Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs

6-Shogaol alleviates high-fat diet induced hepatic steatosis through miR-3066-5p/Grem2 pathway

The purpose of this study is to investigate the mechanism by which 6-shogaol ameliorates hepatic steatosis via miRNA-mRNA interaction analysis. C57BL/6 J mice were fed a high-fat diet (HFD) for 12 weeks, during which 6-shogaol was administered orally. The liver lipid level, liver function and oxidative damage in mice were evaluated. mRNA sequencing, miRNA sequencing, and RT-qPCR were employed to compare the expression profiles between the HFD group and the 6-shogaol-treated group. High-throughput sequencing was used to construct the mRNA and miRNA libraries. Target prediction and integration analysis identified eight potential miRNA-mRNA pairs involved in hepatic steatosis, which were subsequently validated in liver tissues and AML12 cells. The findings revealed that 6-shogaol modulates the miR-3066-5p/Grem2 pathway, thereby improving hepatic steatosis. This study provides new insights into the mechanisms through which 6-shogaol alleviates hepatic steatosis, establishing a foundation for future research on natural active compounds for the treatment of metabolic diseases.

Mechanosensitive pathways are regulated by mechanosensitive miRNA clusters in endothelial cells

Shear stress is known to affect many processes in (patho-) physiology through a complex, multi-molecular mechanism, termed mechanotransduction. The sheer complexity of the process has raised questions how mechanotransduction is regulated. Here, we comprehensively evaluate the literature about the role of small non-coding miRNA in the regulation of mechanotransduction. Regulation of mRNA by miRNA is rather complex, depending not only on the concentration of mRNA to miRNA, but also on the amount of mRNA competing for a single mRNA. The only mechanism to counteract the latter factor is through overarching structures of miRNA. Indeed, two overarching structures are present miRNA families and miRNA clusters, and both will be discussed in details, regarding the latest literature and a previous conducted study focussed on mechanotransduction. Both the literature and our own data support a new hypothesis that miRNA-clusters predominantly regulate mechanotransduction, affecting 65% of signalling pathways. In conclusion, a new and important mode of regulation of mechanotransduction is proposed, based on miRNA clusters. This finding implicates new avenues for treatment of mechanotransduction and atherosclerosis.

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders.

Modelling and measuring intracellular competition for finite resources during gene expression

Dissecting the competition between genes for shared expressional resources is of fundamental importance for understanding the interplay between cellular components. Owing to the relationship between gene expression and cellular fitness, genomes are shaped by evolution to improve resource allocation. Whereas experimental approaches to investigate intracellular competition require technical resources and human expertise, computational models and in silico simulations allow vast numbers of experiments to be carried out and controlled easily, and with significantly reduced costs. Thus, modelling competition has a pivotal role in understanding the effects of competition on the biophysics of the cell. In this article, we review various computational models proposed to describe the different types of competition during gene expression. We also present relevant synthetic biology experiments and their biotechnological implications, and discuss the open questions in the field.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

Long non-coding RNA HOTAIR in cervical cancer: Molecular marker, mechanistic insight, and therapeutic target

Cervical cancer is a common gynecologic malignant tumor with high mortality. HOX transcript antisense RNA (HOTAIR), a trans-acting long non-coding RNA (lncRNA) containing six exons in humans, is transcribed from the antisense strand of homeobox gene C cluster. This lncRNA serves as a modular scaffold for gene silencing and protein ubiquitination. In patients with cervical cancer, elevated HOTAIR levels are significantly associated with poor prognosis. HOTAIR plays an oncogenic role in cervical cancer by promoting cell proliferation, migration, invasion and autophagy, inhibiting cell apoptosis, stimulating angiogenesis, accelerating cell cycle progression, and inducing epithelial-mesenchymal transition. Moreover, blockade of HOTAIR by artesunate or propofol shows promise for further development of this lncRNA as a potential therapeutic target in cervical cancer. In this review, we summarized the latest advances regarding the role of HOTAIR in cervical cancer with an emphasis on its diagnostic and prognostic values.

From Endogenous to Synthetic microRNA-Mediated Regulatory Circuits: An Overview

MicroRNAs are short non-coding RNAs that are evolutionarily conserved and are pivotal post-transcriptional mediators of gene regulation. Together with transcription factors and epigenetic regulators, they form a highly interconnected network whose building blocks can be classified depending on the number of molecular species involved and the type of interactions amongst them. Depending on their topology, these molecular circuits may carry out specific functions that years of studies have related to the processing of gene expression noise. In this review, we first present the different over-represented network motifs involving microRNAs and their specific role in implementing relevant biological functions, reviewing both theoretical and experimental studies. We then illustrate the recent advances in synthetic biology, such as the construction of artificially synthesised circuits, which provide a controlled tool to test experimentally the possible microRNA regulatory tasks and constitute a starting point for clinical applications.

Competing endogenous RNA crosstalk at system level

microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network's topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.