A combined RNA-Seq and comparative genomics approach identifies 1,085 candidate structured RNAs expressed in human microbiomes

Fremin, Brayon J.; Bhatt, Ami S.

doi:10.1101/2020.03.31.018887

Cited by 5 publications

(8 citation statements)

References 45 publications

(48 reference statements)

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“…The in-house-generated data can be found under BioProject accession no. PRJNA510123 ( 8 , 17 ). Ribo-Seq and RNA-Seq for E. coli generated by Li et al in 2014 can be found under BioProject accession no.…”

Section: Methodsmentioning

confidence: 99%

Structured RNA Contaminants in Bacterial Ribo-Seq

Fremin

Bhatt

2020

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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.

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Section: Methodsmentioning

confidence: 99%

Structured RNA Contaminants in Bacterial Ribo-Seq

Fremin

Bhatt

2020

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“…Reads from all samples used are publicly available. The in-house generated data can be found under this accession: PRJNA510123 11,14 . Ribo-Seq and RNA-Seq for E. coli generated by Li et al, 2014 can be found under this bioproject: PRJNA232843 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Ribo-Seq has the advantage of capturing in vivo RNA structures, in high-throughput, and can immediately be applied to the vast existing datasets. Additionally, Ribo-Seq data can be leveraged to identify novel structured RNAs, many of which are yet to be discovered 14 .…”

Section: Observationmentioning

confidence: 99%

Repurposing Ribo-Seq to provide insights into structured RNAs

Fremin

Bhatt

2020

Preprint

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8Ribosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. 9 However, this method can enrich RNAs that are not bound by ribosomes, but rather, are 10 protected from degradation in another way. For example, Escherichia coli Ribo-Seq libraries 11 also capture reads from most non-coding RNAs (ncRNAs). These fragments of ncRNAs pass all 12 size selection steps of the Ribo-Seq protocol and survive hours of MNase treatment, presumably 13 without protection from the ribosome or other macromolecules or proteins. Since bacterial 14 ribosome profiling does not directly isolate ribosomes, but instead uses broad size range cutoffs 15 to fractionate actively translated RNAs, it is understandable that some ncRNAs are retained after 16 size selection. However, how these 'contaminants' survive MNase treatment is unclear. Through 17 analyzing metaRibo-Seq reads across ssrS, a well established structured RNA in E. coli, and 18 structured direct repeats from Clustered Regularly Interspaced Short Palindromic Repeats 19 (CRISPR) arrays in Ruminococcus lactaris, we observed that these RNAs are protected from 20 MNase treatment by virtue of their secondary structure. Therefore, large volumes of data 21 previously discarded as contaminants in bacterial Ribo-Seq experiments can, in fact, be used to 22 gain information regarding the in vivo secondary structure of ncRNAs, providing unique insight 23 into their native functional structures. 25Importance 26 We observe that 'contaminant' signals in bacterial Ribo-Seq experiments that are often 27 disregarded and discarded, in fact, strongly overlap with structured regions of ncRNAs. 28 Structured ncRNAs are pivotal mediators of bioregulation in bacteria and their functions are 29 often reliant on their specific structures. We present an approach to access important RNA 30 structural information through merely repurposing 'contaminant' signals in bacterial Ribo-Seq 31 experiments. This powerful approach enables us to partially resolve RNA structures, identify 32 novel structured RNAs, and elucidate RNA structure-function relationships in bacteria at a large-33 scale and in vivo. 34 35 Observation 36Ribosome profiling (Ribo-Seq) in bacteria is a method that enriches for ribosome-37 protected RNAs and therefore, enables the study of active translation events 1,2 . However, this 38 enrichment is not highly selective for ribosomes as relevant protocols do not specifically isolate 39 ribosomes, but rather select for them within an expected size range. Ribo-Seq is especially 40 challenging in bacteria because, unlike in yeast and eukaryotes, bacteria have a broad size 41 distribution of ribosome-protected footprints, ranging from 15-40 nucleotides 3 . The size range 42 that should be selected can vary across Ribo-Seq protocols; at present, most Ribo-Seq 43 experiments on bacteria target a size range of 15 to 45 nucleotides, as was used by Li et al, 44 2014 1 . Hence, bacterial ribosome profiling protocols must adopt less stringent size selection to 45 comprehensively...

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“…Using this basic assumption, comparative genomic analyses across groups of macroorganisms as diverse as Drosophila and mammals have yielded insight into novel proteins (Clark et al 2007;Lawrie & Petrov 2014). Now, recent efforts have been made to apply this approach to the microbiome (Fremin & Bhatt 2020). Specifically, Sberro et al (2019) recently performed a comparative analysis on shotgun microbiome metagenomic data and discovered thousands of novel small genes.…”

Section: ) Inference Of Functionalitymentioning

confidence: 99%

“…Coupled with the higher gene density in microorganisms due to overlapping open reading frames (Johnson & Chisholm 2004), this prediction suggests that the gut microbiome is a particularly apt system for researchers who wish to leverage statistical evidence provided by comparative population genomics to confirm the purported functionality of a given gene. Indeed, researchers are likely already acting on this prediction, as recent efforts combined comparative genomics with RNA-seq to identify ~2,000 novel structural RNAs in the microbiome (Fremin & Bhatt 2020). With hundreds of species harboring genomes with high gene density across billions of hosts, the gut microbiome is still very much a proverbial "gold rush" for the discovery and characterization of novel proteins and RNAs.…”

Section: ) Inference Of Functionalitymentioning

confidence: 99%

Comparative Population Genetics in the Human Gut Microbiome

Shoemaker¹,

Chen²,

Garud³

2021

Preprint

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The genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of co-existing species in this microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we re- analyzed patterns of purifying selection across ~40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.

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A combined RNA-Seq and comparative genomics approach identifies 1,085 candidate structured RNAs expressed in human microbiomes

Cited by 5 publications

References 45 publications

Structured RNA Contaminants in Bacterial Ribo-Seq

Structured RNA Contaminants in Bacterial Ribo-Seq

Repurposing Ribo-Seq to provide insights into structured RNAs

Comparative Population Genetics in the Human Gut Microbiome

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