ceRNA crosstalk stabilizes protein expression and affects the correlation pattern of interacting proteins

Alcohol consumption and esophageal cancer risk: unveiling DLEU2 as a key immune modulator through Mendelian randomization and transcriptomic analysis

Esophageal cancer (EC) is a leading cause of cancer-related mortality globally, with alcohol consumption being a significant risk factor. However, the genetic and molecular mechanisms linking alcohol intake to EC remain unclear. This study utilized Mendelian randomization analysis to establish a causal relationship between alcohol consumption and EC (OR: 4.11 [95% CI 1.83-9.23]). Transcriptomic analysis identified 83 differentially expressed genes (log₂ fold change > 1, false discovery rate [FDR] < 0.05), among which DLEU2 was uniquely transcribed into a long non-coding RNA (lncRNA). Pan-cancer analysis revealed its association with the tumor immune microenvironment and cancer progression. Single-cell RNA sequencing localized DLEU2 expression predominantly to T cells, particularly exhausted subpopulations, and pseudo-temporal analysis demonstrated increased DLEU2 expression during late T cell differentiation stages, co-expressing immune suppression markers, with consistent expression patterns observed across multiple patient-derived samples. Additionally, cell communication analysis suggested that DLEU2 modulates TNF signaling through TNFRSF1A/B pathways, contributing to immune evasion and poor prognosis. These findings position DLEU2 as a pivotal regulator of the immune landscape in EC and a potential prognostic biomarker and therapeutic target.

The Role of Ovarian Granulosa Cells Related-ncRNAs in Ovarian Dysfunctions: Mechanism Research and Clinical Exploration

Ovarian dysfunctions, encompassing conditions such as polycystic ovary syndrome (PCOS), premature ovarian failure (POF), premature ovarian insufficiency (POI), and diminished ovarian reserve (DOR), are closely linked to disruptions in follicular development, often tied to granulosa cell (GC) abnormalities. Despite ongoing research, the precise mechanisms underlying these dysfunctions remain elusive. Increasing evidence highlights the pivotal role of non-coding RNAs (ncRNAs) in the pathogenesis of ovarian dysfunctions. As transcripts that do not encode proteins, ncRNAs are capable of regulating gene expression at various levels. They influence GCs by modulating key biological processes including proliferation, apoptosis, autophagy, cell cycle progression, steroidogenesis, mitochondrial function, inflammatory responses, and aging. Disruptions in GC development and function can lead to impaired follicular development, consequently contributing to ovarian dysfunctions. Thus, ncRNAs are likely integral to the regulatory mechanisms underlying these pathologies, exhibiting distinct expression patterns in affected individuals. This review delves into the regulatory roles of ncRNAs in GCs and their implications for ovarian dysfunctions (PCOS, POF, POI, DOR), offering insights into potential biomarkers for ovarian function assessment and novel therapeutic approaches for treating these conditions.

Construction of long non-coding RNA- and microRNA-mediated competing endogenous RNA networks in alcohol-related esophageal cancer

The current study aimed to explore the lncRNA–miRNA–mRNA networks associated with alcohol-related esophageal cancer (EC). RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas and the differentially expressed genes (DEGs), long non-coding RNAs (lncRNAs, DELs), and miRNAs (DEMs) in patients with alcohol-related and non-alcohol-related EC were identified. Prognostic RNAs were identified by performing Kaplan–Meier survival analyses. Weighted gene co-expression network analysis was employed to build the gene modules. The lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) networks were constructed based on our in silico analyses using data from miRcode, starBase, and miRTarBase databases. Functional enrichment analysis was performed for the genes in the identified ceRNA networks. A total of 906 DEGs, 40 DELs, and 52 DEMs were identified. There were eight lncRNAs and miRNAs each, including ST7-AS2 and miR-1269, which were significantly associated with the survival rate of patients with EC. Of the seven gene modules, the blue and turquoise modules were closely related to disease progression; the genes in this module were selected to construct the ceRNA networks. SNHG12–miR-1–ST6GAL1, SNHG3–miR-1–ST6GAL1, SPAG5-AS1–miR-133a–ST6GAL1, and SNHG12–hsa-miR-33a–ST6GA interactions, associated with the N-glycan biosynthesis pathway, may have key roles in alcohol-related EC. Thus, the identified biomarkers provide a novel insight into the molecular mechanism of alcohol-related EC.

MALAT1/miR-320a in bone marrow mesenchymal stem cells function may shed light on mechanisms underlying osteoporosis

INTRODUCTION

Growing evidence supports the involvement of long noncoding RNAs (lncRNAs) in bone metabolism and diseases. This study aims to investigate the involvement of the lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in the pathological process of osteoporosis and the effects of MALAT1 on regulation of BMSC differentiation through competitive endogenous RNA (ceRNA) mechanisms.

