| 1 | 基于STAT3/miRNA反馈环路探讨中医药促脑梗死后血管新生的调控机制显示文摘脑梗死是脑血流供应障碍引起脑组织缺血缺氧,出现相应神经功能症状的临床疾病,是最常见的脑血管病之一,严重威胁到人类健康,脑梗死的防治研究具有重要的社会意义。血管新生是脑梗死内科治疗的关键切入点,信号传导和转录激活子(STAT)/缺氧诱导因子-1(HIF-1)/血管内皮生长因子(VEGF)通路是介导脑梗死后血管新生的重要通路。本文以血管新生为切入点,以STAT/HIF-1/VEGF信号通路的上游分子STAT3,微小RNA(miRNA)为主要研究对象,综述芯片测序、实验研究、中医病机、中医治法的研究成果,结合'转录因子(TF)—miRNA'调控模式与'微观整体'思想,提出在中医基础理论指导下立方的中药复方和有效成分可能通过STAT3/miRNA反馈环路激活STAT/HIF-1/VEGF信号通路促脑梗死后血管新生,为中医药治疗脑梗死疾病提出了深层次的分子机制及新的方向。 | 杨仁义 周德生 傅馨莹 颜思阳 龚翠兰 马强 刘利娟 | 2020 | 中国实验方剂学杂志2020，26，20： | 10 |
| 2 | Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants显示文摘Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity. | Xuelei Lai Arnaud Stigliani Gilles Vachon Cristel Carles Cezary Smaczniak Chloe Zubieta Kerstin Kaufmann Francois Parcy | 2019 | Molecular Plant2019，12，6： | 2 |
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