A quantitative understanding of microRNA-mediated competing endogenous RNA regulation

Dynamic Transcription Machineries Guide the Synthesis of Temporally Operating DNAzymes, Gated and Cascaded DNAzyme Catalysis

Transient transcription machineries play important roles in the dynamic modulation of gene expression and the sequestered regulation of cellular networks. The present study emulates such processes by designing artificial reaction modules consisting of transcription machineries that guide the transient synthesis of catalytic DNAzymes, the transient operation of gated DNAzymes, and the temporal activation of an intercommunicated DNAzyme cascade. The reaction modules rely on functional constituents that lead to the triggered activation of transcription machineries in the presence of the nucleoside triphosphates oligonucleotide fuel, yielding the transient formation and dissipative depletion of the intermediate DNAzyme(s) products. The kinetics of the transient DNAzyme networks are computationally simulated, allowing to predict and experimentally validate the performance of the systems under different auxiliary conditions. The study advances the field of systems chemistry by introducing transcription machinery-based networks for the dynamic control over transient catalysis─a primary step toward life-like cellular assemblies.

The Regulatory Mechanisms and Clinical Significance of Lnc SNHG4 in Cancer

BACKGROUND

LncRNAs have been reported to be involved in a variety of biological functions, including gene expression, cell growth, and differentiation. They may also serve as oncogenes or tumor suppressor genes in diseases. lncRNAs that can encode small nucleolar RNAs (snoRNAs) have been named small nucleolar RNA host genes (SNHGs).

OBJECTIVE

In this review article, we readily review the regulatory mechanisms and clinical significance of Lnc SNHG4 in cancer.

METHODS

We systematically investigated databases, like Scopus, PubMed, Embase, Google Scholar, and Cochrane Library database for all research articles, and have provided an overview regarding the biological functions and mechanisms of lncRNA SNHG4 in tumorigenesis.

RESULTS

Compared to neighboring normal tissues, SNHG4 is significantly dysregulated in various tumor tissues. SNHG4 upregulation is mainly associated with advanced tumor stage, tumor size, TNM stage, and decreased overall survival. In addition, aberrant SNHG4 expression promotes cell proliferation, metastasis, migration, and invasion of cancer cells.

CONCLUSION

SNHG4 may serve as a new therapeutic target and prognostic biomarker in patients with cancer.

In silico and in vitro protocols for quantifying gene expression noise modulated by microRNAs

Characterizing the noise modulation pattern of microRNA is valuable for both microRNA function analysis and synthetic biology applications. Here we propose a coarse-grained model to simulate how the properties of microRNAs, competing RNAs, and microRNA response elements affect gene expression noise. We also detail an experimental protocol based on synthetic gene circuits and flow cytometry to quantify the noise. This framework is easy-to-use for the study and application of both microRNA and gene expression noise.

For complete details on the use and execution of this protocol, please refer to Wei et al. (2021).

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Simulate how microRNA modulates gene expression noise

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Consider the impact of competing RNAs and microRNA target composition to noise

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Quantify gene expression noise by synthetic gene circuits and flow cytometer

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

Time to infer miRNA sponge modules

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

The long non-coding RNA MEG3 plays critical roles in the pathogenesis of cholesterol gallstone

Background

Cholesterol gallstone (CG) is the most common gallstone disease, which is induced by biliary cholesterol supersaturation. The purpose of this study is to investigate the pathogenesis of CG.

Methods

Sixteen mice were equally and randomly divided into model group and normal control group. The model group was fed with lithogenic diets to induce CG, and then gallbladder bile lipid analysis was performed. After RNA-seq library was constructed, differentially expressed mRNAs (DE-mRNAs) and differentially expressed lncRNAs (DE-lncRNAs) between model group and normal control group were analyzed by DESeq2 package. Using the cluster Profiler package, enrichment analysis for the DE-mRNAs was carried out. Based on Cytoscape software, the protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network were built. Using quantitative real-time reverse transcription-PCR (qRT-PCR) analysis, the key RNAs were validated.

Results

The mouse model of CG was suc cessfully established, and then 181 DE-mRNAs and 33 DE-lncRNAs between model and normal groups were obtained. Moreover, KDM4A was selected as a hub node in the PPI network, and lncRNA MEG3 was considered as a key lncRNA in the regulatory network. Additionally, the miR-107-5p/miR-149-3p/miR-346-3-MEG3 regulatory pairs and MEG3-PABPC4/CEP131/NUMB1 co-expression pairs existed in the regulatory network. The qRT-PCR analysis showed that KDM4A expression was increased, and the expressions of MEG3, PABPC4, CEP131, and NUMB1 were downregulated.

Conclusion

These RNAs might be related to the pathogenesis of CG.

Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.

The number of titrated microRNA species dictates ceRNA regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

Modeling ncRNA-Mediated Circuits in Cell Fate Decision

Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions on elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework on real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA), as well as the role of mathematical modeling of ncRNA in synthetic biology. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decision.

MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction

Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature.

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

MicroRNAs, Regulatory Networks, and Comorbidities: Decoding Complex Systems

MicroRNAs (miRNAs) are small noncoding RNAs involved in the posttranscriptional regulation of messenger RNAs (mRNAs). Each miRNA targets a specific set of mRNAs. Upon binding the miRNA inhibits mRNA translation or facilitate mRNA degradation. miRNAs are frequently deregulated in several pathologies including cancer and cardiovascular diseases. Since miRNAs have a crucial role in fine-tuning the expression of their targets, they have been proposed as biomarkers of disease progression and prognostication.In this chapter we discuss different approaches for computational predictions of miRNA targets based on sequence complementarity and integration of expression data. In the last section of the chapter we discuss new opportunities in the study of miRNA regulatory networks in the context of temporal disease progression and comorbidities.