MATERIAL AND METHODS

The expression of MALAT1 and miR-320a was determined using RT-PCR in bone tissue derived from female SD (Sprague Dawley) rats with osteoporosis. Immunohistochemical (IHC) staining was used to evaluate the expression of neuropilin-1 (NRP-1) and β-catenin. Bone marrow mesenchymal stem cells (BMSCs) were divided into 4 groups: control, NC (negative control), MALAT1 siRNA, and miR-320a mimics. Forty-eight hours later, the effect of MALAT1 on the miR-320a expression, proliferation and osteogenic differentiation of BMSCs was investigated. Two weeks later, the cell activity, alkaline phosphatase (ALP) activity, and mRNA expression of Osterix and Runx2 were evaluated. Three weeks later, alizarin red staining of calcified nodules and Western blot analysis of the expression of β-catenin, NRP-1, osteocalcin (OCN), and osteopontin (OPN) were performed.

RESULTS

Downregulated MALAT1or upregulated miR-320a expression inhibited the activity and osteogenic differentiation of BMSCs, resulting in low ALP activity and NRP-1 expression, fewer calcified nodules, decreased mRNA levels of Osterix and Runx2, and inhibited expression of NRP-1, OCN, and OPN. MALAT1 silencing did not decrease the protein level of β-catenin in the cytoplasm but suppressed that in the nucleus.

CONCLUSIONS

Downregulated MALAT1 and upregulated miR-320a expression play an important role in the pathological process of osteoporosis, via inhibition of the osteogenic differentiation of BMSCs.

Single-cell Transcriptomes Reveal Characteristics of MicroRNA in Gene Expression Noise Reduction

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

Construction and comprehensive analysis of a competitive endogenous RNA network to reveal potential biomarkers for the malignant differentiation of glioma

BACKGROUND

Long noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; therefore, lncRNAs are considered major components of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. This study explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in the malignant differentiation of low-grade glioma (LGG) to glioblastoma (GBM) and their potential impact on the prognosis of patients with GBM.

METHODS

LncRNA and messenger RNA (mRNA) data were extracted from the Cancer Genome Atlas (TCGA) database from 156 GBM samples and 529 LGG samples. Separately, the miRNA expression data were downloaded from the Gene Expression Omnibus database, with the GSE112009 dataset containing miRNA expression data from 10 GBM samples and 15 LGG samples. Weighted gene coexpression network analysis was performed to screen the glioma grade-related lncRNAs. Then, a ceRNA network was established. The database for annotation, visualization, and integrated discovery was adopted to conduct functional enrichment analysis based on 57 upregulated differentially expressed mRNAs in the ceRNA network. Finally, Kaplan-Meier curves were created for the survival analysis of 13 hub lncRNA by combining the clinical data of GBM patients in TCGA.

RESULTS

A ceRNA network including 16 lncRNAs, 18 miRNAs, and 78 mRNAs specific to the malignant differentiation of LGG to GBM was established. The 57 upregulated differentially expressed mRNAs in the ceRNA network were significantly enriched in 35 gene ontology terms and 5 pathways. The survival analysis showed that 2 lncRNAs (LINC00261 and HOXA10-AS) were prognostic biomarkers for patients with GBM in TCGA.

CONCLUSION

The proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the malignant differentiation of LGG to GBM. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for GBM.

Small RNA-driven feed-forward loop: fine-tuning of protein synthesis through sRNA-mediated crosstalk

Often in bacterial regulatory networks, small non-coding RNAs (sRNA) interact with several mRNA species. The competition among mRNAs for binding to the common pool of sRNA might lead to crosstalk between the mRNAs. This is similar to the competing endogenous RNA effect that leads to complex gene regulation with stabilized gene expression in Eukaryotes. Here, we study an sRNA-driven feed-forward loop (sFFL) where the top-tier regulator, an sRNA, translationally activates the target protein (TP) as well as a transcriptional activator of the TP through binding to the respective mRNAs. We show that the sRNA-mediated crosstalk between the two mRNA species enables the sFFL to function in three different regimes depending on the synthesis rate of the transcriptional activator mRNA. Of these three regimes, there exists a sensitive regime where the TP level shows interesting features depending on the precise mechanism of target translation. In the case of translation entirely from sRNA-mRNA bound complexes, the TP level becomes maximum around the sensitive regime. Through stochastic analysis and simulations, we show that relative fluctuations in the TP level is minimized here. For translation both from mRNA and sRNA-mRNA bound complexes, the target expression shows a threshold response across the sensitive regime.

