scholarly journals RGS4 RNA Secondary Structure Mediates Staufen2 RNP Assembly in Neurons

2021 ◽  
Vol 22 (23) ◽  
pp. 13021
Author(s):  
Sandra M. Fernández-Moya ◽  
Janina Ehses ◽  
Karl E. Bauer ◽  
Rico Schieweck ◽  
Anob M. Chakrabarti ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Underlying Mechanism ◽  
Secondary Structures ◽  
Rna Granules ◽  
Rna Hairpins ◽  
Sequence Elements ◽  
Rna Hairpin

RNA-binding proteins (RBPs) act as posttranscriptional regulators controlling the fate of target mRNAs. Unraveling how RNAs are recognized by RBPs and in turn are assembled into neuronal RNA granules is therefore key to understanding the underlying mechanism. While RNA sequence elements have been extensively characterized, the functional impact of RNA secondary structures is only recently being explored. Here, we show that Staufen2 binds complex, long-ranged RNA hairpins in the 3′-untranslated region (UTR) of its targets. These structures are involved in the assembly of Staufen2 into RNA granules. Furthermore, we provide direct evidence that a defined Rgs4 RNA duplex regulates Staufen2-dependent RNA localization to distal dendrites. Importantly, disrupting the RNA hairpin impairs the observed effects. Finally, we show that these secondary structures differently affect protein expression in neurons. In conclusion, our data reveal the importance of RNA secondary structure in regulating RNA granule assembly, localization and eventually translation. It is therefore tempting to speculate that secondary structures represent an important code for cells to control the intracellular fate of their mRNAs.

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

BMC Genomics ◽  
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/.

scholarly journals Prediction and analysis of functional RNA structures within the integrative genomics viewer

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Warren B Rouse ◽  
Ryan J Andrews ◽  
Nicholas J Booher ◽  
Jibo Wang ◽  
Michael E Woodman ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Drug Targets ◽  
Rna Binding ◽  
Secondary Structure Prediction ◽  
Rna Binding Proteins ◽  
Published Data ◽  
Nucleotide Polymorphisms ◽  
Integrative Genomics

ABSTRACT In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

scholarly journals Amount of RNA secondary structure required to induce an alternative splice.

1987 ◽  
Vol 7 (9) ◽  
pp. 3194-3198 ◽  
Author(s):  
D Solnick ◽  
S I Lee
Keyword(s):  
Alternative Splicing ◽  
Secondary Structure ◽  
Alternative Splice ◽  
Rna Secondary Structure ◽  
Secondary Structures ◽  
Splice Sites ◽  
Perfect Complementarity ◽  
Set Up

We set up an alternative splicing system in vitro in which the relative amounts of two spliced RNAs, one containing and the other lacking a particular exon, were directly proportional to the length of an inverted repeat inserted into the flanking introns. We then used the system to measure the effect of intramolecular complementarity on alternative splicing in vivo. We found that an alternative splice was induced in vivo only when the introns contained more than approximately 50 nucleotides of perfect complementarity, that is, only when the secondary structure was much more stable than most if not all possible secondary structures in natural mRNA precursors. We showed further that intron insertions containing long complements to splice sites and a branch point inhibited splicing in vitro but not in vivo. These results raise the possibility that in cells most pre-mRNA secondary structures either are not maintained long enough to influence splicing choices, or never form at all.

scholarly journals RNA secondary structure prediction using deep learning with thermodynamic integration

2020 ◽  
Author(s):  
Kengo Sato ◽  
Manato Akiyama ◽  
Yasubumi Sakakibara
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Thermodynamic Integration ◽  
Rna Secondary Structure Prediction ◽  
Rna Secondary Structures ◽  
Non Coding Rnas

RNA secondary structure prediction is one of the key technologies for revealing the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models have achieved extremely high performance in terms of prediction accuracy, the risk of overfitting for such models has been reported. In this work, we propose a new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions. Similar to our previous work, the folding scores, which are computed by a deep neural network, are integrated with traditional thermodynamic parameters to enable robust predictions. We also propose thermodynamic regularization for training our model without overfitting it to the training data. Our algorithm (MXfold2) achieved the most robust and accurate predictions in computational experiments designed for newly discovered non-coding RNAs, with significant 2–10 % improvements over our previous algorithm (MXfold) and standard algorithms for predicting RNA secondary structures in terms of F-value.

scholarly journals Ras Suppresses TXNIP Expression by Restricting Ribosome Translocation

2018 ◽  
Author(s):  
Zhizhou Ye ◽  
Donald E. Ayer
Keyword(s):  
Protein Synthesis ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Aerobic Glycolysis ◽  
Protein Translation ◽  
Underlying Mechanism ◽  
Metabolic Reprogramming ◽  
Coding Region ◽  
Interacting Protein ◽  
Thioredoxin Interacting Protein

ABSTRACTOncogenic Ras upregulates aerobic glycolysis to meet the bioenergetic and biosynthetic demands of rapidly growing cells. In contrast, Thioredoxin interacting protein (TXNIP) is a potent inhibitor of glucose uptake and is frequently downregulated in human cancers. Our lab previously discovered that Ras activation suppresses TXNIP transcription and translation. In this report, we developed a system to study how Ras affects TXNIP translation in the absence of transcriptional affects. We show that whereas Ras drives a global increase in protein translation, it suppresses TXNIP protein synthesis by reducing the rate at which ribosomes transit the coding region of TXNIP mRNA. To investigate the underlying mechanism(s), we randomized or optimized the codons in the TXNIP message without altering the TXNIP primary amino acid sequence. Translation from these mRNA variants is still repressed by Ras, intimating that mRNA secondary structure, miRNAs, RNA binding proteins, or codon usage do not contribute to the blockade of TXNIP synthesis. Rather, we show that the N-terminus of the growing TXNIP polypeptide is the target for Ras-dependent translational repression. Our work demonstrates how Ras suppresses TXNIP translation elongation in the face of a global upregulation of protein synthesis and provides new insight into Ras-dependent metabolic reprogramming.

