Live-cell mapping of organelle-associated RNAs via proximity biotinylation combined with protein-RNA crosslinking

Protein targeting to the ER membrane: multiple pathways and shared machinery

The endoplasmic reticulum (ER) serves as a central hub for protein production and sorting in eukaryotic cells, processing approximately one-third of the cellular proteome. Protein targeting to the ER occurs through multiple pathways that operate both during and independent of translation. The classical translation-dependent pathway, mediated by cytosolic factors like signal recognition particle, recognizes signal peptides or transmembrane helices in nascent proteins, while translation-independent mechanisms utilize RNA-based targeting through specific sequence elements and RNA-binding proteins. At the core of these processes lies the Sec61 complex, which undergoes dynamic conformational changes and coordinates with numerous accessory factors to facilitate protein translocation and membrane insertion across and into the endoplasmic reticulum membrane. This review focuses on the molecular mechanisms of protein targeting to the ER, from the initial recognition of targeting signals to the dynamics of the translocation machinery, highlighting recent discoveries that have revealed unprecedented complexity in these cellular trafficking pathways.

Dynamic role of long noncoding RNA in liver diseases: pathogenesis and diagnostic aspects

Liver disease (LD) is complex pathological condition that has emerged as a major threat to human health and the quality of life. Nonetheless, the molecular mechanisms underlying the pathogenesis of LD have not yet been fully elucidated. Recently, a large amount of evidence has shown that long noncoding RNAs (lncRNAs) play important roles in diverse biological processes in the liver. The dysregulation of lncRNAs in the liver, for example, can affect tumor proliferation, migration, and invasion, contribute to hepatic metabolism disorder of lipid and glucose, and shape of hepatic tumoral microenvironment. Thus, a comprehensive understanding of the functional roles of lncRNAs in LD pathogenesis may provide new perspectives for the development of novel diagnostic and therapeutic tools. In the present review, we summarize the current findings on the relationship between lncRNAs and LD, including the modes of action of lncRNAs, the biological significance of lncRNAs in the pathogenesis of LD, especially in hepatocellular carcinoma (HCC) as well as in some non-neoplastic disorders, and the potential use of lncRNAs as diagnostic biomarkers and therapeutic targets for LD.

Fast Degradation of MecciRNAs by SUPV3L1/ELAC2 Provides a Novel Opportunity to Tackle Heart Failure With Exogenous MecciRNA

BACKGROUND

Circular RNAs derived from both nuclear and mitochondrial genomes are identified in animal cells. Mitochondria-encoded circular RNAs (mecciRNAs) are attracting more attention, and several members of mecciRNAs have already been recognized in regulating mitochondrial functions. Mitochondria dysfunctions are well-known to participate in heart failure (HF). This study was designed to investigate the RNA metabolism of mecciRNAs and the relevant roles and potential application of mecciRNAs in HF.

METHODS

Compared with highly stable nuclear genome-encoded circular RNAs, the fast degradation feature of mecciRNAs is identified by RNA sequencing and a series of molecular, biochemical, and cellular experiments. The substantial protective effects of in vitro synthesized mecciRNAs were tested in both doxorubicin- and pressure overload-induced mouse models of HF.

RESULTS

We discover that mecciRNAs are promptly degraded by an animal-conserved complex of helicase SUPV3L1 (suppressor of var1, 3-like protein 1) and endoribonuclease ELAC2 (elaC ribonuclease Z 2). MecciRNA degradation complex and mecciRNAs interact with mitochondrial permeability transition pore and its regulators including TRAP1 (TNF receptor-associated protein 1) and CypD (cyclophilin D). MecciRNAs regulate mitochondrial levels of TRAP1 and CypD to modulate the opening of mitochondrial permeability transition pore and the release of mitochondrial reactive oxygen species. Exogenously applied mecciRNAs interact with cytosolic TRAP1 and increase mitochondrial levels of TRAP1, and lead to a more closed state of mitochondrial permeability transition pore to constrain deleterious reactive oxygen species release. HF conditions lead to stimulated mecciRNA degradation, and administration of in vitro synthesized mecciRNAs exhibits substantial protective effects in both doxorubicin- and pressure overload-induced mouse models of HF.

CONCLUSIONS

This study demonstrates the fast degradation of mecciRNAs and the associated regulations of mitochondrial reactive oxygen species release of mitochondrial permeability transition pore by mecciRNAs. HF conditions lead to dysregulated mecciRNA degradation, and exogenous mecciRNAs demonstrate treatment potential in mouse models of HF.

A Comprehensive Review on RNA Subcellular Localization Prediction

The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.

Mapping RNA-Protein Interactions via Proximity Labeling-Based Approaches

RNA-protein interactions are fundamental to a wide range of biological processes, and understanding these interactions in their native cellular context is both vital and challenging. Traditional methods for studying RNA-protein interactions rely on crosslinking, which can introduce artifacts. Recently, proximity labeling-based techniques have emerged as powerful alternatives, offering a crosslinking-free approach to investigate these interactions. This review highlights recent advancements in the development and application of proximity labeling methods, focusing on both RNA-centric and protein-centric strategies for profiling cellular RNA-protein interactions. By examining these innovative approaches, we aim to provide insights into their potential for enhancing our understanding of RNA-protein dynamics in various biological settings.

