Localization Of Messenger RNA Research Articles

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

MRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Localization of RNAs to the mitochondria-mechanisms and functions.

The mammalian mitochondrial proteome comprises over 1000 proteins, with the majority translated from nuclear-encoded messenger RNAs (mRNAs). Mounting evidence suggests many of these mRNAs are localized to the outer mitochondrial membrane (OMM) in a pre- or cotranslational state. Upon reaching the mitochondrial surface, these mRNAs are locally translated to produce proteins that are cotranslationally imported into mitochondria. Here, we summarize various mechanisms cells use to localize RNAs, including transfer RNAs (tRNAs), to the OMM and recent technological advancements in the field to study these processes. While most early studies in the field were carried out in yeast, recent studies reveal RNA localization to the OMM and their regulation in higher organisms. Various factors regulate this localization process, including RNA sequence elements, RNA-binding proteins (RBPs), cytoskeletal motors, and translation machinery. In this review, we also highlight the role of RNA structures and modifications in mitochondrial RNA localization and discuss how these features can alter the binding properties of RNAs. Finally, in addition to RNAs related to mitochondrial function, RNAs involved in other cellular processes can also localize to the OMM, including those implicated in the innate immune response and piRNA biogenesis. As impairment of messenger RNA (mRNA) localization and regulation compromise mitochondrial function, future studies will undoubtedly expand our understanding of how RNAs localize to the OMM and investigate the consequences of their mislocalization in disorders, particularly neurodegenerative diseases, muscular dystrophies, and cancers.

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles.

The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence representation that is more memory and time-efficient than well-known k-mer sequence representation. Combining protein-protein interaction data, which is essential for numerous biological processes, with our novel distance-based subsequence profiles of mRNA sequences produces more accurate features. On two benchmark datasets, CeFra-Seq and RNALocate, the performance of NN-RNALoc is compared to powerful predictive models proposed in previous works (mRNALoc, RNATracker, mLoc-mRNA, DM3Loc, iLoc-mRNA, and EL-RMLocNet), and a ground neural (DNN5-mer) network. Compared to the previous methods, NN-RNALoc significantly reduces computation time and also outperforms them in terms of accuracy. This study's source code and datasets are freely accessible at https://github.com/NeginBabaiha/NN-RNALoc.

Statistical modeling of mRNP transport in dendrites: A comparative analysis of β-actin and Arc mRNP dynamics.

Localization of messenger RNA (mRNA) in dendrites is crucial for regulating gene expression during long-term memory formation. mRNA binds to RNA-binding proteins (RBPs) to form messenger ribonucleoprotein (mRNP) complexes that are transported by motor proteins along microtubules to their target synapses. However, the dynamics by which mRNPs find their target locations in the dendrite have not been well understood. Here, we investigated the motion of endogenous β-actin and Arc mRNPs in dissociated mouse hippocampal neurons using the MS2 and PP7 stem-loop systems, respectively. By evaluating the statistical properties of mRNP movement, we found that the aging Lévy walk model effectively describes both β-actin and Arc mRNP transport in proximal dendrites. A critical difference between β-actin and Arc mRNPs was the aging time, the time lag between transport initiation and measurement initiation. The longer mean aging time of β-actin mRNP (~100 s) compared with that of Arc mRNP (~30 s) reflects the longer half-life of constitutively expressed β-actin mRNP. Furthermore, our model also permitted us to estimate the ratio of newly generated and pre-existing β-actin mRNPs in the dendrites. This study offers a robust theoretical framework for mRNP transport, which provides insight into how mRNPs locate their targets in neurons.

MSLP: mRNA subcellular localization predictor based on machine learning techniques

BackgroundSubcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.MethodsIn this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.ResultsConsidering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.AvailabilityWe have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP.

Phosphorylation controls the oligomeric state of She2 and mRNA localization in yeast.

Messenger RNA (mRNA) localization is an important mechanism controlling local protein synthesis. In budding yeast, asymmetric localization of transcripts such as ASH1 mRNA to the bud tip depends on the She2 RNA-binding protein. She2 assembles as a tetramer to bind RNA, but the regulation of this process as part of the mRNA locasome is still unclear. Here, we performed a phosphoproteomic analysis of She2 in vivo and identified new phosphosites, several of which are located at the dimerization or tetramerization interfaces of She2. Remarkably, phosphomimetic mutations at these residues disrupt the capacity of She2 to promote Ash1 asymmetric accumulation. A detailed analysis of one of these residues, T109, shows that a T109D mutation inhibits She2 oligomerization and its interaction with She3 and the importin-α Srp1. She2 proteins harboring the T109D mutation also display reduced expression. More importantly, this phosphomimetic mutation strongly impairs the capacity of She2 to bind RNA and disrupts ASH1 mRNA localization. These results demonstrate that the control of She2 oligomerization by phosphorylation constitutes an important regulatory step in the mRNA localization pathway.

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

Localization of Kif1c mRNA to cell protrusions dictates binding partner specificity of the encoded protein.

