Differences in Vaccine and SARS-CoV-2 Replication Derived mRNA: Implications for Cell Biology and Future Disease

Codon optimization describes the process used to increase protein production by use of alternative but synonymous codon changes. In SARS-CoV-2 mRNA vaccines codon optimizations can result in differential secondary conformations that inevitably affect a protein’s function with significant consequences to the cell. Importantly, when codon optimization increases the GC content of synthetic mRNAs, there can be an inevitable enrichment of G-quartets which potentially form G-quadruplex structures. The emerging G-quadruplexes are favorable binding sites of RNA binding proteins like helicases that inevitably affect epigenetic reprogramming of the cell by altering transcription, translation and replication. In this study, we performed a RNAfold analysis to investigate alterations in secondary structures of mRNAs in SARS-CoV-2 vaccines due to codon optimization. We show a significant increase in the GC content of mRNAs in vaccines as compared to native SARS-CoV-2 RNA sequences encoding the spike protein. As the GC enrichment leads to more G-quadruplex structure formations, these may contribute to potential pathological processes initiated by SARS-CoV-2 molecular vaccination.

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FMRP promotes RNA localization to neuronal projections through interactions between its RGG domain and G-quadruplex RNA sequences

eLife ◽

10.7554/elife.52621 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 6

Author(s):

Raeann Goering ◽

Laura I Hudish ◽

Bryan B Guzman ◽

Nisha Raj ◽

Gary J Bassell ◽

...

Keyword(s):

Fragile X Syndrome ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Fragile X ◽

Rna Localization ◽

Null Mouse ◽

Rna Sequences ◽

Rna Molecules ◽

G Quadruplex

The sorting of RNA molecules to subcellular locations facilitates the activity of spatially restricted processes. We have analyzed subcellular transcriptomes of FMRP-null mouse neuronal cells to identify transcripts that depend on FMRP for efficient transport to neurites. We found that these transcripts contain an enrichment of G-quadruplex sequences in their 3′ UTRs, suggesting that FMRP recognizes them to promote RNA localization. We observed similar results in neurons derived from Fragile X Syndrome patients. We identified the RGG domain of FMRP as important for binding G-quadruplexes and the transport of G-quadruplex-containing transcripts. Finally, we found that the translation and localization targets of FMRP were distinct and that an FMRP mutant that is unable to bind ribosomes still promoted localization of G-quadruplex-containing messages. This suggests that these two regulatory modes of FMRP may be functionally separated. These results provide a framework for the elucidation of similar mechanisms governed by other RNA-binding proteins.

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Regulation and Function of RNA Pseudouridylation in Human Cells

Annual Review of Genetics ◽

10.1146/annurev-genet-112618-043830 ◽

2020 ◽

Vol 54 (1) ◽

pp. 309-336

Author(s):

Erin K. Borchardt ◽

Nicole M. Martinez ◽

Wendy V. Gilbert

Keyword(s):

Messenger Rna ◽

Noncoding Rna ◽

Protein Production ◽

Rna Binding ◽

Rna Binding Proteins ◽

Human Cells ◽

Rna Sequences ◽

Rna Targets ◽

Pseudouridine Synthases ◽

And Function

Recent advances in pseudouridine detection reveal a complex pseudouridine landscape that includes messenger RNA and diverse classes of noncoding RNA in human cells. The known molecular functions of pseudouridine, which include stabilizing RNA conformations and destabilizing interactions with varied RNA-binding proteins, suggest that RNA pseudouridylation could have widespread effects on RNA metabolism and gene expression. Here, we emphasize how much remains to be learned about the RNA targets of human pseudouridine synthases, their basis for recognizing distinct RNA sequences, and the mechanisms responsible for regulated RNA pseudouridylation. We also examine the roles of noncoding RNA pseudouridylation in splicing and translation and point out the potential effects of mRNA pseudouridylation on protein production, including in the context of therapeutic mRNAs.

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Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

10.1101/146175 ◽

2017 ◽

Cited By ~ 3

Author(s):

Xiaoyong Pan ◽

Peter Rijnbeek ◽

Junchi Yan ◽

Hong-Bin Shen

Keyword(s):

Neural Networks ◽

Binding Sites ◽

Large Scale ◽

Rna Binding ◽

Short Term Memory ◽

Rna Binding Proteins ◽

Point Of View ◽

Rna Sequences ◽

Close Relationship ◽

Binding Sequence

AbstractRNA regulation is significantly dependent on its binding protein partner, which is known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized, especially on the structure point of view. Informative signals hiding and interdependencies between sequence and structure specificities are two challenging problems for both predicting RBP binding sites and accurate sequence and structure motifs mining.In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, which are appropriate for subsequent convolution operations. To reveal the hidden binding knowledge from the observations, the CNNs are applied to learn the abstract motif features. Considering the close relationship between sequences and predicted structures, we use the BLSTM to capture the long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets, and the results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage is that iDeepS is able to automatically extract both binding sequence and structure motifs, which will improve our transparent understanding of the mechanisms of binding specificities of RBPs. iDeepS is available at https://github.com/xypan1232/iDeepS.