Decoding subcellular RNA localization one molecule at a time

Eukaryotic cells are highly structured and composed of multiple membrane-bound and membraneless organelles. Subcellular RNA localization is a critical regulator of RNA function, influencing various biological processes. At any given moment, RNAs must accurately navigate the three-dimensional subcellular environment to ensure proper localization and function, governed by numerous factors, including splicing, RNA stability, modifications, and localizing sequences. Aberrant RNA localization can contribute to the development of numerous diseases. Here, we explore diverse RNA localization mechanisms and summarize advancements in methods for determining subcellular RNA localization, highlighting imaging techniques transforming our ability to study RNA dynamics at the single-molecule level.

Quantification of subcellular RNA localization through direct detection of RNA oxidation

Across cell types and organisms, thousands of RNAs display asymmetric subcellular distributions. Studying this process requires quantifying abundances of specific RNAs at precise subcellular locations. To analyze subcellular transcriptomes, multiple proximity-based techniques have been developed in which RNAs near a localized bait protein are specifically labeled, facilitating their biotinylation and purification. However, these complex methods are often laborious and require expensive enrichment reagents. To streamline the analysis of localized RNA populations, we developed Oxidation-Induced Nucleotide Conversion sequencing (OINC-seq). In OINC-seq, RNAs near a genetically encoded, localized bait protein are specifically oxidized in a photo-controllable manner. These oxidation events are then directly detected and quantified using high-throughput sequencing and our software package, PIGPEN, without the need for biotin-mediated enrichment. We demonstrate that OINC-seq can induce and quantify RNA oxidation with high specificity in a dose- and light-dependent manner. We further show the spatial specificity of OINC-seq by using it to quantify subcellular transcriptomes associated with the cytoplasm, ER, nucleus, and the inner and outer membranes of mitochondria. Finally, using transgenic zebrafish, we demonstrate that OINC-seq allows proximity-mediated RNA labeling in live animals. In sum, OINC-seq together with PIGPEN provide an accessible workflow for analyzing localized RNAs across different biological systems.

Phosphorylation of a nuclear condensate regulates cohesion and mRNA retention

Nuclear speckles are membraneless organelles that associate with active transcription sites and participate in post-transcriptional mRNA processing. During the cell cycle, nuclear speckles dissolve following phosphorylation of their protein components. Here, we identify the PP1 family as the phosphatases that counteract kinase-mediated dissolution. PP1 overexpression increases speckle cohesion and leads to retention of mRNA within speckles and the nucleus. Using APEX2 proximity labeling combined with RNA-sequencing, we characterize the recruitment of specific RNAs. We find that many transcripts are preferentially enriched within nuclear speckles compared to the nucleoplasm, particularly chromatin- and nucleus-associated transcripts. While total polyadenylated RNA retention increases with nuclear speckle cohesion, the ratios of most mRNA species to each other are constant, indicating non-selective retention. We further find that cellular responses to heat shock, oxidative stress, and hypoxia include changes to the phosphorylation and cohesion of nuclear speckles and to mRNA retention. Our results demonstrate that tuning the material properties of nuclear speckles provides a mechanism for the acute control of mRNA localization.

EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance

BACKGROUND

The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this. These methods face the problem of class imbalance in multi-label classification, causing models to favor majority classes and overlook minority classes during training. Additionally, traditional feature extraction methods have high computational costs, incomplete features, and may lead to the loss of critical information. On the other hand, deep learning methods face challenges related to hardware performance and training time when handling complex sequences. They may suffer from the curse of dimensionality and overfitting problems. Therefore, there is an urgent need for more efficient and accurate prediction models.

RESULTS

To address these issues, we propose a multi-label classifier, EDCLoc, for predicting mRNA subcellular localization. EDCLoc reduces training pressure through a stepwise pooling strategy and applies grouped convolution blocks of varying sizes at different levels, combined with residual connections, to achieve efficient feature extraction and gradient propagation. The model employs global max pooling at the end to further reduce feature dimensions and highlight key features. To tackle class imbalance, we improved the focal loss function to enhance the model's focus on minority classes. Evaluation results show that EDCLoc outperforms existing methods in most subcellular regions. Additionally, the position weight matrix extracted by multi-scale CNN filters can match known RNA-binding protein motifs, demonstrating EDCLoc's effectiveness in capturing key sequence features.

CONCLUSIONS

EDCLoc outperforms existing prediction tools in most subcellular regions and effectively mitigates class imbalance issues in multi-label classification. These advantages make EDCLoc a reliable choice for multi-label mRNA subcellular localization. The dataset and source code used in this study are available at https://github.com/DellCode233/EDCLoc .

lncRNAs maintain the functional phase state of nucleolar prion-like protein to facilitate rRNA processing

Liquid-to-solid phase transition of proteins with prion-like domains (PLDs) has been associated with neurodegenerative diseases and aging. High protein concentration is one important aspect triggering the transition; however, several prion-like proteins, including fibrillarin (FBL), an important phase-separated protein in the nucleolus for pre-rRNA processing, show relatively high expression levels in certain cells, especially cancer cells, without obvious phase transitions and growth arrest. How cells maintain prion-like protein proteostasis is still unknown. Here, we attempt to answer the question, with FBL as an example. We find that lncRNA DNAJC3-AS1 can buffer the behavior of FBL condensation and maintain the state and function of fibrillar component/dense fibrillar component (FC/DFC) units in human cell lines through two mechanisms, not only facilitating FBL condensation but also inhibiting excessive aggregation by binding multiple PLDs and partially blocking their interactions. We propose that lncRNAs could supply buffered systems to sustain functional phase states of prion-like proteins.

