MapToCleave: high-throughput profiling of microRNA biogenesis in living cells

Abstract

Previous large-scale studies have uncovered many features that determine the processing of microRNA (miRNA) precursors, however, they have been conducted in vitro. Here we introduce MapToCleave, a new method to simultaneously profile processing of thousands of distinct RNA structures in living cells. Our new in cell method captures essentially all the biogenesis features that have been discovered through near two decades of in vitro studies - providing support for both approaches. We find that miRNA precursors with a stable lower basal stem are more efficiently processed and also have higher expression in vivo in tissues from twenty animal species. We systematically compare the importance of known and novel sequence and structural features and test biogenesis of miRNA precursors from ten animal and plant species in human cells. Lastly, we provide evidence that the GHG motif better predicts processing when defined as a structure rather than sequence motif, consistent with recent cryo-EM studies. In summary, we apply a new screening assay in living cells to reveal the importance of lower basal stem stability for miRNA processing and in vivo expression.

Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine

“…Importantly, each microRNA gene and family is associated with a detailed phylogenetic reconstruction of the evolutionary node of origin and estimated age. This dataset, hence, represents a starting point to better understand features of microRNAs (Kang et al, 2021) and to generate better tools for the prediction of microRNAs.…”

Rapid Adaptation of Cellular Metabolic Rate to the MicroRNA Complements of Mammals and its Relevance to the Evolution of Endothermy

“…However, these omissions were clearly technical artifacts, since losses of microRNA families have rarely been observed in well-annotated repertoires (Tarver et al, 2018). We and others have recently successfully employed MirGeneDB complements in a range of comparative (Fromm et al, 2021; Hu et al, 2021), phylogenetic (Ma et al, 2021; Rosani et al, 2021) and evolutionary studies (Peterson et al, 2022), as a validation cohort for experimental findings (Kang et al, 2021; Baronti et al, 2020), and as a standard reference for the development of novel bioinformatics tools (https://github.com/sinanugur/MirMachine).…”