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Qiao Y, Yang R, Liu Y, Chen J, Zhao L, Huo P, Wang Z, Bu D, Wu Y, Zhao Y. DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures. Comput Struct Biotechnol J 2024; 23:617-625. [PMID: 38274994 PMCID: PMC10808905 DOI: 10.1016/j.csbj.2023.12.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/04/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rui Yang
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Liu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxin Chen
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Peipei Huo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhihao Wang
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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2
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Reisman EG, Hawley JA, Hoffman NJ. Exercise-Regulated Mitochondrial and Nuclear Signalling Networks in Skeletal Muscle. Sports Med 2024; 54:1097-1119. [PMID: 38528308 PMCID: PMC11127882 DOI: 10.1007/s40279-024-02007-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2024] [Indexed: 03/27/2024]
Abstract
Exercise perturbs energy homeostasis in skeletal muscle and engages integrated cellular signalling networks to help meet the contraction-induced increases in skeletal muscle energy and oxygen demand. Investigating exercise-associated perturbations in skeletal muscle signalling networks has uncovered novel mechanisms by which exercise stimulates skeletal muscle mitochondrial biogenesis and promotes whole-body health and fitness. While acute exercise regulates a complex network of protein post-translational modifications (e.g. phosphorylation) in skeletal muscle, previous investigations of exercise signalling in human and rodent skeletal muscle have primarily focused on a select group of exercise-regulated protein kinases [i.e. 5' adenosine monophosphate-activated protein kinase (AMPK), protein kinase A (PKA), Ca2+/calmodulin-dependent protein kinase (CaMK) and mitogen-activated protein kinase (MAPK)] and only a small subset of their respective protein substrates. Recently, global mass spectrometry-based phosphoproteomic approaches have helped unravel the extensive complexity and interconnection of exercise signalling pathways and kinases beyond this select group and phosphorylation and/or translocation of exercise-regulated mitochondrial and nuclear protein substrates. This review provides an overview of recent advances in our understanding of the molecular events associated with acute endurance exercise-regulated signalling pathways and kinases in skeletal muscle with a focus on phosphorylation. We critically appraise recent evidence highlighting the involvement of mitochondrial and nuclear protein phosphorylation and/or translocation in skeletal muscle adaptive responses to an acute bout of endurance exercise that ultimately stimulate mitochondrial biogenesis and contribute to exercise's wider health and fitness benefits.
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Affiliation(s)
- Elizabeth G Reisman
- Exercise and Nutrition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Level 5, 215 Spring Street, Melbourne, VIC, 3000, Australia
| | - John A Hawley
- Exercise and Nutrition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Level 5, 215 Spring Street, Melbourne, VIC, 3000, Australia
| | - Nolan J Hoffman
- Exercise and Nutrition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Level 5, 215 Spring Street, Melbourne, VIC, 3000, Australia.
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3
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Ng YK, Blazev R, McNamara JW, Dutt M, Molendijk J, Porrello ER, Elliott DA, Parker BL. Affinity Purification-Mass Spectrometry and Single Fiber Physiology/Proteomics Reveals Mechanistic Insights of C18ORF25. J Proteome Res 2024; 23:1285-1297. [PMID: 38480473 DOI: 10.1021/acs.jproteome.3c00716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
C18ORF25 was recently shown to be phosphorylated at S67 by AMP-activated protein kinase (AMPK) in the skeletal muscle, following acute exercise in humans. Phosphorylation was shown to improve the ex vivo skeletal muscle contractile function in mice, but our understanding of the molecular mechanisms is incomplete. Here, we profiled the interactome of C18ORF25 in mouse myotubes using affinity purification coupled to mass spectrometry. This analysis included an investigation of AMPK-dependent and S67-dependent protein/protein interactions. Several nucleocytoplasmic and contractile-associated proteins were identified, which revealed a subset of GTPases that associate with C18ORF25 in an AMPK- and S67 phosphorylation-dependent manner. We confirmed that C18ORF25 is localized to the nucleus and the contractile apparatus in the skeletal muscle. Mice lacking C18Orf25 display defects in calcium handling specifically in fast-twitch muscle fibers. To investigate these mechanisms, we developed an integrated single fiber physiology and single fiber proteomic platform. The approach enabled a detailed assessment of various steps in the excitation-contraction pathway including SR calcium handling and force generation, followed by paired single fiber proteomic analysis. This enabled us to identify >700 protein/phenotype associations and 36 fiber-type specific differences, following loss of C18Orf25. Taken together, our data provide unique insights into the function of C18ORF25 and its role in skeletal muscle physiology.
