| Project/Area Number |
21K15509
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 50010:Tumor biology-related
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| Research Institution | Kumamoto University |
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Principal Investigator |
CHANG CHIHHSIANG 熊本大学, 大学院生命科学研究部(医), 特別研究員 (40898805)
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| Project Period (FY) |
2021-04-01 – 2022-03-31
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| Project Status |
Discontinued (Fiscal Year 2021)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2022: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2021: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | proteomics / proteases / cancer stem cell / phosphoproteomics / N-terminomics / glioma stem cell |
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| Outline of Research at the Start |
Glioma stem cells (GSCs) are considered responsible for the therapeutic resistance and recurrence of malignant glioma. GSC clones having the potential to differentiate into malignant gliomas, were established and subjected to mass spectrometry(MS)-based proteomics in my present laboratory. We found that the kinases and proteases are uniquely regulated in GSCs during their differentiation. In this study, a unique integration of of phosphoproteomics and N-terminomics together with global proteomics, will be established and optimized for the crosstalk network screening in the clone of GSCs.
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| Outline of Annual Research Achievements |
The six GSC clones established from GBM cell lines were used for profiling their global proteome by MS-based proteomics. We firstly examined the identified number of proteins in urea or phase transfer surfactant-aided (PTS) trypsin digestion and concluded that the PTS digestion preformed the better identification for both proteins as well as peptides. Second, we applied the PTS digestion to all six GSCs cell lines. For each GSCs cell line, three time points were applied for protein extraction in the serum-induced differentiation or stem cell maintenance state, respectively. Third, all of samples were analyzed by reverse-phase-nanoLC/MS/MS, and the MS dataset were processed via MaxQuant followed by Persues. From MS-based proteomics, we have identified 7350 proteins and obtained 5329 quantified proteins. There are 224 were proteases such as ADAMs, MMPs, Caspases and Calpains, and these protease expressions were correlated with the several kinase networks such as TKR-MAPK-c-myc-CD44 etc. In conclusion, the results of this study provide an in-depth dynamic profiling of more than 200 proteases, and the next step is to classify the key regulators of proteases during differentiation.
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