RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing.
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ABSTRACT:
SUBMITTER: Wu KE
PROVIDER: S-EPMC7297119 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The SARS-CoV-2 coronavirus is driving a global pandemic, but its biological mechanisms are less well understood. SARS-CoV-2 is an RNA virus whose multiple genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery, located across distinct cytotopic locations. Subcellular localization of its viral RNA could play important roles in viral replication and host antiviral immune response. Here we perform computational modeling of SARS-CoV-2 viral RNA localization across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes to the human transcriptome and other coronaviruses and perform systematic sub-sequence analyses to identify the responsible signals. Using state-of-the-art machine learning models, we predict that the SARS-CoV-2 RNA genome and all sgRNAs are enriched in the host mitochondrial matrix and nucleolus. The 5' and 3' viral untranslated regions possess the strongest and most distinct localization signals. We discuss the mitochondrial localization signal in relation to the formation of double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus.
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Project description:The RNase III enzyme Drosha is a key factor in microRNA (miRNA) biogenesis and as such indispensable for cellular homeostasis and developmental processes. Together with its co-factor DGCR8, it converts the primary transcript (pri-miRNA) into the precursor hairpin (pre-miRNA) in the nucleus. While the middle and the C-terminal domain are crucial for pri-miRNA processing and DGCR8 binding, the function of the N-terminus remains cryptic. Different studies have linked this region to the subcellular localization of Drosha, stabilization and response to stress. In this study, we identify alternatively spliced Drosha transcripts that are devoid of a part of the arginine/serine-rich (RS-rich) domain and expressed in a large set of human cells. In contrast to their expected habitation, we find two isoforms also present in the cytoplasm, while the other two isoforms reside exclusively in the nucleus. Their processing activity for pri-miRNAs and the binding to co-factors remains unaltered. In multiple cell lines, the endogenous mRNA expression of the Drosha isoforms correlates with the localization of endogenous Drosha proteins. The pri-miRNA processing efficiency is not significantly different between groups of cells with or without cytoplasmic Drosha expression. In summary, we discovered novel isoforms of Drosha with differential subcellular localization pointing toward additional layers of complexity in the regulation of its activity.
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Project description:Eukaryotes and bacteria regulate the activity of some proteins by localizing them to discrete subcellular structures, and eukaryotes localize some RNAs for the same purpose. To explore whether bacteria also spatially regulate RNAs, the localization of tmRNA was determined using fluorescence in situ hybridization. tmRNA is a small regulatory RNA that is ubiquitous in bacteria and that interacts with translating ribosomes in a reaction known as trans-translation. In Caulobacter crescentus, tmRNA was localized in a cell-cycle-dependent manner. In G(1)-phase cells, tmRNA was found in regularly spaced foci indicative of a helix-like structure. After initiation of DNA replication, most of the tmRNA was degraded, and the remaining molecules were spread throughout the cytoplasm. Immunofluorescence assays showed that SmpB, a protein that binds tightly to tmRNA, was colocalized with tmRNA in the helix-like pattern. RNase R, the nuclease that degrades tmRNA, was localized in a helix-like pattern that was separate from the SmpB-tmRNA complex. These results suggest a model in which tmRNA-SmpB is localized to sequester tmRNA from RNase R, and localization might also regulate tmRNA-SmpB interactions with ribosomes.
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Project description:Epithelial-Splicing-Regulatory-Protein 1 (Esrp1) is a cell-type specific RNA-binding protein (RBP) that is essential for mammalian development through maintenance of epithelial cell properties including barrier function. Esrp1 also regulates splicing during the epithelial to mesenchymal transition (EMT). It contains three highly conserved RNA recognition motifs (RRMs) in the absence of other clearly defined protein domains. Esrp1 itself is also alternatively spliced to produce multiple protein isoforms. Here we determined that two competing alternative 5' splice sites in exon 12 yield Esrp1 isoforms with differential nucleocytoplasmic localization. We carried out a detailed characterization of the Esrp1 peptide that is sufficient to confer nuclear localization. Furthermore, we identified splice variants encoding distinct nuclear and cytoplasmic isoforms of fusilli, the D. Melanogaster Esrp1 ortholog. Our observations demonstrate that the production of both nuclear and cytoplasmic Esrp1 isoforms through alternative splicing is phylogenetically conserved; strongly suggesting it is biologically significant. Thus, while previous studies have described extensive regulation by nuclear Esrp1 to promote epithelial specific splicing, it will be of great interest to study the contribution of cytoplasmic Esrp1 in maintenance of epithelial cell functions.
