Making use of transcription factor enrichment to identify functional microRNA-regulons

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Detection of features predictive of microRNA targets by integration of network data

Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHuman 7.2 predictions to identify useful features for the prediction of microRNA targets. These enriched miRNA families would have silencing activity for neural transcripts overlapping the repressive activity on neural genes of REST. We analyze properties of associated microRNA binding sites and contrast the outcome to the background. We found that the selected subset presents significant differences respect to the background: (i) lower GC-content in the vicinity of the predicted miRNA binding site, (ii) more target genes with multiple identical microRNA binding sites and (iii) a higher density of predicted microRNA binding sites close to the 3’ terminal end of the 3’-UTR. These results suggest that network selection of miRNA-mRNA pairs could provide useful features to improve microRNA target prediction.

MiR-455-5p upregulation in umbilical cord mesenchymal stem cells attenuates endometrial injury and promotes repair of damaged endometrium via Janus kinase/signal transducer and activator of transcription 3 signaling

Umbilical cord mesenchymal stem cells (UCMSCs) are regarded as an ideal source for clinical use. Increasing evidence has suggested that microRNAs (miRNAs) work as a crucial regulator in the development of plentiful diseases, including intrauterine adhesions (IUA). Herein, we investigated the specific impacts of UCMSCs overexpressing miR-455-5p in IUA. UCMSCs were cocultured with endometrial stromal cells (ESCs). Thirty-two female mice were divided into four different treated groups: sham, model, model + UCMSC-miR-NC and model + UCMSC-miR-455-5p. Mice in model groups were induced by uterine curettage. MiR-455-5p overexpressed UCMSCs facilitated the proliferation and cell cycle progression of ESCs according to 5-ethynyl-2′-deoxyuridine assay and flow cytometry analysis. Hematoxylin-eosin and Masson staining revealed that miR-455-5p upregulation in UCMSCs increased the number of endometrial glands and suppressed endometrial fibrosis in murine uterine tissues. Western blotting displayed that miR-455-5p overexpressed UCMSCs promoted the activation of Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) signaling in ESCs and murine uterine tissues. Mechanistically, miR-455-5p targeted 3’ untranslated region of suppressor of cytokine signaling 3 (SOCS3), which was confirmed by luciferase reporter assay. Reverse transcription quantitative polymerase chain reaction demonstrated that miR-455-5p was lowly expressed and SOCS3 was highly expressed in murine uterine tissues of IUA model. Moreover, Pearson correlation analysis showed that their expression was inversely correlated. Rescue assays suggested that inhibiting JAK/STAT3 signaling reversed effects of miR-455-5p on the behaviors of ESCs. The results indicated that miR-455-5p overexpression in UCMSCs helps to attenuate endometrial injury and repair damaged endometrium by activating SOCS3-mediated JAK/STAT3 signaling.