CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in Drosophila and human cells

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.

Expanding roles of circRNAs in cardiovascular diseases

CircRNAs are a class of single-stranded RNAs characterized by covalently looped structures. Emerging advances have promoted our understanding of circRNA biogenesis, nuclear export, biological functions, and functional mechanisms. Roles of circRNAs in diverse diseases have been increasingly recognized in the past decade, with novel approaches in bioinformatics analysis and new strategies in modulating circRNA levels, which have made circRNAs the hot spot for therapeutic applications. Moreover, due to the intrinsic features of circRNAs such as high stability, conservation, and tissue-/stage-specific expression, circRNAs are believed to be promising prognostic and diagnostic markers for diseases. Aiming cardiovascular disease (CVD), one of the leading causes of mortality worldwide, we briefly summarize the current understanding of circRNAs, provide the recent progress in circRNA functions and functional mechanisms in CVD, and discuss the future perspectives both in circRNA research and therapeutics based on existing knowledge.

RBM22 regulates RNA polymerase II 5' pausing, elongation rate, and termination by coordinating 7SK-P-TEFb complex and SPT5

BACKGROUND

Splicing factors are vital for the regulation of RNA splicing, but some have also been implicated in regulating transcription. The underlying molecular mechanisms of their involvement in transcriptional processes remain poorly understood.

RESULTS

Here, we describe a direct role of splicing factor RBM22 in coordinating multiple steps of RNA Polymerase II (RNAPII) transcription in human cells. The RBM22 protein widely occupies the RNAPII-transcribed gene locus in the nucleus. Loss of RBM22 promotes RNAPII pause release, reduces elongation velocity, and provokes transcriptional readthrough genome-wide, coupled with production of transcripts containing sequences from downstream of the gene. RBM22 preferentially binds to the hyperphosphorylated, transcriptionally engaged RNAPII and coordinates its dynamics by regulating the homeostasis of the 7SK-P-TEFb complex and the association between RNAPII and SPT5 at the chromatin level.

CONCLUSIONS

Our results uncover the multifaceted role of RBM22 in orchestrating the transcriptional program of RNAPII and provide evidence implicating a splicing factor in both RNAPII elongation kinetics and termination control.

System-wide analysis of RNA and protein subcellular localization dynamics

Although the subcellular dynamics of RNA and proteins are key determinants of cell homeostasis, their characterization is still challenging. Here we present an integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution by combining two methods: localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT) to map RNA and protein to organelles (nucleus, endoplasmic reticulum and mitochondria) and membraneless compartments (cytosol, nucleolus and cytosolic granules). Interrogating all RNA subcellular locations at once enables system-wide quantification of the proportional distribution of RNA. We obtain a cell-wide overview of localization dynamics for 31,839 transcripts and 5,314 proteins during the unfolded protein response, revealing that endoplasmic reticulum-localized transcripts are more efficiently recruited to cytosolic granules than cytosolic RNAs, and that the translation initiation factor eIF3d is key to sustaining cytoskeletal function. Overall, we provide the most comprehensive overview so far of RNA and protein subcellular localization dynamics.

CD8 T-cell diversification: Asymmetric cell division and its functional implications

Establishment of cellular diversity is a basic requirement for the development of multicellular organisms. Cellular diversification can be induced by asymmetric cell division (ACD), during which the emerging two daughter cells unequally inherit lineage specific cargo (including transcription factors, receptors for specific signaling inputs, metabolic platforms, and possibly different epigenetic landscapes), resulting in two daughter cells endowed with different fates. While ACD is strongly involved in lineage choices in mammalian stem cells, its role in fate diversification in lineage committed cell subsets that still exhibit plastic potential, such as T-cells, is currently investigated. In this review, we focus predominantly on the role of ACD in fate diversification of CD8 T-cells. Further, we discuss the impact of differential T-cell receptor stimulation strengths and differentiation history on ACD-mediated fate diversification and highlight a particular importance of ACD in the development of memory CD8 T-cells upon high-affinity stimulation conditions.

Examining SRP pathway function in mRNA localization to the endoplasmic reticulum

Signal recognition particle (SRP) pathway function in protein translocation across the endoplasmic reticulum (ER) is well established; its role in RNA localization to the ER remains, however, unclear. In current models, mRNAs undergo translation- and SRP-dependent trafficking to the ER, with ER localization mediated via interactions between SRP-bound translating ribosomes and the ER-resident SRP receptor (SR), a heterodimeric complex comprising SRA, the SRP-binding subunit, and SRB, an integral membrane ER protein. To study SRP pathway function in RNA localization, SR knockout (KO) mammalian cell lines were generated and the consequences of SR KO on steady-state and dynamic mRNA localization examined. CRISPR/Cas9-mediated SRPRB KO resulted in profound destabilization of SRA. Pairing siRNA silencing of SRPRA in SRPRB KO cells yielded viable SR KO cells. Steady-state mRNA compositions and ER-localization patterns in parental and SR KO cells were determined by cell fractionation and deep sequencing. Notably, steady-state cytosol and ER mRNA compositions and partitioning patterns were largely unaltered by loss of SR expression. To examine SRP pathway function in RNA localization dynamics, the subcellular trafficking itineraries of newly exported mRNAs were determined by 4-thiouridine (4SU) pulse-labeling/4SU-seq/cell fractionation. Newly exported mRNAs were distinguished by high ER enrichment, with ER localization being SR-independent. Intriguingly, under conditions of translation initiation inhibition, the ER was the default localization site for all newly exported mRNAs. These data demonstrate that mRNA localization to the ER can be uncoupled from the SRP pathway function and reopen questions regarding the mechanism of RNA localization to the ER.

