RESUMO
MicroRNAs (miRNAs), in association with Argonaute (AGO) proteins, direct repression by pairing to sites within mRNAs. Compared to pairing preferences of the miRNA seed region (nucleotides 2-8), preferences of the miRNA 3' region are poorly understood, due to the sparsity of measured affinities for the many pairing possibilities. We used RNA bind-n-seq with purified AGO2-miRNA complexes to measure relative affinities of >1000 3'-pairing architectures for each miRNA. In some cases, optimal 3' pairing increased affinity by >500 fold. Some miRNAs had two high-affinity 3'-pairing modes-one of which included additional nucleotides bridging seed and 3' pairing to enable high-affinity pairing to miRNA nucleotide 11. The affinity of binding and the position of optimal pairing both tracked with the occurrence of G or oligo(G/C) nucleotides within the miRNA. These and other results advance understanding of miRNA targeting, providing insight into how optimal 3' pairing is determined for each miRNA.
Assuntos
MicroRNAs , Proteínas Argonautas/genética , Proteínas Argonautas/metabolismo , Sítios de Ligação , MicroRNAs/metabolismo , Nucleotídeos/metabolismo , RNA Mensageiro/metabolismoRESUMO
MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA 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 sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific 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.