Dataset Information

SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in transcriptomes of virus and host

ABSTRACT: RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists to identify the proteins bound to specific regions in endogenous RNAs in an unbiased fashion. Here, we develop SHIFTR (Selective RNase-H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected RNA regions in live cells. Compared to state-of-the-art RNA antisense purification techniques, SHIFTR is superior in accuracy, captures close to zero background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA biotypes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with multiplexed RNA interactome release in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5ÃŠÂ¹ and 3ÃŠÂ¹-terminal regions of the authentic SARS-CoV-2 RNA genome in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type, which has the potential to revolutionize our understanding of the transcriptomes of pathogens and their hosts.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell

SUBMITTER: Frank Stein

LAB HEAD: Mathias Munschauer

PROVIDER: PXD044722 | Pride | 2024-01-18

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 102

Similar Datasets

Project description:Proteomics, the temporal study of proteins expressed by an organism, is a powerful technique that can reveal how organisms respond to biological perturbations, such as disease and environmental stress. Yet, the use of proteomics for addressing ecological questions has been limited, partly due to inadequate protocols for the sampling and preparation of animal tissues from the field. Although RNAlater is an ideal alternative to freezing for tissue preservation in transcriptomics studies, its suitability for the field could be more broadly examined. Moreover, existing protocols require samples to be preserved immediately to maintain protein integrity, yet the effects of delays in preservation on proteomic analyses have not been thoroughly tested. Hence, we optimised a proteomic workflow for wild-caught samples. First, we conducted a preliminary in-lab test using SDS-PAGE analysis on aquaria-reared Octopus berrima confirming that RNAlater can effectively preserve proteins up to 6 h after incubation, supporting its use in the field. Subsequently, we collected arm tips from wild-caught Octopus berrima and preserved them in homemade RNAlater immediately, 3 h, and 6 h after euthanasia. Processed tissue samples were analysed by liquid chromatography tandem mass spectrometry to ascertain protein differences between time delay in tissue preservation, as well as the influence of sex, tissue type, and tissue homogenisation methods. Over 3500 proteins were identified from all tissues, with bioinformatic analysis revealing protein abundances were largely consistent regardless of sample treatment. However, nearly 10 % additional proteins were detected from tissues homogenised with metal beads compared to liquid nitrogen methods, indicating the beads were more efficient at extracting proteins. Our optimised workflow demonstrates that sampling non-model organisms from remote field sites is achievable and can facilitate extensive proteomic coverage without compromising protein integrity.

			Action	DRS
	F111914.dat	Other
	F112244.dat	Other
	F117176.dat	Other
	F117179.dat	Other
	F117180.dat	Other

Dataset Information

SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in transcriptomes of virus and host

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets