Regulatory non-coding RNAs: everything is possible, but what is important?

CyCoNP lncRNA establishes cis and trans RNA-RNA interactions to supervise neuron physiology

The combination of morphogenetic and transcription factors together with the synergic aid of noncoding RNAs and their cognate RNA binding proteins contribute to shape motor neurons (MN) identity. Here, we extend the noncoding perspective of human MN, by detailing the molecular and biological activity of CyCoNP (as Cytoplasmic Coordinator of Neural Progenitors) a highly expressed and MN-enriched human lncRNA. Through in silico prediction, in vivo RNA purification and loss of function experiments followed by RNA-sequencing, we found that CyCoNP sustains a specific neuron differentiation program, required for the physiology of both neuroblastoma cells and hiPSC-derived MN, which mainly involves miR-4492 and NCAM1 mRNA. We propose a novel lncRNA-mediated 'dual mode' of action, in which CyCoNP acts in trans as a classical RNA sponge by sequestering miR-4492 from its pro-neuronal targets, including NCAM1 mRNA, and at the same time it plays an additional role in cis by interacting with NCAM1 mRNA and regulating the availability and localization of the miR-4492 in its proximity. These data highlight novel insights into the noncoding RNA-mediated control of human neuron physiology and point out the importance of lncRNA-mediated interactions for the spatial distribution of regulatory molecules.

ChimericFragments: computation, analysis and visualization of global RNA networks

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

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.

FOXO1, a tiny protein with intricate interactions: Promising therapeutic candidate in lung cancer

Nowadays, lung cancer is the most common cause of cancer-related deaths in both men and women globally. Despite the development of extremely efficient targeted agents, lung cancer progression and drug resistance remain serious clinical issues. Increasing knowledge of the molecular mechanisms underlying progression and drug resistance will enable the development of novel therapeutic methods. It has been revealed that transcription factors (TF) dysregulation, which results in considerable expression modifications of genes, is a generally prevalent phenomenon regarding human malignancies. The forkhead box O1 (FOXO1), a member of the forkhead transcription factor family with crucial roles in cell fate decisions, is suggested to play a pivotal role as a tumor suppressor in a variety of malignancies, especially in lung cancer. FOXO1 is involved in diverse cellular processes and also has clinical significance consisting of cell cycle arrest, apoptosis, DNA repair, oxidative stress, cancer prevention, treatment, and chemo/radioresistance. Based on the critical role of FOXO1, this transcription factor appears to be an appropriate target for future drug discovery in lung cancers. This review focused on the signaling pathways, and molecular mechanisms involved in FOXO1 regulation in lung cancer. We also discuss pharmacological compounds that are currently being administered for lung cancer treatment by affecting FOXO1 and also point out the essential role of FOXO1 in drug resistance. Future preclinical research should assess combination drug strategies to stimulate FOXO1 and its upstream regulators as potential strategies to treat resistant or advanced lung cancers.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Novel Insights into Circular RNAs in Metastasis in Breast Cancer: An Update

Circular RNAs (circRNAs) are single-stranded closed non-coding RNA molecules that are aberrantly expressed and produce tumor-specific gene signatures in human cancers. They exert biological functions by acting as transcriptional regulators, microRNA sponges, and protein scaffolds, regulating the formation of protein-RNA complexes and, ultimately, regulating gene expression. Triple-negative breast cancer (TNBC) is one of the most aggressive cancers of the mammary gland and has a poor prognosis. Studies of circRNAs in TNBC are limited but have demonstrated these molecules' pivotal roles in cell proliferation, invasion, metastasis, and resistance to chemo/radiotherapy, suggesting that they could be potential prognostic biomarkers and novel therapeutic targets. Here, we reviewed the status of actual knowledge about circRNA biogenesis and functions and summarized novel findings regarding their roles in TNBC development and progression. In addition, we discussed recent data about the importance of exosomes in the transport and export of circRNAs in TNBC. Deep knowledge of circRNA functions in metastasis and therapy responses could be an invaluable guide in the identification of novel therapeutic targets for advancing the treatment of TNBC.

The DNA binding high mobility group box protein family functionally binds RNA

Nucleic acid binding proteins regulate transcription, splicing, RNA stability, RNA localization, and translation, together tailoring gene expression in response to stimuli. Upon discovery, these proteins are typically classified as either DNA or RNA binding as defined by their in vivo functions; however, recent evidence suggests dual DNA and RNA binding by many of these proteins. High mobility group box (HMGB) proteins have a DNA binding HMGB domain, act as transcription factors and chromatin remodeling proteins, and are increasingly understood to interact with RNA as means to regulate gene expression. Herein, multiple layers of evidence that the HMGB family are dual DNA and RNA binding proteins is comprehensively reviewed. For example, HMGB proteins directly interact with RNA in vitro and in vivo, are localized to RNP granules involved in RNA processing, and their protein interactors are enriched in RNA binding proteins involved in RNA metabolism. Importantly, in cell-based systems, HMGB-RNA interactions facilitate protein-protein interactions, impact splicing outcomes, and modify HMGB protein genomic or cellular localization. Misregulation of these HMGB-RNA interactions are also likely involved in human disease. This review brings to light that as a family, HMGB proteins are likely to bind RNA which is essential to HMGB protein biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Challenges in structural modeling of RNA-protein interactions

