The biochemical basis of microRNA targeting efficacy [3]. The biochemical basis of microRNA targeting efficacy [3]

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. Overall design: mRNA sequencing of HeLa cells serially transfected with synthetic miRNA duplexes and a massively parallel reporter library. This study consists of 12 libraries with 6 different miRNA transfected in replicate.

微小RNA（microRNAs，miRNAs）可在Argonaute蛋白（Argonaute proteins）的介导下，引导对信使RNA（mRNA）靶标的抑制过程。尽管已有多种方法为靶标识别研究提供了重要见解，但目前miRNA-靶标结合亲和力的测量数据较为稀缺，这限制了学界对靶向效能的理解与预测能力。本研究对RNA结合测序（RNA bind-n-seq）技术进行适配改造，实现了对Argonaute-miRNA复合物与所有长度≤12核苷酸序列之间相对结合亲和力的检测。该技术揭示了三类关键发现：每种miRNA所特有的非经典靶标位点、经典靶标位点亲和力的miRNA特异性差异，以及每个位点侧翼双核苷酸对结合亲和力高达100倍的调控效应。基于上述数据，我们构建了miRNA介导抑制过程的生化模型，并通过卷积神经网络（convolutional neural network）将该模型推广至所有miRNA序列。该模型显著提升了细胞内抑制效应的预测精度，为将miRNA定量整合至基因调控网络提供了生化层面的理论依据。总体实验设计：对依次转染合成miRNA双链体与大规模平行报告基因文库的海拉（HeLa）细胞进行mRNA测序。本研究共包含12个文库，对应6种不同miRNA的重复转染实验。