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Kim JH, Park SH, Han J, Ko PW, Kwon D, Suk K. Gliome database: a comprehensive web-based tool to access and analyze glia secretome data. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5879255. [PMID: 32743661 PMCID: PMC7396318 DOI: 10.1093/database/baaa057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/12/2020] [Accepted: 07/02/2020] [Indexed: 12/15/2022]
Abstract
Glial cells are phenotypically heterogeneous non-neuronal components of the central and peripheral nervous systems. These cells are endowed with diverse functions and molecular machineries to detect and regulate neuronal or their own activities by various secreted mediators, such as proteinaceous factors. In particular, glia-secreted proteins form a basis of a complex network of glia-neuron or glia-glia interactions in health and diseases. In recent years, the analysis and profiling of glial secretomes have raised new expectations for the diagnosis and treatment of neurological disorders due to the vital role of glia in numerous physiological or pathological processes of the nervous system. However, there is no online database of glia-secreted proteins available to facilitate glial research. Here, we developed a user-friendly 'Gliome' database (available at www.gliome.org), a web-based tool to access and analyze glia-secreted proteins. The database provides a vast collection of information on 3293 proteins that are released from glia of multiple species and have been reported to have differential functions under diverse experimental conditions. It contains a web-based interface with the following four key features regarding glia-secreted proteins: (i) fundamental information, such as signal peptide, SecretomeP value, functions and Gene Ontology category; (ii) differential expression patterns under distinct experimental conditions; (iii) disease association; and (iv) interacting proteins. In conclusion, the Gliome database is a comprehensive web-based tool to access and analyze glia-secretome data obtained from diverse experimental settings, whereby it may facilitate the integration of bioinformatics into glial research.
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Affiliation(s)
- Jong-Heon Kim
- Brain Science and Engineering Institute, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea
| | - Su-Hyeong Park
- Department of Pharmacology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea.,D&P BIOTECH, 807 Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea
| | - Jin Han
- Department of Pharmacology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea
| | - Pan-Woo Ko
- Department of Neurology, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, Republic of Korea
| | - Dongseop Kwon
- School of Software Convergence, Myongji University, 34 Geobukgol-ro, Seodaemun-gu, Seoul, 03674, Republic of Korea
| | - Kyoungho Suk
- Brain Science and Engineering Institute, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea.,Department of Pharmacology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea
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Gómez-Caudillo L, Ortega-Lozano AJ, Martínez-Batallar ÁG, Rosas-Vargas H, Minauro-Sanmiguel F, Encarnación-Guevara S. Principal component analysis on LC‑MS/MS and 2DE‑MALDI‑TOF in glioblastoma cell lines reveals that mitochondria act as organelle sensors of the metabolic state in glioblastoma. Oncol Rep 2020; 44:661-673. [PMID: 32468038 PMCID: PMC7336416 DOI: 10.3892/or.2020.7625] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 04/08/2020] [Indexed: 02/06/2023] Open
Abstract
Glioblastoma is a difficult disease to diagnose. Proteomic techniques are commonly applied in biomedical research, and can be useful for early detection, making an accurate diagnosis and reducing mortality. The relevance of mitochondria in brain development and function is well known; therefore, mitochondria may influence the development of glioblastoma. The T98G (with oxidative metabolism) and U87MG (with glycolytic metabolism) cell lines are considered to be useful glioblastoma models for studying these tumors and the role of mitochondria in key aspects of this disease, such as prognosis, metastasis and apoptosis. In the present study, principal component analysis of protein abundance data identified by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF) from 2D gels indicated that representative mitochondrial proteins were associated with glioblastoma. The selected proteins were organized into T98G- and U87MG-specific protein-protein interaction networks to demonstrate the representativeness of both proteomic techniques. Gene Ontology overrepresentation analysis based on the relevant proteins revealed that mitochondrial processes were associated with metabolic changes, invasion and metastasis in glioblastoma, along with other non-mitochondrial processes, such as DNA translation, chaperone responses and autophagy. Despite the lower resolution of 2D electrophoresis, principal component analysis yielded information of comparable quality to that of LC-MS/MS. The present analysis pipeline described a specific and more complete metabolic status for each cell line, defined a clear mitochondrial performance for distinct glioblastoma tumors, and introduced a useful strategy to understand the heterogeneity of glioblastoma.
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Affiliation(s)
- Leopoldo Gómez-Caudillo
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos 62210, Mexico
| | - Ariadna J Ortega-Lozano
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos 62210, Mexico
| | - Ángel G Martínez-Batallar
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos 62210, Mexico
| | - Haydee Rosas-Vargas
- Medical Research Unit in Human Genetics, Hospital of Pediatrics, National Medical Center XXI Century, Mexican Social Security Institute, Mexico City 06720, Mexico
| | - Fernando Minauro-Sanmiguel
- Medical Research Unit in Human Genetics, Hospital of Pediatrics, National Medical Center XXI Century, Mexican Social Security Institute, Mexico City 06720, Mexico
| | - Sergio Encarnación-Guevara
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos 62210, Mexico
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