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Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

BMC Genomics ◽

10.1186/s12864-020-07239-w ◽

2020 ◽

Vol 21 (S13) ◽

Author(s):

Lei Deng ◽

Youzhi Liu ◽

Yechuan Shi ◽

Wenhao Zhang ◽

Chun Yang ◽

...

Keyword(s):

Neural Networks ◽

Secondary Structure ◽

Binding Sites ◽

Large Scale ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Secondary Structures ◽

Rna Sequences ◽

Distributed Representations

Abstract Background RNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA transport, and modulation of mRNA translation and decay. The identification of RBP binding sites is a crucial step in understanding the biological mechanism of post-transcriptional gene regulation. However, the determination of RBP binding sites on a large scale is a challenging task due to high cost of biochemical assays. Quite a number of studies have exploited machine learning methods to predict binding sites. Especially, deep learning is increasingly used in the bioinformatics field by virtue of its ability to learn generalized representations from DNA and protein sequences. Results In this paper, we implemented a novel deep neural network model, DeepRKE, which combines primary RNA sequence and secondary structure information to effectively predict RBP binding sites. Specifically, we used word embedding algorithm to extract features of RNA sequences and secondary structures, i.e., distributed representation of k-mers sequence rather than traditional one-hot encoding. The distributed representations are taken as input of convolutional neural networks (CNN) and bidirectional long-term short-term memory networks (BiLSTM) to identify RBP binding sites. Our results show that deepRKE outperforms existing counterpart methods on two large-scale benchmark datasets. Conclusions Our extensive experimental results show that DeepRKE is an efficacious tool for predicting RBP binding sites. The distributed representations of RNA sequences and secondary structures can effectively detect the latent relationship and similarity between k-mers, and thus improve the predictive performance. The source code of DeepRKE is available at https://github.com/youzhiliu/DeepRKE/.

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Zooming in on protein–RNA interactions: a multi-level workflow to identify interaction partners

Biochemical Society Transactions ◽

10.1042/bst20191059 ◽

2020 ◽

Vol 48 (4) ◽

pp. 1529-1543

Author(s):

Alessio Colantoni ◽

Jakob Rupert ◽

Andrea Vandelli ◽

Gian Gaetano Tartaglia ◽

Elsa Zacco

Keyword(s):

Binding Sites ◽

Rna Binding ◽

Rna Binding Proteins ◽

Rna Sequences ◽

Binding Partners ◽

Interaction Partners ◽

Multi Level ◽

Non Coding Rnas ◽

Computational Predictions

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

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FMRP promotes RNA localization to neuronal projections through interactions between its RGG domain and G-quadruplex RNA sequences

10.1101/784728 ◽

2019 ◽

Cited By ~ 2

Author(s):

Raeann Goering ◽

Laura I. Hudish ◽

Bryan B. Guzman ◽

Nisha Raj ◽

Gary J. Bassell ◽

...

Keyword(s):

Single Molecule ◽

Rna Binding ◽

Rna Binding Proteins ◽

Fragile X ◽

Rna Localization ◽

Null Mouse ◽

Rna Sequences ◽

Local Protein Synthesis ◽

Rna Molecules ◽

G Quadruplex

ABSTRACTThe sorting of RNA molecules to distinct subcellular locations facilitates the activity of spatially restricted processes through local protein synthesis. This process affects thousands of transcripts yet precisely how these RNAs are trafficked to their destinations remains generally unclear. Here we have analyzed subcellular transcriptomes of FMRP-null mouse neuronal cells to identify transcripts that depend on FMRP for efficient transport to neurites. We found that these FMRP RNA localization targets contain a large enrichment of G-quadruplex sequences, particularly in their 3′ UTRs, suggesting that FMRP recognizes these sequences to promote the localization of transcripts that contain them. Fractionation of neurons derived from human Fragile X Syndrome patients revealed a high degree of conservation in the identity of FMRP localization targets between human and mouse as well as an enrichment of G-quadruplex sequences in human FMRP RNA localization targets. Using high-throughput RNA/protein interaction assays and single-molecule RNA FISH, we identified the RGG domain of FMRP as important for both interaction with G-quadruplex RNA sequences and the neuronal transport of G-quadruplex-containing transcripts. Finally, we used ribosome footprinting to identify translational regulatory targets of FMRP. The translational regulatory targets were not enriched for G-quadruplex sequences and were largely distinct from the RNA localization targets of FMRP, indicating that the two functions can be biochemically separated and are mediated through different target recognition mechanisms. These results establish a molecular mechanism underlying FMRP-mediated neuronal RNA localization and provide a framework for the elucidation of similar mechanisms governed by other RNA-binding proteins.

