Transcriptomics

Dataset Information

0

Information-dependent Enrichment Analysis Reveals Time-dependent Transcriptional Regulation of the Estrogen Pathway of Toxicity


ABSTRACT: The twenty-first century vision for toxicology involves a transition away from high-dose animal studies to in vitro and computational models (NRC 2007). This transition requires mapping pathways of toxicity by understanding how in vitro systems respond to chemical perturbation. Uncovering transcription factors/signaling networks responsible for gene expression patterns are essential for defining pathways of toxicity, and ultimately, for determining the chemical modes of action through which a toxicant acts. Traditionally transcription factor identification is achieved via chromatin immunoprecipitation studies and summarized by calculating which transcription factors are statistically associated with up- and downregulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be as useful for determining transcription factor associations. To move away from an arbitrary statistical or fold-change based cutoffs we developed, in the context of the Mapping the Human Toxome project, an enrichment paradigm called Information Dependent Enrichment Analysis (IDEA) to guide identification of the transcription factor network. We used a test case of activation in MCF‑7 cells by 17β estradiol (E2). Using this new approach, we established a time course for transcriptional and functional responses to E2. ERα and ERβ were associated with short-term transcriptional changes in response to E2. Sustained exposure led to recruitment of additional transcription factors and alteration of cell-cycle machinery. TFAP2C and SOX2 were the transcription factors most highly correlated with dose. E2F7, E2F1 and Foxm1, which are involved in cell proliferation, were enriched only at 24h. IDEA should be useful for identifying candidate pathways of toxicity. IDEA outperforms Gene-set Enrichment Analysis (GSEA) and provides similar results to Weighted Gene Correlation Network Analysis, a platform that helps to identify genes not annotated to pathways.

ORGANISM(S): Homo sapiens

PROVIDER: GSE84981 | GEO | 2017/06/05

SECONDARY ACCESSION(S): PRJNA335817

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2022-05-31 | GSE197122 | GEO
2020-01-31 | GSE144580 | GEO
2014-06-30 | GSE52351 | GEO
2014-06-30 | GSE57804 | GEO
2014-06-30 | E-GEOD-57804 | biostudies-arrayexpress
2021-08-24 | GSE167038 | GEO
2014-06-30 | E-GEOD-52351 | biostudies-arrayexpress
2011-05-09 | E-GEOD-26834 | biostudies-arrayexpress
2020-04-30 | GSE129622 | GEO
| PRJEB29568 | ENA