Heterogeneity coordinates bacterial multi-gene expression in single cells

For a genetically identical microbial population, multi-gene expression in various environments requires effective allocation of limited resources and precise control of heterogeneity among individual cells. However, it is unclear how resource allocation and cell-to-cell variation jointly shape the overall performance. Here we demonstrate a Simpson’s paradox during overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. Yet this phenomenon was not observed between two competing mRNAs in single cells. Our analytical framework shows that the phenomenon arises from competition for translational resource, with the correlation modulated by both mRNA and ribosome variability. Thus, heterogeneity plays a key role in single-cell multi-gene expression and provides the population with an evolutionary advantage, as demonstrated in this study.

Microbes perform multitasking for a wide range of purposes, including survival, adaptation, colonization, and evolution. Both modelling and experimental results at the ensemble level reveal trade-offs between different tasks due to resource competition, but it is unclear how single cells allocate limited intracellular resources to perform multitasking, and how does a population coordinate single cell performances during multitasking to maximize population efficiencies. In this study, we address this question by using bacterial multi-gene overexpression as the basic form of multitasking. We discovered and analyzed a statistical phenomenon called Simpson’s paradox, where competing proteins in single cells correlate positively at each constant condition, although the proteins correlate negatively when all conditions are combined. We demonstrate that the phenomenon arises from competition for translational resources, with the correlation modulated by heterogeneity of both mRNA and ribosomes. We further show that heterogeneity coordinates multiple functional modules, conferring an evolutionary advantage on the population. Our work discloses that heterogeneity in the form of Simpson’s paradox is an important phenomenon in coordinating multi-gene expression.

Cross-Talk Between circRNAs and mRNAs Modulates MiRNA-mediated Circuits and Affects Melanoma Plasticity

CircularRNAs (circRNAs) are non-coding RNAs which compete for microRNA (miRNA) binding, influencing the abundance and stability of other RNA species. Herein we have investigated the effect of circRNAs on the mir200-ZEB1 feedback loop in relationship with the aggressiveness of human melanoma cells. We first compared the level of expression of key factors in the mir200-ZEB1 feedback loop in primary human melanoma cells compared with their matching metastatic one and found a correlation between the aggressiveness of the cells and the level of expression of ZEB1 and SNAI1. We also analyzed factors in the mir200-ZEB1 feedback loop, including circZEB1, during the phenotypic switching of human melanoma cells. Our results showed a correlation between the level of ZEB1 and SNAI1 and the fraction of cancer stem cells in the population. The level of circZEB1 was, however, consistently high during the entire phenotypic transformation. To understand this result we propose a mathematical model of the regulatory circuit. According to the model, the experimental observations can be explained by the presence of a back-splicing factor limiting circRNA production.

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.

FEZF1-AS1: a novel vital oncogenic lncRNA in multiple human malignancies

Long noncoding RNAs (LncRNAs) refer to the RNA with a length of >200 nucleotides, which lack or have no open reading coding frame and have higher tissue and organ specificity compared with the protein coding genes. A surging number of studies have shown that lncRNA is involved in numerous essential regulatory processes, such as X chromosome silencing, genomic imprinting, chromatin modification, transcriptional activation, transcriptional interference and nuclear transport, which are closely related to the occurrence and development of human malignancies. FEZ family Zinc Finger 1-Antisense RNA 1 (FEZF1-AS1) of FEZ family is a recently discovered lncRNA. FEZF1-AS1 is highly expressed in pancreatic cancer, colorectal cancer, lung adenocarcinoma and other human malignancies, and is associated with poor prognosis. As an oncogene, it plays crucial role in the proliferation, migration, invasion and Warburg effect of various tumor cells. In addition, FEZF1-AS1 is also involved in the regulation of multiple signal pathways such as epithelial–mesenchymal transition (EMT), signal transducer and activator of transcription 3 (STAT3) and Wnt/ β-catenin. In this paper, the recent research progress of FEZF1-AS1 in tumorigenesis and development is reviewed systematically.

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

Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting, e.g., the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.

Stochastic sequestration dynamics: a minimal model with extrinsic noise for bimodal distributions and competitors correlation

Many biological processes are known to be based on molecular sequestration. This kind of dynamics involves two types of molecular species, namely targets and sequestrants, that bind to form a complex. In the simple framework of mass-action law, key features of these systems appear to be threshold-like profiles of the amounts of free molecules as a function of the parameters determining their possible maximum abundance. However, biochemical processes are probabilistic and take place in stochastically fluctuating environments. How these different sources of noise affect the final outcome of the network is not completely characterised yet. In this paper we specifically investigate the effects induced by a source of extrinsic noise onto a minimal stochastic model of molecular sequestration. We analytically show how bimodal distributions of the targets can appear and characterise them as a result of noise filtering mediated by the threshold response. We then address the correlations between target species induced by the sequestrant and discuss how extrinsic noise can turn the negative correlation caused by competition into a positive one. Finally, we consider the more complex scenario of competitive inhibition for enzymatic kinetics and discuss the relevance of our findings with respect to applications.

Statistics of optimal information flow in ensembles of regulatory motifs

Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N, (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.