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Affiliation(s)
- Yaan-Kit Ng
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
| | - Ronnie Blazev
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
| | - James W McNamara
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
- Murdoch Children's Research Institute and Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Parkville, 3052 VIC, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Melbourne, 3052 VIC, Australia
| | - Mriga Dutt
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
| | - Jeffrey Molendijk
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
| | - Enzo R Porrello
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Murdoch Children's Research Institute and Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Parkville, 3052 VIC, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Melbourne, 3052 VIC, Australia
- Department of Paediatrics, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne, Melbourne, 3010 VIC, Australia
| | - David A Elliott
- Murdoch Children's Research Institute and Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Parkville, 3052 VIC, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Melbourne, 3052 VIC, Australia
- Department of Paediatrics, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne, Melbourne, 3010 VIC, Australia
| | - Benjamin L Parker
- Department of Anatomy & Physiology, The University of Melbourne, Parkville, 3052 VIC, Australia
- Centre for Muscle Research, The University of Melbourne, Parkville, 3052 VIC, Australia
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4
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van Gerwen J, Masson SWC, Cutler HB, Vegas AD, Potter M, Stöckli J, Madsen S, Nelson ME, Humphrey SJ, James DE. The genetic and dietary landscape of the muscle insulin signalling network. eLife 2024; 12:RP89212. [PMID: 38329473 PMCID: PMC10942587 DOI: 10.7554/elife.89212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway's organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasising the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.
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Affiliation(s)
- Julian van Gerwen
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Stewart WC Masson
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Harry B Cutler
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Alexis Diaz Vegas
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Meg Potter
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Jacqueline Stöckli
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Søren Madsen
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Marin E Nelson
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Sean J Humphrey
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
- Faculty of Medicine and Health, University of SydneySydneyAustralia
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5
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Zhang K, Xie N, Ye H, Miao J, Xia B, Yang Y, Peng H, Xu S, Wu T, Tao C, Ruan J, Wang Y, Yang S. Glucose restriction enhances oxidative fiber formation: A multi-omic signal network involving AMPK and CaMK2. iScience 2024; 27:108590. [PMID: 38161415 PMCID: PMC10755363 DOI: 10.1016/j.isci.2023.108590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/23/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Skeletal muscle is a highly plastic organ that adapts to different metabolic states or functional demands. This study explored the impact of permanent glucose restriction (GR) on skeletal muscle composition and metabolism. Using Glut4m mice with defective glucose transporter 4, we conducted multi-omics analyses at different ages and after low-intensity treadmill training. The oxidative fibers were significantly increased in Glut4m muscles. Mechanistically, GR activated AMPK pathway, promoting mitochondrial function and beneficial myokine expression, and facilitated slow fiber formation via CaMK2 pathway. Phosphorylation-activated Perm1 may synergize AMPK and CaMK2 signaling. Besides, MAPK and CDK kinases were also implicated in skeletal muscle protein phosphorylation during GR response. This study provides a comprehensive signaling network demonstrating how GR influences muscle fiber types and metabolic patterns. These insights offer valuable data for understanding oxidative fiber formation mechanisms and identifying clinical targets for metabolic diseases.
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Affiliation(s)
- Kaiyi Zhang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
- Precision Livestock and Nutrition Unit, Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, Liège University, 5030 Gembloux, Belgium
| | - Ning Xie
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Huaqiong Ye
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Jiakun Miao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Boce Xia
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yu Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Huanqi Peng
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Shuang Xu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Tianwen Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Cong Tao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Jinxue Ruan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
| | - Yanfang Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Shulin Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
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6
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Leutert M, Barente AS, Fukuda NK, Rodriguez-Mias RA, Villén J. The regulatory landscape of the yeast phosphoproteome. Nat Struct Mol Biol 2023; 30:1761-1773. [PMID: 37845410 PMCID: PMC10841839 DOI: 10.1038/s41594-023-01115-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 09/05/2023] [Indexed: 10/18/2023]
Abstract
The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.
