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Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024; 52:W238-W247. [PMID: 38709873 PMCID: PMC11223847 DOI: 10.1093/nar/gkae346] [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] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
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Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
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Amahong K, Zhang W, Liu Y, Li T, Huang S, Han L, Tao L, Zhu F. RVvictor: Virus RNA-directed molecular interactions for RNA virus infection. Comput Biol Med 2024; 169:107886. [PMID: 38157777 DOI: 10.1016/j.compbiomed.2023.107886] [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] [Received: 08/06/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.
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Affiliation(s)
- Kuerbannisha Amahong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Teng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Lianyi Han
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, 315211, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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Lv D, Li D, Cai Y, Guo J, Chu S, Yu J, Liu K, Jiang T, Ding N, Jin X, Li Y, Xu J. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res 2024; 52:D1155-D1162. [PMID: 37823596 PMCID: PMC10767844 DOI: 10.1093/nar/gkad824] [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] [Received: 08/12/2023] [Revised: 09/07/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.
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Affiliation(s)
- Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Sen Chu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Kefan Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province 150000, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
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Park JW, Tyl MD, Cristea IM. Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection. Biomolecules 2023; 13:biom13050869. [PMID: 37238738 DOI: 10.3390/biom13050869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs.
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Affiliation(s)
- Ji Woo Park
- Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ 08544, USA
| | - Matthew D Tyl
- Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ 08544, USA
| | - Ileana M Cristea
- Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ 08544, USA
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Varshney N, Mishra AK. Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery. Proteomes 2023; 11:proteomes11020016. [PMID: 37218921 DOI: 10.3390/proteomes11020016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023] Open
Abstract
Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.
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Affiliation(s)
- Neha Varshney
- Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 93093, USA
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA
| | - Abhinava K Mishra
- Molecular, Cellular and Developmental Biology Department, University of California, Santa Barbara, CA 93106, USA
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Akmal MA, Hassan MA, Muhammad S, Khurshid KS, Mohamed A. An analytical study on the identification of N-linked glycosylation sites using machine learning model. PeerJ Comput Sci 2022; 8:e1069. [PMID: 36262138 PMCID: PMC9575850 DOI: 10.7717/peerj-cs.1069] [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: 04/19/2022] [Accepted: 07/25/2022] [Indexed: 06/16/2023]
Abstract
N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.
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Affiliation(s)
- Muhammad Aizaz Akmal
- Department of Computer Science, University of Engineering and Technology, KSK, Lahore, Punjab, Pakistan
| | - Muhammad Awais Hassan
- Department of Computer Science, University of Engineering and Technology, Lahore, Punjab, Pakistan
| | - Shoaib Muhammad
- Department of Computer Science, University of Engineering and Technology, Lahore, Punjab, Pakistan
| | - Khaldoon S. Khurshid
- Department of Computer Science, University of Engineering and Technology, Lahore, Punjab, Pakistan
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Shvets D, Vinogradova S. Occurrence and Genetic Characterization of Grapevine Pinot Gris Virus in Russia. PLANTS (BASEL, SWITZERLAND) 2022; 11:1061. [PMID: 35448789 PMCID: PMC9028157 DOI: 10.3390/plants11081061] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
Grapevine Pinot gris virus (GPGV) is a widespread grapevine pathogen associated with symptoms of leaf mottling and deformation. In order to study the distribution and genetic diversity of GPGV in Russia, we tested 1347 grapevine samples from 3 regions of Russia-the Krasnodar Krai, Stavropol Krai, and Republic of Crimea-using duplex real-time RT-PCR. GPGV was detected in 993 grapevines, both symptomatic and asymptomatic. In 119 isolates, we sequenced complete movement protein (MP) and coat protein (CP) genes of the GPGV genome. The percentage of identity of the obtained nucleotide MP/CP sequences with the closest isolates from the GenBank was 97.75-99.56%. A phylogenetic analysis showed that these Russian GPGV isolates are mainly grouped with previously described representative asymptomatic isolates. New post-translational modifications of the MP and CP at the positions of polymorphisms in the genomes of Russian isolates were predicted. The present work is the first study on the distribution and genetic diversity of GPGV in Russia.
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Comprehensive identification of protein orthologs in the family Ascoviridae facilitates an understanding of phylogenomics, protein conservation, and phosphorylation. Arch Virol 2022; 167:1075-1087. [PMID: 35246734 DOI: 10.1007/s00705-022-05402-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/18/2022] [Indexed: 11/02/2022]
Abstract
Analysis of orthology is important for understanding protein conservation, function, and phylogenomics. In this study, we performed a comprehensive analysis of gene orthology in the family Ascoviridae based on identification of 366 protein homologue groups and phylogenetic analysis of 34 non-single-copy proteins. Our findings revealed 90 newly annotated proteins, five newly identified core proteins for the family Ascoviridae, and 14 core proteins for the genus Ascovirus. A phylogenomic tree of 11 Ascoviridae members was constructed based on a concatenation of 35 of the 45 ortholog groups. In combination with phosphoproteomic results and conservation estimations, 30 conserved phosphorylation sites on 17 phosphoproteins were identified from a total of 176 phosphosites on 57 phosphoproteins from Heliothis virescens ascovirus 3h (HvAV-3h), providing potential research targets for investigating the role of these protein in the regulation of viral infection. This study will facilitate genome annotation and comparison of further Ascoviridae members as well as functional genomic investigations.
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Databases, Knowledgebases, and Software Tools for Virus Informatics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1368:1-19. [DOI: 10.1007/978-981-16-8969-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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