Construction of an oesophageal cancer-specific ceRNA network based on miRNA, lncRNA, and mRNA expression data

AIM

To explore the expression profiles of microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and mRNAs in oesophageal squamous cell carcinoma (ESCC) in order to construct an oesophageal cancer-specific competing endogenous RNA (ceRNA) network.

METHODS

In this work, the expression data of miRNAs, lncRNAs, and mRNAs in ESCC were obtained. An oesophageal cancer-specific ceRNA network was then constructed and investigated.

RESULTS

CeRNAs have the ability to reduce the targeting activity of miRNAs, leading to the de-repression of specific mRNAs with common miRNA response elements. CeRNA interactions have a critical effect in gene regulation and cancer development.

CONCLUSION

This study suggests a novel perspective on potential oesophageal cancer mechanisms as well as novel pathways for modulating ceRNA networks for treating cancers.

Independent channels for miRNA biosynthesis ensure efficient static and dynamic control in the regulation of the early stages of myogenesis

Motivated by recent experimental work, we define and study a deterministic model of the complex miRNA-based regulatory circuit that putatively controls the early stage of myogenesis in human. We aim in particular at a quantitative understanding of (i) the roles played by the separate and independent miRNA biosynthesis channels (one involving a miRNA-decoy system regulated by an exogenous controller, the other given by transcription from a distinct genomic locus) that appear to be crucial for the differentiation program, and of (ii) how competition to bind miRNAs can efficiently control molecular levels in such an interconnected architecture. We show that optimal static control via the miRNA-decoy system constrains kinetic parameters in narrow ranges where the channels are tightly cross-linked. On the other hand, the alternative locus for miRNA transcription can ensure that the fast concentration shifts required by the differentiation program are achieved, specifically via non-linear response of the target to even modest surges in the miRNA transcription rate. While static, competition-mediated regulation can be achieved by the miRNA-decoy system alone, both channels are essential for the circuit's overall functionality, suggesting that that this type of joint control may represent a minimal optimal architecture in different contexts.

TMED2 promotes epithelial ovarian cancer growth

TMED2 is involved in morphogenesis of the mouse embryo and placenta. We found that expression of TMED2 was higher in epithelial ovarian cancer tissues than normal ovarian tissues. Silencing TMED2 decreased cell proliferation, migration, and invasion. Ectopic expression of TMED2 increased cell proliferation, migration and invasion. Silencing TMED2 inhibited ovarian cancer growth in mice. Silencing TMED2 inhibited IGF2/IGF1R/PI3K/Akt pathway. In agreement, ectopically expressed TMED2 activated IGF2/IGF1R/PI3K/Akt pathway. Mechanistic study revealed that TMED2 directly binds to AKT2, thereby facilitating its phosphorylation. We also found that TMED2 increased IGF1R expression by competing for miR-30a. Thus, TMED2 is oncogenic and a potential target for epithelial ovarian cancer therapy.

Translating ceRNA Susceptibilities into Correlation Functions

Competition to bind microRNAs induces an effective positive cross talk between their targets, which are therefore known as "competing endogenous RNAs" (ceRNAs). Although such an effect is known to play a significant role in specific situations, estimating its strength from data and experimentally in physiological conditions appears to be far from simple. Here, we show that the susceptibility of ceRNAs to different types of perturbations affecting their competitors (and hence their tendency to cross talk) can be encoded in quantities as intuitive and as simple to measure as correlation functions. This scenario is confirmed by extensive numerical simulations and validated by re-analyzing phosphatase and tensin homolog's cross-talk pattern from The Cancer Genome Atlas breast cancer database. These results clarify the links between different quantities used to estimate the intensity of ceRNA cross talk and provide, to our knowledge, new keys to analyze transcriptional data sets and effectively probe ceRNA networks in silico.

MicroRNA-mediated regulatory circuits: outlook and perspectives

MicroRNAs have been found to be necessary for regulating genes implicated in almost all signaling pathways, and consequently their dysfunction influences many diseases, including cancer. Understanding of the complexity of the microRNA-mediated regulatory network has grown in terms of size, connectivity and dynamics with the development of computational and, more recently, experimental high-throughput approaches for microRNA target identification. Newly developed studies on recurrent microRNA-mediated circuits in regulatory networks, also known as network motifs, have substantially contributed to addressing this complexity, and therefore to helping understand the ways by which microRNAs achieve their regulatory role. This review provides a summarizing view of the state-of-the-art, and perspectives of research efforts on microRNA-mediated regulatory motifs. In this review, we discuss the topological properties characterizing different types of circuits, and the regulatory features theoretically enabled by such properties, with a special emphasis on examples of circuits typifying their biological significance in experimentally validated contexts. Finally, we will consider possible future developments, in particular regarding microRNA-mediated circuits involving long non-coding RNAs and epigenetic regulators.