Quantitative Proteome Analysis of Brain Subregions and Spinal Cord from Experimental Autoimmune Encephalomyelitis Mice by TMT-Based Mass Spectrometry
- Authors
- Hasan, Mahbub; Min, Hophil; Rahaman, Khandoker Asiqur; Muresan, Anca Raluca; Kim, Hyeyoon; Han, Dohyun; Kwon, Oh-Seung
- Issue Date
- 2019-03
- Publisher
- John Wiley & Sons Ltd.
- Citation
- Proteomics, v.19, no.5
- Abstract
- Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS); its cause is unknown. To understand the pathogenesis of MS, researchers often use the experimental autoimmune encephalomyelitis (EAE) mouse model. Here, the aim is to build a proteome map of the biological changes that occur during MS at the major onset sites-the brain and the spinal cord. Quantitative proteome profiling is performed in five specific brain regions and the spinal cord of EAE and healthy mice with high-resolution mass spectrometry based on tandem mass tags. On average, 7400 proteins per region are quantified, with the most differentially expressed proteins in the spinal cord (1691), hippocampus (104), frontal cortex (83), cerebellum (63), brainstem (50), and caudate nucleus (41). Moreover, region-specific and commonly expressed proteins in each region are identified and bioinformatics analysis is performed. Pathway analysis reveals that protein clusters resemble their functions in disease pathogenesis (i.e., by inducing inflammatory responses, immune activation, and cell-cell adhesion). In conclusion, the study provides an understanding of the pathogenesis of MS in the EAE animal model. It is expected that the comprehensive proteome map of the brain and spinal cord can be used to identify biomarkers for the pathogenesis of MS.
- Keywords
- TISSUE TRANSGLUTAMINASE; EAE MODEL; MULTIPLE; DISEASE; PROTEINS; IDENTIFICATION; PATHOGENESIS; EXPRESSION; GENES; experimental autoimmune encephalomyelitis; multiple sclerosis; proteome map; quantitative proteomics
- ISSN
- 1615-9853
- URI
- https://pubs.kist.re.kr/handle/201004/120277
- DOI
- 10.1002/pmic.201800355
- Appears in Collections:
- KIST Article > 2019
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