RNA-seq profiling identified a three-lncRNA panel in serum as potential biomarker for muscle-invasive bladder cancer

Background

Preoperative determination of muscular infiltration is crucial for appropriate treatment planning in patients with muscle-invasive bladder cancer (MIBC). We aimed to explore early diagnostic biomarkers in serum for MIBC in this study.

Methods

The expression profiles of long noncoding RNA (lncRNA) were initially screened by high-throughput sequencing and evaluation of potential lncRNAs were conducted by two phases of RT-qPCR assays using serum samples from 190 patients with MIBC and 190 non-muscle-invasive BC (NMIBC) patients. Multivariate logistic regression analysis was applied to establish a diagnostic signature with high accuracy and Fagan's nomogram was plotted to promote clinical application. Bioinformatics analysis was used to determine the potential miRNA-mRNA binding of candidate lncRNAs.

Results

We identified three differentially expressed lncRNAs (LINC00565, LINC00592 and NDUFA6-AS1) and established a 3-lncRNA panel which demonstrated high diagnostic accuracy for MIBC with an AUC of 0.903 (95% CI: 0.850-0.942) and 0.875 (95% CI: 0.802-0.928) in the training and validation set. Moreover, construction and assessment of Fagan'nomogram demonstrated that the 3-lncRNA panel could exhibit practical and helpful values for clinical use. Finally, a network map based on LINC00565 was constructed and we found that the expression of miR-143-5p and miR-4516 were significantly correlated with LINC00565 in MIBC.

Conclusion

Our findings indicated that the constructed 3-lncRNA panel in serum showed favorable diagnostic capacity and might serve as promising non-invasive biomarkers in the early diagnosis of MIBC.

Decoding protein-RNA interactions using CLIP-based methodologies

Protein-RNA interactions are central to all RNA processing events, with pivotal roles in the regulation of gene expression and cellular functions. Dysregulation of these interactions has been increasingly linked to the pathogenesis of human diseases. High-throughput approaches to identify RNA-binding proteins and their binding sites on RNA - in particular, ultraviolet crosslinking followed by immunoprecipitation (CLIP) - have helped to map the RNA interactome, yielding transcriptome-wide protein-RNA atlases that have contributed to key mechanistic insights into gene expression and gene-regulatory networks. Here, we review these recent advances, explore the effects of cellular context on RNA binding, and discuss how these insights are shaping our understanding of cellular biology. We also review the potential therapeutic applications arising from new knowledge of protein-RNA interactions.

Spatially Controlled MicroRNA Imaging in Mitochondria via Enzymatic Activation of Hybridization Chain Reaction

Live-cell imaging of RNA in specific subcellular compartments is essential for elucidating the rich repertoire of cellular functions, but it has been limited by a lack of simple, precisely controlled methods. Here such an approach is presented via the combination of hybridization chain reaction and spatially restricted enzymatic activation with organelle-targeted delivery. The system can localize engineered DNA hairpins in the mitochondria, where target RNA-initiated chain reaction of hybridization events is selectively activated by a specific enzyme, enabling amplified RNA imaging with high precision. It is demonstrated that the approach is compatible with live cell visualization and enables the regulatable imaging of microRNA in mitochondria. Since in situ activation of the signal amplification with enzyme eliminates the need for genetically encoded protein overexpression, it is envisioned that this simple platform will be broadly applicable for precise RNA imaging with subcellular resolution in a variety of biological processes.

Quantification of subcellular RNA localization through direct detection of RNA oxidation

Across cell types and organisms, thousands of RNAs display asymmetric subcellular distributions. The study of this process often requires quantifying abundances of specific RNAs at precise subcellular locations. To analyze subcellular transcriptomes, multiple proximity-based techniques have been developed in which RNAs near a localized bait protein are specifically labeled, facilitating their biotinylation and purification. However, these complex methods are often laborious and require expensive enrichment reagents. To streamline the analysis of localized RNA populations, we developed Oxidation-Induced Nucleotide Conversion sequencing (OINC-seq). In OINC-seq, RNAs near a genetically encoded, localized bait protein are specifically oxidized in a photo-controllable manner. These oxidation events are then directly detected and quantified using high-throughput sequencing and our software package, PIGPEN, without the need for biotin-mediated enrichment. We demonstrate that OINC-seq can induce and quantify RNA oxidation with high specificity in a dose- and light-dependent manner. We further show the spatial specificity of OINC-seq by using it to quantify subcellular transcriptomes associated with the cytoplasm, ER, nucleus, and the inner and outer membranes of mitochondria. Finally, using transgenic zebrafish, we demonstrate that OINC-seq allows proximity-mediated RNA labeling in live animals. In sum, OINC-seq together with PIGPEN provide an accessible workflow for the analysis of localized RNAs across different biological systems.

Multiomic characterization of RNA microenvironments by oligonucleotide-mediated proximity-interactome mapping

RNA molecules form complex networks of molecular interactions that are central to their function and to cellular architecture. But these interaction networks are difficult to probe in situ. Here, we introduce Oligonucleotide-mediated proximity-interactome MAPping (O-MAP), a method for elucidating the biomolecules near an RNA of interest, within its native context. O-MAP uses RNA-fluorescence in situ hybridization-like oligonucleotide probes to deliver proximity-biotinylating enzymes to a target RNA in situ, enabling nearby molecules to be enriched by streptavidin pulldown. This induces exceptionally precise biotinylation that can be easily optimized and ported to new targets or sample types. Using the noncoding RNAs 47S, 7SK and Xist as models, we develop O-MAP workflows for discovering RNA-proximal proteins, transcripts and genomic loci, yielding a multiomic characterization of these RNAs' subcellular compartments and new regulatory interactions. O-MAP requires no genetic manipulation, uses exclusively off-the-shelf parts and requires orders of magnitude fewer cells than established methods, making it accessible to most laboratories.