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Affiliation(s)
- Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Anthony S Barente
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Noelle K Fukuda
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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7
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Shilenok I, Kobzeva K, Stetskaya T, Freidin M, Soldatova M, Deykin A, Soldatov V, Churnosov M, Polonikov A, Bushueva O. SERPINE1 mRNA Binding Protein 1 Is Associated with Ischemic Stroke Risk: A Comprehensive Molecular-Genetic and Bioinformatics Analysis of SERBP1 SNPs. Int J Mol Sci 2023; 24:8716. [PMID: 37240062 PMCID: PMC10217814 DOI: 10.3390/ijms24108716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
The SERBP1 gene is a well-known regulator of SERPINE1 mRNA stability and progesterone signaling. However, the chaperone-like properties of SERBP1 have recently been discovered. The present pilot study investigated whether SERBP1 SNPs are associated with the risk and clinical manifestations of ischemic stroke (IS). DNA samples from 2060 unrelated Russian subjects (869 IS patients and 1191 healthy controls) were genotyped for 5 common SNPs-rs4655707, rs1058074, rs12561767, rs12566098, and rs6702742 SERBP1-using probe-based PCR. The association of SNP rs12566098 with an increased risk of IS (risk allele C; p = 0.001) was observed regardless of gender or physical activity level and was modified by smoking, fruit and vegetable intake, and body mass index. SNP rs1058074 (risk allele C) was associated with an increased risk of IS exclusively in women (p = 0.02), non-smokers (p = 0.003), patients with low physical activity (p = 0.04), patients with low fruit and vegetable consumption (p = 0.04), and BMI ≥25 (p = 0.007). SNPs rs1058074 (p = 0.04), rs12561767 (p = 0.01), rs12566098 (p = 0.02), rs6702742 (p = 0.036), and rs4655707 (p = 0.04) were associated with shortening of activated partial thromboplastin time. Thus, SERBP1 SNPs represent novel genetic markers of IS. Further studies are required to confirm the relationship between SERBP1 polymorphism and IS risk.
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Affiliation(s)
- Irina Shilenok
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
- Division of Neurology, Kursk Emergency Hospital, 305035 Kursk, Russia
| | - Ksenia Kobzeva
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
| | - Tatiana Stetskaya
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
| | - Maxim Freidin
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, UK
- Laboratory of Population Genetics, Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Science, 634050 Tomsk, Russia
| | - Maria Soldatova
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
| | - Alexey Deykin
- Laboratory of Genome Editing for Biomedicine and Animal Health, Belgorod State National Research University, 308015 Belgorod, Russia
- Department of Pharmacology and Clinical Pharmacology, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Vladislav Soldatov
- Laboratory of Genome Editing for Biomedicine and Animal Health, Belgorod State National Research University, 308015 Belgorod, Russia
- Department of Pharmacology and Clinical Pharmacology, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Alexey Polonikov
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 305041 Kursk, Russia
| | - Olga Bushueva
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 305041 Kursk, Russia
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Poplawski P, Alseekh S, Jankowska U, Skupien-Rabian B, Iwanicka-Nowicka R, Kossowska H, Fogtman A, Rybicka B, Bogusławska J, Adamiok-Ostrowska A, Hanusek K, Hanusek J, Koblowska M, Fernie AR, Piekiełko-Witkowska A. Coordinated reprogramming of renal cancer transcriptome, metabolome and secretome associates with immune tumor infiltration. Cancer Cell Int 2023; 23:2. [PMID: 36604669 PMCID: PMC9814214 DOI: 10.1186/s12935-022-02845-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. The molecules (proteins, metabolites) secreted by tumors affect their extracellular milieu to support cancer progression. If secreted in amounts detectable in plasma, these molecules can also serve as useful, minimal invasive biomarkers. The knowledge of ccRCC tumor microenvironment is fragmentary. In particular, the links between ccRCC transcriptome and the composition of extracellular milieu are weakly understood. In this study, we hypothesized that ccRCC transcriptome is reprogrammed to support alterations in tumor microenvironment. Therefore, we comprehensively analyzed ccRCC extracellular proteomes and metabolomes as well as transcriptomes of ccRCC cells to find molecules contributing to renal tumor microenvironment. METHODS Proteomic and metabolomics analysis of conditioned media isolated from normal kidney cells as well as five ccRCC cell lines was performed using mass spectrometry, with the following ELISA validation. Transcriptomic analysis was done using microarray analysis and validated using real-time PCR. Independent transcriptomic and proteomic datasets of ccRCC tumors were used for the analysis of gene and protein expression as well as the level of the immune infiltration. RESULTS Renal cancer secretome contained 85 proteins detectable in human plasma, consistently altered in all five tested ccRCC cell lines. The top upregulated extracellular proteins included SPARC, STC2, SERPINE1, TGFBI, while downregulated included transferrin and DPP7. The most affected extracellular metabolites were increased 4-hydroxy-proline, succinic acid, cysteine, lactic acid and downregulated glutamine. These changes were associated with altered expression of genes encoding the secreted proteins (SPARC, SERPINE1, STC2, DPP7), membrane transporters (SLC16A4, SLC6A20, ABCA12), and genes involved in protein trafficking and secretion (KIF20A, ANXA3, MIA2, PCSK5, SLC9A3R1, SYTL3, and WNTA7). Analogous expression changes were found in ccRCC tumors. The expression of SPARC predicted the infiltration of ccRCC tumors with endothelial cells. Analysis of the expression of the 85 secretome genes in > 12,000 tumors revealed that SPARC is a PanCancer indicator of cancer-associated fibroblasts' infiltration. CONCLUSIONS Transcriptomic reprogramming of ccRCC supports the changes in an extracellular milieu which are associated with immune infiltration. The proteins identified in our study represent valuable cancer biomarkers detectable in plasma.
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Affiliation(s)
- Piotr Poplawski
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Saleh Alseekh
- grid.418390.70000 0004 0491 976XMax-Planck Institute of Molecular Plant Physiology, Golm, 14476 Potsdam, Germany ,grid.510916.a0000 0004 9334 5103Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Urszula Jankowska
- grid.5522.00000 0001 2162 9631Proteomics and Mass Spectrometry Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Bozena Skupien-Rabian
- grid.5522.00000 0001 2162 9631Proteomics and Mass Spectrometry Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Roksana Iwanicka-Nowicka
- grid.12847.380000 0004 1937 1290Laboratory of Systems Biology, Faculty of Biology, University of Warsaw, 02-106 Warsaw, Poland ,grid.413454.30000 0001 1958 0162Laboratory for Microarray Analysis, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Helena Kossowska
- grid.12847.380000 0004 1937 1290Laboratory of Systems Biology, Faculty of Biology, University of Warsaw, 02-106 Warsaw, Poland
| | - Anna Fogtman
- grid.413454.30000 0001 1958 0162Laboratory for Microarray Analysis, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Beata Rybicka
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Joanna Bogusławska
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Anna Adamiok-Ostrowska
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Karolina Hanusek
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Jan Hanusek
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
| | - Marta Koblowska
- grid.12847.380000 0004 1937 1290Laboratory of Systems Biology, Faculty of Biology, University of Warsaw, 02-106 Warsaw, Poland ,grid.413454.30000 0001 1958 0162Laboratory for Microarray Analysis, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Alisdair R. Fernie
- grid.418390.70000 0004 0491 976XMax-Planck Institute of Molecular Plant Physiology, Golm, 14476 Potsdam, Germany ,grid.510916.a0000 0004 9334 5103Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Agnieszka Piekiełko-Witkowska
- grid.414852.e0000 0001 2205 7719Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland
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9
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Johnson JL, Yaron TM, Huntsman EM, Kerelsky A, Song J, Regev A, Lin TY, Liberatore K, Cizin DM, Cohen BM, Vasan N, Ma Y, Krismer K, Robles JT, van de Kooij B, van Vlimmeren AE, Andrée-Busch N, Käufer NF, Dorovkov MV, Ryazanov AG, Takagi Y, Kastenhuber ER, Goncalves MD, Hopkins BD, Elemento O, Taatjes DJ, Maucuer A, Yamashita A, Degterev A, Uduman M, Lu J, Landry SD, Zhang B, Cossentino I, Linding R, Blenis J, Hornbeck PV, Turk BE, Yaffe MB, Cantley LC. An atlas of substrate specificities for the human serine/threonine kinome. Nature 2023; 613:759-766. [PMID: 36631611 PMCID: PMC9876800 DOI: 10.1038/s41586-022-05575-3] [Citation(s) in RCA: 149] [Impact Index Per Article: 149.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 11/17/2022] [Indexed: 01/13/2023]
Abstract
Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.
