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RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.

ABSTRACT: RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.

SUBMITTER: Wang J

PROVIDER: S-EPMC10516376 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.

Wang Jun J Horlacher Marc M Cheng Lixin L Winther Ole O

Briefings in bioinformatics 20230901 5

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as w ...[more]

PMID: 37466130

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Project description:BackgroundHuman angiotensin-converting enzyme 2 (ACE2), a type I transmembrane receptor physiologically acting as a carboxypeptidase enzyme within the renin-angiotensin system (RAS), is a critical mediator of infection by several severe acute respiratory syndrome (SARS) corona viruses. For instance, it has been demonstrated that ACE2 is the primary receptor for the SARS-CoV-2 entry to many human cells through binding to the viral spike S protein. Consequently, genetic variability in ACE2 gene has been suggested to contribute to the variable clinical manifestations in COVID-19. Many of those genetic variations result in missense variants within the amino acid sequence of ACE2. The potential effects of those variations on binding to the spike protein have been speculated and, in some cases, demonstrated experimentally. However, their effects on ACE2 protein folding, trafficking and subcellular targeting have not been established.ResultsIn this study we aimed to examine the potential effects of 28 missense variants (V801G, D785N, R768W, I753T, L731F, L731I, I727V, N720D, R710H, R708W, S692P, E668K, V658I, N638S, A627V, F592L, G575V, A501T, I468V, M383I, G173S, N159S, N149S, D38E, N33D, K26R, I21T, and S19P) distributed across the ACE2 receptor domains on its subcellular trafficking and targeting through combinatorial approach involving in silico analysis and experimental subcellular localization analysis. Our data show that none of the studied missense variants (including 3 variants predicted to be deleterious R768W, G575V, and G173S) has a significant effect on ACE2 intracellular trafficking and subcellular targeting to the plasma membrane.ConclusionAlthough the selected missense variants display no significant change in ACE2 trafficking and subcellular localization, this does not rule out their effect on viral susceptibility and severity. Further studies are required to investigate the effect of ACE2 variants on its expression, binding, and internalization which might explain the variable clinical manifestations associated with the infection.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Piero Carninci mailto:carninci@riken.jp). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows 5' cap analysis gene expression (CAGE) tags and clusters in RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in multiple cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). A CAGE cluster is a region of overlapping tags with an assigned value that represents the expression level. The data in this track were produced as part of the ENCODE Transcriptome Project. Release 2 has three new downloads only files per experiment (Clusters, TSS Gencode 7 and TSS HMM) and four new cell lines (A459, AG04450, BJ and SK-N-SH_RA). Release 1 on hg19 contained the original data on hg18 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeRikenCage) that was remapped and indicated in this release as Generation 0 since that data had no replicates. If there is both old and new generation data available for a particular experiment, only the new generation data is displayed and the older data is available for download. The new data for this track was done with a different process and has standard replicate numbers. The replicate labeling in the genome browser view is a counter indicating the total number of replicates submitted. The producing lab has replicate numbers that correspond to their internal bio-replicate numbering. Where these two numbering systems conflict, both are listed in the long label of the specific track. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). RNA molecules longer than 200 nt were isolated from each subcellular compartment and then were fractionated into polyA+ and polyA- fractions as described in these protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/general/rnaExtracts.txt). The CAGE tags were sequenced from the 5' ends of cap-trapped cDNAs produced using RIKEN CAGE technology (Kodzius et al. 2006; Valen et al. 2009). To create the tag, a linker was attached to the 5' end of polyA+ or polyA- reverse-transcribed cDNAs which were selected by cap trapping (Carninci et al. 1996). The first 27 bp of the cDNA were cleaved using class II restriction enzymes. A linker was then attached to the 3' end of the cDNA. After PCR amplification, the tags were sequenced (36 bp single reads) using Illumina's Genome analyzer. Tags were mapped to the human genome (hg19) using the program delve (T. Lassmann manuscript in preparation). Delve is a new probabilistic aligner focused on giving the best possible alignment of reads to a genome rather than focusing on speed. It calculates the mapping accuracy (probability of each alignment being true or not) for each alignment. There is no set limit on the number of errors allowed and therefore the mapping rate is commonly 100%. However, for analysis it is recommended to discard alignments with low mapping qualities. Exceptions to the above protocol are the polyA- RNA samples from K562 cytosol, K562 nucleus, and prostate whole cell which were sequenced using ABI SOLiD (http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html) technology. These reads were mapped using Bowtie using default parameters. Clusters were defined as regions of overlapping CAGE reads. The expression level was computed as the number of reads making up the cluster, divided by the total number of reads sequenced, times 1 million.

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RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.

Publications

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.

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