Nucleocytoplasmic mRNA redistribution accompanies RNA binding protein mislocalization in ALS motor neurons and is restored by VCP ATPase inhibition

Amyotrophic lateral sclerosis (ALS) is characterized by nucleocytoplasmic mislocalization of the RNA-binding protein (RBP) TDP-43. However, emerging evidence suggests more widespread mRNA and protein mislocalization. Here, we employed nucleocytoplasmic fractionation, RNA sequencing, and mass spectrometry to investigate the localization of mRNA and protein in induced pluripotent stem cell-derived motor neurons (iPSMNs) from ALS patients with TARDBP and VCP mutations. ALS mutant iPSMNs exhibited extensive nucleocytoplasmic mRNA redistribution, RBP mislocalization, and splicing alterations. Mislocalized proteins exhibited a greater affinity for redistributed transcripts, suggesting a link between RBP mislocalization and mRNA redistribution. Notably, treatment with ML240, a VCP ATPase inhibitor, partially restored mRNA and protein localization in ALS mutant iPSMNs. ML240 induced changes in the VCP interactome and lysosomal localization and reduced oxidative stress and DNA damage. These findings emphasize the link between RBP mislocalization and mRNA redistribution in ALS motor neurons and highlight the therapeutic potential of VCP inhibition.

Profiling the polyadenylated transcriptome of extracellular vesicles with long-read nanopore sequencing

BACKGROUND

While numerous studies have described the transcriptomes of extracellular vesicles (EVs) in different cellular contexts, these efforts have typically relied on sequencing methods requiring RNA fragmentation, which limits interpretations on the integrity and isoform diversity of EV-targeted RNA populations. It has been assumed that mRNA signatures in EVs are likely to be fragmentation products of the cellular mRNA material, and the extent to which full-length mRNAs are present within EVs remains to be clarified.

RESULTS

Using long-read nanopore RNA sequencing, we sought to characterize the full-length polyadenylated (poly-A) transcriptome of EVs released by human chronic myelogenous leukemia K562 cells. We detected 443 and 280 RNAs that were respectively enriched or depleted in EVs. EV-enriched poly-A transcripts consist of a variety of biotypes, including mRNAs, long non-coding RNAs, and pseudogenes. Our analysis revealed that 10.58% of all EV reads, and 18.67% of all cellular (WC) reads, corresponded to known full-length transcripts, with mRNAs representing the largest biotype for each group (EV = 58.13%, WC = 43.93%). We also observed that for many well-represented coding and non-coding genes, diverse full-length transcript isoforms were present in EV specimens, and these isoforms were reflective-of but often in different ratio compared to cellular samples.

CONCLUSION

This work provides novel insights into the compositional diversity of poly-A transcript isoforms enriched within EVs, while also underscoring the potential usefulness of nanopore sequencing to interrogate secreted RNA transcriptomes.

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.

Deciphering shared attributes of plant long non-coding RNAs through a comparative computational approach

Over the past decade, long non-coding RNA (lncRNA), which lacks protein-coding potential, has emerged as an essential regulator of the genome. The present study examined 13,599 lncRNAs in Arabidopsis thaliana, 11,565 in Oryza sativa, and 32,397 in Zea mays for their characteristic features and explored the associated genomic and epigenomic features. We found lncRNAs were distributed throughout the chromosomes and the Helitron family of transposable elements (TEs) enriched, while the terminal inverted repeat depleted in lncRNA transcribing regions. Our analyses determined that lncRNA transcribing regions show rare or weak signals for most epigenetic marks except for H3K9me2 and cytosine methylation in all three plant species. LncRNAs showed preferential localization in the nucleus and cytoplasm; however, the distribution ratio in the cytoplasm and nucleus varies among the studied plant species. We identified several conserved endogenous target mimic sites in the lncRNAs among the studied plants. We found 233, 301, and 273 unique miRNAs, potentially targeting the lncRNAs of A. thaliana, O. sativa, and Z. mays, respectively. Our study has revealed that miRNAs, which interact with lncRNAs, target genes that are involved in a diverse array of biological and molecular processes. The miRNA-targeted lncRNAs displayed a strong affinity for several transcription factors, including ERF and BBR-BPC, mutually present in all three plants, advocating their conserved functions. Overall, the present study showed that plant lncRNAs exhibit conserved genomic and epigenomic characteristics and potentially govern the growth and development of plants.