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Affiliation(s)
- Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional PhD Program in Computational Biology & Medicine, Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center and The Rockefeller University, New York, NY, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alexander Kerelsky
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Junho Song
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Amit Regev
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ting-Yu Lin
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Cell and Developmental Biology Program, New York, NY, USA
| | - Katarina Liberatore
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Daniel M Cizin
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin M Cohen
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Neil Vasan
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yilun Ma
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Konstantin Krismer
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for Precision Cancer Medicine, Koch Institute for Integrative Cancer Biology, Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jaylissa Torres Robles
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Bert van de Kooij
- Center for Precision Cancer Medicine, Koch Institute for Integrative Cancer Biology, Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anne E van Vlimmeren
- Center for Precision Cancer Medicine, Koch Institute for Integrative Cancer Biology, Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicole Andrée-Busch
- Institute of Genetics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Norbert F Käufer
- Institute of Genetics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Maxim V Dorovkov
- Department of Pharmacology, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Alexey G Ryazanov
- Department of Pharmacology, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Yuichiro Takagi
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Edward R Kastenhuber
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Marcus D Goncalves
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Division of Endocrinology, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin D Hopkins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivier Elemento
- Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Dylan J Taatjes
- Department of Biochemistry, University of Colorado, Boulder, CO, USA
| | - Alexandre Maucuer
- SABNP, Univ Evry, INSERM U1204, Université Paris-Saclay, Evry, France
| | - Akio Yamashita
- Department of Investigative Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-cho, Japan
| | - Alexei Degterev
- Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine, Boston, MA, USA
| | - Mohamed Uduman
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Jingyi Lu
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Sean D Landry
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Bin Zhang
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Ian Cossentino
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Rune Linding
- Rewire Tx, Humboldt-Universität zu Berlin, Berlin, Germany
| | - John Blenis
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
- Department of Biochemistry, Weill Cornell Medicine, New York, NY, USA
| | - Peter V Hornbeck
- Department Of Bioinformatics, Cell Signaling Technology, Danvers, MA, USA
| | - Benjamin E Turk
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA.
| | - Michael B Yaffe
- Center for Precision Cancer Medicine, Koch Institute for Integrative Cancer Biology, Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Divisions of Acute Care Surgery, Trauma, and Surgical Critical Care, and Surgical Oncology, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Surgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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10
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Welch N, Singh SS, Musich R, Mansuri MS, Bellar A, Mishra S, Chelluboyina AK, Sekar J, Attaway AH, Li L, Willard B, Hornberger TA, Dasarathy S. Shared and unique phosphoproteomics responses in skeletal muscle from exercise models and in hyperammonemic myotubes. iScience 2022; 25:105325. [PMID: 36345342 PMCID: PMC9636548 DOI: 10.1016/j.isci.2022.105325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/22/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Skeletal muscle generation of ammonia, an endogenous cytotoxin, is increased during exercise. Perturbations in ammonia metabolism consistently occur in chronic diseases, and may blunt beneficial skeletal muscle molecular responses and protein homeostasis with exercise. Phosphorylation of skeletal muscle proteins mediates cellular signaling responses to hyperammonemia and exercise. Comparative bioinformatics and machine learning-based analyses of published and experimentally derived phosphoproteomics data identified differentially expressed phosphoproteins that were unique and shared between hyperammonemic murine myotubes and skeletal muscle from exercise models. Enriched processes identified in both hyperammonemic myotubes and muscle from exercise models with selected experimental validation included protein kinase A (PKA), calcium signaling, mitogen-activated protein kinase (MAPK) signaling, and protein homeostasis. Our approach of feature extraction from comparative untargeted "omics" data allows for selection of preclinical models that recapitulate specific human exercise responses and potentially optimize functional capacity and skeletal muscle protein homeostasis with exercise in chronic diseases.
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Affiliation(s)
- Nicole Welch
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Gastroenterology and Hepatology, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Shashi Shekhar Singh
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ryan Musich
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | - M. Shahid Mansuri
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Annette Bellar
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Saurabh Mishra
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | | | - Jinendiran Sekar
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Amy H. Attaway
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ling Li
- Proteomics Core, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53706, USA
| | - Belinda Willard
- Proteomics Core, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53706, USA
| | - Troy A. Hornberger
- Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53706, USA
| | - Srinivasan Dasarathy
- Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Gastroenterology and Hepatology, Cleveland Clinic, Cleveland, OH 44195, USA
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11
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Personalized phosphoproteomics identifies functional signaling. Nat Biotechnol 2022; 40:576-584. [PMID: 34857927 DOI: 10.1038/s41587-021-01099-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 09/16/2021] [Indexed: 02/07/2023]
Abstract
Protein phosphorylation dynamically integrates environmental and cellular information to control biological processes. Identifying functional phosphorylation amongst the thousands of phosphosites regulated by a perturbation at a global scale is a major challenge. Here we introduce 'personalized phosphoproteomics', a combination of experimental and computational analyses to link signaling with biological function by utilizing human phenotypic variance. We measure individual subject phosphoproteome responses to interventions with corresponding phenotypes measured in parallel. Applying this approach to investigate how exercise potentiates insulin signaling in human skeletal muscle, we identify both known and previously unidentified phosphosites on proteins involved in glucose metabolism. This includes a cooperative relationship between mTOR and AMPK whereby the former directly phosphorylates the latter on S377, for which we find a role in metabolic regulation. These results establish personalized phosphoproteomics as a general approach for investigating the signal transduction underlying complex biology.
