Cross-Linking Ligation and Sequencing of Hybrids (qCLASH) Reveals an Unpredicted miRNA Targetome in Melanoma Cells

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3'-untranslated regions (3'-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

Deciphering the role of LOC124905135-related non-coding RNA cluster in human cancers: A comprehensive review

Non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and long ncRNAs (lncRNAs), are essential regulators of processes, such as the cell cycle and apoptosis. In addition to interacting with intracellular complexes and participating in diverse molecular pathways, ncRNAs can be used as clinical diagnostic biomarkers and therapeutic targets for fighting cancer. Studying ncRNA gene clusters is crucial for understanding their role in cancer and developing new treatments. LOC124905135 is a protein-coding gene encoding a collagen alpha-1(III) chain-like protein, and also acts as a gene for several ncRNAs, including miR-3619, PRR34 antisense RNA 1 (PRR34-AS1), PRR34, long intergenic ncRNA 2939 (LINC02939), LOC112268288, and MIRLET7BHG. It also serves as a host gene for three miRNAs (hsa-let7-A3, hsa-miR-4763, and hsa-let-7b). Notably, the ncRNAs derived from this particular genomic region significantly affect various cell functions, including the cell cycle and apoptosis. This cluster of ncRNAs is dysregulated in several types of cancer, exhibiting mutations, alterations in copy number, and being subject to DNA methylation and histone modification. In summary, the ncRNAs derived from the LOC124905135 cluster could be used as targets for diagnosis, therapy monitoring, and drug discovery in human cancers.

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNA-target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field.

MicroRNA-mediated regulation of nonsense-mediated mRNA decay factors: Insights into microRNA prediction tools and profiling techniques

Nonsense-mediated mRNA decay (NMD) stands out as a prominent RNA surveillance mechanism within eukaryotes, meticulously overseeing both RNA abundance and integrity by eliminating aberrant transcripts. These defective transcripts are discerned through the concerted efforts of translating ribosomes, eukaryotic release factors (eRFs), and trans-acting NMD factors, with Up-Frameshift 3 (UPF3) serving as a noteworthy component. Remarkably, in humans, UPF3 exists in two paralogous forms, UPF3A (UPF3) and UPF3B (UPF3X). Beyond its role in quality control, UPF3 wields significant influence over critical cellular processes, including neural development, synaptic plasticity, and axon guidance. However, the precise regulatory mechanisms governing UPF3 remain elusive. MicroRNAs (miRNAs) emerge as pivotal post-transcriptional gene regulators, exerting substantial impact on diverse pathological and physiological pathways. This comprehensive review encapsulates our current understanding of the intricate regulatory nexus between NMD and miRNAs, with particular emphasis on the essential role played by UPF3B in neurodevelopment. Additionally, we bring out the significance of the 3'-untranslated region (3'-UTR) as the molecular bridge connecting NMD and miRNA-mediated gene regulation. Furthermore, we provide an in-depth exploration of diverse computational tools tailored for the prediction of potential miRNA targets. To complement these computational approaches, we delineate experimental techniques designed to validate predicted miRNA-mRNA interactions, empowering readers with the knowledge necessary to select the most appropriate methodology for their specific research objectives.

Non-Canonical Targets of MicroRNAs: Role in Transcriptional Regulation, Disease Pathogenesis and Potential for Therapeutic Targets

MicroRNAs are a class of regulatory, non-coding small ribonucleic acid (RNA) molecules found in eukaryotes. Dysregulated expression of microRNAs can lead to downregulation or upregulation of their target gene. In general, microRNAs bind with the Argonaute protein and its interacting partners to form a silencing complex. This silencing complex binds with fully or partial complementary sequences in the 3'-UTR of their cognate target mRNAs and leads to degradation of the transcripts or translational inhibition, respectively. However, recent developments point towards the ability of these microRNAs to bind to the promoters, enhancers or coding sequences, leading to upregulation of their target genes. This review briefly summarizes the various non-canonical binding sites of microRNAs and their regulatory roles in various diseased conditions.

Hybkit: a Python API and command-line toolkit for hybrid sequence data from chimeric RNA methods

SUMMARY

Experimental methods using microRNA/target ligation have recently provided significant insights into microRNA functioning through generation of chimeric (hybrid) RNA sequences. Here, we introduce Hybkit, a Python3 API, and command-line toolkit for analysis of hybrid sequence data in the "hyb" file format to enable customizable evaluation and annotation of hybrid characteristics. The Hybkit API includes a suite of python objects for developing custom analyses of hybrid data as well as miRNA-specific analysis methods, built-in plotting of analysis results, and incorporation of predicted miRNA/target interactions in Vienna format.

