新CRISPR技術可用於研究增強子-啟動子調控模型
作者:
小柯機器人發布時間:2019/12/2 13:48:30
2019年11月29日,《自然—遺傳學》雜誌發表了MIT-哈佛大學博德研究所Jesse M. Engreitz、Eric S. Lander等研究人員的合作成果。他們利用新研發的技術,建立了數千種CRISPR擾動引起的增強子-啟動子調控的接觸活動模型。
研究人員開發了一種叫做CRISPRi-FlowFISH實驗方法來打亂基因組中的增強子,並將其用於測試30個基因中的超過3500個潛在增強子與基因的連接。
研究人員發現一個簡單的接觸活動模型在預測CRISPR數據集中的複雜連接方面遠勝過先前的方法。通過接觸活動模型,研究人員可以在染色質狀態測量的基礎上,構建特定細胞類型中增強子與基因的連接的全基因組圖譜。
CRISPRi-FlowFISH和接觸活動模型共同提供了一種系統的方法來定位和預測哪些增強子調節哪些基因,並將有助於解釋非編碼基因組中數千種疾病風險變體的功能。
據了解,人類基因組中的增強子元件可控制基因在特定細胞類型中的表達方式,並包含成千上萬的遺傳變異,這些變異會影響常見疾病。然而,人們仍然不知道增強子如何調控特定基因,並且缺乏通用規則來預測不同細胞類型的增強子與基因的連接。
附:英文原文
Title: Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations
Author: Charles P. Fulco, Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, Rockwell Anyoha, Benjamin R. Doughty, Tejal A. Patwardhan, Tung H. Nguyen, Michael Kane, Elizabeth M. Perez, Neva C. Durand, Caleb A. Lareau, Elena K. Stamenova, Erez Lieberman Aiden, Eric S. Lander, Jesse M. Engreitz
Issue&Volume: 2019-11-29
Abstract: Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1,2,3,4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test>3,500potential enhancer–gene connections for 30genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
DOI: 10.1038/s41588-019-0538-0
Source: https://www.nature.com/articles/s41588-019-0538-0