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12
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Smith PR, Pandit SC, Loerch S, Campbell ZT. The space between notes: emerging roles for translationally silent ribosomes. Trends Biochem Sci 2022; 47:477-491. [PMID: 35246374 DOI: 10.1016/j.tibs.2022.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 01/02/2023]
Abstract
In addition to their central functions in translation, ribosomes can adopt inactive structures that are fully assembled yet devoid of mRNA. We describe how the abundance of idle eukaryotic ribosomes is influenced by a broad range of biological conditions spanning viral infection, nutrient deprivation, and developmental cues. Vacant ribosomes may provide a means to exclude ribosomes from translation while also shielding them from degradation, and the variable identity of factors that occlude ribosomes may impart distinct functionality. We propose that regulated changes in the balance of idle and active ribosomes provides a means to fine-tune translation. We provide an overview of idle ribosomes, describe what is known regarding their function, and highlight questions that may clarify their biological roles.
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Affiliation(s)
- Patrick R Smith
- The University of Texas at Dallas, Department of Biological Sciences, Richardson, TX, USA
| | - Sapna C Pandit
- University of California, Santa Cruz, Department of Chemistry and Biochemistry, Santa Cruz, CA, USA
| | - Sarah Loerch
- University of California, Santa Cruz, Department of Chemistry and Biochemistry, Santa Cruz, CA, USA
| | - Zachary T Campbell
- The University of Texas at Dallas, Department of Biological Sciences, Richardson, TX, USA; The Center for Advanced Pain Studies (CAPS), University of Texas at Dallas, Richardson, TX, USA.
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13
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Akberdin IR, Kiselev IN, Pintus SS, Sharipov RN, Vertyshev AY, Vinogradova OL, Popov DV, Kolpakov FA. A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle. Int J Mol Sci 2021; 22:10353. [PMID: 34638694 PMCID: PMC8508736 DOI: 10.3390/ijms221910353] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 09/22/2021] [Indexed: 11/29/2022] Open
Abstract
Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling-metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause-effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.
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Affiliation(s)
- Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya N. Kiselev
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Sergey S. Pintus
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Ruslan N. Sharipov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | | | - Olga L. Vinogradova
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Daniil V. Popov
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Fedor A. Kolpakov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
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14
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Vepkhvadze TF, Vorotnikov AV, Popov DV. Electrical Stimulation of Cultured Myotubes in vitro as a Model of Skeletal Muscle Activity: Current State and Future Prospects. BIOCHEMISTRY (MOSCOW) 2021; 86:597-610. [PMID: 33993862 DOI: 10.1134/s0006297921050084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Skeletal muscles comprise more than a third of human body mass and critically contribute to regulation of body metabolism. Chronic inactivity reduces metabolic activity and functional capacity of muscles, leading to metabolic and other disorders, reduced life quality and duration. Cellular models based on progenitor cells isolated from human muscle biopsies and then differentiated into mature fibers in vitro can be used to solve a wide range of experimental tasks. The review discusses the aspects of myogenesis dynamics and regulation, which might be important in the development of an adequate cell model. The main function of skeletal muscle is contraction; therefore, electrical stimulation is important for both successful completion of myogenesis and in vitro modeling of major processes induced in the skeletal muscle by acute or regular physical exercise. The review analyzes the drawbacks of such cellular model and possibilities for its optimization, as well as the prospects for its further application to address fundamental aspects of muscle physiology and biochemistry and explore cellular and molecular mechanisms of metabolic diseases.
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Affiliation(s)
- Tatiana F Vepkhvadze
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, 123007, Russia
| | - Alexander V Vorotnikov
- National Medical Research Center of Cardiology, Ministry of Healthcare of the Russian Federation, Moscow, 121552, Russia
| | - Daniil V Popov
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, 123007, Russia. .,Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
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15
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McKendry J, Stokes T, Mcleod JC, Phillips SM. Resistance Exercise, Aging, Disuse, and Muscle Protein Metabolism. Compr Physiol 2021; 11:2249-2278. [PMID: 34190341 DOI: 10.1002/cphy.c200029] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Skeletal muscle is the organ of locomotion, its optimal function is critical for athletic performance, and is also important for health due to its contribution to resting metabolic rate and as a site for glucose uptake and storage. Numerous endogenous and exogenous factors influence muscle mass. Much of what is currently known regarding muscle protein turnover is owed to the development and use of stable isotope tracers. Skeletal muscle mass is determined by the meal- and contraction-induced alterations of muscle protein synthesis and muscle protein breakdown. Increased loading as resistance training is the most potent nonpharmacological strategy by which skeletal muscle mass can be increased. Conversely, aging (sarcopenia) and muscle disuse lead to the development of anabolic resistance and contribute to the loss of skeletal muscle mass. Nascent omics-based technologies have significantly improved our understanding surrounding the regulation of skeletal muscle mass at the gene, transcript, and protein levels. Despite significant advances surrounding the mechanistic intricacies that underpin changes in skeletal muscle mass, these processes are complex, and more work is certainly needed. In this article, we provide an overview of the importance of skeletal muscle, describe the influence that resistance training, aging, and disuse exert on muscle protein turnover and the molecular regulatory processes that contribute to changes in muscle protein abundance. © 2021 American Physiological Society. Compr Physiol 11:2249-2278, 2021.
