miRNA and lncRNA Expression Networks Modulate Cell Cycle and DNA Repair Inhibition in Senescent Prostate Cells.

Cellular senescence is a state of permanent growth arrest that arises once cells reach the limit of their proliferative capacity. It creates an inflammatory microenvironment favouring the initiation and progression of various age-related diseases, including prostate cancer. Non-coding RNAs (ncRNAs) have emerged as important regulators of cellular gene expression. Nonetheless, very little is known about the interplay of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and how deregulation of ncRNA networks promotes cellular senescence. To investigate this, human prostate epithelial cells were cultured through different passages until senescent, and their RNA was extracted and sequenced using RNA sequencing (RNAseq) and microRNA sequencing (miRNA-seq) miRNAseq. Differential expression (DE) gene analysis was performed to compare senescent and proliferating cells with Limma, miRNA-target interactions with multiMiR, lncRNA-target interactions using TCGA data and network evaluation with miRmapper. We found that miR-335-3p, miR-543 and the lncRNAs H19 and SMIM10L2A all play central roles in the regulation of cell cycle and DNA repair processes. Expression of most genes belonging to these pathways were down-regulated by senescence. Using the concept of network centrality, we determined the top 10 miRNAs and lncRNAs, with miR-335-3p and H19 identified as the biggest hubs for miRNAs and lncRNA respectively. These ncRNAs regulate key genes belonging to pathways involved in cell senescence and prostate cancer demonstrating their central role in these processes and opening the possibility for their use as biomarkers or therapeutic targets to mitigate against prostate ageing and carcinogenesis.

GAIL: An interactive webserver for inference and dynamic visualization of gene-gene associations based on gene ontology guided mining of biomedical literature.

In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining and provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. We evaluate the utility and performance of GAIL with applications to gene signatures associated with systemic lupus erythematosus and breast cancer. Results show that GAIL allows effective interrogation and visualization of gene-gene networks and their subnetworks, which facilitates biological understanding of gene-gene associations. GAIL is available at http://chunglab.io/GAIL/.

Interplay Between MicroRNAs and Targeted Genes in Cellular Homeostasis of Adult Zebrafish (Danio rerio).

BACKGROUND: Cellular homeostasis is regulated by the intricate interplay between a plethora of signaling pathways and "energetic sensors" in organs. In order to maintain energy balance, induction or repression of metabolic pathways must be regulated and act in concert with the energetic demands of the cell at a given point in time. A new class of small noncoding RNAs, the microRNAs (miRNAs), has added yet further complexity to the control of metabolic homeostasis. OBJECTIVE: Understanding the damages induced by toxins in the liver and the intestine as well as the interplay between the miRNome and transcriptome first requires baseline characterization in these tissues in healthy animals under cellular homeostasis. METHODS: The liver (main site for detoxification) and the gut (primary exposure routes for contaminant exposure) were dissected out (wildtype fish), total and small RNA extracted, mRNA and miRNA libraries constructed and subjected to high throughput sequencing. Differential Expression (DE) analysis was performed comparing liver with gut and an "miRNA matrix" that integrates the miRNA-seq and mRNA-seq data was constructed. RESULTS: Both the miRNome and transcriptome of the liver and gut tissues were characterized and putative novel miRNAs were identified. Exploration of the "miRNA matrix" regulatory network revealed that miRNAs uniquely expressed in the liver or gut tissue regulated fundamental cellular processes important for both organs, and that commonly expressed miRNAs in both tissues regulated biological processes that were specific to either the liver or the gut. CONCLUSION: The result of our analyses revealed new insights into microRNA function in these tissues.

miRmapper: A Tool for Interpretation of miRNA⁻mRNA Interaction Networks.

It is estimated that 30% of all genes in the mammalian cells are regulated by microRNA (miRNAs). The most relevant miRNAs in a cellular context are not necessarily those with the greatest change in expression levels between healthy and diseased tissue. Differentially expressed (DE) miRNAs that modulate a large number of messenger RNA (mRNA) transcripts ultimately have a greater influence in determining phenotypic outcomes and are more important in a global biological context than miRNAs that modulate just a few mRNA transcripts. Here, we describe the development of a tool, "miRmapper", which identifies the most dominant miRNAs in a miRNA⁻mRNA network and recognizes similarities between miRNAs based on commonly regulated mRNAs. Using a list of miRNA⁻target gene interactions and a list of DE transcripts, miRmapper provides several outputs: (1) an adjacency matrix that is used to calculate miRNA similarity utilizing the Jaccard distance; (2) a dendrogram and (3) an identity heatmap displaying miRNA clusters based on their effect on mRNA expression; (4) a miRNA impact table and (5) a barplot that provides a visual illustration of this impact. We tested this tool using nonmetastatic and metastatic bladder cancer cell lines and demonstrated that the most relevant miRNAs in a cellular context are not necessarily those with the greatest fold change. Additionally, by exploiting the Jaccard distance, we unraveled novel cooperative interactions between miRNAs from independent families in regulating common target mRNAs; i.e., five of the top 10 miRNAs act in synergy.