Investigating the role of super-enhancer RNAs underlying embryonic stem cell differentiation

BACKGROUND

Super-enhancer RNAs (seRNAs) are a kind of noncoding RNA transcribed from super-enhancer regions. The regulation mechanism and functional role of seRNAs are still unclear. Although super-enhancers play a critical role in the core transcriptional regulatory circuity of embryonic stem cell (ESC) differentiation, whether seRNAs have similar properties should be further investigated.

RESULTS

We analyzed cap analysis gene expression sequencing (CAGE-seq) datasets collected during the differentiation of embryonic stem cells (ESCs) to cardiomyocytes to identify the seRNAs. A non-negative matrix factorization algorithm was applied to decompose the seRNA profiles and reveal two hidden stages during the ESC differentiation. We further identified 95 and 78 seRNAs associated with early- and late-stage ESC differentiation, respectively. We found that the binding sites of master regulators of ESC differentiation, including NANOG, FOXA2, and MYC, were significantly observed in the loci of the stage-specific seRNAs. Based on the investigation of genes coexpressed with seRNA, these stage-specific seRNAs might be involved in cardiac-related functions such as myofibril assembly and heart development and act in trans to regulate the co-expressed genes.

CONCLUSIONS

In this study, we used a computational approach to demonstrate the possible role of seRNAs during ESC differentiation.

Identification of lncRNA functions in lung cancer based on associated protein-protein interaction modules

Long non-coding RNAs (lncRNAs) have been found to play important roles in various biological processes; however, many of their functions remain unclear. In this study, we present a novel approach to identify the lncRNA-associated protein-protein interaction (PPI) modules and ascertain their functions in human lung squamous cell carcinoma. We collected lncRNA and mRNA expression profiles of lung squamous cell carcinoma from The Cancer Genome Atlas. To identify the lncRNA-associated PPI modules, lncRNA-mRNA co-expression networks were first constructed based on the mutual ranks of expression correlations. Next, we examined whether the co-expressed mRNAs of a specific lncRNA were closely connected by PPIs. For this, a significantly connected mRNA set was considered to be the lncRNA-associated PPI module. Finally, the prospective functions of a lncRNA was inferred using Gene Ontology enrichment analysis on the associated module. We found that lncRNA-associated PPI modules were subtype-dependent and each subtype had unique molecular mechanisms. In addition, antisense lncRNAs and sense genes tended to be functionally associated. Our results might provide new directions for understanding lncRNA regulations in lung cancer. The analysis pipeline was implemented in a web tool, available at http://lncin.ym.edu.tw/.

A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions

Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independently regulate their downstream targets, collaborate with each other to regulate gene expression. However, their synergistic roles in protein-protein interactions (PPIs) remain mostly unknown. In this paper, we present a novel framework (called CoRePPI) for inferring TF and miRNA co-regulation of PPIs. Particularly, CoRePPI is aimed at discovering the co-regulation specific to a condition of interest, by using heterogeneous data, including miRNA and messenger RNA (mRNA) expression profiles, putative miRNA targets, TF targets and PPIs. CoRePPI firstly finds the network motifs indicating the co-regulation of PPIs by TFs and miRNAs in tumor and normal conditions separately. Then by identifying the differential motifs found in one condition but not in the other, it builds the networks consisting of TFs, miRNAs and their co-regulated PPIs specific to different conditions respectively. To validate CoRePPI, we apply it to the Pan-Cancer dataset which includes the expression profiles of 12 cancer types from TCGA. Through network topology analysis, we found that the tumor and normal CoRePPI networks are scale-free. Furthermore, the results of differential and intersected network analysis between the tumor and normal CoRePPI networks suggest that only a small fraction of the regulatory relationships between TFs and miRNAs are conserved in both conditions but they co-regulate different downstream PPIs in tumor and normal conditions; and in different conditions the majority of the regulatory relationships between TFs and miRNAs are different although they may regulate the same PPIs in their respective conditions. The CoRePPI sub-networks constructed for the three types of cancers (breast cancer, lung cancer and ovarian cancer) are all scale-free, and the intersection of these CoRePPI sub-networks can be utilized as the biomarker CoRePPI sub-network of the three types of cancers. The PPI enrichment analyses of the tumor and normal CoRePPI networks suggest that the co-regulating TFs and miRNAs are significantly associated with the specific biological processes, diseases and pathways. In addition, comparing with the two non-condition-specific approaches, the tumor CoRePPI network is found to have the most enriched cancer-related PPIs. Altogether, the results uncover the combined regulatory patterns of TFs and miRNAs on the PPIs, and may provide new insights for research in cancer-associated TFs and miRNAs.