MicroRNA-Target Interactions Reloaded: Identification of Potentially Functional Sequence Variants Within Validated MicroRNA-Target Interactions

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.

The Paradoxical Behavior of microRNA-211 in Melanomas and Other Human Cancers

Cancer initiation, progression, and metastasis leverage many regulatory agents, such as signaling molecules, transcription factors, and regulatory RNA molecules. Among these, regulatory non-coding RNAs have emerged as molecules that control multiple cancer types and their pathologic properties. The human microRNA-211 (MIR211) is one such molecule, which affects several cancer types, including melanoma, glioblastoma, lung adenocarcinomas, breast, ovarian, prostate, and colorectal carcinoma. Previous studies suggested that in certain tumors MIR211 acts as a tumor suppressor while in others it behaves as an oncogenic regulator. Here we summarize the known molecular genetic mechanisms that regulate MIR211 gene expression and molecular pathways that are in turn controlled by MIR211 itself. We discuss how cellular and epigenetic contexts modulate the biological effects of MIR211, which exhibit pleiotropic effects. For example, up-regulation of MIR211 expression down-regulates Warburg effect in melanoma tumor cells associated with an inhibition of the growth of human melanoma cells in vitro, and yet these conditions robustly increase tumor growth in xenografted mice. Signaling through the DUSP6-ERK5 pathway is modulated by MIR211 in BRAFV600E driven melanoma tumors, and this function is involved in the resistance of tumor cells to the BRAF inhibitor, Vemurafenib. We discuss several alternate but testable models, involving stochastic cell-to-cell expression heterogeneity due to multiple equilibria involving feedback circuits, intracellular communication, and genetic variation at miRNA target sties, to reconcile the paradoxical effects of MIR211 on tumorigenesis. Understanding the precise role of this miRNA is crucial to understanding the genetic basis of melanoma as well as the other cancer types where this regulatory molecule has important influences. We hope this review will inspire novel directions in this field.

Childhood Obesity Risk in Relationship to Perilipin 1 (PLIN1) Gene Regulation by Circulating microRNAs

Childhood obesity is a growing public health burden in many countries. The lipid perilipin 1 (PLIN1) gene is involved in regulation of lipolysis, and thus represents a viable candidate mechanism for obesity genetics research in children. In addition, the regulation of candidate gene expression by circulating microRNAs (miRNAs) offers a new research venue for diagnostic innovation. We report new findings on associations among circulating miRNAs, regulation of the PLIN1 gene, and susceptibility to childhood obesity. In a sample of 135 unrelated subjects, 35 children with obesity (between ages 3 and 13) and 100 healthy controls (between ages 4 and 16), we examined the expression levels of four candidate miRNAs (hsa-miR-4777-3p, hsa-miR-642b-3p, hsa-miR-3671-1, and hsa-miR-551b-2) targeting the PLIN1 as measured by real-time polymerase chain reaction in whole blood samples. We found that the full genetic model, including the four candidate miRNAs and the PLIN1 gene, explained a statistically significant 12.7% of the variance in childhood obesity risk (p = 0.0034). The four miRNAs together explained 10.1% of the risk (p = 0.008). The percentage of variation in childhood obesity risk explained by hsa-miR-642b-3p and age was 19%. In accordance with biological polarity of the observed association, for example, hsa-miR-642b-3p was upregulated, while the PLIN1 expression decreased in obese participants compared to healthy controls. To the best of our knowledge, this is the first clinical association study of these candidate miRNAs targeting the PLIN1 in childhood obesity. These data offer new molecular leads for future clinical biomarker and diagnostic discovery for childhood obesity.

A Map of the microRNA Regulatory Networks Identified by Experimentally Validated microRNA-Target Interactions in Five Domestic Animals: Cattle, Pig, Sheep, Dog, and Chicken

Domestic animals are members of the broader ecological context, in which humans are situated. Yet, genomics and systems science research have lagged behind and been relatively underappreciated in domestic animals compared to human genetics/genomics. Harnessing big data calls for omics data mapping studies in a broad range of mammals. To this end, microRNAs (miRNAs) regulate posttranscriptional expression of target genes, hence, governing different biological pathways and physiological processes. The knowledge of miRNA regulatory networks and maps is important for understanding regulation of gene expression and functions in both humans and domestic animals. However, complete miRNA regulatory networks have not yet been described in all species, particularly in domestic animals. We report here an original analysis so as to map the miRNA regulatory networks in domestic animals based on miRNA-target interactions (MTIs). Validated MTIs for five species; cattle, pig, sheep, dog, and chicken were extracted from the miRTarBase. miRNA regulomes were visualized using the Cytoscape software. The data in cattle, chicken, and pig were sufficient to visualize networks, identify central molecules, and subnetworks associated with the same phenotype; however, the MTI data in dog and sheep are still limited. We found several hub genes with large number of interactions, for example, 1 miRNA (bta-miR-17-5p) interacting with 27 genes and 7 miRNAs interacting with the same gene (tumor necrosis factor [TNF]) in cattle. In addition, two single-nucleotide polymorphisms were identified within the seed region of a previously demonstrated MTI, namely, between HMGB3 (high mobility group box 3) gene and bta-miR-17-5p. In summary, this miRNA regulome mapping study will enable and guide further studies of genome function in mammals with a view to applications in human as well as veterinary medicine. Furthermore, these miRNA regulomes can help to clarify fundamental pathways in cell biology and reveal molecular insights on phenotypic trait variability in common complex diseases and response phenotypes of drugs or other health interventions for precision medicine in the future.