| 研究課題/領域番号 |
19K17662
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| 研究機関 | 群馬大学 |
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研究代表者 |
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| 研究期間 (年度) |
2019-04-01 – 2021-03-31
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| キーワード | 喘息 / メタボロミクス / asthma / metabolomics |
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| 研究実績の概要 |
1. Optimized high-throughput urine sample preparation platform using 96-well plates. Measurement of urine specific gravity has been automated improving the throughput and increasing precision. Sample preparation has been optimized using liquid handler (Agilent Bravo).
2. Optimized two LC-MS HILIC chromatography methods covering a wide range of polar urinary metabolites. Method for constructing chemical standard libraries from all ion fragmentation data published (Tada et al 2019) and libraries expanded to contain urinary metabolites. Currently our HILIC libraries contain >400 compounds including asthma drugs and metabolites of interest.
3. Sample preparation workflow and LC-MS methods optimized and tested in several sample sets. Internal standard CVs (coefficients of variation) in quality control samples <10% and in study samples <15% showing that methods are robust. A panel of parameters established to monitor LC-MS data quality.
4. Data processing workflow established in MS-DIAL software enabling confident identification of metabolites using libraries as well as investigation of unidentified peaks.
5. Main asthma longitudinal cohort samples (>800) been randomized and prepared for measurements.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Basically, the project is progressing following the initial plan. Untargeted metabolomics workflow and methods have been optimized and characterized. Constructed chemical standards libraries including urinary metabolites, and developed data processing and curation workflow in MS-DIAL. Main asthma longitudinal cohort (>800 samples) measurements and analysis ongoing.
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| 今後の研究の推進方策 |
1. Processing and comprehensive annotation of metabolomics data from the main longitudinal cohort. Acquired data will be processed and curated MS-DIAL software. Metabolites will be identified using an in-house libraries as well as external databases. Batch signal drifts will be corrected using algorithms developed by our collaborators at Edith Cowan University in Australia.
2. Longitudinal metabolomics data analysis and integration with other datasets will be performed in collaboration with University of Amsterdam.
3. Confirmation and exploration of metabolites of interest will be performed in other respiratory longitudinal sample sets data available in the laboratory.
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