演題詳細

一般口演 / Oral Session

一般口演 18 (Oral Session 18) :エピジェネティクス

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日程
2013年10月11日(金)
時間
15:25 - 16:25
会場
第3会場 / Room No.3 (さっぽろ芸文館 3F 蓬莱)
座長・司会
瀧原 義宏 (Yoshihiro Takihara):1
1:広島大学原爆放射線医科学研究所 幹細胞機能学研究分野
 
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A novel approach to identify pathogenic mutations based on epigenetic information

演題番号 : OS-1-92

小田原 淳 (Jun Odawara):1,2、湯田 淳一朗 (Junichiro Yuda):1、宮脇 恒太 (Kohta Miyawaki):1、林 正康 (Masayasu Hayashi):1、前原 一満 (Kazumitsu Maehara):2、河野 健太郎 (Kentaro Kohno):1、島 隆宏 (Takahiro Shima):1、長崎 正朗 (Masao Nagasaki):3、大川 恭行 (Yasuyuki Ohkawa):2、赤司 浩一 (Koichi Akashi):1

1:Dep. of Medecine and Biosystemic Sciences, Kyushu Univ., Japan、2:Dep. of Advanced Medical Initiatives, Kyushu Univ., Japan、3:Div. of Biomedical Information Analysis, Dep. of Integrative Genomics, Tohoku Univ., Japan

 

To explore single nucleotide variants (SNVs) as candidates for hematological malignancies such as AML, it is common to compare identified variants with those present in known SNV databases. Here we propose a new approach to narrow down mutations involved in leukemogenesis. We have already reported that the distribution of histone variant H3.3 has a significant impact on cellular differentiation. We have further demonstrated that the deposition of H3.3 on hematopoietic genes occurs specifically in mouse hematopoietic stem/progenitor cells prior to differentiation. However, in leukemic cells, this selective H3.3 incorporation was diminished. To identify the factors that induce H3.3 incorporation during hematopoiesis, we used the public database provided by the ENCODE project. In this database, more than 2,000 ChIPseq data sets are already available. We have constructed a system to manage all these datasets and to explore the factors closely related to H3.3 comprehensively. Interestingly, some of the correlations between our H3.3 ChIPSeq data and the ENCODE data were significantly different between AML cells and normal cells. Using this approach, we identified some of hematopoietic transcription factors such as CEBPB and YY1 were associated with the decrease in H3.3 incorporation in AML. In addition, by comparing these transcription factors and SNVs obtained from Exome-Sequence, we clarified links between these transcription factors and particular SNVs in common pathways. Our approach has the potential to extract oncogenic variants from many SNVs using H3.3 ChIPSeq data.

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