The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization

Abstract

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.

Comparative toxicity and liver transcriptomics of legacy and emerging brominated flame retardants following 5-day exposure in the rat

“…We chose liver and thyroid tissues for study, because both are target organs for toxicity after exposure to other brominated chemicals (Dunnick et al 1997;National Toxicology Program 2015). Toxicogenomic data after a five-day exposure scenario provides information on mechanisms, disease pathways and biomarkers that can be used for predicting long term toxicity (Mezencev and Auerbach 2019;National Academy of Sciences 2005).The goals of the current study are to provide information for comparing the toxicity of legacy and emerging flame retardants and to prioritize the need for more in-depth flame retardant toxicity studies.…”