bioRxivMicrobiologyPreprint
Repurposing Ribo-Seq to provide insights into structured RNAs
Abstract
Ribosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. However, this method can enrich RNAs that are not bound by ribosomes, but rather, are protected from degradation in another way. For example, Escherichia coli Ribo-Seq libraries also capture reads from most non-coding RNAs (ncRNAs). These fragments of ncRNAs pass all size selection steps of the Ribo-Seq protocol and survive hours of MNase treatment, presumably without protection from the ribosome or other macromolecules or proteins. Since bacterial ribosome profiling does not directly isolate ribosomes, but instead uses broad size range cutoffs to fractionate actively translated RNAs, it is understandable that some ncRNAs are retained after size selection. However, how these contaminants survive MNase treatment is unclear. Through analyzing metaRibo-Seq reads across ssrS, a well established structured RNA in E. coli, and structured direct repeats from Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) arrays in Ruminococcus lactaris, we observed that these RNAs are protected from MNase treatment by virtue of their secondary structure. Therefore, large volumes of data previously discarded as contaminants in bacterial Ribo-Seq experiments can, in fact, be used to gain information regarding the in vivo secondary structure of ncRNAs, providing unique insight into their native functional structures. ImportanceWe observe that contaminant signals in bacterial Ribo-Seq experiments that are often disregarded and discarded, in fact, strongly overlap with structured regions of ncRNAs. Structured ncRNAs are pivotal mediators of bioregulation in bacteria and their functions are often reliant on their specific structures. We present an approach to access important RNA structural information through merely repurposing contaminant signals in bacterial Ribo-Seq experiments. This powerful approach enables us to partially resolve RNA structures, identify novel structured RNAs, and elucidate RNA structure-function relationships in bacteria at a large-scale and in vivo.
- organism: E. coli
- organism: rat
- assay: Ribo-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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