Mechanisms of RNA export and nuclear retention

With the identification of huge amount of noncoding RNAs in recent years, the concept of RNA localization has extended from traditional mRNA export to RNA export of mRNA and ncRNA as well as nuclear retention of ncRNA. This review aims to summarize the recent findings from studies on the mechanisms of export of different RNAs and nuclear retention of some lncRNAs in higher eukaryotes, with a focus on splicing-dependent TREX recruitment for the export of spliced mRNA and the sequence-dependent mechanism of mRNA export in the absence of splicing. In addition, evidence to support the involvement of m⁶ A modification in RNA export with the coordination between the methylase complex and TREX complex as well as sequence-dependent nuclear retention of lncRNA is recapitulated. Finally, a model of sequence-dependent RNA localization is proposed along with the many questions that remain to be answered. This article is categorized under: RNA Export and Localization > RNA Localization RNA Export and Localization > Nuclear Export/Import.

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.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization

Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction.

Profiling subcellular localization of nuclear-encoded mitochondrial gene products in zebrafish

Most mitochondrial proteins are encoded by nuclear genes, synthetized in the cytosol and targeted into the organelle. To characterize the spatial organization of mitochondrial gene products in zebrafish (Danio rerio), we sequenced RNA from different cellular fractions. Our results confirmed the presence of nuclear-encoded mRNAs in the mitochondrial fraction, which in unperturbed conditions, are mainly transcripts encoding large proteins with specific properties, like transmembrane domains. To further explore the principles of mitochondrial protein compartmentalization in zebrafish, we quantified the transcriptomic changes for each subcellular fraction triggered by the chchd4a ^-/- mutation, causing the disorders in the mitochondrial protein import. Our results indicate that the proteostatic stress further restricts the population of transcripts on the mitochondrial surface, allowing only the largest and the most evolutionary conserved proteins to be synthetized there. We also show that many nuclear-encoded mitochondrial transcripts translated by the cytosolic ribosomes stay resistant to the global translation shutdown. Thus, vertebrates, in contrast to yeast, are not likely to use localized translation to facilitate synthesis of mitochondrial proteins under proteostatic stress conditions.

Illuminating RNA biology through imaging

RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.

Transcriptome-scale methods for uncovering subcellular RNA localization mechanisms

Across a variety of systems, thousands of RNAs are localized to specific subcellular locations. However, for the vast majority of these RNAs, the mechanisms that underlie their transport are unknown. Historically, these mechanisms were uncovered for a single transcript at a time by laboriously testing the ability of RNA fragments to direct transcript localization. Recently developed methods profile the content of subcellular transcriptomes using high-throughput sequencing, allowing the analysis of the localization of thousands of transcripts at once. By identifying commonalities shared among multiple localized transcripts, these methods have the potential to rapidly expand our understanding of RNA localization mechanisms.

The Role of Alternative Polyadenylation in the Regulation of Subcellular RNA Localization

Alternative polyadenylation (APA) is a widespread and conserved regulatory mechanism that generates diverse 3' ends on mRNA. APA patterns are often tissue specific and play an important role in cellular processes such as cell proliferation, differentiation, and response to stress. Many APA sites are found in 3' UTRs, generating mRNA isoforms with different 3' UTR contents. These alternate 3' UTR isoforms can change how the transcript is regulated, affecting its stability and translation. Since the subcellular localization of a transcript is often regulated by 3' UTR sequences, this implies that APA can also change transcript location. However, this connection between APA and RNA localization has only recently been explored. In this review, we discuss the role of APA in mRNA localization across distinct subcellular compartments. We also discuss current challenges and future advancements that will aid our understanding of how APA affects RNA localization and molecular mechanisms that drive these processes.

LncRNA-Chromatin Pull-Down Using Biotin-Conjugated DNA Probes

Long noncoding RNAs (lncRNAs) are a class of RNA molecules that have been associated with several important biological processes and linked to numerous diseases. Due to their cell type- and tissue specific expression, lncRNAs are involved in a wide range of molecular pathways. To fully understand how a lncRNA is linked to a biological process, its mechanism of action needs to be uncovered. Nuclear retained lncRNAs have been described to modulate gene expression directly or indirectly by interacting with chromatin and associated factors. Described here is an RNA pull-down strategy, which enables the identification of chromatin regions directly bound by a lncRNA of interest. This method is an important step toward investigating how lncRNAs regulate gene expression and/or chromatin states.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

Analysis of eukaryotic lincRNA sequences indicates signatures of hindered translation linked to selection pressure

Long intergenic noncoding RNAs (lincRNAs) represent a large fraction of transcribed loci in eukaryotic genomes. Although classified as noncoding, most lincRNAs contain open reading frames (ORFs), and it remains unclear why cytoplasmic lincRNAs are not or very inefficiently translated. Here, we analyzed signatures of hindered translation in lincRNA sequences from five eukaryotes, covering a range of natural selection pressures. In fission yeast and Caenorhabditis elegans, that is, species under strong selection, we detected significantly shorter ORFs, a suboptimal sequence context around start codons for translation initiation, and trinucleotides (“codons”) corresponding to less abundant tRNAs than for neutrally evolving control sequences, likely impeding translation elongation. For human, we detected signatures for cell-type-specific hindrance of lincRNA translation, in particular codons in abundant cytoplasmic lincRNAs corresponding to lower expressed tRNAs than control codons, in three out of five human cell lines. We verified that varying tRNA expression levels between cell lines are reflected in the amount of ribosomes bound to cytoplasmic lincRNAs in each cell line. We further propose that codons at ORF starts are particularly important for reducing ribosome-binding to cytoplasmic lincRNA ORFs. Altogether, our analyses indicate that in species under stronger selection lincRNAs evolved sequence features generally hindering translation and support cell-type-specific hindrance of translation efficiency in human lincRNAs. The sequence signatures we have identified may improve predicting peptide-coding and genuine noncoding lincRNAs in a cell type.