scholarly journals The structural basis for RNA selectivity by the IMP family of RNA-binding proteins

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jeetayu Biswas ◽  
Vivek L. Patel ◽  
Varun Bhaskar ◽  
Jeffrey A. Chao ◽  
Robert H. Singer ◽  
...  
Keyword(s):  
Neural Development ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Stability ◽  
Underlying Mechanism ◽  
Structural Basis ◽  
General Mechanism ◽  
Binding Domains ◽  
Variable Loops

Abstract The IGF2 mRNA-binding proteins (ZBP1/IMP1, IMP2, IMP3) are highly conserved post-transcriptional regulators of RNA stability, localization and translation. They play important roles in cell migration, neural development, metabolism and cancer cell survival. The knockout phenotypes of individual IMP proteins suggest that each family member regulates a unique pool of RNAs, yet evidence and an underlying mechanism for this is lacking. Here, we combine systematic evolution of ligands by exponential enrichment (SELEX) and NMR spectroscopy to demonstrate that the major RNA-binding domains of the two most distantly related IMPs (ZBP1 and IMP2) bind to different consensus sequences and regulate targets consistent with their knockout phenotypes and roles in disease. We find that the targeting specificity of each IMP is determined by few amino acids in their variable loops. As variable loops often differ amongst KH domain paralogs, we hypothesize that this is a general mechanism for evolving specificity and regulation of the transcriptome.

scholarly journals G3BP1-linked mRNA partitioning supports selective protein synthesis in response to oxidative stress

2020 ◽  
Vol 48 (12) ◽  
pp. 6855-6873 ◽  
Author(s):  
Syam Prakash Somasekharan ◽  
Fan Zhang ◽  
Neetu Saxena ◽  
Jia Ni Huang ◽  
I-Chih Kuo ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Mrna Translation ◽  
Stress Adaptation ◽  
Rna Granules ◽  
Before And After ◽  
Selective Translation ◽  
Energy Consuming ◽  
Limit Energy

Abstract Cells limit energy-consuming mRNA translation during stress to maintain metabolic homeostasis. Sequestration of mRNAs by RNA binding proteins (RBPs) into RNA granules reduces their translation, but it remains unclear whether RBPs also function in partitioning of specific transcripts to polysomes (PSs) to guide selective translation and stress adaptation in cancer. To study transcript partitioning under cell stress, we catalogued mRNAs enriched in prostate carcinoma PC-3 cell PSs, as defined by polysome fractionation and RNA sequencing (RNAseq), and compared them to mRNAs complexed with the known SG-nucleator protein, G3BP1, as defined by spatially-restricted enzymatic tagging and RNAseq. By comparing these compartments before and after short-term arsenite-induced oxidative stress, we identified three major categories of transcripts, namely those that were G3BP1-associated and PS-depleted, G3BP1-dissociated and PS-enriched, and G3BP1-associated but also PS-enriched. Oxidative stress profoundly altered the partitioning of transcripts between these compartments. Under arsenite stress, G3BP1-associated and PS-depleted transcripts correlated with reduced expression of encoded mitochondrial proteins, PS-enriched transcripts that disassociated from G3BP1 encoded cell cycle and cytoprotective proteins whose expression increased, while transcripts that were both G3BP1-associated and PS-enriched encoded proteins involved in diverse stress response pathways. Therefore, G3BP1 guides transcript partitioning to reprogram mRNA translation and support stress adaptation.

scholarly journals Cataloguing and Selection of mRNAs Localized to Dendrites in Neurons and Regulated by RNA-Binding Proteins in RNA Granules

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 167 ◽  
Author(s):  
Ohashi ◽  
Shiina
Keyword(s):  
Synaptic Plasticity ◽  
Cell Fate ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Binding Protein ◽  
Memory Formation ◽  
Long Term Memory ◽  
Local Translation ◽  
Rna Granules

Spatiotemporal translational regulation plays a key role in determining cell fate and function. Specifically, in neurons, local translation in dendrites is essential for synaptic plasticity and long-term memory formation. To achieve local translation, RNA-binding proteins in RNA granules regulate target mRNA stability, localization, and translation. To date, mRNAs localized to dendrites have been identified by comprehensive analyses. In addition, mRNAs associated with and regulated by RNA-binding proteins have been identified using various methods in many studies. However, the results obtained from these numerous studies have not been compiled together. In this review, we have catalogued mRNAs that are localized to dendrites and are associated with and regulated by the RNA-binding proteins fragile X mental retardation protein (FMRP), RNA granule protein 105 (RNG105, also known as Caprin1), Ras-GAP SH3 domain binding protein (G3BP), cytoplasmic polyadenylation element binding protein 1 (CPEB1), and staufen double-stranded RNA binding proteins 1 and 2 (Stau1 and Stau2) in RNA granules. This review provides comprehensive information on dendritic mRNAs, the neuronal functions of mRNA-encoded proteins, the association of dendritic mRNAs with RNA-binding proteins in RNA granules, and the effects of RNA-binding proteins on mRNA regulation. These findings provide insights into the mechanistic basis of protein-synthesis-dependent synaptic plasticity and memory formation and contribute to future efforts to understand the physiological implications of local regulation of dendritic mRNAs in neurons.

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