PUBLICATION

Metabolic disruption of zebrafish (Danio rerio) embryos by bisphenol A. An integrated metabolomic and transcriptomic approach

Authors
Ortiz-Villanueva, E., Navarro-Martín, L., Jaumot, J., Benavente, F., Sanz-Nebot, V., Piña, B., Tauler, R.
ID
ZDB-PUB-170808-6
Date
2017
Source
Environmental pollution (Barking, Essex : 1987)   231: 22-36 (Journal)
Registered Authors
Piña, Benjamin
Keywords
Bisphenol A, Metabolic disruption, Non-targeted metabolomics, Transcriptomics, Zebrafish
MeSH Terms
  • Animals
  • Benzhydryl Compounds/toxicity*
  • Chromatography, Liquid
  • Endocrine Disruptors/toxicity*
  • Fish Proteins/chemistry
  • Fish Proteins/genetics*
  • Fish Proteins/metabolism
  • Mass Spectrometry
  • Metabolome/drug effects
  • Metabolomics/methods
  • Phenols/toxicity*
  • Transcriptome/drug effects
  • Zebrafish/genetics
  • Zebrafish/growth & development
  • Zebrafish/metabolism*
PubMed
28780062 Full text @ Environ. Pollut.
CTD
28780062
Abstract
Although bisphenol A (BPA) is commonly recognized as an endocrine disruptor, the metabolic consequences of its exposure are still poorly understood. In this study, we present a non-targeted LC-MS based metabolomic analysis in combination with a full-genome, high-throughput RNA sequencing (RNA-Seq) to reveal the metabolic effects and the subjacent regulatory pathways of exposing zebrafish embryos to BPA during the first 120 hours post-fertilization. We applied multivariate data analysis methods to extract biochemical information from the LC-MS and RNA-Seq complex datasets and to perform testable predictions of the phenotypic adverse effects. Metabolomic and transcriptomic data revealed a similar subset of altered pathways, despite the large difference in the number of identified biomarkers (around 50 metabolites and more than 1000 genes). These results suggest that even a moderate coverage of zebrafish metabolome may be representative of the global metabolic changes. These multi-omic responses indicate a specific metabolic disruption by BPA affecting different signaling pathways, such as retinoid and prostaglandin metabolism. The combination of transcriptomic and metabolomic data allowed a dynamic interpretation of the results that could not be drawn from either single dataset. These results illustrate the utility of -omic integrative analyses for characterizing the physiological effects of toxicants beyond the mere indication of the affected pathways.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping