Raphaël Coudret

Transcriptome profile analysis reveals specific signatures of pollutants in Atlantic eels

Identifying specific effects of contaminants in a multi-stress field context remain a challenge in ecotoxicology. In this context, “omics” technologies, by allowing the simultaneous measurement of numerous biological endpoints, could help unravel the in situ toxicity of contaminants. In this study, wild Atlantic eels were sampled in 8 sites presenting a broad contamination gradient in France and Canada. The global hepatic transcriptome of animals was determined by RNA-Seq. In parallel, the contamination level of fish to 8 metals and 25 organic pollutants was determined. Factor analysis for multiple testing was used to identify genes that are most likely to be related to a single factor. Among the variables analyzed, arsenic (As), cadmium (Cd), lindane (γ-HCH) and the hepato-somatic index (HSI) were found to be the main factors affecting eel’s transcriptome. Genes associated with As exposure were involved in the mechanisms that have been described during As vasculotoxicity in mammals. Genes correlated with Cd were involved in cell cycle and energy metabolism. For γ-HCH, genes were involved in lipolysis and cell growth. Genes associated with HSI were involved in protein, lipid and iron metabolisms. Our study proposes specific gene signatures of pollutants and their impacts in fish exposed to multi-stress conditions.

A new sliced inverse regression method for multivariate response

A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. The choice of the dimension of the EDR space is discussed. Moreover, a way to cluster components of y related to the same EDR space is provided. Thus, the proposed multivariate SIR method can be used properly on each cluster instead of blindly applying it on all components of y. The numerical performances of multivariate SIR are illustrated on a simulation study. An application to the Minneapolis elementary schools data is also provided. Although the proposed methodology relies on SIR, it opens the door for new regression approaches with a multivariate response. They could be built similarly based on other reduction dimension methods.