Subcellular localization of messenger RNA (mRNA) is a widespread phenomenon that can impact the regulation and function of the encoded protein. In nonneuronal cells, specific mRNAs localize to cell protrusions, and proper mRNA localization is required for cell migration. However, the mechanisms by which mRNA localization regulates protein function in this setting remain unclear. Here, we examined the functional consequences of localization of the mRNA encoding KIF1C. KIF1C is a kinesin motor protein required for cell migration and mRNA trafficking, including trafficking of its own mRNA. We show that Kif1c mRNA localization does not regulate KIF1C's protein abundance, distribution, or ability to traffic other mRNAs. Conversely, Kif1c mRNA localization to protrusions is required for directed cell migration. We used mass spectrometry to identify binding partners of endogenous KIF1C, which revealed dramatic dysregulation of the number and identity of KIF1C interactors in response to Kif1c mRNA mislocalization. These results therefore uncovered a mechanistic connection between mRNA localization to cell protrusions and the specificity of protein-protein interactions. We anticipate that this mechanism is not limited to Kif1c and is likely to be a general principle that impacts the functions of proteins encoded by protrusion-enriched mRNAs in nonneuronal cells.

Analysis of mRNA Subcellular Distribution in Collective Cell Migration.

The movement of groups of cells by collective cell migration requires division of labor between group members. Therefore, distinct cell identities, unique cell behaviors, and specific cellular roles are acquired by cells undergoing collective movement. A key driving force behind the acquisition of discrete cell states is the precise control of where, when, and how genes are expressed, both at the subcellular and supracellular level. Unraveling the mechanisms underpinning the spatiotemporal control of gene expression in collective cell migration requires not only suitable experimental models but also high-resolution imaging of messenger RNA and protein localization during this process. In recent times, the highly stereotyped growth of new blood vessels by sprouting angiogenesis has become a paradigm for understanding collective cell migration, and consequently this has led to the development of numerous user-friendly in vitro models of angiogenesis. In parallel, single-molecule fluorescent in situ hybridization (smFISH) has come to the fore as a powerful technique that allows quantification of both RNA number and RNA spatial distribution in cells and tissues. Moreover, smFISH can be combined with immunofluorescence to understand the precise interrelationship between RNA and protein distribution. Here, we describe methods for use of smFISH and immunofluorescence microscopy in in vitro angiogenesis models to enable the investigation of RNA and protein expression and localization during endothelial collective cell migration.

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Subcellular and regional localization of mRNA translation in midbrain dopamine neurons.

SUMMARYMidbrain dopaminergic (mDA) neurons exhibit extensive dendritic and axonal arborizations, but local protein synthesis is not characterized in these neurons. Here, we investigate messenger RNA (mRNA) localization and translation in mDA neuronal axons and dendrites, both of which release dopamine (DA). Using highly sensitive ribosome-bound RNA sequencing and imaging approaches, we find no evidence for mRNA translation in mDA axons. In contrast, mDA neuronal dendrites in the substantia nigra pars reticulata (SNr) contain ribosomes and mRNAs encoding the major components of DA synthesis, release, and reuptake machinery. Surprisingly, we also observe dendritic localization of mRNAs encoding synaptic vesicle-related proteins, including those involved in exocytic fusion. Our results are consistent with a role for local translation in the regulation of DA release from dendrites, but not from axons. Our translatome data define a molecular signature of sparse mDA neurons in the SNr, including the enrichment of Atp2a3/SERCA3, an atypical ER calcium pump.

Correlative Localization Analysis Between mRNA and Enhanced Green Fluorescence Protein-Fused Protein by a Single-Molecule Fluorescence in situ Hybridization Using an egfp Probe in Aspergillus oryzae.

Although subcellular localization analysis of proteins fused with enhanced green fluorescence protein (EGFP) has been widely conducted in filamentous fungi, little is known about the localization of messenger RNAs (mRNAs) encoding the EGFP-fused proteins. In this study, we performed single-molecule fluorescence in situ hybridization (smFISH) using an egfp probe to simultaneously visualize EGFP-fused proteins and their mRNAs in the hyphal cells of the filamentous fungus Aspergillus oryzae. We investigated the subcellular localization of mRNAs encoding cytoplasmic EGFP, an actin marker protein Lifeact tagged with EGFP, and several EGFP-fused proteins AoSec22, AoSnc1, AoVam3, and AoUapC that localize to the endoplasmic reticulum (ER), the apical vesicle cluster Spitzenkörper, vacuolar membrane, and plasma membrane, respectively. Visualization of these mRNAs by smFISH demonstrated that each mRNA exhibited distinct localization patterns likely depending on the mRNA sequence. In particular, we revealed that mRNAs encoding Lifeact-EGFP, EGFP-AoSec22, EGFP-AoVam3, and AoUapC-EGFP, but not cytoplasmic EGFP and EGFP-AoSnc1, were preferentially localized at the apical cell, suggesting certain mechanisms to regulate the existence of these transcripts among hyphal regions. Our findings provide the distinct localization information of each mRNA in the hyphal cells of A. oryzae.

Spatiotemporal Insights Into RNA-Organelle Interactions in Neurons.

