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.

Polynomial search and global modeling: Two algorithms for modeling chaos

Global modeling aims to build mathematical models of concise description. Polynomial Model Search (PoMoS) and Global Modeling (GloMo) are two complementary algorithms (freely downloadable at the following address: http://www.cesbio.ups-tlse.fr/us/pomos_et_glomo.html) designed for the modeling of observed dynamical systems based on a small set of time series. Models considered in these algorithms are based on ordinary differential equations built on a polynomial formulation. More specifically, PoMoS aims at finding polynomial formulations from a given set of 1 to N time series, whereas GloMo is designed for single time series and aims to identify the parameters for a selected structure. GloMo also provides basic features to visualize integrated trajectories and to characterize their structure when it is simple enough: One allows for drawing the first return map for a chosen Poincaré section in the reconstructed space; another one computes the Lyapunov exponent along the trajectory. In the present paper, global modeling from single time series is considered. A description of the algorithms is given and three examples are provided. The first example is based on the three variables of the Rössler attractor. The second one comes from an experimental analysis of the copper electrodissolution in phosphoric acid for which a less parsimonious global model was obtained in a previous study. The third example is an exploratory case and concerns the cycle of rainfed wheat under semiarid climatic conditions as observed through a vegetation index derived from a spatial sensor.