mRNA Subcellular Localization Research Articles

MRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization

Subcellular localization of mRNA is related to protein synthesis, cell polarity, cell movement and other biological regulation mechanisms. The distribution of mRNAs in subcellulars is similar to that of proteins, and most mRNAs are distributed in multiple subcellulars. Recently, some computational methods have been designed to predict the subcellular localization of mRNA. However, these methods only employed a sin-gle level of mRNA features and did not employ the position encoding of nucleotides in mRNA. In this paper, an ensemble learning prediction model is proposed, named MulStack, which is based on random forest and deep learning for multilabel mRNA subcellular localization. The proposed method employs two levels of mRNA features, including sequence-level and residue-level features, and position encoding is employed for the first time in the field of subcellular localization of mRNA. Random forest is employed to learn mRNA sequence-level feature, deep learning is employed to learn mRNA sequence-level feature and mRNA residue-level combined with position encoding. And the outputs of random forest and deep learning model will be weighted sum as the prediction probability. Compared with existing methods, the results show that MulStack is the best in the localization of the nucleus, cytosol and exosome. In addition, position weight matrices (PWMs) are extracted by convolutional neural networks (CNNs) that can be matched with known RNA binding protein motifs. Gene ontology (GO) enrichment analysis shows biological processes, molecular functions and cellular components of mRNA genes. The prediction web server of MulStack is freely accessible at http://bliulab.net/MulStack.

Unified mRNA Subcellular Localization Predictor based on machine learning techniques

BackgroundThe mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.MethodsIn this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).ResultsThe proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.AvailabilityWe have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP.

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/).

Abstract 18452: Microtubule Motors Coordinate mRNA Export and Transport to Direct Local Translation in the Cardiomyocyte

Introduction: The post-mitotic cardiomyocyte (CM) must maintain proteostasis while also preserving the ability to remodel, particularly during times of cardiac growth. How the CM couples transcription and translation in space and time for normal maintenance of the cell is understudied, and thus it is also unclear how these programs are co-opted to enable hypertrophy. Our lab’s previous work has indicated the necessity of microtubules (MTs) and kinesin-1 in proper localization of transcripts, translation and growth of the CM, yet the molecular mechanisms that direct specific mRNAs to subcellular regions for local translation remain opaque. Research Questions: We hypothesize that changes to MT PTMs are sufficient to alter the subcellular localization of motors, mRNA and translation. We additionally postulate that changes to the MT network during initial cardiac growth facilitate mRNA transport and translation. Methods/Approach: To visualize mRNA and protein localization in CMs, we couple immunofluorescence (IF) and smFISH. To measure translation, we methionine-deplete and use methionine-analogues and click chemistry. To measure cell size, we use live cell imaging of a membrane dye. Results: We find that the CM contains at least three subdomains for mRNA localization and local translation, and that some transcripts require proper localization for translation. Nearly any change to the MT detyrosination or tyrosination status is sufficient to mislocalize motors and mRNA. We observe mRNA accumulation at the nuclear short axis when detyrosinated tubulin is increased at the nuclear poles using taxol. This mRNA accumulation is accompanied by dynactin1 and kinesin-1 accumulation as well as nucleoporin relocalization. Taxol treatment in neonatal rat CMs results in a similarly asymmetric mRNA and nucleoporin localization, concomitant with an increase in width along this mRNA axis. Conclusions: A precise balance of dynein/dynactin1 and kinesin-1 in the CM is necessary for mRNA localization, which is at least partly established by MT PTMs. Stabilizing the MT network reveals a role for dynein/dynactin1 and nucleoporins in mRNA export and transport. We propose that perturbed mRNA export/localization may alter downstream translation to direct the growth of the CM.

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.

In Search of a Function for the N6-Methyladenosine in Epitranscriptome, Autophagy and Neurodegenerative Diseases.

Changes in epitranscriptome with N6-methyladenine (m6A) modification could be involved in the development of multiple diseases, which might be a prevalent modification of messenger RNAs (mRNAs) in eukaryotes. The m6A modification might be performed through the action of methyltransferases, demethylases, and methylation-binding proteins. Importantly, the m6A methylation may be associated with various neurological disorders including Alzheimer's disease (AD), Parkinson's disease (PD), depression, aging-related diseases, and/or aging itself. In addition, the m6A methylation might functionally regulate the eukaryotic transcriptome by influencing the splicing, export, subcellular localization, translation, stability, and decay of mRNAs. Neurodegenerative diseases may possess a wide variety of phenotypes, depending on the neurons that degenerate on occasion. Interestingly, an increasing amount of evidence has indicated that m6A modification could modulate the expression of autophagy-related genes and promote autophagy in neuronal cells. Oxidative stresses such as reactive oxygen species (ROS) could stimulate the m6A RNA methylation, which may also be related to the regulation of autophagy and/or the development of neurodegenerative diseases. Both m6A modification and autophagy could also play critical roles in regulating the health condition of neurons. Therefore, a comprehensive understanding of the m6A and autophagy relationship in human diseases may benefit in developing therapeutic strategies in the future. This paper reviews advances in the understanding of the regulatory mechanisms of m6A modification in the occurrence and development of neurodegenerative diseases and/or aging, discussing the possible therapeutic procedures related to mechanisms of m6A RNA methylation and autophagy.