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CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

Current Bioinformatics ◽

10.2174/1574893615666210108093950 ◽

2021 ◽

Vol 15 ◽

Author(s):

Lichao Zhang ◽

Zihong Huang ◽

Liang Kong

Keyword(s):

Computational Model ◽

Binding Sites ◽

Noncoding Rna ◽

Computational Models ◽

Information Sources ◽

Rna Binding ◽

Rna Binding Proteins ◽

Gradient Boosting ◽

Position Information ◽

Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

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Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state

eLife ◽

10.7554/elife.03915 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 55

Author(s):

Yarden Katz ◽

Feifei Li ◽

Nicole J Lambert ◽

Ethan S Sokol ◽

Wai-Leong Tam ◽

...

Keyword(s):

Cancer Cells ◽

Cell Biology ◽

Rna Binding ◽

Rna Binding Proteins ◽

State Regulation ◽

Ribosome Profiling ◽

Luminal Cell ◽

Mesenchymal Transition ◽

Cell State

The conserved Musashi (Msi) family of RNA binding proteins are expressed in stem/progenitor and cancer cells, but generally absent from differentiated cells, consistent with a role in cell state regulation. We found that Msi genes are rarely mutated but frequently overexpressed in human cancers and are associated with an epithelial-luminal cell state. Using ribosome profiling and RNA-seq analysis, we found that Msi proteins regulate translation of genes implicated in epithelial cell biology and epithelial-to-mesenchymal transition (EMT), and promote an epithelial splicing pattern. Overexpression of Msi proteins inhibited the translation of Jagged1, a factor required for EMT, and repressed EMT in cell culture and in mammary gland in vivo. Knockdown of Msis in epithelial cancer cells promoted loss of epithelial identity. Our results show that mammalian Msi proteins contribute to an epithelial gene expression program in neural and mammary cell types.

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SSMART: Sequence-structure motif identification for RNA-binding proteins

10.1101/287953 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alina Munteanu ◽

Neelanjan Mukherjee ◽

Uwe Ohler

Keyword(s):

Binding Sites ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Sequence Motif ◽

Primary Sequence ◽

Sequence Structure ◽

Rna Targets

AbstractMotivationRNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized.ResultsWe developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3‘UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP.AvailabilitySSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/[email protected]

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Genomic and cDNA selection-amplification identifies transcriptome-wide binding sites for the Drosophila protein sex-lethal

PLoS ONE ◽

10.1371/journal.pone.0250592 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0250592

Author(s):

Hiren Banerjee ◽

Ravinder Singh

Keyword(s):

Binding Sites ◽

Binding Proteins ◽

Rna Binding ◽

Developmental Stages ◽

Rna Binding Proteins ◽

Systematic Analysis ◽

Downstream Targets ◽

Drosophila Protein ◽

Sex Lethal ◽

Female Specific

Background Downstream targets for a large number of RNA-binding proteins remain to be identified. The Drosophila master sex-switch protein Sex-lethal (SXL) is an RNA-binding protein that controls splicing, polyadenylation, or translation of certain mRNAs to mediate female-specific sexual differentiation. Whereas some targets of SXL are known, previous studies indicate that additional targets of SXL have escaped genetic screens. Methodology/Principal findings Here, we have used an alternative molecular approach of GEnomic Selective Enrichment of Ligands by Exponential enrichment (GESELEX) using both the genomic DNA and cDNA pools from several Drosophila developmental stages to identify new potential targets of SXL. Our systematic analysis provides a comprehensive view of the Drosophila transcriptome for potential SXL-binding sites. Conclusion/Significance We have successfully identified new SXL-binding sites in the Drosophila transcriptome. We discuss the significance of our analysis and that the newly identified binding sites and sequences could serve as a useful resource for the research community. This approach should also be applicable to other RNA-binding proteins for which downstream targets are unknown.

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