Spatial omics advances for in situ RNA biology

Spatial regulation of RNA plays a critical role in gene expression regulation and cellular function. Understanding spatially resolved RNA dynamics and translation is vital for bringing new insights into biological processes such as embryonic development, neurobiology, and disease pathology. This review explores past studies in subcellular, cellular, and tissue-level spatial RNA biology driven by diverse methodologies, ranging from cell fractionation, in situ and proximity labeling, imaging, spatially indexed next-generation sequencing (NGS) approaches, and spatially informed computational modeling. Particularly, recent advances have been made for near-genome-scale profiling of RNA and multimodal biomolecules at high spatial resolution. These methods enabled new discoveries into RNA's spatiotemporal kinetics, RNA processing, translation status, and RNA-protein interactions in cells and tissues. The evolving landscape of experimental and computational strategies reveals the complexity and heterogeneity of spatial RNA biology with subcellular resolution, heralding new avenues for RNA biology research.

Mapping subcellular RNA localization with proximity labeling

The subcellular localization of RNA is critical to a variety of physiological and pathological processes. Dissecting the spatiotemporal regulation of the transcriptome is key to understanding cell function and fate. However, it remains challenging to effectively enrich and catalogue RNAs from various subcellular structures using traditional approaches. In recent years, proximity labeling has emerged as an alternative strategy for efficient isolation and purification of RNA from these intricate subcellular compartments. This review focuses on examining RNA-related proximity labeling tools and exploring their application in elucidating the spatiotemporal regulation of RNA at the subcellular level.

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

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

Role of long noncoding RNAs in diabetes-associated peripheral arterial disease

Diabetes mellitus (DM) is a metabolic disease that heightens the risks of many vascular complications, including peripheral arterial disease (PAD). Various types of cells, including but not limited to endothelial cells (ECs), vascular smooth muscle cells (VSMCs), and macrophages (MΦs), play crucial roles in the pathogenesis of DM-PAD. Long non-coding RNAs (lncRNAs) are epigenetic regulators that play important roles in cellular function, and their dysregulation in DM can contribute to PAD. This review focuses on the developing field of lncRNAs and their emerging roles in linking DM and PAD. We review the studies investigating the role of lncRNAs in crucial cellular processes contributing to DM-PAD, including those in ECs, VSMCs, and MΦ. By examining the intricate molecular landscape governed by lncRNAs in these relevant cell types, we hope to shed light on the roles of lncRNAs in EC dysfunction, inflammatory responses, and vascular remodeling contributing to DM-PAD. Additionally, we provide an overview of the research approach and methodologies, from identifying disease-relevant lncRNAs to characterizing their molecular and cellular functions in the context of DM-PAD. We also discuss the potential of leveraging lncRNAs in the diagnosis and therapeutics for DM-PAD. Collectively, this review provides a summary of lncRNA-regulated cell functions contributing to DM-PAD and highlights the translational potential of leveraging lncRNA biology to tackle this increasingly prevalent and complex disease.

Tools to investigate the cell surface: Proximity as a central concept in glycoRNA biology

Proximity is a fundamental concept in chemistry and biology, referring to the convergence of molecules to facilitate new molecular interactions or reactions. Hybrid biopolymers like glycosylphosphatidylinositol (GPI)-anchored proteins, ubiquitinated proteins, glycosylated RNAs (glycoRNAs), and RNAylated proteins exemplify this by covalent bonding of moieties that are often orthogonally active. Hybrid molecules like glycoRNAs are localized to new physical spaces, generating new interfaces for biological functions. To fully investigate the compositional and spatial features of molecules like glycoRNAs, flexible genetic and chemical tools that encompass different encoding and targeting biopolymers are required. Here we discuss concepts of molecular proximity and explore newer proximity labeling technologies that facilitate applications in RNA biology, cell surface biology, and the interface therein with a particular focus on glycoRNA biology. We review the advantages and disadvantages of methods pertaining to cell surface RNA identification and provide insights into the vast opportunities for method development in this area.

Mapping RNA-protein interactions with subcellular resolution using colocalization CLIP

RNA-binding proteins (RBPs) are essential for RNA metabolism and profoundly impact health and disease. The subcellular organization of RBP interaction networks with target RNAs remains largely unexplored. Here, we develop colocalization CLIP (coCLIP), a method that combines cross-linking and immunoprecipitation (CLIP) with proximity labeling, to explore in-depth the subcellular RNA interactions of the RBP human antigen R (HuR). Using this method, we uncover HuR's dynamic and location-specific interactions with RNA, revealing alterations in sequence preferences and interactions in the nucleus, cytosol, or stress granule (SG) compartments. We uncover HuR's unique binding preferences within SGs during arsenite stress, illuminating intricate interactions that conventional methodologies cannot capture. Overall, coCLIP provides a powerful method for revealing RBP-RNA interactions based on localization and lays the foundation for an advanced understanding of RBP models that incorporate subcellular location as a critical determinant of their functions.