Analysis of subcellular transcriptomes by RNA proximity labeling with Halo-seq

Thousands of RNA species display nonuniform distribution within cells. However, quantification of the spatial patterns adopted by individual RNAs remains difficult, in part by a lack of quantitative tools for subcellular transcriptome analysis. In this study, we describe an RNA proximity labeling method that facilitates the quantification of subcellular RNA populations with high spatial specificity. This method, termed Halo-seq, pairs a light-activatable, radical generating small molecule with highly efficient Click chemistry to efficiently label and purify spatially defined RNA samples. We compared Halo-seq with previously reported similar methods and found that Halo-seq displayed a higher efficiency of RNA labeling, indicating that it is well suited to the investigation of small, precisely localized RNA populations. We then used Halo-seq to quantify nuclear, nucleolar and cytoplasmic transcriptomes, characterize their dynamic nature following perturbation, and identify RNA sequence features associated with their composition. Specifically, we found that RNAs containing AU-rich elements are relatively enriched in the nucleus. This enrichment becomes stronger upon treatment with the nuclear export inhibitor leptomycin B, both expanding the role of HuR in RNA export and generating a comprehensive set of transcripts whose export from the nucleus depends on HuR.

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.

Application of RNA subcellular fraction estimation method to explore RNA localization regulation

RNA localization is involved in multiple biological processes. Recent advances in subcellular fractionation based sequencing approaches uncovered localization pattern on a global scale. Most of existing methods adopt relative localization ratios (such as ratios of separately normalized TPMs of different subcellular fractions without considering the difference in total RNA abundances in different fractions), however, absolute ratios may yield different results on the preference to different cellular compartment. Experimentally, adding external Spike-in RNAs to different fractionation can be used to obtain absolute ratios. In addition, a spike-in independent computational approach based on multiple linear regression model can also be used. However, currently no custom tool is available. To solve this problem, we developed a method called Subcellular Fraction Abundance Estimator (SFAE) to correctly estimate relative RNA abundances of different subcellular fractionations. The ratios estimated by our method were consistent with existing reports. By applying the estimated ratios for different fractions, we explored the RNA localization pattern in cell lines and also predicted RBP motifs that were associated with different localization patterns. In addition, we showed that different isoforms of same genes could exhibit distinct localization patterns. To conclude, we believed our tool will facilitate future subcellular fractionation related sequencing study to explore the function of RNA localization in various biological problems.

Puromycin Labeling Coupled with Proximity Ligation Assays to Define Sites of mRNA Translation in Drosophila Embryos and Human Cells

Genetic mutations, whether they occur within protein-coding or noncoding regions of the genome, can affect various aspects of gene expression by influencing the complex network of intra- and intermolecular interactions that occur between cellular nucleic acids and proteins. One aspect of gene expression control that can be impacted is the intracellular trafficking and translation of mRNA molecules. To study the occurrence and dynamics of translational regulation, researchers have developed approaches such as genome-wide ribosome profiling and artificial reporters that enable single molecule imaging. In this paper, we describe a complementary and optimized approach that combines puromycin labeling with a proximity ligation assay (Puro-PLA) to define sites of translation of specific mRNAs in tissues or cells. This method can be used to study the mechanisms driving the translation of select mRNAs and to access the impact of genetic mutations on local protein synthesis. This approach involves the treatment of cell or tissue specimens with puromycin to label nascently translated peptides, rapid fixation, followed by immunolabeling with appropriate primary and secondary antibodies coupled to PLA oligonucleotide probes, ligation, amplification, and signal detection via fluorescence microscopy. Puro-PLA can be performed at small scale in individual tubes or in chambered slides, or in a high-throughput setup with 96-well plate, for both in situ and in vitro experimentation.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Persistent mRNA localization defects and cell death in ALS neurons caused by transient cellular stress

Persistent cytoplasmic aggregates containing RNA binding proteins (RBPs) are central to the pathogenesis of late-onset neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS). These aggregates share components, molecular mechanisms, and cellular protein quality control pathways with stress-induced RNA granules (SGs). Here, we assess the impact of stress on the global mRNA localization landscape of human pluripotent stem cell-derived motor neurons (PSC-MNs) using subcellular fractionation with RNA sequencing and proteomics. Transient stress disrupts subcellular RNA and protein distributions, alters the RNA binding profile of SG- and ALS-relevant RBPs and recapitulates disease-associated molecular changes such as aberrant splicing of STMN2. Although neurotypical PSC-MNs re-establish a normal subcellular localization landscape upon recovery from stress, cells harboring ALS-linked mutations are intransigent and display a delayed-onset increase in neuronal cell death. Our results highlight subcellular molecular distributions as predictive features and underscore the utility of cellular stress as a paradigm to study ALS-relevant mechanisms.

