Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non-model Organisms.

Short ("seed") or extended base pairing between microRNAs (miRNAs) and their target RNAs enables post-transcriptional silencing in many organisms. These interactions allow the computational prediction of potential targets. In model organisms, predicted targets are frequently validated experimentally; hence meaningful miRNA-regulated processes are reported. However, in non-models, these reports mostly rely on computational prediction alone. Many times, further bioinformatic analyses such as Gene Ontology (GO) enrichment are based on these in silico projections. Here such approaches are reviewed, their caveats are highlighted and the ease of picking false targets from predicted lists is demonstrated. Discoveries that shed new light on how miRNAs evolved to regulate targets in various phyletic groups are discussed, in addition to the pitfalls of target identification in non-model organisms. The goal is to prevent the misuse of bioinformatic tools, as they cannot bypass the biological understanding of miRNA-target regulation.

miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer.

BACKGROUND: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used. RESULTS: Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer. CONCLUSIONS: Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .

Improving prediction accuracy using decision-tree-based meta-strategy and multi-threshold sequential-voting exemplified by miRNA target prediction.

Lots of computational predictors have been developed for fast and large-scale analysis of biological data. However, many of them were developed long time ago when training datasets or sets of input features were rather small. Consequently, the utility of these predictors in much large datasets, which are very common in nowadays, need to be examined carefully. In addition, with the rapid development of scientific research, the expectation on the prediction accuracy of computational predictors is continuously uplifting. Therefore, developing novel strategies to improve the prediction accuracies of computational predictors becomes critical. In this study, the predictive results of existing individual miRNA target predictors were integrated into a decision-tree to make meta-prediction. When the multi-threshold sequential-voting technique was used, the prediction accuracy of the decision-tree was significantly improved by at least thirty percentage points compared to the individual predictors.

Quantifying the strength of miRNA-target interactions.

We quantify the strength of miRNA-target interactions with MIRZA, a recently introduced biophysical model. We show that computationally predicted energies of interaction correlate strongly with the energies of interaction estimated from biochemical measurements of Michaelis-Menten constants. We further show that the accuracy of the MIRZA model can be improved taking into account recently emerged experimental data types. In particular, we use chimeric miRNA-mRNA sequences to infer a MIRZA-CHIMERA model and we provide a framework for inferring a similar model from measurements of rate constants of miRNA-mRNA interaction in the context of Argonaute proteins. Finally, based on a simple model of miRNA-based regulation, we discuss the importance of interaction energy and its variability between targets for the modulation of miRNA target expression in vivo.

Joint analysis of miRNA and mRNA expression data.

miRNAs are small RNA molecules ('22 nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.

Altered microRNA expression in bovine skeletal muscle with age.

Age-dependent decline in skeletal muscle function leads to several inherited and acquired muscular disorders in elderly individuals. The levels of microRNAs (miRNAs) could be altered during muscle maintenance and repair. We therefore performed a comprehensive investigation for miRNAs from five different periods of bovine skeletal muscle development using next-generation small RNA sequencing. In total, 511 miRNAs, including one putatively novel miRNA, were identified. Thirty-six miRNAs were differentially expressed between prenatal and postnatal stages of muscle development including several myomiRs (miR-1, miR-206 and let-7 families). Compared with miRNA expression between different muscle tissues, 14 miRNAs were up-regulated and 22 miRNAs were down-regulated in the muscle of postnatal stage. In addition, a novel miRNA was predicted and submitted to the miRBase database as bta-mir-10020. A dual luciferase reporter assay was used to demonstrate that bta-mir-10020 directly targeted the 3'-UTR of the bovine ANGPT1 gene. The overexpression of bta-mir-10020 significantly decreased the DsRed fluorescence in the wild-type expression cassette compared to the mutant type. Using three computational approaches - miranda, pita and rnahybrid - these differentially expressed miRNAs were also predicted to target 3609 bovine genes. Disease and biological function analyses and the KEGG pathway analysis revealed that these targets were statistically enriched in functionality for muscle growth and disease. Our miRNA expression analysis findings from different states of muscle development and aging significantly expand the repertoire of bovine miRNAs now shown to be expressed in muscle and could contribute to further studies on growth and developmental disorders in this tissue type.