AVAILABILITY AND IMPLEMENTATION

Hybkit is provided free and open source under the GNU GPL license at github.com/RenneLab/hybkit and archived on Zenodo (doi.org/10.5281/zenodo.7834299). Hybkit distributions are also provided via PyPI (pypi.org/project/hybkit), Conda (bioconda.github.io/recipes/hybkit/README.html), and Docker (quay.io/repository/biocontainers/hybkit).

A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

The RNA interactome in the Hallmarks of Cancer

Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

Detecting RNA-RNA interactome

The last decade has seen a robust increase in various types of novel RNA molecules and their complexity in gene regulation. RNA molecules play a critical role in cellular events by interacting with other biomolecules, including protein, DNA, and RNA. It has been established that RNA-RNA interactions play a critical role in several biological processes by regulating the biogenesis and function of RNA molecules. Interestingly, RNA-RNA interactions regulate the biogenesis of diverse RNA molecules, including mRNAs, microRNAs, tRNAs, and circRNAs, through splicing or backsplicing. Structured RNAs like rRNA, tRNA, and snRNAs achieve their functional conformation by intramolecular RNA-RNA interactions. In addition, functional consequences of many intermolecular RNA-RNA interactions have been extensively studied in the regulation of gene expression. Hence, it is essential to understand the mechanism and functions of RNA-RNA interactions in eukaryotes. Conventionally, RNA-RNA interactions have been identified through diverse biochemical methods for decades. The advent of high-throughput RNA-sequencing technologies has revolutionized the identification of global RNA-RNA interactome in cells and their importance in RNA structure and function in gene expression regulation. Although these technologies revealed tens of thousands of intramolecular and intermolecular RNA-RNA interactions, we further look forward to future unbiased and quantitative high-throughput technologies for detecting transcriptome-wide RNA-RNA interactions. With the ability to detect RNA-RNA interactome, we expect that future studies will reveal the higher-order structures of RNA molecules and multi-RNA hybrids impacting human health and diseases. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Sequencing of Argonaute-bound microRNA/mRNA hybrids reveals regulation of the unfolded protein response by microRNA-320a

MicroRNAs (miRNA) are short non-coding RNAs widely implicated in gene regulation. Most metazoan miRNAs utilize the RNase III enzymes Drosha and Dicer for biogenesis. One notable exception is the RNA polymerase II transcription start sites (TSS) miRNAs whose biogenesis does not require Drosha. The functional importance of the TSS-miRNA biogenesis is uncertain. To better understand the function of TSS-miRNAs, we applied a modified Crosslinking, Ligation, and Sequencing of Hybrids on Argonaute (AGO-qCLASH) to identify the targets for TSS-miRNAs in HCT116 colorectal cancer cells with or without DROSHA knockout. We observed that miR-320a hybrids dominate in TSS-miRNA hybrids identified by AGO-qCLASH. Targets for miR-320a are enriched for the eIF2 signaling pathway, a downstream component of the unfolded protein response. Consistently, in miR-320a mimic- and antagomir- transfected cells, differentially expressed gene products are associated with eIF2 signaling. Within the AGO-qCLASH data, we identified the endoplasmic reticulum (ER) chaperone calnexin as a direct miR-320a down-regulated target, thus connecting miR-320a to the unfolded protein response. During ER stress, but not amino acid deprivation, miR-320a up-regulates ATF4, a critical transcription factor for resolving ER stress. In summary, our study investigates the targetome of the TSS-miRNAs in colorectal cancer cells and establishes miR-320a as a regulator of unfolded protein response.

DISE/6mer seed toxicity-a powerful anti-cancer mechanism with implications for other diseases

micro(mi)RNAs are short noncoding RNAs that through their seed sequence (pos. 2-7/8 of the guide strand) regulate cell function by targeting complementary sequences (seed matches) located mostly in the 3' untranslated region (3' UTR) of mRNAs. Any short RNA that enters the RNA induced silencing complex (RISC) can kill cells through miRNA-like RNA interference when its 6mer seed sequence (pos. 2-7 of the guide strand) has a G-rich nucleotide composition. G-rich seeds mediate 6mer Seed Toxicity by targeting C-rich seed matches in the 3' UTR of genes critical for cell survival. The resulting Death Induced by Survival gene Elimination (DISE) predominantly affects cancer cells but may contribute to cell death in other disease contexts. This review summarizes recent findings on the role of DISE/6mer Seed Tox in cancer; its therapeutic potential; its contribution to therapy resistance; its selectivity, and why normal cells are protected. In addition, we explore the connection between 6mer Seed Toxicity and aging in relation to cancer and certain neurodegenerative diseases.