Sequence-dependent recruitment of SRSF1 and SRSF7 to intronless lncRNA NKILA promotes nuclear export via the TREX/TAP pathway

The TREX-TAP pathway is vital for mRNA export. For spliced mRNA, the TREX complex is recruited during splicing; however, for intronless mRNA, recruitment is sequence dependent. However, the export of cytoplasmic long noncoding RNA (lncRNA) is poorly characterized. We report the identification of a cytoplasmic accumulation region (CAR-N) in the intronless lncRNA, NKILA. CAR-N removal led to strong nuclear retention of NKILA, and CAR-N insertion promoted the export of cDNA transcripts. In vitro RNP purification via CAR-N, mass spectrometry, and siRNA screening revealed that SRSF1 and SRSF7 were vital to NKILA export, and identified a cluster of SRSF1/7 binding sites within a 55 nucleotide sequence in CAR-N. Significant nuclear enrichment of NKILA was observed for NKILA lacking CAR-N or the cluster of binding sites in knock-in models. Depletion of TREX-TAP pathway components resulted in strong nuclear retention of NKILA. RNA and protein immunoprecipitation verified that SRSF1/7 were bound to NKILA and interacted with UAP56 and ALYREF. Moreover, NKILA lacking CAR-N was unable to inhibit breast cancer cell migration. We concluded that the binding of SRSF1/7 to clustered motifs in CAR-N facilitated TREX recruitment, promoting the export of NKILA, and confirmed the importance of NKILA localization to its function.

Alternative 3' UTRs play a widespread role in translation-independent mRNA association with the endoplasmic reticulum

Transcripts encoding membrane and secreted proteins are known to associate with the endoplasmic reticulum (ER) through translation. Here, using cell fractionation, polysome profiling, and 3' end sequencing, we show that transcripts differ substantially in translation-independent ER association (TiERA). Genes in certain functional groups, such as cell signaling, tend to have significantly higher TiERA potentials than others, suggesting the importance of ER association for their mRNA metabolism, such as localized translation. The TiERA potential of a transcript is determined largely by size, sequence content, and RNA structures. Alternative polyadenylation (APA) isoforms can have distinct TiERA potentials because of changes in transcript features. The widespread 3' UTR lengthening in cell differentiation leads to greater transcript association with the ER, especially for genes that are capable of expressing very long 3' UTRs. Our data also indicate that TiERA is in dynamic competition with translation-dependent ER association, suggesting limited space on the ER for mRNA association.

LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival

BACKGROUND

The sequence content of the 3' UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging.

RESULTS

To combat this problem, we developed LABRAT, an APA isoform quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA isoform abundance in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between relative APA isoform abundance and overall gene expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA isoforms, finding that dozens promote broad, directional shifts in relative APA isoform abundance both in vitro and in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated.

CONCLUSIONS

We conclude that LABRAT has the ability to accurately quantify APA isoform ratios from RNAseq data across a variety of sample types. Further, LABRAT is able to derive biologically meaningful insights that connect APA isoform regulation to cellular and molecular phenotypes.

mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

BACKGROUND

Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs.

RESULTS

The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1-6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools.

CONCLUSIONS

This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server "mLoc-mRNA" is accessible at http://cabgrid.res.in:8080/mlocmrna/ . The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention

The spatial partitioning of the transcriptome in the cell is an important form of gene-expression regulation. Here, we address how intron retention influences the spatio-temporal dynamics of transcripts from two clinically relevant genes: TERT (Telomerase Reverse Transcriptase) pre-mRNA and TUG1 (Taurine-Upregulated Gene 1) lncRNA. Single molecule RNA FISH reveals that nuclear TERT transcripts uniformly and robustly retain specific introns. Our data suggest that the splicing of TERT retained introns occurs during mitosis. In contrast, TUG1 has a bimodal distribution of fully spliced cytoplasmic and intron-retained nuclear transcripts. We further test the functionality of intron-retention events using RNA-targeting thiomorpholino antisense oligonucleotides to block intron excision. We show that intron retention is the driving force for the nuclear compartmentalization of these RNAs. For both RNAs, altering this splicing-driven subcellular distribution has significant effects on cell viability. Together, these findings show that stable retention of specific introns can orchestrate spatial compartmentalization of these RNAs within the cell. This process reveals that modulating RNA localization via targeted intron retention can be utilized for RNA-based therapies.

