Dataset Information

Microarray dataset supporting a role for ATF4 in isoginkgetin-induced gene expression in HCT116 cells.

ABSTRACT: Isoginkgetin (IGG) is a compound originally derived from the leaves of Ginkgo biloba trees. It was subsequently identified through a chemical screen to be an inhibitor of both the major and minor spliceosome, with an IC50 value of 30 µM [1]. Little is currently known about the overall effects of spliceosome inhibition on human cells. Here, we treated HCT116 and a p53 null subline of colon cancer cells with 30 µM IGG for 8 hours. Total RNA was isolated, and Affymetrix oligonucleotide microarray analysis was completed using samples from two biologically independent experiments. A relatively small number of transcripts were differentially expressed in these cell lines. There was considerable overlap in the upregulated but not the downregulated transcripts. PANTHER Reactome analysis of these shared upregulated transcripts identified enriched pathways involving the ATF4 transcription factor important in the integrated stress response [2].

SUBMITTER: van Zyl E

PROVIDER: S-EPMC9019238 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray dataset supporting a role for ATF4 in isoginkgetin-induced gene expression in HCT116 cells.

van Zyl Erin E Tolls Victoria V McKay Bruce C BC

Data in brief 20220401

Isoginkgetin (IGG) is a compound originally derived from the leaves of <i>Ginkgo biloba</i> trees. It was subsequently identified through a chemical screen to be an inhibitor of both the major and minor spliceosome, with an IC50 value of 30 µM [1]. Little is currently known about the overall effects of spliceosome inhibition on human cells. Here, we treated HCT116 and a p53 null subline of colon cancer cells with 30 µM IGG for 8 hours. Total RNA was isolated, and Affymetrix oligonucleotide micro ...[more]

PMID: 35463055

Similar Datasets

Project description:Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates' 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

Project description:Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

Dataset Information

Microarray dataset supporting a role for ATF4 in isoginkgetin-induced gene expression in HCT116 cells.

Publications

Microarray dataset supporting a role for ATF4 in isoginkgetin-induced gene expression in HCT116 cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets