In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 41 ( 2016-10-11), p. 11591-11596
Abstract:
The functional annotation of transcriptomes and identification of noncoding RNA (ncRNA) classes has been greatly facilitated by the advent of next-generation RNA sequencing which, by reading the nucleotide order of transcripts, theoretically allows the rapid profiling of all transcripts in a cell. However, primary sequence per se is a poor predictor of function, as ncRNAs dramatically vary in length and structure and often lack identifiable motifs. Therefore, to visualize an informative RNA landscape of organisms with potentially new RNA biology that are emerging from microbiome and environmental studies requires the use of more functionally relevant criteria. One such criterion is the association of RNAs with functionally important cognate RNA-binding proteins. Here we analyze the full ensemble of cellular RNAs using gradient profiling by sequencing (Grad-seq) in the bacterial pathogen Salmonella enterica , partitioning its coding and noncoding transcripts based on their network of RNA–protein interactions. In addition to capturing established RNA classes based on their biochemical profiles, the Grad-seq approach enabled the discovery of an overlooked large collective of structured small RNAs that form stable complexes with the conserved protein ProQ. We show that ProQ is an abundant RNA-binding protein with a wide range of ligands and a global influence on Salmonella gene expression. Given its generic ability to chart a functional RNA landscape irrespective of transcript length and sequence diversity, Grad-seq promises to define functional RNA classes and major RNA-binding proteins in both model species and genetically intractable organisms.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1609981113
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2016
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12