Inter-dependent Centrosomal Co-localization of the cen and ik2 cis-Natural Antisense mRNAs in Drosophila

Overlapping genes are prevalent in most genomes, but the extent to which this organization influences regulatory events operating at the post-transcriptional level remains unclear. Studying the cen and ik2 genes of Drosophila melanogaster, which are convergently transcribed as cis-natural antisense transcripts (cis-NATs) with overlapping 3' UTRs, we found that their encoded mRNAs strikingly co-localize to centrosomes. These transcripts physically interact in a 3' UTR-dependent manner, and the targeting of ik2 requires its 3' UTR sequence and the presence of cen mRNA, which serves as the main driver of centrosomal co-localization. The cen transcript undergoes localized translation in proximity to centrosomes, and its localization is perturbed by polysome-disrupting drugs. By interrogating global fractionation-sequencing datasets generated from Drosophila and human cellular models, we find that RNAs expressed as cis-NATs tend to co-localize to specific subcellular fractions. This work suggests that post-transcriptional interactions between RNAs with complementary sequences can dictate their localization fate in the cytoplasm.

Translating Messages in Different Neighborhoods

Cellular distribution of biomolecules is important for regulating their function. In this issue of Developmental Cell, Chouaib et al., 2020 employ genetically tagged human cell lines to investigate the subcellular distribution of specific mRNAs and their encoded proteins, revealing several instances of localized translation with distinctive regulatory implications.

LNCcation: lncRNA localization and function

Subcellular localization of RNAs has gained attention in recent years as a prevalent phenomenon that influences numerous cellular processes. This is also evident for the large and relatively novel class of long noncoding RNAs (lncRNAs). Because lncRNAs are defined as RNA transcripts >200 nucleotides that do not encode protein, they are themselves the functional units, making their subcellular localization critical to their function. The discovery of tens of thousands of lncRNAs and the cumulative evidence involving them in almost every cellular activity render assessment of their subcellular localization essential to fully understanding their biology. In this review, we summarize current knowledge of lncRNA subcellular localization, factors controlling their localization, emerging themes, including the role of lncRNA isoforms and the involvement of lncRNAs in phase separation bodies, and the implications of lncRNA localization on their function and on cellular behavior. We also discuss gaps in the current knowledge as well as opportunities that these provide for novel avenues of investigation.

A putative stem-loop structure in Drosophila crumbs is required for mRNA localisation in epithelia and germline cells

Crumbs (Crb) is an evolutionarily conserved transmembrane protein localised to the apical membrane of epithelial cells. Loss or mislocalisation of Crb is often associated with disruption of apicobasal cell polarity. crb mRNA is also apically enriched in epithelial cells, and, as shown here, accumulates in the oocyte of developing egg chambers. We narrowed down the localisation element (LE) of crb mRNA to 47 nucleotides, which form a putative stem-loop structure that may be recognised by Egalitarian (Egl). Mutations in conserved nucleotides abrogate apical transport. crb mRNA enrichment in the oocyte is affected in egl mutant egg chambers. A CRISPR-based genomic deletion of the crb locus that includes the LE disrupts asymmetric crb mRNA localisation in epithelia and prevents its accumulation in the oocyte during early stages of oogenesis, but does not affect Crb protein localisation in embryonic and follicular epithelia. However, flies lacking the LE show ectopic Crb protein expression in the nurse cells. These data suggest an additional role for the Drosophila 3'-UTR in regulating translation in a tissue-specific manner.This article has an associated First Person interview with the first author of the paper.

SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning

MOTIVATION

mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed.

RESULTS

In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA.

Subcellular mRNA Localization Regulates Ribosome Biogenesis in Migrating Cells

Translation of ribosomal protein-coding mRNAs (RP-mRNAs) constitutes a key step in ribosome biogenesis, but the mechanisms that modulate RP-mRNA translation in coordination with other cellular processes are poorly defined. Here, we show that subcellular localization of RP-mRNAs acts as a key regulator of their translation during cell migration. As cells migrate into their surroundings, RP-mRNAs localize to the actin-rich cell protrusions. This localization is mediated by La-related protein 6 (LARP6), an RNA-binding protein that is enriched in protrusions. Protrusions act as hotspots of translation for RP-mRNAs, enhancing RP synthesis, ribosome biogenesis, and the overall protein synthesis in migratory cells. In human breast carcinomas, epithelial-to-mesenchymal transition (EMT) upregulates LARP6 expression to enhance protein synthesis and support invasive growth. Our findings reveal LARP6-mediated mRNA localization as a key regulator of ribosome biogenesis during cell migration and demonstrate a role for this process in cancer progression downstream of EMT.

mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization

Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regulate the amount of protein translated from each mRNA, thus allowing a tight temporal regulation of translation or buffering of protein levels from bursty transcription. Besides, mRNA localization performs a variety of additional roles like long-distance signaling, facilitating assembly of protein complexes and coordination of developmental processes. Here, we describe a novel machine-learning based tool, mRNALoc, to predict five sub-cellular locations of eukaryotic mRNAs using cDNA/mRNA sequences. During five fold cross-validations, the maximum overall accuracy was 65.19, 75.36, 67.10, 99.70 and 73.59% for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. Assessment on independent datasets revealed the prediction accuracies of 58.10, 69.23, 64.55, 96.88 and 69.35% for extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The corresponding values of AUC were 0.76, 0.75, 0.70, 0.98 and 0.74 for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The mRNALoc standalone software and web-server are freely available for academic use under GNU GPL at http://proteininformatics.org/mkumar/mrnaloc.

