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

The human genome encodes a multitude of novel miRNAs

Human cells generate a vast complexity of noncoding RNAs, the "RNA dark matter," which includes a vast small RNA (sRNA) transcriptome. The biogenesis, biological relevance, and mechanisms of action of most of these transcripts remain unknown, and they are widely assumed to represent degradation products. Here, we aimed to functionally characterize human sRNA transcriptome by attempting to answer the following question-can a significant number of novel sRNAs correspond to novel members of known classes, specifically, microRNAs (miRNAs)? By developing and validating a miRNA discovery pipeline, we show that at least 2726 novel canonical miRNAs, majority of which represent novel miRNA families, exist in just one human cell line compared to just 1914 known miRNA loci. Moreover, potentially tens of thousands of miRNAs remain to be discovered. Strikingly, many novel miRNAs map to exons of protein-coding genes emphasizing a complex and interleaved architecture of the genome. The existence of so many novel members of a functional class of sRNAs suggest that the human sRNA transcriptome harbors a multitude of novel regulatory molecules. Overall, these results suggest that we are at the very beginning of understanding the true functional complexity of the sRNA component of the "RNA dark matter."

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

Regulatory features aid interpretation of 3'UTR variants

Our ability to determine the clinical impact of variants in 3' untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3' UTR variants from several datasets. Variants in putative regulatory elements, including RNA-binding protein motifs, eCLIP peaks, and microRNA sites, are up to 16 times more likely than variants not in these elements to have gene expression and phenotype associations. Variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3' UTR variants in ClinVar are up to 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3' UTR variant and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in individuals.

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.

Consequences of depleting TNRC6, AGO, and DROSHA proteins on expression of microRNAs

The potential for microRNAs (miRNAs) to regulate gene expression remains incompletely understood. DROSHA initiates the biogenesis of miRNAs while variants of Argonaute (AGO) and trinucleotide repeat containing six (TNRC6) family proteins form complexes with miRNAs to facilitate RNA recognition and gene regulation. Here we investigate the fate of miRNAs in the absence of these critical RNAi protein factors. Knockout of DROSHA expression reduces levels of some miRNAs annotated in miRBase but not others. The identity of miRNAs with reduced expression matches the identity of miRNAs previously identified by experimental approaches. The MirGeneDB resource offers the closest alignment with experimental results. In contrast, the loss of TNRC6 proteins had much smaller effects on miRNA levels. Knocking out AGO proteins, which directly contact the mature miRNA, decreased expression of the miRNAs most strongly associated with AGO2 as determined from enhanced crosslinking immunoprecipitation (AGO2-eCLIP). Evaluation of miRNA binding to endogenously expressed AGO proteins revealed that miRNA:AGO association was similar for AGO1, AGO2, AGO3, and AGO4. Our data emphasize the need to evaluate annotated miRNAs based on approximate cellular abundance, DROSHA-dependence, and physical association with AGO when forming hypotheses related to their function.

Screening of Drosophila microRNA-degradation sequences reveals Argonaute1 mRNA's role in regulating miR-999

MicroRNAs (miRNA) load onto AGO proteins to target mRNAs for translational repression or degradation. However, miRNA degradation can be triggered when extensively base-paired with target RNAs, which induces confirmational change of AGO and recruitment of ZSWIM8 ubiquitin ligase to mark AGO for proteasomal degradation. This target RNA-directed miRNA degradation (TDMD) mechanism appears to be evolutionarily conserved, but recent studies have focused on mammalian systems. Here, we performed AGO1-CLASH in Drosophila S2 cells, with Dora (ortholog of vertebrate ZSWIM8) knockout mediated by CRISPR-Cas9 to identify five TDMD triggers (sequences that can induce miRNA degradation). Interestingly, one trigger in the 3' UTR of AGO1 mRNA induces miR-999 degradation. CRISPR-Cas9 knockout of the AGO1 trigger in S2 cells and in Drosophila specifically elevates miR-999, with concurrent repression of the miR-999 targets. AGO1 trigger knockout flies respond poorly to hydrogen peroxide-induced stress, demonstrating the physiological importance of this TDMD event.

MicroRNAs as the Sentinels of Redox and Hypertrophic Signalling

Oxidative stress and inflammation are associated with skeletal muscle function decline with ageing or disease or inadequate exercise and/or poor diet. Paradoxically, reactive oxygen species and inflammatory cytokines are key for mounting the muscular and systemic adaptive responses to endurance and resistance exercise. Both ageing and lifestyle-related metabolic dysfunction are strongly linked to exercise redox and hypertrophic insensitivity. The adaptive inability and consequent exercise intolerance may discourage people from physical training resulting in a vicious cycle of under-exercising, energy surplus, chronic mitochondrial stress, accelerated functional decline and increased susceptibility to serious diseases. Skeletal muscles are malleable and dynamic organs, rewiring their metabolism depending on the metabolic or mechanical stress resulting in a specific phenotype. Endogenous RNA silencing molecules, microRNAs, are regulators of these metabolic/phenotypic shifts in skeletal muscles. Skeletal muscle microRNA profiles at baseline and in response to exercise have been observed to differ between adult and older people, as well as trained vs. sedentary individuals. Likewise, the circulating microRNA blueprint varies based on age and training status. Therefore, microRNAs emerge as key regulators of metabolic health/capacity and hormetic adaptability. In this narrative review, we summarise the literature exploring the links between microRNAs and skeletal muscle, as well as systemic adaptation to exercise. We expand a mathematical model of microRNA burst during adaptation to exercise through supporting data from the literature. We describe a potential link between the microRNA-dependent regulation of redox-signalling sensitivity and the ability to mount a hypertrophic response to exercise or nutritional cues. We propose a hypothetical model of endurance exercise-induced microRNA "memory cloud" responsible for establishing a landscape conducive to aerobic as well as anabolic adaptation. We suggest that regular aerobic exercise, complimented by a healthy diet, in addition to promoting mitochondrial health and hypertrophic/insulin sensitivity, may also suppress the glycolytic phenotype and mTOR signalling through miRNAs which in turn promote systemic metabolic health.