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      Spaceflight on the ISS changed the skeletal muscle proteome of two astronauts

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          Abstract

          Skeletal muscle undergoes atrophy and loss of force during long space missions, when astronauts are persistently exposed to altered gravity and increased ionizing radiation. We previously carried out mass spectrometry-based proteomics from skeletal muscle biopsies of two astronauts, taken before and after a mission on the International Space Station. The experiments were part of an effort to find similarities between spaceflight and bed rest, a ground-based model of unloading, focused on proteins located at the costameres. We here extend the data analysis of the astronaut dataset and show compartment-resolved changes in the mitochondrial proteome, remodeling of the extracellular matrix and of the antioxidant response. The astronauts differed in their level of onboard physical exercise, which correlated with their respective preservation of muscle mass and force at landing in previous analyses. We show that the mitochondrial proteome downregulation during spaceflight, particularly the inner membrane and matrix, was dramatic for both astronauts. The expression of autophagy regulators and reactive oxygen species scavengers, however, showed partially opposite expression trends in the two subjects, possibly correlating with their level of onboard exercise. As mitochondria are primarily affected in many different tissues during spaceflight, we hypothesize that reactive oxygen species (ROS) rather than mechanical unloading per se could be the primary cause of skeletal muscle mitochondrial damage in space. Onboard physical exercise might have a strong direct effect on the prevention of muscle atrophy through mechanotransduction and a subsidiary effect on mitochondrial quality control, possibly through upregulation of autophagy and anti-oxidant responses.

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          Proteomics. Tissue-based map of the human proteome.

          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. Copyright © 2015, American Association for the Advancement of Science.
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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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              Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

              Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
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                Author and article information

                Contributors
                mmurgia@biochem.mpg.de
                Journal
                NPJ Microgravity
                NPJ Microgravity
                NPJ Microgravity
                Nature Publishing Group UK (London )
                2373-8065
                5 June 2024
                5 June 2024
                2024
                : 10
                : 60
                Affiliations
                [1 ]Department of Biomedical Sciences, University of Padova, ( https://ror.org/00240q980) 35131 Padua, Italy
                [2 ]Max-Planck-Institute of Biochemistry, ( https://ror.org/04py35477) 82152 Martinsried, Germany
                [3 ]Institute of Aerospace Medicine, German Aerospace Center, ( https://ror.org/04bwf3e34) Cologne, Germany
                [4 ]Department of Pediatrics and Adolescent Medicine, University Hospital Cologne, ( https://ror.org/05mxhda18) Cologne, Germany
                [5 ]Science and Research Center Koper, Institute for Kinesiology Research, ( https://ror.org/00nykqr56) 6000 Koper, Slovenia
                [6 ]Department of Molecular Medicine, University of Pavia, ( https://ror.org/00s6t1f81) Pavia, Italy
                [7 ]GRID grid.419425.f, ISNI 0000 0004 1760 3027, IRCCS Policlinico San Matteo Foundation, ; Pavia, Italy
                [8 ]NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, ( https://ror.org/035b05819) Copenhagen, Denmark
                [9 ]Veneto Institute of Molecular Medicine, ( https://ror.org/0048jxt15) 35129 Padua, Italy
                [10 ]CIR-MYO Myology Center, 35121 Padua, Italy
                Author information
                http://orcid.org/0000-0001-6244-3358
                http://orcid.org/0000-0002-2223-8963
                http://orcid.org/0000-0001-8080-361X
                http://orcid.org/0000-0002-4960-7490
                Article
                406
                10.1038/s41526-024-00406-3
                11153545
                38839773
                16b0483b-3c40-49ec-a861-560cfaf396a8
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 February 2024
                : 22 May 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003981, Agenzia Spaziale Italiana (Italian Space Agency);
                Award ID: DC-VUM-2017-006
                Award ID: DC-VUM-2017-006
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                physiology,biochemistry
                physiology, biochemistry

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