Back to the Future: Rethinking the Great Potential of lncRNAS for Optimizing Chemotherapeutic Response in Ovarian Cancer

Ovarian cancer (OC) is one of the most fatal cancers in women worldwide. Currently, platinum- and taxane-based chemotherapy is the mainstay for the treatment of OC. Yet, the emergence of chemoresistance results in therapeutic failure and significant relapse despite a consistent rate of primary response. Emerging evidence substantiates the potential role of lncRNAs in determining the response to standard chemotherapy in OC. The objective of this narrative review is to provide an integrated, synthesized overview of the current state of knowledge regarding the role of lncRNAs in the emergence of resistance to platinum- and taxane-based chemotherapy in OC. In addition, we sought to develop conceptual frameworks for harnessing the therapeutic potential of lncRNAs in strategies aimed at enhancing the chemotherapy response of OC. Furthermore, we offered significant new perspectives and insights on the interplay between lncRNAs and the molecular circuitries implicated in chemoresistance to determine their impacts on therapeutic response. Although this review summarizes robust data concerning the involvement of lncRNAs in the emergence of acquired resistance to platinum- and taxane-based chemotherapy in OC, effective approaches for translating these lncRNAs into clinical practice warrant further investigation.

Ribosomes as a nexus between translation and cancer progression: Focus on ribosomal Receptor for Activated C Kinase 1 (RACK1) in breast cancer

Ribosomes coordinate spatiotemporal control of gene expression, contributing to the acquisition and maintenance of cancer phenotype. The link between ribosomes and cancer is found in the roles of individual ribosomal proteins in tumorigenesis and cancer progression, including the ribosomal protein, receptor for activated C kinase 1 (RACK1). RACK1 regulates cancer cell invasion and is localized in spreading initiation centres, structural adhesion complexes containing RNA binding proteins and poly-adenylated mRNAs that suggest a local translation process. As RACK1 is a ribosomal protein directly involved in translation and in breast cancer progression, we propose a new molecular mechanism for breast cancer cell migration and invasion, which considers the molecular differences between epithelial and mesenchymal cell profiles in order to characterize and provide novel targets for therapeutic strategies. Hence, we provide an analysis on how ribosomes translate cancer progression with a final focus on the ribosomal protein RACK1 in breast cancer.

RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing

Subcellular localization is essential to RNA biogenesis, processing, and function across the gene expression life cycle. However, the specific nucleotide sequence motifs that direct RNA localization are incompletely understood. Fortunately, new sequencing technologies have provided transcriptome-wide atlases of RNA localization, creating an opportunity to leverage computational modeling. Here we present RNA-GPS, a new machine learning model that uses nucleotide-level features to predict RNA localization across eight different subcellular locations-the first to provide such a wide range of predictions. RNA-GPS's design enables high-throughput sequence ablation and feature importance analyses to probe the sequence motifs that drive localization prediction. We find localization informative motifs to be concentrated on 3'-UTRs and scattered along the coding sequence, and motifs related to splicing to be important drivers of predicted localization, even for cytotopic distinctions for membraneless bodies within the nucleus or for organelles within the cytoplasm. Overall, our results suggest transcript splicing is one of many elements influencing RNA subcellular localization.

Principles and mechanisms of asymmetric cell division

Asymmetric cell division (ACD) is an evolutionarily conserved mechanism used by prokaryotes and eukaryotes alike to control cell fate and generate cell diversity. A detailed mechanistic understanding of ACD is therefore necessary to understand cell fate decisions in health and disease. ACD can be manifested in the biased segregation of macromolecules, the differential partitioning of cell organelles, or differences in sibling cell size or shape. These events are usually preceded by and influenced by symmetry breaking events and cell polarization. In this Review, we focus predominantly on cell intrinsic mechanisms and their contribution to cell polarization, ACD and binary cell fate decisions. We discuss examples of polarized systems and detail how polarization is established and, whenever possible, how it contributes to ACD. Established and emerging model organisms will be considered alike, illuminating both well-documented and underexplored forms of polarization and ACD.

Prediction of mRNA subcellular localization using deep recurrent neural networks

MOTIVATION

Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood.

RESULTS

We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker's strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts.

AVAILABILITY AND IMPLEMENTATION

Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Subcellular Spatial Transcriptomes: Emerging Frontier for Understanding Gene Regulation

RNAs are trafficked and localized with exquisite precision inside the cell. Studies of candidate messenger RNAs have shown the vital importance of RNA subcellular location in development and cellular function. New sequencing- and imaging-based methods are providing complementary insights into subcellular localization of RNAs transcriptome-wide. APEX-seq and ribosome profiling as well as proximity-labeling approaches have revealed thousands of transcript isoforms are localized to distinct cytotopic locations, including locations that defy biochemical fractionation and hence were missed by prior studies. Sequences in the 3' and 5' untranslated regions (UTRs) serve as "zip codes" to direct transcripts to particular locales, and it is clear that intronic and retrotransposable sequences within transcripts have been co-opted by cells to control localization. Molecular motors, nuclear-to-cytosol RNA export, liquid-liquid phase separation, RNA modifications, and RNA structure dynamically shape the subcellular transcriptome. Location-based RNA regulation continues to pose new mysteries for the field, yet promises to reveal insights into fundamental cell biology and disease mechanisms.