RNA-binding protein CCDC137 activates AKT signaling and promotes hepatocellular carcinoma through a novel non-canonical role of DGCR8 in mRNA localization

BackgroundRNA binding proteins (RBPs)—regulated gene expression play a vital role in various pathological processes, including the progression of cancer. However, the role of RBP in hepatocellular carcinoma (HCC) remains much unknown. In this study, we aimed to explore the contribution of RBP CCDC137 in HCC development.MethodsWe analyzed the altered expression level and clinical significance of CCDC137 in database and HCC specimens. In vitro cell assays and in vivo spontaneous mouse models were used to assess the function of CCDC137. Finally, the molecular mechanisms of how CCDC137 regulates gene expression and promotes HCC was explored.ResultsCCDC137 is aberrantly upregulated in HCC and correlates with poor clinical outcomes in HCC patients. CCDC137 markedly promoted HCC proliferation and progression in vitro and in vivo. Mechanistically, CCDC137 binds with FOXM1, JTV1, LASP1 and FLOT2 mRNAs, which was revealed by APOBEC1-mediated profiling, to increase their cytoplasmic localization and thus enhance their protein expressions. Upregulation of FOXM1, JTV1, LASP1 and FLOT2 subsequently synergistically activate AKT signaling and promote HCC. Interestingly, we found that CCDC137 binds with the microprocessor protein DGCR8 and DGCR8 has a novel non-canonical function in mRNA subcellular localization, which mediates the cytoplasmic distribution of mRNAs regulated by CCDC137.ConclusionsOur results identify a critical proliferation-related role of CCDC137 and reveal a novel CCDC137/DGCR8/mRNA localization/AKT axis in HCC progression, which provide a potential target for HCC therapy.

MRNA stability and m6A are major determinants of subcellular mRNA localization in neurons

For cells to perform their biological functions, they need to adopt specific shapes and form functionally distinct subcellular compartments. This is achieved in part via an asymmetric distribution of mRNAs within cells. Currently, the main model of mRNA localization involves specific sequences called "zipcodes" that direct mRNAs to their proper locations. However, while thousands of mRNAs localize within cells, only a few zipcodes have been identified, suggesting that additional mechanisms contribute to localization. Here, we assess the role of mRNA stability in localization by combining the isolation of the soma and neurites of mouse primary cortical and mESC-derived neurons, SLAM-seq, m6A-RIP-seq, the perturbation of mRNA destabilization mechanisms, and the analysis of multiple mRNA localization datasets. We show that depletion of mRNA destabilization elements, such as m6A, AU-rich elements, and suboptimal codons, functions as a mechanism that mediates the localization of mRNAs associated with housekeeping functions to neurites in several types of neurons.

Hitting the mark: Localization of mRNA and biomolecular condensates in health and disease.

Subcellular mRNA localization is critical to a multitude of biological processes such as development of cellular polarity, embryogenesis, tissue differentiation, protein complex formation, cell migration, and rapid responses to environmental stimuli and synaptic depolarization. Our understanding of the mechanisms of mRNA localization must now be revised to include formation and trafficking of biomolecular condensates, as several biomolecular condensates that transport and localize mRNA have recently been discovered. Disruptions in mRNA localization can have catastrophic effects on developmental processes and biomolecular condensate biology and have been shown to contribute to diverse diseases. A fundamental understanding of mRNA localization is essential to understanding how aberrations in this biology contribute the etiology of numerous cancers though support of cancer cell migration and biomolecular condensate dysregulation, as well as many neurodegenerative diseases, through misregulation of mRNA localization and biomolecular condensate biology. This article is categorized under: RNA Export and Localization > RNA Localization RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Improved enzymatic labeling of fluorescent in situ hybridization probes applied to the visualization of retained introns in cells.

