming of human fibroblasts. Here we show that four human cardiac transcription factors, including GATA binding protein 4, Hand2, T-box5, and myocardin, and two microRNAs, miR-1 and miR-133, activated cardiac marker expression in neonatal and adult human fibroblasts. After maintenance in culture for 4-11 wk, human fibroblasts reprogrammed with these proteins and microRNAs displayed sarcomere-like structures and calcium transients, and a small subset of such cells exhibited spontaneous contractility. These phenotypic changes were accompanied by expression of a broad range of cardiac genes and suppression of nonmyocyte genes. These findings indicate that human fibroblasts can be reprogrammed to cardiac-like myocytes by forced expression of cardiac transcription factors with muscle-specific microRNAs and represent a step toward possible therapeutic application of this reprogramming approach.
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              Epidemiology and natural history of atrial fibrillation: clinical implications.

              With a substantial impact on morbidity and mortality, the growing "epidemic" of atrial fibrillation (AF) intersects with a number of conditions, including aging, thromboembolism, hemorrhage, hypertension and left ventricular dysfunction. Currently, the epidemiology and natural history of AF govern all aspects of its clinical management. The ongoing global investigative efforts toward understanding AF are also driven by epidemiologic findings. New developments, by affecting the natural history of the disease, could eventually alter the nature of decision making in patients with AF. The crucial issue of rate versus rhythm control awaits completion of the AF Follow-up Investigation of Rhythm Management trial. The processes of electrical and structural remodeling that perpetuate AF appear to be reversible. In the era of functional genomics, the molecular basis of this ubiquitous arrhythmia is in the process of being defined. Unraveling the molecular genetics of AF might provide new insights into the structural and electrical phenotypes resulting from genetic mutations and, as such, new approaches to treatment of this arrhythmia at the ion channel and cellular levels. Thus, current adverse trends are superimposed on a background of a rapidly developing knowledge base and potentially exciting new therapeutic options. Consequently, an understanding of the epidemiology and natural history of AF is crucial to the future allocation of resources and the utilization of an expanding range of therapies aimed at reducing the impact of this disease on a changing patient population.
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                Author and article information

                Contributors
                krane@dhm.mhn.de
                mmann@biochem.mpg.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 November 2017
                13 November 2017
                2017
                : 8
                : 1469
                Affiliations
                [1 ]ISNI 0000 0004 0491 845X, GRID grid.418615.f, Department of Proteomics and Signal Transduction, , Max Planck Institute of Biochemistry, ; Martinsried, 82152 Germany
                [2 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Novo Nordisk Foundation Center for Protein Research, , Faculty of Health Sciences, University of Copenhagen, ; Copenhagen, 2200 Denmark
                [3 ]ISNI 0000 0001 0695 783X, GRID grid.472754.7, Department of Cardiovascular Surgery, , German Heart Center Munich at the Technische Universität München, ; Munich, 80636 Germany
                [4 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Forensic Institute, , Ludwig-Maximilians-University, ; Munich, 80336 Germany
                [5 ]DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, 80802 Germany
                Author information
                http://orcid.org/0000-0002-1505-1658
                http://orcid.org/0000-0003-4729-175X
                http://orcid.org/0000-0003-1292-4799
                Article
                1747
                10.1038/s41467-017-01747-2
                5684139
                29133944
                e461d6e8-b1d0-49c6-8549-723ade27e606
                © The Author(s) 2017

                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
                : 19 June 2017
                : 13 October 2017
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