Noncoding RNA MaIL1 is an integral component of the TLR4-TRIF pathway

RNA has been proposed as an important scaffolding factor in the nucleus, aiding protein complex assembly in the dense intracellular milieu. Architectural contributions of RNA to cytosolic signaling pathways, however, remain largely unknown. Here, we devised a multidimensional gradient approach, which systematically locates RNA components within cellular protein networks. Among a subset of noncoding RNAs (ncRNAs) cosedimenting with the ubiquitin-proteasome system, our approach unveiled ncRNA MaIL1 as a critical structural component of the Toll-like receptor 4 (TLR4) immune signal transduction pathway. RNA affinity antisense purification-mass spectrometry (RAP-MS) revealed MaIL1 binding to optineurin (OPTN), a ubiquitin-adapter platforming TBK1 kinase. MaIL1 binding stabilized OPTN, and consequently, loss of MaIL1 blunted OPTN aggregation, TBK1-dependent IRF3 phosphorylation, and type I interferon (IFN) gene transcription downstream of TLR4. MaIL1 expression was elevated in patients with active pulmonary infection and was highly correlated with IFN levels in bronchoalveolar lavage fluid. Our study uncovers MaIL1 as an integral RNA component of the TLR4-TRIF pathway and predicts further RNAs to be required for assembly and progression of cytosolic signaling networks in mammalian cells.

Localization elements and zip codes in the intracellular transport and localization of messenger RNAs in Saccharomyces cerevisiae

Intracellular trafficking and localization of mRNAs provide a mechanism of regulation of expression of genes with excellent spatial control. mRNA localization followed by localized translation appears to be a mechanism of targeted protein sorting to a specific cell-compartment, which is linked to the establishment of cell polarity, cell asymmetry, embryonic axis determination, and neuronal plasticity in metazoans. However, the complexity of the mechanism and the components of mRNA localization in higher organisms prompted the use of the unicellular organism Saccharomyces cerevisiae as a simplified model organism to study this vital process. Current knowledge indicates that a variety of mRNAs are asymmetrically and selectively localized to the tip of the bud of the daughter cells, to the vicinity of endoplasmic reticulum, mitochondria, and nucleus in this organism, which are connected to diverse cellular processes. Interestingly, specific cis-acting RNA localization elements (LEs) or RNA zip codes play a crucial role in the localization and trafficking of these localized mRNAs by providing critical binding sites for the specific RNA-binding proteins (RBPs). In this review, we present a comprehensive account of mRNA localization in S. cerevisiae, various types of localization elements influencing the mRNA localization, and the RBPs, which bind to these LEs to implement a number of vital physiological processes. Finally, we emphasize the significance of this process by highlighting their connection to several neuropathological disorders and cancers. This article is categorized under: RNA Export and Localization > RNA Localization.

Characterization of Hepatocellular Carcinoma Cell Lines Using a Fractionation-Then-Sequencing Approach Reveals Nuclear-Enriched HCC-Associated lncRNAs

Background: Advances in sequencing technologies have greatly improved our understanding of long noncoding RNA (lncRNA). These transcripts with lengths of >200 nucleotides may play significant regulatory roles in various biological processes. Importantly, the dysregulation of better characterized lncRNAs has been associated with multiple types of cancers, including hepatocellular carcinoma (HCC). There are many studies on altered lncRNA expression levels, very few, however, have focused on their subcellular localizations, from which accumulating evidences have indicated their close relationships to lncRNA functions. A transcriptome-wide investigation of the subcellular distributions of lncRNAs might thus provide new insights into their roles and functions in cancers.

Results: In this study, we subjected eight patient-derived HCC cell lines to subcellular fractionation and independently sequenced RNAs from the nuclear and cytoplasmic compartments. With the integration of tumor and tumor-adjacent RNA-seq datasets of liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas (TCGA), de novo transcriptome assembly and differential expression analysis were conducted successively and identified 26 nuclear-enriched HCC-associated lncRNAs shared between the HCC samples and the TCGA datasets, including the reported cancer driver PXN-AS1. The majority of nuclear-enriched HCC-associated lncRNAs were associated with the survival outcomes of HCC patients, exhibited characteristics similar to those of many experimentally supported HCC prognostic lncRNAs, and were co-expressed with protein-coding genes that have been linked to disease progression in various cancer types.

Conclusion: We adopted a fractionation-then-sequencing approach on multiple patient-derived HCC samples and identified nuclear-enriched, HCC-associated lncRNAs that could serve as important targets for HCC diagnosis and therapeutic development. This approach could be widely applicable to other studies into the disease etiologies of lncRNA.

Spatiotemporal Organization of the E. coli Transcriptome: Translation Independence and Engagement in Regulation

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E. coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E. coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.