Neurons exhibit spatial compartmentalization of gene expression where localization of messenger RNAs (mRNAs) to distal processes allows for site-specific distribution of proteins through local translation. Recently, there have been reports of coordination between mRNA transport with vesicular and organellar trafficking. In this review, we will highlight the latest literature on axonal and dendritic local protein synthesis with links to mRNA–organelle cotransport followed by emerging technologies necessary to study these phenomena. Recent high-resolution imaging studies have led to insights into the dynamics of RNA–organelle interactions, and we can now peer into these intricate interactions within subcellular compartments of neurons.

DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.

Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computational methods for mRNA subcellular localization prediction provide a useful approach for studying mRNA functions. However, few computational methods were designed for mRNA subcellular localization prediction and their performance have room for improvement. Especially, there is still no available tool to predict for mRNAs that have multiple localization annotations. In this paper, we propose a multi-head self-attention method, DM3Loc, for multi-label mRNA subcellular localization prediction. Evaluation results show that DM3Loc outperforms existing methods and tools in general. Furthermore, DM3Loc has the interpretation ability to analyze RNA-binding protein motifs and key signals on mRNAs for subcellular localization. Our analyses found hundreds of instances of mRNA isoform-specific subcellular localizations and many significantly enriched gene functions for mRNAs in different subcellular localizations.

In Vivo Cross-Linking Followed by polyA Enrichment to Identify Yeast mRNA Binding Proteins.

mRNA binding proteins regulate gene expression by controlling the processing, localization, decay, and translation of messenger RNAs (mRNAs). To fully understand these mechanisms of posttranscriptional gene regulation, it is necessary to identify the complete set of mRNA binding proteins. In recent years, several assays have been developed to accomplish this goal in a wide variety of organisms. This work describes a method for the systematic identification of mRNA binding proteins in Saccharomyces cerevisiae. This method applies in vivo UV cross-linking, affinity pull-down of polyA(+) mRNAs, and analysis by mass spectrometry to identify proteins that directly bind to mRNAs.

Molecular mechanisms behind mRNA localization in axons.

Messenger RNA (mRNA) localization allows spatiotemporal regulation of the proteome at the subcellular level. This is observed in the axons of neurons, where mRNA localization is involved in regulating neuronal development and function by orchestrating rapid adaptive responses to extracellular cues and the maintenance of axonal homeostasis through local translation. Here, we provide an overview of the key findings that have broadened our knowledge regarding how specific mRNAs are trafficked and localize to axons. In particular, we review transcriptomic studies investigating mRNA content in axons and the molecular principles underpinning how these mRNAs arrived there, including cis-acting mRNA sequences and trans-acting proteins playing a role. Further, we discuss evidence that links defective axonal mRNA localization and pathological outcomes.

Messenger RNA expression and localization of xenin in the gastrointestinal tract in sheep

The present study aimed to determine the primary sequence of ovine xenin and clarify the mRNA expression and peptide localization of xenin in the gastrointestinal tract in sheep. The colocalization of xenin and glucose-dependent insulinotropic polypeptide was also compared in the antrum and duodenum. Analysis of the nucleotide sequence of ovine xenin revealed a high degree (97.9%) of sequence homology of the sequence between sheep and cattle, and the amino acids sequence determined for ovine xenin coincided (100%) with that of other mammalian species. Real-time quantitative PCR for ovine xenin did not show regional difference in the mRNA expression ratio of xenin. In contrast to the real-time quantitative PCR results, anti-xenin positive cells were abundantly localized in the abomasal antrum (P < 0.01) and at a lesser amount in the duodenum, but no antixenin positive cells were observed in the other regions. Anti-xenin single-positive cells were in a majority in the abomasal antrum, whereas anti-xenin single-positive cells, and anti-GIP single-positive cells, and double-positive cells were even colocalized in the duodenum. These results suggest that abomasal antrum is a major source of xenin in the ovine gastrointestinal tract.

A Disorder-to-Order Transition Mediates RNA Binding of the Caenorhabditis elegans Protein MEX-5

CCCH-type tandem zinc finger (TZF) domains are found in many RNA-binding proteins (RBPs) that regulate the essential processes of post-transcriptional gene expression and splicing through direct protein-RNA interactions. In Caenorhabditis elegans, RBPs control the translation, stability, or localization of maternal messenger RNAs required for patterning decisions before zygotic gene activation. MEX-5 (Muscle EXcess) is a C. elegans protein that leads a cascade of RBP localization events that is essential for axis polarization and germline differentiation after fertilization. Here, we report that at room temperature, the CCCH-type TZF domain of MEX-5 contains an unstructured zinc finger that folds upon binding of its RNA target. We have characterized the structure and dynamics of the TZF domain of MEX-5 and designed a variant MEX-5 in which both fingers are fully folded in the absence of RNA. Within the thermal range experienced by C. elegans, the population of the unfolded state of the TZF domain of MEX-5 varies. We observe that the TZF domain becomes less disordered at lower temperatures and more disordered at higher temperatures. However, in the temperature range in which C. elegans is fertile, when MEX-5 needs to be functional, only one of the two zinc fingers is folded.