Modified Cross-Linking, Ligation, and Sequencing of Hybrids (qCLASH) to Identify MicroRNA Targets

This protocol was designed to identify microRNA (miRNA) targetomes from smaller‐input samples by performing a simplified workflow of the Cross‐Linking and Sequencing of Hybrids (CLASH) technique developed in the Tollervey group. In this ribonomics‐based technique, Cross‐Linking and Immunoprecipitation (CLIP) of Argonaute (Ago) is combined with an RNA ligase reaction that yields covalently bound “hybrids” between miRNAs and their target RNAs. While this iteration of CLIP identifies “high‐confidence” or “unambiguous” miRNA targets, the added ligation step is highly inefficient and therefore requires large numbers of cultured cells. To make this powerful approach applicable to smaller cell numbers, we created qCLASH, incorporating a workflow that performs all enzymatic reactions on bead‐bound complexes and omits gel purification of immunoprecipitated Ago complexes associated with major loss of RNA. At a sequencing depth of 100 million reads per library, which is highly feasible with rapidly decreasing sequencing costs, qCLASH, when used with three biological replicates, results in thousands of high‐confidence miRNA targets. qCLASH was first developed to identify viral miRNA targetomes of endothelial cells infected with Kaposi's sarcoma−associated herpesvirus. Since then, qCLASH has been applied to Epstein‐Barr virus− and MHV68‐infected cells, and more recently to metastatic melanoma and breast cancer cells. Currently, protocols are under development to apply qCLASH to human solid tumor specimens. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.

Basic Protocol: Quick Cross‐Linking and Sequencing of Hybrids (qCLASH)

Support Protocol: Optimization of Ago immunoprecipitation

MicroRNAs and Stem-like Properties: The Complex Regulation Underlying Stemness Maintenance and Cancer Development

Embryonic stem cells (ESCs) have the extraordinary properties to indefinitely proliferate and self-renew in culture to produce different cell progeny through differentiation. This latter process recapitulates embryonic development and requires rounds of the epithelial-mesenchymal transition (EMT). EMT is characterized by the loss of the epithelial features and the acquisition of the typical phenotype of the mesenchymal cells. In pathological conditions, EMT can confer stemness or stem-like phenotypes, playing a role in the tumorigenic process. Cancer stem cells (CSCs) represent a subpopulation, found in the tumor tissues, with stem-like properties such as uncontrolled proliferation, self-renewal, and ability to differentiate into different cell types. ESCs and CSCs share numerous features (pluripotency, self-renewal, expression of stemness genes, and acquisition of epithelial-mesenchymal features), and most of them are under the control of microRNAs (miRNAs). These small molecules have relevant roles during both embryogenesis and cancer development. The aim of this review was to recapitulate molecular mechanisms shared by ESCs and CSCs, with a special focus on the recently identified classes of microRNAs (noncanonical miRNAs, mirtrons, isomiRs, and competitive endogenous miRNAs) and their complex functions during embryogenesis and cancer development.

A noncanonical microRNA derived from the snaR-A noncoding RNA targets a metastasis inhibitor

MicroRNAs (miRNAs) are small noncoding RNAs that function as critical posttranscriptional regulators in various biological processes. While most miRNAs are generated from processing of long primary transcripts via sequential Drosha and Dicer cleavage, some miRNAs that bypass Drosha cleavage can be transcribed as part of another small noncoding RNA. Here, we develop the target-oriented miRNA discovery (TOMiD) bioinformatic analysis method to identify Drosha-independent miRNAs from Argonaute crosslinking and sequencing of hybrids (Ago-CLASH) data sets. Using this technique, we discovered a novel miRNA derived from a primate specific noncoding RNA, the small NF90 associated RNA A (snaR-A). The miRNA derived from snaR-A (miR-snaR) arises independently of Drosha processing but requires Exportin-5 and Dicer for biogenesis. We identify that miR-snaR is concurrently up-regulated with the full snaR-A transcript in cancer cells. Functionally, miR-snaR associates with Ago proteins and targets NME1, a key metastasis inhibitor, contributing to snaR-A's role in promoting cancer cell migration. Our findings suggest a functional link between a novel miRNA and its precursor noncoding RNA.