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana.

Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive. In this study, we performed a comprehensive analysis of mature precursor miRNA (pre-miRNA) sequences from Arabidopsis to understand their features and regulatory targets. The influence of isomiR terminal heterogeneity in target binding was examined comprehensively. Our comprehensive analyses suggested a novel computational strategy that utilizes miRNA and its isomiRs to enhance the accuracy of their regulatory target prediction in Arabidopsis. A few targets are shared by several members of isomiRs; however, this phenomenon was not typical. Gene Ontology (GO) enrichment analysis showed that commonly targeted mRNAs were enriched for certain GO terms. Moreover, comparison of these commonly targeted genes with validated targets from published data demonstrated that the validated targets are bound by most isomiRs and not only the canonical miRNA. Furthermore, the biological role of isomiRs in target cleavage was supported by degradome data. Incorporating this finding, we predicted potential target genes of several miRNAs and confirmed them by experimental assays. This study proposes a novel strategy to improve the accuracy of predicting miRNA targets through combined use of miRNA with its isomiRs.

Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction.

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts through direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with a decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such deep learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for the further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in the clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein-protein interactions.

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets.

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

Turning off a few overexpressed genes in prostate cancer with microRNAs using a 7mer-seed match model.

PURPOSE: This research aims to identify the miRNAs that could target the genes overexpressed in prostate cancer so that miRNA-based therapeutics could be developed. METHODS: A 7mer-m8 model of microRNA targeting was utilized in order to analyse the relationship between microRNAs and overexpressed genes. The efficiency of miRNA binding was investigated using various parameters namely free energy (AMFE), GC and GC3 content, translation efficiency, cosine similarity metric, mRNA stability, free energy of RNA duplex, and base compositional difference. BLAST2GO software was used to elucidate the functional roles of the genes overexpressed in prostate cancer. RESULTS: The current research reveals that the coding sequences of the genes were found targeted with multiple miRNAs. For instance, the HPN gene was targeted by the microRNA miR-4279 at two distinct sites i.e. 263-278 and 746-761 in the coding sequence. In the present study, it was observed that the target region of the genes exhibited a comparatively high GC and GC3 contents in comparison to the flanking regions. A low translational rate and weak relationship between RSCU and tRNA were obtained which may be due to the absence of optimal codons. CONCLUSION: In this study, we have uncovered the human miRNAs that have potential for binding to the coding sequences of 14 most overexpressed genes in prostate cancer and thereby could silence those genes.

In Silico Analysis of MicroRNA Expression Data in Liver Cancer.

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

miRBind: A Deep Learning Method for miRNA Binding Classification.

The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

MicroRNA target prediction tools for animals: Where we are at and where we are going to - A systematic review.

MicroRNAs (miRNAs) are non-coding RNAs containing 19-26 nucleotides, and they directly regulate the translation of mRNAs by binding to them. MiRNAs participate in various physiological processes and are associated with the development of diseases, such as cancer. Therefore, understanding miRNAs regulation on targets is crucial for understanding the mechanisms of diseases and for obtaining a more suitable treatment. In animals, the base complementarity between miRNAs and the mRNA is imperfect, hindering the prediction of these targets. Thus, over the past 15 years, several computational tools have emerged for the prediction of miRNA targets in animals, generally with a focus on human expression data. Taking into account the wide range of prediction tools, a systematic review is presented here to analyze and classify these methods and features to enable the most appropriate choice according to the needs of each researcher. In this study, only articles whose methods met the inclusion and exclusion criteria established in the protocol were considered. The search was performed in November 2020, in two search engines PubMed and VHL Regional Portal. Among the initial 5315 journals found in the two searches, 78 articles were accepted, comprising 49 different tools analyzed and grouped by features and method similarities. As we limited our criteria to animals, all tools found in our search were suitable for human studies. The results demonstrated the evolution of prediction tools, including the most used features, such as alignment and thermodynamics, the methods used, as well as performance issues. It is possible to conclude that the currently available miRNA target prediction tools and methods can be aggregated with new features or other methods to improve accuracy.

A Study on microRNAs Targeting the Genes Overexpressed in Lung Cancer and their Codon Usage Patterns.