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Affiliation(s)
- James McKendry
- Exercise Metabolism Research Group, Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
| | - Tanner Stokes
- Exercise Metabolism Research Group, Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
| | - Jonathan C Mcleod
- Exercise Metabolism Research Group, Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
| | - Stuart M Phillips
- Exercise Metabolism Research Group, Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
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16
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Abstract
Since ancient times, the health benefits of regular physical activity/exercise have been recognized and the classic studies of Morris and Paffenbarger provided the epidemiological evidence in support of such an association. Cardiorespiratory fitness, often measured by maximal oxygen uptake, and habitual physical activity levels are inversely related to mortality. Thus, studies exploring the biological bases of the health benefits of exercise have largely focused on the cardiovascular system and skeletal muscle (mass and metabolism), although there is increasing evidence that multiple tissues and organ systems are influenced by regular exercise. Communication between contracting skeletal muscle and multiple organs has been implicated in exercise benefits, as indeed has other interorgan "cross-talk." The application of molecular biology techniques and "omics" approaches to questions in exercise biology has opened new lines of investigation to better understand the beneficial effects of exercise and, in so doing, inform the optimization of exercise regimens and the identification of novel therapeutic strategies to enhance health and well-being.
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Affiliation(s)
- Mark Hargreaves
- Department of Anatomy & Physiology, The University of Melbourne, Melbourne, Victoria, Australia
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17
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Hargreaves M, Spriet LL. Skeletal muscle energy metabolism during exercise. Nat Metab 2020; 2:817-828. [PMID: 32747792 DOI: 10.1038/s42255-020-0251-4] [Citation(s) in RCA: 396] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/25/2020] [Indexed: 12/12/2022]
Abstract
The continual supply of ATP to the fundamental cellular processes that underpin skeletal muscle contraction during exercise is essential for sports performance in events lasting seconds to several hours. Because the muscle stores of ATP are small, metabolic pathways must be activated to maintain the required rates of ATP resynthesis. These pathways include phosphocreatine and muscle glycogen breakdown, thus enabling substrate-level phosphorylation ('anaerobic') and oxidative phosphorylation by using reducing equivalents from carbohydrate and fat metabolism ('aerobic'). The relative contribution of these metabolic pathways is primarily determined by the intensity and duration of exercise. For most events at the Olympics, carbohydrate is the primary fuel for anaerobic and aerobic metabolism. Here, we provide an overview of exercise metabolism and the key regulatory mechanisms ensuring that ATP resynthesis is closely matched to the ATP demand of exercise. We also summarize various interventions that target muscle metabolism for ergogenic benefit in athletic events.
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Affiliation(s)
- Mark Hargreaves
- Department of Physiology, University of Melbourne, Melbourne, Victoria, Australia.
| | - Lawrence L Spriet
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada.
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18
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Bouhaddou M, Memon D, Meyer B, White KM, Rezelj VV, Correa Marrero M, Polacco BJ, Melnyk JE, Ulferts S, Kaake RM, Batra J, Richards AL, Stevenson E, Gordon DE, Rojc A, Obernier K, Fabius JM, Soucheray M, Miorin L, Moreno E, Koh C, Tran QD, Hardy A, Robinot R, Vallet T, Nilsson-Payant BE, Hernandez-Armenta C, Dunham A, Weigang S, Knerr J, Modak M, Quintero D, Zhou Y, Dugourd A, Valdeolivas A, Patil T, Li Q, Hüttenhain R, Cakir M, Muralidharan M, Kim M, Jang G, Tutuncuoglu B, Hiatt J, Guo JZ, Xu J, Bouhaddou S, Mathy CJP, Gaulton A, Manners EJ, Félix E, Shi Y, Goff M, Lim JK, McBride T, O'Neal MC, Cai Y, Chang JCJ, Broadhurst DJ, Klippsten S, De Wit E, Leach AR, Kortemme T, Shoichet B, Ott M, Saez-Rodriguez J, tenOever BR, Mullins RD, Fischer ER, Kochs G, Grosse R, García-Sastre A, Vignuzzi M, Johnson JR, Shokat KM, Swaney DL, Beltrao P, Krogan NJ. The Global Phosphorylation Landscape of SARS-CoV-2 Infection. Cell 2020; 182:685-712.e19. [PMID: 32645325 PMCID: PMC7321036 DOI: 10.1016/j.cell.2020.06.034] [Citation(s) in RCA: 684] [Impact Index Per Article: 171.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/09/2020] [Accepted: 06/23/2020] [Indexed: 02/07/2023]
Abstract
The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.