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.

Bioorthogonal masked acylating agents for proximity-dependent RNA labelling

RNA localization is highly regulated, with subcellular organization driving context-dependent cell physiology. Although proximity-based labelling technologies that use highly reactive radicals or carbenes provide a powerful method for unbiased mapping of protein organization within a cell, methods for unbiased RNA mapping are scarce and comparably less robust. Here we develop α-alkoxy thioenol and chloroenol esters that function as potent acylating agents upon controlled ester unmasking. We pair these probes with subcellular-localized expression of a bioorthogonal esterase to establish a platform for spatial analysis of RNA: bioorthogonal acylating agents for proximity labelling and sequencing (BAP-seq). We demonstrate that, by selectively unmasking the enol probe in a locale of interest, we can map RNA distribution in membrane-bound and membrane-less organelles. The controlled-release acylating agent chemistry and corresponding BAP-seq method expand the scope of proximity labelling technologies and provide a powerful approach to interrogate the cellular organization of RNAs.

Neuronal activity rapidly reprograms dendritic translation via eIF4G2:uORF binding

Learning and memory require activity-induced changes in dendritic translation, but which mRNAs are involved and how they are regulated are unclear. In this study, to monitor how depolarization impacts local dendritic biology, we employed a dendritically targeted proximity labeling approach followed by crosslinking immunoprecipitation, ribosome profiling and mass spectrometry. Depolarization of primary cortical neurons with KCl or the glutamate agonist DHPG caused rapid reprogramming of dendritic protein expression, where changes in dendritic mRNAs and proteins are weakly correlated. For a subset of pre-localized messages, depolarization increased the translation of upstream open reading frames (uORFs) and their downstream coding sequences, enabling localized production of proteins involved in long-term potentiation, cell signaling and energy metabolism. This activity-dependent translation was accompanied by the phosphorylation and recruitment of the non-canonical translation initiation factor eIF4G2, and the translated uORFs were sufficient to confer depolarization-induced, eIF4G2-dependent translational control. These studies uncovered an unanticipated mechanism by which activity-dependent uORF translational control by eIF4G2 couples activity to local dendritic remodeling.

Transcriptome regulation by PARP13 in basal and antiviral states in human cells

The RNA-binding protein PARP13 is a primary factor in the innate antiviral response, which suppresses translation and drives decay of bound viral and host RNA. PARP13 interacts with many proteins encoded by interferon-stimulated genes (ISG) to activate antiviral pathways including co-translational addition of ISG15, or ISGylation. We performed enhanced crosslinking immunoprecipitation (eCLIP) and RNA-seq in human cells to investigate PARP13's role in transcriptome regulation for both basal and antiviral states. We find that the antiviral response shifts PARP13 target localization, but not its binding preferences, and that PARP13 supports the expression of ISGylation-related genes, including PARP13's cofactor, TRIM25. PARP13 associates with TRIM25 via RNA-protein interactions, and we elucidate a transcriptome-wide periodicity of PARP13 binding around TRIM25. Taken together, our study implicates PARP13 in creating and maintaining a cellular environment poised for an antiviral response through limiting PARP13 translation, regulating access to distinct mRNA pools, and elevating ISGylation machinery expression.

Denaturing purifications demonstrate that PRC2 and other widely reported chromatin proteins do not appear to bind directly to RNA in vivo

Polycomb repressive complex 2 (PRC2) is reported to bind to many RNAs and has become a central player in reports of how long non-coding RNAs (lncRNAs) regulate gene expression. Yet, there is a growing discrepancy between the biochemical evidence supporting specific lncRNA-PRC2 interactions and functional evidence demonstrating that PRC2 is often dispensable for lncRNA function. Here, we revisit the evidence supporting RNA binding by PRC2 and show that many reported interactions may not occur in vivo. Using denaturing purification of in vivo crosslinked RNA-protein complexes in human and mouse cell lines, we observe a loss of detectable RNA binding to PRC2 and chromatin-associated proteins previously reported to bind RNA (CTCF, YY1, and others), despite accurately mapping bona fide RNA-binding sites across others (SPEN, TET2, and others). Taken together, these results argue for a critical re-evaluation of the broad role of RNA binding to orchestrate various chromatin regulatory mechanisms.

Creative approaches using proximity labeling to gain new biological insights

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

Mitochondria-Associated Transcriptome Profiling via Localizable Aggregation-Induced Emission Photosensitizers in Live Cells

In recent decades, there has been increasing interest in studying mitochondria through transcriptomic research. Various exogenous fusion protein-based proximity labeling methods have been reported that focus on the site of one particular protein/peptide and might also influence the corresponding localization or interactome. To enable unbiased and high spatial-resolution profiling of mitochondria-associated transcriptomes in live cells, a flexible RNA proximity labeling approach was developed using aggregation-induced emission (AIE) type photosensitizers (PSs) that possess great mitochondria-targeting capabilities. Their accumulation in an enclosed mitochondrial environment tends to enhance the fluorescence emission and reactive oxygen species generation. By comparing the in vitro optical properties, photosensitization processes, as well as the in cellulo mitochondrial specificity and RNA labeling performance of four AIE PSs, high-throughput sequencing analysis was conducted using TFPy-mediated RNA proximity labeling in live HeLa cells. This approach successfully captured a comprehensive list of transcripts, including mitochondria-encoded RNAs, as well as some nuclear-derived RNAs located at the outer mitochondrial membrane and interacting organelles. This small molecule-based proximity labeling method bypasses complex genetic manipulation and transfection steps, making it readily applicable for diverse research purposes.

