美国怀特黑德生物医学研究所David P. Bartel研究团队对microRNA靶向效率的生化基础进行了解析。相关论文2019年12月5日在线发表于国际学术期刊《科学》。

研究人员对RNA bind-n-seq进行了调整，以能够测量Argonaute–miRNA复合物与所有≤12个核苷酸序列之间的相对结合亲和力。该方法揭示了每个miRNA特有的非经典靶位点、经典靶位点亲和力中miRNA特异性差异以及位于每个位点两侧的二核苷酸的100倍影响。这些数据使得能够构建miRNA介导的抑制的生化模型，并使用卷积神经网络将其扩展到所有miRNA序列。该模型大大改善了对细胞抑制的预测，从而为将miRNA定量整合到基因调控网络中提供了生化基础。

据了解，miRNA在Argonaute蛋白质内起作用，以介导mRNA靶标的抑制。尽管各种方法已为靶标识别提供了见识，但miRNA与靶标亲和力测量的稀疏性限制了对靶标功效的理解和预测。

附：英文原文

Title: The biochemical basis of microRNA targeting efficacy

Author: Sean E. McGeary, Kathy S. Lin, Charlie Y. Shi, Thy Pham, Namita Bisaria, Gina M. Kelley, David P. Bartel

Issue&Volume: 2019/12/05

Abstract: MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

DOI: 10.1126/science.aav1741

Source: https://science.sciencemag.org/content/early/2019/12/04/science.aav1741