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Affiliation(s)
- Mehdi Bouhaddou
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Danish Memon
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Bjoern Meyer
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | - Kris M White
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Veronica V Rezelj
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | - Miguel Correa Marrero
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Benjamin J Polacco
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - James E Melnyk
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute
| | - Svenja Ulferts
- Institute for Clinical and Experimental Pharmacology and Toxicology, University of Freiburg, Freiburg 79104, Germany
| | - Robyn M Kaake
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jyoti Batra
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Alicia L Richards
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Erica Stevenson
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David E Gordon
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ajda Rojc
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kirsten Obernier
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jacqueline M Fabius
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Margaret Soucheray
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lisa Miorin
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Elena Moreno
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Cassandra Koh
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | - Quang Dinh Tran
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | - Alexandra Hardy
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | - Rémy Robinot
- Virus & Immunity Unit, Department of Virology, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France; Vaccine Research Institute, 94000 Creteil, France
| | - Thomas Vallet
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France
| | | | - Claudia Hernandez-Armenta
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alistair Dunham
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Sebastian Weigang
- Institute of Virology, Medical Center - University of Freiburg, Freiburg 79104, Germany
| | - Julian Knerr
- Institute for Clinical and Experimental Pharmacology and Toxicology, University of Freiburg, Freiburg 79104, Germany
| | - Maya Modak
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Diego Quintero
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yuan Zhou
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Aurelien Dugourd
- Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Alberto Valdeolivas
- Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Trupti Patil
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Qiongyu Li
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruth Hüttenhain
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Merve Cakir
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Monita Muralidharan
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Minkyu Kim
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gwendolyn Jang
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Beril Tutuncuoglu
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joseph Hiatt
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jeffrey Z Guo
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jiewei Xu
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sophia Bouhaddou
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
| | - Christopher J P Mathy
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Anna Gaulton
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Emma J Manners
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Eloy Félix
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ying Shi
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute
| | - Marisa Goff
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jean K Lim
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | | | | | | | | | - Emmie De Wit
- NIH/NIAID/Rocky Mountain Laboratories, Hamilton, MT 59840, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tanja Kortemme
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian Shoichet
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA
| | - Melanie Ott
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Benjamin R tenOever
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - R Dyche Mullins
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute
| | | | - Georg Kochs
- Institute of Virology, Medical Center - University of Freiburg, Freiburg 79104, Germany; Faculty of Medicine, University of Freiburg, Freiburg 79008, Germany
| | - Robert Grosse
- Institute for Clinical and Experimental Pharmacology and Toxicology, University of Freiburg, Freiburg 79104, Germany; Faculty of Medicine, University of Freiburg, Freiburg 79008, Germany; Centre for Integrative Biological Signalling Studies (CIBSS), Freiburg 79104, Germany.
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Marco Vignuzzi
- Viral Populations and Pathogenesis Unit, CNRS UMR 3569, Institut Pasteur, 75724 Paris, Cedex 15, France.
| | - Jeffery R Johnson
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Kevan M Shokat
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute.
| | - Danielle L Swaney
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Pedro Beltrao
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Nevan J Krogan
- QBI COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94158, USA; J. David Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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19
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Caveolae: Formation, dynamics, and function. Curr Opin Cell Biol 2020; 65:8-16. [PMID: 32146331 DOI: 10.1016/j.ceb.2020.02.001] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/28/2020] [Accepted: 02/02/2020] [Indexed: 12/22/2022]
Abstract
Caveolae are abundant surface pits formed by the assembly of cytoplasmic proteins on a platform generated by caveolin integral membrane proteins and membrane lipids. This membranous assembly can bud off into the cell or can be disassembled releasing the cavin proteins into the cytosol. Disassembly can be triggered by increased membrane tension, or by stress stimuli, such as UV. Here, we discuss recent mechanistic studies showing how caveolae are formed and how their unique properties allow them to function as multifunctional protective and signaling structures.
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