Unified mRNA Subcellular Localization Predictor based on machine learning techniques

BACKGROUND

The 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.

METHODS

In 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).

RESULTS

The 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.

AVAILABILITY

We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

Revealing protein trafficking by proximity labeling-based proteomics

Protein trafficking is a fundamental process with profound implications for both intracellular and intercellular functions. Proximity labeling (PL) technology has emerged as a powerful tool for capturing precise snapshots of subcellular proteomes by directing promiscuous enzymes to specific cellular locations. These enzymes generate reactive species that tag endogenous proteins, enabling their identification through mass spectrometry-based proteomics. In this comprehensive review, we delve into recent advancements in PL-based methodologies, placing particular emphasis on the label-and-fractionation approach and TransitID, for mapping proteome trafficking. These methodologies not only facilitate the exploration of dynamic intracellular protein trafficking between organelles but also illuminate the intricate web of intercellular and inter-organ protein communications.

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.

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.

Proteomic Analyses of the G Protein-Coupled Estrogen Receptor GPER1 Reveal Constitutive Links to Endoplasmic Reticulum, Glycosylation, Trafficking, and Calcium Signaling

The G protein-coupled estrogen receptor 1 (GPER1) has been proposed to mediate rapid responses to the steroid hormone estrogen. However, despite a strong interest in its potential role in cancer, whether it is indeed activated by estrogen and how this works remain controversial. To provide new tools to address these questions, we set out to determine the interactome of exogenously expressed GPER1. The combination of two orthogonal methods, namely APEX2-mediated proximity labeling and immunoprecipitation followed by mass spectrometry, gave us high-confidence results for 73 novel potential GPER1 interactors. We found that this GPER1 interactome is not affected by estrogen, a result that mirrors the constitutive activity of GPER1 in a functional assay with a Rac1 sensor. We specifically validated several hits highlighted by a gene ontology analysis. We demonstrate that CLPTM1 interacts with GPER1 and that PRKCSH and GANAB, the regulatory and catalytic subunits of α-glucosidase II, respectively, associate with CLPTM1 and potentially indirectly with GPER1. An imbalance in CLPTM1 levels induces nuclear association of GPER1, as does the overexpression of PRKCSH. Moreover, we show that the Ca2+ sensor STIM1 interacts with GPER1 and that upon STIM1 overexpression and depletion of Ca2+ stores, GPER1 becomes more nuclear. Thus, these new GPER1 interactors establish interesting connections with membrane protein maturation, trafficking, and calcium signaling.

The development of proximity labeling technology and its applications in mammals, plants, and microorganisms

Protein‒protein, protein‒RNA, and protein‒DNA interaction networks form the basis of cellular regulation and signal transduction, making it crucial to explore these interaction networks to understand complex biological processes. Traditional methods such as affinity purification and yeast two-hybrid assays have been shown to have limitations, as they can only isolate high-affinity molecular interactions under nonphysiological conditions or in vitro. Moreover, these methods have shortcomings for organelle isolation and protein subcellular localization. To address these issues, proximity labeling techniques have been developed. This technology not only overcomes the limitations of traditional methods but also offers unique advantages in studying protein spatial characteristics and molecular interactions within living cells. Currently, this technique not only is indispensable in research on mammalian nucleoprotein interactions but also provides a reliable approach for studying nonmammalian cells, such as plants, parasites and viruses. Given these advantages, this article provides a detailed introduction to the principles of proximity labeling techniques and the development of labeling enzymes. The focus is on summarizing the recent applications of TurboID and miniTurbo in mammals, plants, and microorganisms. Video Abstract.

Controlled Level of Contamination Coupled to Deep Sequencing (CoLoC-seq) Probes the Global Localisation Topology of Organelle Transcriptomes

Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host-pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles. Key features • Tested on human mitochondria; potentially applicable to cell cultures, non-model organisms, extracellular vesicles, enveloped viruses, tissues; does not require genetic manipulations or highly pure organelles. • In the case of human cells, the required amount of starting material is ~2,500 cm2 of 80% confluent cells (or ~3 × 108 HEK293 cells). • CoLoC-seq implements a special RNA-seq strategy to selectively capture intact transcripts, which requires RNases generating 5'-hydroxyl and 2'/3'-phosphate termini (e.g., RNase A, RNase I). • Relies on nonlinear regression software with customisable exponential functions.

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.

Role of long non-coding RNAs in cancer: From subcellular localization to nanoparticle-mediated targeted regulation

Long non-coding RNAs (lncRNAs) are a class of RNA transcripts more than 200 nucleotides in length that play crucial roles in cancer development and progression. With the rapid development of high-throughput sequencing technology, a considerable number of lncRNAs have been identified as novel biomarkers for predicting the prognosis of cancer patients and/or therapeutic targets for cancer therapy. In recent years, increasing evidence has shown that the biological functions and regulatory mechanisms of lncRNAs are closely associated with their subcellular localization. More importantly, based on the important roles of lncRNAs in regulating cancer progression (e.g., growth, therapeutic resistance, and metastasis) and the specific ability of nucleic acids (e.g., siRNA, mRNA, and DNA) to regulate the expression of any target genes, much effort has been exerted recently to develop nanoparticle (NP)-based nucleic acid delivery systems for in vivo regulation of lncRNA expression and cancer therapy. In this review, we introduce the subcellular localization and regulatory mechanisms of various functional lncRNAs in cancer and systemically summarize the recent development of NP-mediated nucleic acid delivery for targeted regulation of lncRNA expression and effective cancer therapy.