As reported by WHO in 2018, there were 2.09 million victims of lung cancer and 1.76 million fatalities worldwide. Tobacco remains the biggest hazard in causing this lethal disease. To execute the computational analysis, the overexpressed lung cancer genes were retrieved from literature and subsequently their complete coding sequences (CDS) were downloaded. The mature microRNA sequences of human were extracted from miRBASE. The 7mer-m8 perfect seed match between miRNAs and mRNAs was found. Following filtration, 7 genes were selected that possessed binding sites for maximum miRNAs viz., MUC5B (miR-4479, miR-1227-5p, miR-3940-5p, miR-604, miR-4455, miR-4267, miR-6750-3p, miR-4530, miR-5587-5p, miR-4508, miR-4534, miR-4443, miR-4253, miR-1321, miR-4655-5p, miR-4297, miR-4296, miR-1268a, miR-3178, miR-4750-3p, miR-1306-3p, miR-1268b, miR-3656, miR-1233-3p, miR-6804-5p), MUC16 (miR-4456, miR-1205, miR-665, miR-6808-3p, miR-1279, miR-4257, miR-1227-5p, miR-888-3p, miR-4455, miR-4267, miR-4294, miR-1275, miR-4288, miR-1178-5p, miR-4314, miR-6829-3p, miR-548av-5p, miR-1294, miR-5587-5p, miR-3622b-5p, miR-1273f, miR-4770, miR-4327, miR-4318, miR-4531, miR-4534, miR-4443, miR-7106-5p, miR-3125, miR-3650, miR-4325, miR-4266, miR-7976, miR-1290, miR-4500, miR-7160-5p, miR-4291, miR-1306-3p, miR-6130, miR-4430, miR-4725-5p, miR-4441, miR-6077, miR-1304-5p, miR-7515, miR-3182, miR-6134), COL1A1 (miR-3665, miR-1227-5p, miR-6132, miR-2861, miR-4530, miR-3155b, miR-3155a, miR-1292-3p, miR-4497), COL5A1 (miR-7162-5p, miR-3665, miR-6809-3p, miR-4313, miR-4531, miR-4532, miR-3155b, miR-4323, miR-1207-3p, miR-4260, miR-6071, miR-4710, miR-7162-5p), CELSR2 (miR-7150, miR-4308, miR-6132, miR-4770, miR-4534, miR-4492, miR-3960, miR-3178, miR-4291, miR-563), COL7A1 (miR-665, miR-6730-3p, miR-1227-5p, miR-4265, miR-6829-3p, miR-4297, miR-4532, miR-3181, miR-4310, miR-4441, miR-4497, miR-1237-3p), and FAT2 (miR-4267, miR-1275, miR-4770, miR-1825, miR-6895-5p, miR-4535, miR-4493, miR-940, miR-6861-3p, miR-4310, miR-4710, miR-4447, miR-4472). The miRNA-target site and their flank regions were compared with respect to site accessibility, translational rate, and relationship between RSCU and tRNAs. Higher accessibilities to miRNA-binding regions and lower translational rates indicated that miRNAs' binding to their respective targets might be efficient. The presence of rare codons might further augment miRNA targeting. The codon usage bias study of the genes related to lung cancer revealed non-uniform usage of nucleotides and comparatively higher GC content. Lower biasness prevailed in the genes and selective constraint mostly governed them. Lastly, the functionalities of target genes were also revealed. The silencing characteristic of miRNAs might be exploited to design miRNA-mediated therapy that might potentially repress the overexpressed genes in carcinoma.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling.

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

A Computational approach to screen, predict and annotate human and chimpanzee PHEX intronic miRNAs, their gene targets, and regulatory interaction networks.