Fluorescence in situ hybridization (FISH) is a widely used tool for quantifying gene expression and determining the location of RNA molecules in cells. We present an improved method for FISH probe production that yields high-purity probes with a wide range of fluorophores using standard laboratory equipment at low cost. The method modifies an earlier protocol that uses terminal deoxynucleotidyl transferase to add fluorescently labeled nucleotides to synthetic deoxyoligonucleotides. In our protocol, amino-11-ddUTP is joined to an oligonucleotide pool prior to its conjugation to a fluorescent dye, thereby generating pools of probes ready for a variety of modifications. This order of reaction steps allows for high labeling efficiencies regardless of the GC content or terminal base of the oligonucleotides. The degree of labeling (DOL) for spectrally distinct fluorophores (Quasar, ATTO, and Alexa dyes) was mostly >90%, comparable with commercial probes. The ease and low cost of production allowed the generation of probe sets targeting a wide variety of RNA molecules. Using these probes, FISH assays in C2C12 cells showed the expected subcellular localization of mRNAs and pre-mRNAs for Polr2a (RNA polymerase II subunit 2a) and Gapdh, and of the long noncoding RNAs Malat1 and Neat1 Developing FISH probe sets for several transcripts containing retained introns, we found that retained introns in the Gabbr1 and Noc2l transcripts are present in subnuclear foci separate from their sites of synthesis and partially coincident with nuclear speckles. This labeling protocol should have many applications in RNA biology.

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.

Subcellular mRNA localization and local translation of Arhgap11a in radial glial progenitors regulates cortical development

mRNA localization and local translation enable exquisite spatial and temporal control of gene expression, particularly in polarized, elongated cells. These features are especially prominent in radial glial cells (RGCs), which are neural and glial precursors of the developing cerebral cortex and scaffolds for migrating neurons. Yet the mechanisms by which subcellular RGC compartments accomplish their diverse functions are poorly understood. Here, we demonstrate that mRNA localization and local translation of the RhoGAP ARHGAP11A in the basal endfeet of RGCs control their morphology and mediate neuronal positioning. Arhgap11a transcript and protein exhibit conserved localization to RGC basal structures in mice and humans, conferred by the 5' UTR. Proper RGC morphology relies upon active Arhgap11a mRNA transport and localization to the basal endfeet, where ARHGAP11A is locally synthesized. This translation is essential for positioning interneurons at the basement membrane. Thus, local translation spatially and acutely activates Rho signaling in RGCs to compartmentalize neural progenitor functions.

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.

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/.

MRNA Localization to the Endoplasmic Reticulum in Plant Endosperm Cells.

Subcellular mRNA localization is an evolutionarily conserved mechanism to spatially and temporally drive local translation and, in turn, protein targeting. Hence, this mechanism achieves precise control of gene expression and establishes functional and structural networks during cell growth and development as well as during stimuli response. Since its discovery in ascidian eggs, mRNA localization has been extensively studied in animal and yeast cells. Although our knowledge of subcellular mRNA localization in plant cells lags considerably behind other biological systems, mRNA localization to the endoplasmic reticulum (ER) has also been well established since its discovery in cereal endosperm cells in the early 1990s. Storage protein mRNA targeting to distinct subdomains of the ER determines efficient accumulation of the corresponding proteins in different endosomal storage sites and, in turn, underlies storage organelle biogenesis in cereal grains. The targeting process requires the presence of RNA localization elements, also called zipcodes, and specific RNA-binding proteins that recognize and bind these zipcodes and recruit other factors to mediate active transport. Here, we review the current knowledge of the mechanisms and functions of mRNA localization to the ER in plant cells and address directions for future research.

A massively parallel reporter assay reveals focused and broadly encoded RNA localization signals in neurons.

Asymmetric subcellular mRNA localization allows spatial regulation of gene expression and functional compartmentalization. In neurons, localization of specific mRNAs to neurites is essential for cellular functioning. However, it is largely unknown how transcript sorting works in a sequence-specific manner. Here, we combined subcellular transcriptomics and massively parallel reporter assays and tested ∼50 000 sequences for their ability to localize to neurites. Mapping the localization potential of >300 genes revealed two ways neurite targeting can be achieved: focused localization motifs and broadly encoded localization potential. We characterized the interplay between RNA stability and localization and identified motifs able to bias localization towards neurite or soma as well as the trans-acting factors required for their action. Based on our data, we devised machine learning models that were able to predict the localization behavior of novel reporter sequences. Testing this predictor on native mRNA sequencing data showed good agreement between predicted and observed localization potential, suggesting that the rules uncovered by our MPRA also apply to the localization of native full-length transcripts.

Stimulus-specific remodeling of the neuronal transcriptome through nuclear intron-retaining transcripts.