Advances in proteomic phenotyping of microglia in neurodegeneration

Microglia are dynamic resident immune cells of the central nervous system (CNS) that sense, survey, and respond to changes in their environment. In disease states, microglia transform from homeostatic to diverse molecular phenotypic states that play complex and causal roles in neurologic disease pathogenesis, as evidenced by the identification of microglial genes as genetic risk factors for neurodegenerative disease. While advances in transcriptomic profiling of microglia from the CNS of humans and animal models have provided transformative insights, the transcriptome is only modestly reflective of the proteome. Proteomic profiling of microglia is therefore more likely to provide functionally and therapeutically relevant targets. In this review, we discuss molecular insights gained from transcriptomic studies of microglia in the context of Alzheimer's disease as a prototypic neurodegenerative disease, and highlight existing and emerging approaches for proteomic profiling of microglia derived from in vivo model systems and human brain.

Spatially resolved single-cell translatomics at molecular resolution

The precise control of messenger RNA (mRNA) translation is a crucial step in posttranscriptional gene regulation of cellular physiology. However, it remains a challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of ribosome-bound mRNA mapping (RIBOmap), a highly multiplexed three-dimensional in situ profiling method to detect cellular translatome. RIBOmap profiling of 981 genes in HeLa cells revealed cell cycle-dependent translational control and colocalized translation of functional gene modules. We mapped 5413 genes in mouse brain tissues, yielding spatially resolved single-cell translatomic profiles for 119,173 cells and revealing cell type-specific and brain region-specific translational regulation, including translation remodeling during oligodendrocyte maturation. Our method detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks.

Chemical labeling and proteomics for characterization of unannotated small and alternative open reading frame-encoded polypeptides

Thousands of unannotated small and alternative open reading frames (smORFs and alt-ORFs, respectively) have recently been revealed in mammalian genomes. While hundreds of mammalian smORF- and alt-ORF-encoded proteins (SEPs and alt-proteins, respectively) affect cell proliferation, the overwhelming majority of smORFs and alt-ORFs remain uncharacterized at the molecular level. Complicating the task of identifying the biological roles of smORFs and alt-ORFs, the SEPs and alt-proteins that they encode exhibit limited sequence homology to protein domains of known function. Experimental techniques for the functionalization of these gene classes are therefore required. Approaches combining chemical labeling and quantitative proteomics have greatly advanced our ability to identify and characterize functional SEPs and alt-proteins in high throughput. In this review, we briefly describe the principles of proteomic discovery of SEPs and alt-proteins, then summarize how these technologies interface with chemical labeling for identification of SEPs and alt-proteins with specific properties, as well as in defining the interactome of SEPs and alt-proteins.

Endogenous TOM20 Proximity Labeling: A Swiss-Knife for the Study of Mitochondrial Proteins in Human Cells

Biotin-based proximity labeling approaches, such as BioID, have demonstrated their use for the study of mitochondria proteomes in living cells. The use of genetically engineered BioID cell lines enables the detailed characterization of poorly characterized processes such as mitochondrial co-translational import. In this process, translation is coupled to the translocation of the mitochondrial proteins, alleviating the energy cost typically associated with the post-translational import relying on chaperone systems. However, the mechanisms are still unclear with only few actors identified but none that have been described in mammals yet. We thus profiled the TOM20 proxisome using BioID, assuming that some of the identified proteins could be molecular actors of the co-translational import in human cells. The obtained results showed a high enrichment of RNA binding proteins close to the TOM complex. However, for the few selected candidates, we could not demonstrate a role in the mitochondrial co-translational import process. Nonetheless, we were able to demonstrate additional uses of our BioID cell line. Indeed, the experimental approach used in this study is thus proposed for the identification of mitochondrial co-translational import effectors and for the monitoring of protein entry inside mitochondria with a potential application in the prediction of mitochondrial protein half-life.

Proximity Labeling in Plants

Proteins are workhorses in the cell; they form stable and more often dynamic, transient protein-protein interactions, assemblies, and networks and have an intimate interplay with DNA and RNA. These network interactions underlie fundamental biological processes and play essential roles in cellular function. The proximity-dependent biotinylation labeling approach combined with mass spectrometry (PL-MS) has recently emerged as a powerful technique to dissect the complex cellular network at the molecular level. In PL-MS, by fusing a genetically encoded proximity-labeling (PL) enzyme to a protein or a localization signal peptide, the enzyme is targeted to a protein complex of interest or to an organelle, allowing labeling of proximity proteins within a zoom radius. These biotinylated proteins can then be captured by streptavidin beads and identified and quantified by mass spectrometry. Recently engineered PL enzymes such as TurboID have a much-improved enzymatic activity, enabling spatiotemporal mapping with a dramatically increased signal-to-noise ratio. PL-MS has revolutionized the way we perform proteomics by overcoming several hurdles imposed by traditional technology, such as biochemical fractionation and affinity purification mass spectrometry. In this review, we focus on biotin ligase-based PL-MS applications that have been, or are likely to be, adopted by the plant field. We discuss the experimental designs and review the different choices for engineered biotin ligases, enrichment, and quantification strategies. Lastly, we review the validation and discuss future perspectives.

