Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming

Network based multifactorial modelling of miRNA-target interactions

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in humans is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this article, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values. Our approach calculates network-wide effects of perturbations in the expression level of one or more nodes in the presence or absence of miRNA interaction factors such as seed type, binding energy. We carried out the analysis of large-scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer-associated genes and miRNAs. Our network-based approach takes the sponge effect into account and helps to unveil the crosstalk between nodes in miRNA:target network. The model has potential to reveal unforeseen regulations that are only evident in the network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs, and available as R package ceRNAnetsim: https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.

Differential gene expression and gene-set enrichment analysis in Caco-2 monolayers during a 30-day timeline with Dexamethasone exposure

Glucocorticoid hormones affect gene expression via activation of glucocorticoid receptor NR3C1, causing modulation of inflammation and autoimmune activation. The glucocorticoid Dexamethasone is an important pharmaceutical for the treatment of colitis and other inflammatory bowel diseases. While suppressive effects of glucocorticoids on activated immune cells is significant, their effects upon epithelial cells are less well studied. Previous research shows that the effects of Dexamethasone treatment on polarized Caco-2 cell layer permeability is delayed for >10 treatment days (as measured by transepithelial electrical resistance). In vivo intestinal epithelial cells turn over every 3–5 days; we therefore hypothesized that culture age may produce marked effects on gene expression, potentially acting as a confounding variable. To investigate this issue, we cultured polarized Caco-2 monolayers during a 30-day timecourse with ~15 days of continuous Dexamethasone exposure. We collected samples during the timecourse and tested differential expression using a 250-plex gene expression panel and Nanostring nCounter® system. Our custom panel was selectively enriched for KEGG annotations for tight-junction, actin cytoskeleton regulation, and colorectal cancer-associated genes, allowing for focused gene ontology-based pathway enrichment analyses. To test for confounding effects of time and Dexamethasone variables, we used the Nanostring nSolver differential expression data model which includes a mixturenegative binomial modelwith optimization. We identified a time-associated “EMT-like” signature with differential expression seen in important actomyosin cytoskeleton, tight junction, integrin, and cell cycle pathway genes. Dexamethasone treatment resulted in a subtle yet significant counter-signal showing suppression of actomyosin genes and differential expression of various growth factor receptors.

Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index

Both genetic and environmental factors are suggested to influence overweight and obesity risks. Although individual loci and genes have been frequently shown to be associated with body mass index (BMI), the overall interaction of these genes and their role in BMI remains underexplored. Data were collected in 90 healthy, predominately Caucasian participants (51% female) with a mean age of 26.00 ± 9.02 years. Whole blood samples were assayed by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. We integrated and analyzed the clinical and microarray gene expression data from those individuals to understand various systematic gene expression patterns underlying BMI. Conventional differential expression analysis identified seven genes RBM20, SEPT12, AX748233, SLC30A3, WTIP, CASP10, and OR12D3 associated with BMI. Weight gene co-expression network analysis among 4,647 expressed genes identified two gene modules associated with BMI. These two modules, with different extents of gene connectivity, are enriched for catabolic and muscle system processes respectively, and tend to be regulated by zinc finger transcription factors. A total of 246 hub genes were converted to non-hub genes, and 286 non-hub genes were converted to hub genes between normal and overweight individuals, revealing the network dynamics underlying BMI. A total of 28 three-way gene interactions were identified, suggesting the existence of high-order gene expression patterns underlying BMI. Our study demonstrated a variety of systematic gene expression patterns associated with BMI and thus provided novel understanding regarding the genetic factors for overweight and obesity risks on system levels.

Construction and comprehensive analysis of a ceRNA network to reveal potential prognostic biomarkers for hepatocellular carcinoma

Background

Long noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; therefore, lncRNAs are considered a major part of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. The present study explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in hepatocellular carcinoma (HCC) and their potential impact on HCC patient prognosis.

Methods

In this study, we systematically studied the expression profiles and prognostic value of lncRNA, miRNA, and mRNA from a total of 838 HCC patients from five HCC cohorts (TCGA, GSE54236, GSE76427, GSE64041 and GSE14520). The TCGA, GSE54236 and GSE76427 HCC cohorts were utilized to establish a prognosis-related network of dysregulated ceRNAs by bioinformatics methods. The GSE64041 and GSE14520 HCC cohorts were utilized to verify the expression of candidate genes.

Results

In total, 721 lncRNAs, 73 miRNAs, and 1563 mRNAs were aberrantly expressed in HCC samples. A ceRNA network including 26 lncRNAs, four miRNAs, and six mRNAs specific to HCC was established. The survival analysis showed that four lncRNAs (MYCNOS, DLX6-AS1, LINC00221, and CRNDE) and two mRNAs (CCNB1 and SHCBP1) were prognostic biomarkers for patients with HCC in both the TCGA and GEO databases.

Conclusion

The proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the pathogenesis of HCC. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for HCC.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0817-y) contains supplementary material, which is available to authorized users.

Computational Prediction of Functional MicroRNA-mRNA Interactions

Proteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as recognition sequences for their target mRNAs within a larger regulatory machinery. A miRNA can have many target mRNAs and an mRNA can be targeted by many miRNAs which makes it difficult to experimentally discover all miRNA-mRNA interactions. Therefore, computational methods have been developed for miRNA detection and miRNA target prediction. An abundance of available computational tools makes selection difficult. Additionally, interactions are not currently the focus of investigation although they more accurately define the regulation than pre-miRNA detection or target prediction could perform alone. We define an interaction including the miRNA source and the mRNA target. We present computational methods allowing the investigation of these interactions as well as how they can be used to extend regulatory pathways. Finally, we present a list of points that should be taken into account when investigating miRNA-mRNA interactions. In the future, this may lead to better understanding of functional interactions which may pave the way for disease marker discovery and design of miRNA-based drugs.