The knowledge of what separates us genetically from our less-evolved relatives is crucial for gaining new biomedical insight about the human-chimpanzee relatedness that could influence the development of new treatments and diagnostic aids for various ailments. Especially, more than 300 diseases have been mapped to the X chromosome, which has unique and complicated characteristics than other chromosomes in the human genome. Although the genomes of humans and chimpanzees share 99% similarity, significant differences exist between the two species in their non-coding intronic regions. Therefore, this evolutionary-based genome annotation study attempted to computationally compare, contrast, and annotate the homologous miRNAs and their gene regulatory mechanisms in the intronic regions of the PHEX gene on the human X chromosome of the two species. From our results, we identified a total of 1296 human miRNAs and 46, 957 gene targets. Similarly, 30, 563 targets of homologous chimp miRNAs were predicted. miRNAs like hsa-miR-17-5p showed a maximum number of interactions while miRNAs like hsa-miR-107 with the least number of interactions in the human/chimp gene networks. A few top-ranked miRNAs such as hsa-miR-24, hsa-miR-145, hsa-miR-34a, and hsa-miR-378 were observed to be common between the two genera. The cooperativity and multiplicity of certain miRNAs were predicted to regulate the expression of diverse cancer-associated genes such as Cyclin D1, Notch1, CDK-6, E2F3, ALK4, CKDN2A, DHFR, and MAPK14. Nevertheless, further in vitro and in vivo experimental validations of these gene candidates are required before they could be used as potential diagnostic markers and drug targets.

A combinatorial in silico approach for microRNA-target identification: Order out of chaos.

Contemporary computational microRNA(miRNA)-target prediction tools have been playing a vital role in pursuing putative targets for a solitary miRNA or a group of miRNAs. These tools utilise a set of probabilistic algorithms, machine learning techniques and analyse experimentally validated miRNA targets to identify the potential miRNA-target pairs. Unfortunately, current tools generate a huge number of false-positive predictions. A standardized approach with a single tool or a combination of tools is still lacking. Moreover, sensitivity, specificity and overall efficiency of any single tool are yet to be satisfactory. Therefore, a systematic combination of selective online tools combining the factors regarding miRNA-target identification would be valuable as an miRNA-target prediction scheme. The focus of this study was to develop a theoretical framework by combining six available online tools to facilitate the current understanding of miRNA-target identification.

Systematic review and network analysis of microRNAs involved in cardioprotection against myocardial ischemia/reperfusion injury and infarction: Involvement of redox signalling.

Although myocardial ischemia-reperfusion injury (I/R) and its pathological consequences are the leading cause of morbidity and mortality worldwide, cardioprotective therapeutics are still not on the market. Oxidative stress, a major contributing factor to myocardial I/R, changes transcription of coding and non-coding RNAs, alters post-transcriptional modulations, and regulate protein function. MicroRNA (miRNA) expression can be altered by oxidative stress and microRNAs may also regulate cytoprotective mechanisms and exert cardioprotection againts I/R. Transcriptomic analysis of I/R and oxidative stress-induced alterations followed by microRNA-mRNA target interaction network analysis may reveal microRNAs and their mRNA targets that may play a role in cardioprotection and serve as microRNA therapeutics or novel molecular targets for further drug development. Here we provide a summary of a systematic literature review and in silico molecular network analysis to reveal important cardioprotective microRNAs and their molecular targets that may provide cardioprotection via regulation of redox signalling.

miRNA Combinatorics and its Role in Cell State Control-A Probabilistic Approach.

A hallmark of cancer evolution is that the tumor may change its cell identity and improve its survival and fitness. Drastic change in microRNA (miRNA) composition and quantities accompany such dynamic processes. Cancer samples are composed of cells' mixtures of varying stages of cancerous progress. Therefore, cell-specific molecular profiling represents cellular averaging. In this study, we consider the degree to which altering miRNAs composition shifts cell behavior. We used COMICS, an iterative framework that simulates the stochastic events of miRNA-mRNA pairing, using a probabilistic approach. COMICS simulates the likelihood that cells change their transcriptome following many iterations (100 k). Results of COMICS from the human cell line (HeLa) confirmed that most genes are resistant to miRNA regulation. However, COMICS results suggest that the composition of the abundant miRNAs dictates the nature of the cells (across three cell lines) regardless of its actual mRNA steady-state. In silico perturbations of cell lines (i.e., by overexpressing miRNAs) allowed to classify genes according to their sensitivity and resilience to any combination of miRNA perturbations. Our results expose an overlooked quantitative dimension for a set of genes and miRNA regulation in living cells. The immediate implication is that even relatively modest overexpression of specific miRNAs may shift cell identity and impact cancer evolution.

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression.

During tumor progression, cancer cells rewire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.