Structured RNA Contaminants in Bacterial Ribo-Seq

Abstract

Ribosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. However, Ribo-Seq signal can be observed across RNAs that one would not expect to be bound by ribosomes. For example, Escherichia coli Ribo-Seq libraries also capture reads from most noncoding RNAs (ncRNAs). While some of these ncRNAs may overlap coding regions, this alone does not explain the majority of observed signal across ncRNAs. These fragments of ncRNAs in Ribo-Seq data pass all size selection steps of the Ribo-Seq protocol and survive hours of micrococcal nuclease (MNase) treatment. In this work, we specifically focus on Ribo-Seq signal across ncRNAs and provide evidence to suggest that RNA structure, as opposed to ribosome binding, protects them from degradation and allows them to persist in the Ribo-Seq sequencing library preparation. By inspecting these "contaminant reads" in bacterial Ribo-Seq, we show that data previously disregarded in bacterial Ribo-Seq experiments may, in fact, be used to gain partial information regarding the in vivo secondary structure of ncRNAs.IMPORTANCE Structured ncRNAs are pivotal mediators of bioregulation in bacteria, and their functions are often reliant on their specific structures. Here, we first inspect Ribo-Seq reads across noncoding regions, identifying contaminant reads in these libraries. We observe that contaminant reads in bacterial Ribo-Seq experiments that are often disregarded, in fact, strongly overlap with structured regions of ncRNAs. We then perform several bioinformatic analyses to determine why these contaminant reads may persist in Ribo-Seq libraries. Finally, we highlight some structured RNA contaminants in Ribo-Seq and support the hypothesis that structures in the RNA protect them from MNase digestion. We conclude that researchers should be cautious when interpreting Ribo-Seq signal as coding without considering signal distribution. These findings also may enable 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 through the reanalysis of existing Ribo-Seq data sets.

Keywords: RNA structure; metagenomics; metatranscriptomics; microbiome.

FIG 1

Ribo-Seq fragmentation patterns of ssrS suggest that RNA secondary structures protect it from MNase. (A) Interactive Genome Browser (IGV) view of Ribo-Seq signal across ssrS. The black trace above the displayed genomic regions represents the relative coverage of each region by individual sequencing reads. The genes are shown in dark gray. Possible ORFs are shown in light gray. (B) Quantification of the 3′ and 5′ ends of Fremin et al. 2020 (8) Ribo-Seq reads mapping to ssrS in E. coli MG1655. Arrows indicate peaks in signal. (C) Quantification of the 3′ and 5′ ends of Li et al. 2014 (1) Ribo-Seq reads mapping to ssrS in E. coli MG1655. (D) Quantification of the 3′ and 5′ ends of Fremin et al. 2020 (8) MetaRibo-Seq reads mapping to ssrS in E. coli within a fecal sample. (E) Quantification of the 3′ and 5′ ends of Li et al. 2014 (1) RNA-Seq reads mapping to ssrS in E. coli MG1655. (F) Characterized structure of ssrS in E. coli. This structure diagram was created using data from previous work (11–13). Arrows indicate relative positions comparing line graphs (A to D) to this structure diagram.

FIG 2

MetaRibo-Seq signal across CRISPR arrays in two gut commensals suggests that secondary structures of direct repeats protect it from MNase. (A) Ribo-Seq signal across a CRISPR array containing 84 repeats, predicted by minCED (25). This is found in Ruminococcus sp. strain UNK.MGS-30. For reference, this was predicted from sample C in previous work (8). (B) Ribo-Seq signal across an 18-repeat CRISPR array in Ruminococcus lactaris, also predicted by minCED (25). For reference, this was predicted from sample A in previous work (8). Arrows indicate direct repeats.