A unifying model for microRNA-guided silencing of messenger RNAs

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Silencing by the miRNA-guided RNA induced silencing complex (miRISC) is dependent on Ago2-chaperoned base pairing between the miRNA 5′ seed (5′S) and a complementary sequence in the 3′ untranslated region of an mRNA. Prevailing mechanistic understanding posits that initial 5′S pairing can further allow functional base pair expansion into the 3′ non-seed (3′NS), while functionally distinct non-canonical pairing was reported between only the 3′NS and the mRNA coding sequence. We developed single-molecule kinetics through equilibrium Poisson sampling (SiMKEPS) to measure highly precise binding and dissociation rate constants of varying-length target sequences to 5′S and 3′NS in a paradigmatic miRISC isolated from human cells, revealing distinct stable states of miRISC with mutually exclusive 5′S and 3′NS pairing. Our data suggest conformational rearrangements of the Ago2-bound miRNA that regulate alternative 5′S-and 3′NS-driven target recognition. The resulting model reconciles previously disparate observations and deepens our acumen for successfully marshaling RNA silencing therapies.

In brief: In this study, Chatterjee et al. developed a technique to characterize and identify multiple conformations of the RNA-induced silencing complex (RISC) present in human cells. Two distinct pathways of target recognition through different RISC conformations reconcile previously observed divergent findings on the microRNA (miRNA) binding mechanism to messenger RNA (mRNA).

Highlights

  • SiMKEPS is developed to precisely measure binding and dissociation of miRISC:mRNA

  • Single human miRISC molecules exhibit mutually exclusive seed and non-seed pairing

  • SiMKEPS unveils conformational changes of single miRNAs bound to Ago2

  • Resulting model reconciles previously divergent findings, supporting RNAi therapies

Article activity feed