In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Non-coding RNAs in renal cell carcinoma: Implications for drug resistance

Renal cell carcinoma (RCC) represents a malignant tumor of the urinary system. Individuals with early-stage RCC could be cured by surgical treatment, but a considerable number of cases of advanced RCC progress to drug resistance. Recently, numerous reports have demonstrated that a variety of non-coding RNAs (ncRNAs) contribute to tumor occurrence and development. ncRNAs can act as oncogenic or tumor suppressor genes to regulate proliferation, migration, drug resistance and other processes in RCC cells through a variety of signaling pathways. Considering the lack of treatment options for advanced RCC after drug resistance, ncRNAs may be a good choice as biomarkers of drug resistance in RCC and targets to overcome drug resistance. In this review, we discussed the effects of ncRNAs on drug resistance in RCC and the great potential of ncRNAs as a biomarker of or a new therapeutic method in RCC.

SPIDR: a highly multiplexed method for mapping RNA-protein interactions uncovers a potential mechanism for selective translational suppression upon cellular stress

RNA binding proteins (RBPs) play crucial roles in regulating every stage of the mRNA life cycle and mediating non-coding RNA functions. Despite their importance, the specific roles of most RBPs remain unexplored because we do not know what specific RNAs most RBPs bind. Current methods, such as crosslinking and immunoprecipitation followed by sequencing (CLIP-seq), have expanded our knowledge of RBP-RNA interactions but are generally limited by their ability to map only one RBP at a time. To address this limitation, we developed SPIDR (Split and Pool Identification of RBP targets), a massively multiplexed method to simultaneously profile global RNA binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR employs split-pool barcoding coupled with antibody-bead barcoding to increase the throughput of current CLIP methods by two orders of magnitude. SPIDR reliably identifies precise, single-nucleotide RNA binding sites for diverse classes of RBPs simultaneously. Using SPIDR, we explored changes in RBP binding upon mTOR inhibition and identified that 4EBP1 acts as a dynamic RBP that selectively binds to 5'-untranslated regions of specific translationally repressed mRNAs only upon mTOR inhibition. This observation provides a potential mechanism to explain the specificity of translational regulation controlled by mTOR signaling. SPIDR has the potential to revolutionize our understanding of RNA biology and both transcriptional and post-transcriptional gene regulation by enabling rapid, de novo discovery of RNA-protein interactions at an unprecedented scale.

Long noncoding RNAs in cardiovascular disease

PURPOSE OF REVIEW

Here, we review recent findings on the role of long noncoding RNAs (lncRNAs) in cardiovascular disease (CVD). In addition, we highlight some of the latest findings in lncRNA biology, providing an outlook for future avenues of lncRNA research in CVD.

RECENT FINDINGS

Recent publications provide translational evidence from patient studies and animal models for the role of specific lncRNAs in CVD. The molecular effector mechanisms of these lncRNAs are diverse. Overall, cell-type selective modulation of gene expression is the largest common denominator. New methods, such as single-cell profiling and CRISPR/Cas9-screening, reveal additional novel mechanistic principles: For example, many lncRNAs establish RNA-based spatial compartments that concentrate effector proteins. Also, RNA modifications and splicing features can be determinants of lncRNA function.

SUMMARY

lncRNA research is passing the stage of enumerating lncRNAs or recording simplified on-off expression switches. Mechanistic analyses are starting to reveal overarching principles of how lncRNAs can function. Exploring these principles with decisive genetic testing in vivo remains the ultimate test to discern how lncRNA loci, by RNA motifs or DNA elements, affect CVD pathophysiology.

Signaling landscape of mitochondrial non-coding RNAs

Human mitochondria are the vital cell organelle acting as a storehouse of energy generation and diverse regulatory functions. Mitochondrial DNA comprises 93% coding region and 7% non-coding regions, in which the non-coding region hypothesized as responsible for signaling is our specific interest. Here, we explored the unknown functions of mitochondrial non-coding RNAs by studying their respective signaling pathways. We retrieved conserved motifs of interactions from known experimental protein-RNA complexes to model unknown mitochondrial ncRNA sequences. Our results provide the ncRNAs list and show their involvement in four crucial pathways, such as (i) Processing of Capped Intron-Containing Pre-mRNA, (ii) Spliceosome, (iii) Spliceosomal assembly, and (iv) RNA Polymerase II Transcription, respectively. The interactome analysis revealed that the SRSF2 and U2AF2 proteins interact with ncRNAs. Further, we have simulated the selected ncRNA-protein complexes in cell-like environmental conditions and found them stable in terms of energetics. Through our study, we have identified an apparent interaction of mitochondrial ncRNAs with proteins and their role in critical signaling pathways, providing new insights into mitochondrial ncRNA-dependent gene regulation.Communicated by Ramaswamy H. Sarma.