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Project description:The protozoan parasite Trypanosoma brucei is the causative agent of the cattle disease Nagana and human African sleeping sickness. Glycoproteins play key roles in the parasite's survival and infectivity, and the de novo biosyntheses of the sugar nucleotides UDP-galactose (UDP-Gal), UDP-N-acetylglucosamine, and GDP-fucose have been shown to be essential for their growth. The only route to UDP-Gal in T. brucei is through the epimerization of UDP-glucose (UDP-Glc) by UDP-Glc 4'-epimerase. UDP-Glc is also the glucosyl donor for the unfolded glycoprotein glucosyltransferase (UGGT) involved in glycoprotein quality control in the endoplasmic reticulum and is the presumed donor for the synthesis of base J (β-D-glucosylhydroxymethyluracil), a rare deoxynucleotide found in telomere-proximal DNA in the bloodstream form of T. brucei. Considering that UDP-Glc plays such a central role in carbohydrate metabolism, we decided to characterize UDP-Glc biosynthesis in T. brucei. We identified and characterized the parasite UDP-glucose pyrophosphorylase (TbUGP), responsible for the formation of UDP-Glc from glucose-1-phosphate and UTP, and localized the enzyme to the peroxisome-like glycosome organelles of the parasite. Recombinant TbUGP was shown to be enzymatically active and specific for glucose-1-phosphate. The high-resolution crystal structure was also solved, providing a framework for the design of potential inhibitors against the parasite enzyme.
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Project description:BACKGROUND: Protein subcellular localization is a central problem in understanding cell biology and has been the focus of intense research. In order to predict localization from amino acid sequence a myriad of features have been tried: including amino acid composition, sequence similarity, the presence of certain motifs or domains, and many others. Surprisingly, sequence conservation of sorting motifs has not yet been employed, despite its extensive use for tasks such as the prediction of transcription factor binding sites. RESULTS: Here, we flip the problem around, and present a proof of concept for the idea that the lack of sequence conservation can be a novel feature for localization prediction. We show that for yeast, mammal and plant datasets, evolutionary sequence divergence alone has significant power to identify sequences with N-terminal sorting sequences. Moreover sequence divergence is nearly as effective when computed on automatically defined ortholog sets as on hand curated ones. Unfortunately, sequence divergence did not necessarily increase classification performance when combined with some traditional sequence features such as amino acid composition. However a post-hoc analysis of the proteins in which sequence divergence changes the prediction yielded some proteins with atypical (i.e. not MPP-cleaved) matrix targeting signals as well as a few misannotations. CONCLUSION: We report the results of the first quantitative study of the effectiveness of evolutionary sequence divergence as a feature for protein subcellular localization prediction. We show that divergence is indeed useful for prediction, but it is not trivial to improve overall accuracy simply by adding this feature to classical sequence features. Nevertheless we argue that sequence divergence is a promising feature and show anecdotal examples in which it succeeds where other features fail.
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Project description:SARS-CoV-2 genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery. Subcellular localization of its viral RNA could, thus, play important roles in viral replication and host antiviral immune response. We perform computational modeling of SARS-CoV-2 viral RNA subcellular residency across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes with the human transcriptome and other coronaviruses. We predict the SARS-CoV-2 RNA genome and sgRNAs to be enriched toward the host mitochondrial matrix and nucleolus, and that the 5' and 3' viral untranslated regions contain the strongest, most distinct localization signals. We interpret the mitochondrial residency signal as an indicator of intracellular RNA trafficking with respect to double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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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.
2011-12-16 | E-GEOD-34448 | biostudies-arrayexpress
Project description:We introduce APEX-seq, a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome as a resource, revealing extensive patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.
| S-EPMC6786773 | biostudies-literature
Project description:RNA localization is involved in multiple biological processes. Recent advances in subcellular fractionation-based sequencing approaches uncovered localization pattern on a global scale. Most of existing methods adopt relative localization ratios (such as ratios of separately normalized transcripts per millions of different subcellular fractions without considering the difference in total RNA abundances in different fractions), however, absolute ratios may yield different results on the preference to different cellular compartment. Experimentally, adding external Spike-in RNAs to different fractionation can be used to obtain absolute ratios. In addition, a spike-in independent computational approach based on multiple linear regression model can also be used. However, currently, no custom tool is available. To solve this problem, we developed a method called subcellular fraction abundance estimator to correctly estimate relative RNA abundances of different subcellular fractionations. The ratios estimated by our method were consistent with existing reports. By applying the estimated ratios for different fractions, we explored the RNA localization pattern in cell lines and also predicted RBP motifs that were associated with different localization patterns. In addition, we showed that different isoforms of same genes could exhibit distinct localization patterns. To conclude, we believed our tool will facilitate future subcellular fractionation-related sequencing study to explore the function of RNA localization in various biological problems.
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