MSLP: mRNA subcellular localization predictor based on machine learning techniques

BACKGROUND

Subcellular 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.

METHODS

In 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.

RESULTS

Considering 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.

AVAILABILITY

We 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 .

Heme sensing and trafficking in fungi

Fungal pathogens cause life-threatening diseases in humans, and the increasing prevalence of these diseases emphasizes the need for new targets for therapeutic intervention. Nutrient acquisition during infection is a promising target, and recent studies highlight the contributions of endomembrane trafficking, mitochondria, and vacuoles in the sensing and acquisition of heme by fungi. These studies have been facilitated by genetically encoded biosensors and other tools to quantitate heme in subcellular compartments and to investigate the dynamics of trafficking in living cells. In particular, the applications of biosensors in fungi have been extended beyond the detection of metabolites, cofactors, pH, and redox status to include the detection of heme. Here, we focus on studies that make use of biosensors to examine mechanisms of heme uptake and degradation, with guidance from the model fungus Saccharomyces cerevisiae and an emphasis on the pathogenic fungi Candida albicans and Cryptococcus neoformans that threaten human health. These studies emphasize a role for endocytosis in heme uptake, and highlight membrane contact sites involving mitochondria, the endoplasmic reticulum and vacuoles as mediators of intracellular iron and heme trafficking.

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.

Oligonucleotide-directed proximity-interactome mapping (O-MAP): A unified method for discovering RNA-interacting proteins, transcripts and genomic loci in situ

Throughout biology, RNA molecules form complex networks of molecular interactions that are central to their function, but remain challenging to investigate. Here, we introduce Oligonucleotide-mediated proximity-interactome MAPping (O-MAP), a straightforward method for elucidating the biomolecules near an RNA of interest, within its native cellular context. O-MAP uses programmable oligonucleotide probes to deliver proximity-biotinylating enzymes to a target RNA, enabling nearby molecules to be enriched by streptavidin pulldown. O-MAP induces exceptionally precise RNA-localized in situ biotinylation, and unlike alternative methods it enables straightforward optimization of its targeting accuracy. Using the 47S pre-ribosomal RNA and long noncoding RNA Xist as models, we develop O-MAP workflows for unbiased discovery of RNA-proximal proteins, transcripts, and genomic loci. This revealed unexpected co-compartmentalization of Xist and other chromatin-regulatory RNAs and enabled systematic characterization of nucleolar-chromatin interactions across multiple cell lines. O-MAP is portable to cultured cells, organoids, and tissues, and to RNAs of various lengths, abundances, and sequence composition. And, O-MAP requires no genetic manipulation and uses exclusively off-the-shelf parts. We therefore anticipate its application to a broad array of RNA phenomena.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Efficient TurboID-based proximity labelling method for identifying terminal sialic acid glycosylation in living cells

TurboID, a proximity labelling method based on mutant biotin ligase, is an efficient new technique for recognizing protein-protein interactions and has been successfully applied to living cells. Sialic acid is typically the terminal monosaccharide attached to many glycoproteins and plays many important roles in many biological processes. However, the lack of enrichment methods for terminal sialic acid glycosylation in vivo hinders the identification and analysis of this glycosylation. Here, we introduce TurboID to identify terminal sialic acid glycosylation in living cells. SpCBM, the carbohydrate-binding domain of sialidase from Streptococcus pneumoniae, is fused with TurboID and overexpressed in HeLa cells. After streptavidin-based purification and detection by mass spectrometry, 31 terminal sialic acid N-glycosylated sites and 1359 putative terminal sialic acid glycosylated proteins are identified, many of which are located in the cytoplasm and nucleus.

CoLoC-seq probes the global topology of organelle transcriptomes

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from numerous false-positives since incompletely digested contaminant RNAs can still be captured and erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat. CoLoC-seq leverages classical enzymatic kinetics and tracks the depletion dynamics of transcripts in a gradient of an exogenously added RNase, with or without organellar membranes. By means of straightforward mathematical modelling, CoLoC-seq infers the localisation topology of RNAs and robustly distinguishes between genuinely resident, luminal transcripts and merely abundant surface-attached contaminants. Our generic approach performed well on human mitochondria and is in principle applicable to other membrane-bounded organelles, including plastids, compartments of the vacuolar system, extracellular vesicles, and viral particles.

Subcellular distribution, localization, and function of noncoding RNAs

Eukaryotic cells contain subcellular organelles with spatiotemporal regulation to coordinate various biochemical reactions. The various organelles perform their essential biological functions by employing specific biomolecules, including nucleic acids. Recent studies have revealed that noncoding RNAs (ncRNAs) are highly compartmentalized in cells and that their spatial distribution is intimately related to their functions. Dysregulation of subcellular ncRNAs can disrupt cellular homeostasis and cause human diseases. Mitochondria are responsible for energy generation to fuel cell growth and proliferation. Therefore, identifying mitochondria-associated ncRNAs helps to reveal new regulatory mechanisms and physiological functions of mitochondria. In this review, we summarize the latest advances in subcellular ncRNAs derived from either the nuclear or mitochondrial genome. We also discuss available biological approaches for investigating organelle-specific ncRNAs. Exploring the distribution and function of subcellular ncRNAs may facilitate the understanding of endomembrane dynamics and provide potential strategies for clinical transformation. This article is categorized under: RNA Export and Localization > RNA Localization Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Methods > RNA Analyses in Cells.