| 研究課題/領域番号 |
19K08874
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| 研究機関 | 国立研究開発法人理化学研究所 |
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研究代表者 |
ARNER ERIK 国立研究開発法人理化学研究所, 生命医科学研究センター, チームリーダー (20571839)
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| 研究分担者 |
萩原 將太郎 東京女子医科大学, 医学部, 講師 (50306635)
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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| キーワード | Transcriptome analysis |
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| 研究実績の概要 |
We collected samples of NK cells from the active multiple myeloma patients and inactive patients. We collected 12 of the active myeloma patient samples, and 14 of the inactive myeloma. Out of the 12 active multiple samples, 4 were from fresh multiple myeloma cases and 8 were from relapsed cases. Samples from 12 healthy donors were also collected.
Transcriptome analysis was carried out by performing CAGE library preparation, sequencing and initial analysis. 8 samples failed quality control due to having too few mapped CAGE reads (a minimum of 1 million reads was required) and were discarded from further analysis. CAGE transcriptome data was then subjected to several initial analyses: hierachical clustering, variable genes analysis, principal component analysis, and motif activity response analysis. the principal component analysis showed considerable individual variation between the samples, but also that there were differences between samples from multimple myeloma patients and healthy controls. Results from motif activity response analysis were inconclusive and it was decided that further analysis is needed to understand better the transcriptional regulation behind the difference in gene expression between multiple myeloma cases and healthy controls.
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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
Once all samples had been collected, transcriptome analysis proceeded as expected. We will proceed with deeper analysis and follow-up experiments in FY2021.
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| 今後の研究の推進方策 |
Using the CAGE data, we will attempt to reconstruct the NK cell transcriptional regulatory network (TRN) that governs multiple myeloma and is modulated by sSLAMF7.
For identifying the TRN, promoters of differentially expressed genes will be examined for the occurrence of transcription factor binding sites(TFBSs), which makes it possible to infer regulatory edges in the regulatory network. Candidate regulatory interactions will be overlaid with complementary data where possible, for example publicly available ChIP-seq data.
For validation, transcription factors will be chosen and their predicted targets will be investigated. Interactions will be assessed using siRNA knock-down of TFs followed by qRT-PCR of their putative targets.
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| 次年度使用額が生じた理由 |
The remaining budget will be used for follow-up experiments and validation experiments in FY2021.
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