Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Robinson JM

PROVIDER: S-EPMC6748367 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Robinson J M JM Turkington S S Abey S A SA Kenea N N Henderson W A WA

Tissue barriers 20190822 3

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 treatm ...[more]

PMID: 31438773

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Project description:BACKGROUND:Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms. RESULTS:We propose multivariate GSEA (MGSEA) to capture combinatorial relations of gene set enrichment among multiple platform features. MGSEA successfully captures designed feature relations from simulated data. By applying it to the scores of delineating breast cancer and glioblastoma multiforme (GBM) subtypes from The Cancer Genome Atlas (TCGA) datasets of CNV, DNA methylation and mRNA expressions, we find that breast cancer and GBM data yield both similar and distinct outcomes. Among the enriched functional categories, subtype-specific biomarkers are dominated by mRNA expression in many functional categories in both cancer types and also by CNV in many functional categories in breast cancer. The enriched functional categories belonging to distinct combinatorial patterns are involved different oncogenic processes: cell proliferation (such as cell cycle control, estrogen responses, MYC and E2F targets) for mRNA expression in breast cancer, invasion and metastasis (such as cell adhesion and epithelial-mesenchymal transition (EMT)) for CNV in breast cancer, and diverse processes (such as immune and inflammatory responses, cell adhesion, angiogenesis, and EMT) for mRNA expression in GBM. These observations persist in two external datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for breast cancer and Repository for Molecular Brain Neoplasia Data (REMBRANDT) for GBM) and are consistent with knowledge of cancer subtypes. We further compare the characteristics of MGSEA with several extensions of GSEA and point out the pros and cons of each method. CONCLUSIONS:We demonstrated the utility of MGSEA by inferring the combinatorial relations of multiple platforms for cancer subtype delineation in three multi-OMIC datasets: TCGA, METABRIC and REMBRANDT. The inferred combinatorial patterns are consistent with the current knowledge and also reveal novel insights about cancer subtypes. MGSEA can be further applied to any genotype-phenotype association problems with multimodal OMIC data.

Project description:BackgroundGene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results.ResultsTwo breast cancer microarray datasets were analyzed to identify gene sets differentially expressed between histological grade 1 and 3 breast cancer. The correlation of Normalized Enrichment Scores (NES) between gene sets, generated by the original GSEA and GSEA with the appearance frequency of genes incorporated (GSEA-AF), was compared. GSEA-AF resulted in higher correlation between experiments and more overlapping top gene sets. Several cancer related gene sets achieved higher NES in GSEA-AF as well. The same datasets were also analyzed by LRpath and LRpath with the appearance frequency of genes incorporated (LRpath-AF). Two well-studied lung cancer datasets were also analyzed in the same manner to demonstrate the validity of the method, and similar results were obtained.ConclusionsWe introduce an alternative way to integrate KEGG PATHWAY information into gene set enrichment testing. The performance of GSEA and LRpath can be enhanced with the integration of appearance frequency of genes. We conclude that, generally, gene set analysis methods with the integration of information from KEGG PATHWAY performs better both statistically and biologically.

Dataset Information

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

Publications

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

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