The nuclear envelope has long been considered primarily a physical barrier separating nuclear and cytosolic contents. More recently, nuclear compartmentalization has been shown to have additional regulatory functions in controlling gene expression. A sizeable proportion of protein-coding mRNAs is more prevalent in the nucleus than in the cytosol, suggesting regulated mRNA trafficking to the cytosol, but the mechanisms underlying controlled nuclear mRNA retention remain unclear. Here, we provide a comprehensive map of the subcellular localization of mRNAs in mature mouse cortical neurons, and reveal that transcripts retained in the nucleus comprise the majority of stable intron-retaining mRNAs. Systematically probing the fate of nuclear transcripts upon neuronal stimulation, we found opposite effects on sub-populations of transcripts: while some are targeted for degradation, others complete splicing to generate fully mature mRNAs that are exported to the cytosol and mediate rapid increases in protein levels. Finally, different forms of stimulation mobilize distinct groups of intron-retaining transcripts, with this selectivity arising from the activation of specific signaling pathways. Overall, our findings uncover a cue-specific control of intron retention as a major regulator of acute remodeling of the neuronal transcriptome.

Rvb1/Rvb2 proteins couple transcription and translation during glucose starvation.

During times of unpredictable stress, organisms must adapt their gene expression to maximize survival. Along with changes in transcription, one conserved means of gene regulation during conditions that quickly repress translation is the formation of cytoplasmic phase-separated mRNP granules such as P-bodies and stress granules. Previously, we identified that distinct steps in gene expression can be coupled during glucose starvation as promoter sequences in the nucleus are able to direct the subcellular localization and translatability of mRNAs in the cytosol. Here, we report that Rvb1 and Rvb2, conserved ATPase proteins implicated as protein assembly chaperones and chromatin remodelers, were enriched at the promoters and mRNAs of genes involved in alternative glucose metabolism pathways that we previously found to be transcriptionally upregulated but translationally downregulated during glucose starvation in yeast. Engineered Rvb1/Rvb2-binding on mRNAs was sufficient to sequester mRNAs into mRNP granules and repress their translation. Additionally, this Rvb tethering to the mRNA drove further transcriptional upregulation of the target genes. Further, we found that depletion of Rvb2 caused decreased alternative glucose metabolism gene mRNA induction, but upregulation of protein synthesis during glucose starvation. Overall, our results point to Rvb1/Rvb2 coupling transcription, mRNA granular localization, and translatability of mRNAs during glucose starvation. This Rvb-mediated rapid gene regulation could potentially serve as an efficient recovery plan for cells after stress removal.

Functionally Clustered mRNAs Are Distinctly Enriched at Cortical Astroglial Processes and Are Preferentially Affected by FMRP Deficiency.

Mature protoplasmic astroglia in the mammalian CNS uniquely possess a large number of fine processes that have been considered primary sites to mediate astroglia to neuron synaptic signaling. However, localized mechanisms for regulating interactions between astroglial processes and synapses, especially for regulating the expression of functional surface proteins at these fine processes, are largely unknown. Previously, we showed that the loss of the RNA binding protein FMRP in astroglia disrupts astroglial mGluR5 signaling and reduces expression of the major astroglial glutamate transporter GLT1 and glutamate uptake in the cortex of Fmr1 conditional deletion mice. In the current study, by examining ribosome localization using electron microscopy and identifying mRNAs enriched at cortical astroglial processes using synaptoneurosome/translating ribosome affinity purification and RNA-Seq in WT and FMRP-deficient male mice, our results reveal interesting localization-dependent functional clusters of mRNAs at astroglial processes. We further showed that the lack of FMRP preferentially alters the subcellular localization and expression of process-localized mRNAs. Together, we defined the role of FMRP in altering mRNA localization and expression at astroglial processes at the postnatal development (P30-P40) and provided new candidate mRNAs that are potentially regulated by FMRP in cortical astroglia.SIGNIFICANCE STATEMENT Localized mechanisms for regulating interactions between astroglial processes and synapses, especially for regulating the expression of functional surface proteins at these fine processes, are largely unknown. Previously, we showed that the loss of the RNA binding protein FMRP in astroglia disrupts expression of several astroglial surface proteins, such as mGluR5 and major astroglial glutamate transporter GLT1 in the cortex of FMRP-deficient mice. Our current study examined ribosome localization using electron microscopy and identified mRNAs enriched at cortical astroglial processes in WT and FMRP-deficient mice. These results reveal interesting localization-dependent functional clusters of mRNAs at astroglial processes and demonstrate that the lack of FMRP preferentially alters the subcellular localization and expression of process-localized mRNAs.