Supplement to Meta-analysis of gene expression patterns in animal models of prenatal alcohol exposure suggests role for protein synthesis inhibition and chromatin remodeling. Rogic S, Wong A, Pavlidis P.
Alcoholism, clinical and experimental research. 2016 Apr;40(4):717-27
Background: Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioural and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models, however there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets.
Methods: We assembled ten microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and post-natal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures.
Results: For each sub-analysis we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute ethanol treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over-representation of genes involved in protein synthesis, mRNA splicing and chromatin organization.
Conclusions: Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of FASD.
- Files with meta- and core signature genes (n column – number of datasets the gene was present in; core gene column – if ‘*’, the gene is included in the core signature):
- Since data sets were generated on different platforms we used gene-level data to allow for cross-platform integration. In the case where a gene had more than one probeset assigned to it, the p-values for the probesets were Bonferroni-corrected and the lowest corrected p-value was used to represent the gene for that data set (thus if a gene had two probesets, the p-values were multiplied by 2, subject to corrected p≤1.0). The following files contain gene-level p-values used for each dataset and for each direction of change:
- Result files for DE re-analyses of individual datasets (p-values given are for corresponding one-tailed statistics test):