Claude Casellas

Binding of estrogenic compounds to recombinant estrogen receptor-alpha: application to environmental analysis.

Estrogenic activity in environmental samples could be mediated through a wide variety of compounds and by various mechanisms. High-affinity compounds for estrogen receptors (ERs), such as natural or synthetic estrogens, as well as low-affinity compounds such as alkylphenols, phthalates, and polychlorinated biphenyls are present in water and sediment samples. Furthermore, compounds such as polycyclic aromatic hydrocarbons, which do not bind ERs, modulate estrogen activity by means of the aryl hydrocarbon receptor (AhR). In order to characterize compounds that mediate estrogenic activity in river water and sediment samples, we developed a tool based on the ER-αligand-binding domain, which permitted us to estimate contaminating estrogenic compound affinities. We designed a simple transactivation assay in which compounds of high affinity were captured by limited amounts of recombinant ER-αand whose capture led to a selective inhibition of transactivation. This approach allowed us to bring to light that water samples contain estrogenic compounds that display a high affinity for ERs but are present at low concentrations. In sediment samples, on the contrary, we showed that estrogenic compounds possess a low affinity and are present at high concentration. Finally, we used immobilized recombinant ER-αto separate ligands for ER and AhR that are present in river sediments. Immobilized ER-α, which does not retain dioxin-like compounds, enabled us to isolate and concentrate ER ligands to facilitate their further analysis.

Evaluation of simple statistical criteria to qualify a simulation

Statistical and deterministic simulation modelling rely on a complex process made of trials, errors, and gradual improvement of the simulations. The major problem is to be able to quantify the quality of the simulations in order to know if a modification of the concepts, the laws simulating the processes, or the parameters improve it. To try to quantify the quality of simulations using a mathematical criterion we focus on simple linear regression parameters: the values of the slope (a) and the y-intercept (b). The estimated values of these parameters differ depending on which kind of regression model (model I or II) is used. An artificial dataset illustrates that ordinary least-squares regression (OLS; model I regression) leads to results that are not those expected; but using major axis regression (MA; model II regression) instead of OLS leads to the correct answer. The value of a, when it significantly differs from 1, indicates a difference between observed and simulated values proportional to the values of the variable. The value of b, when it significantly differs from 0, indicates a systematic and constant difference between observations and simulations. Taking into account the values of a and b, we define four possible outcomes which allow, at first, to define the quality of a simulation without considering the coefficient of determination, r2: (i) a n.s.d. (not significantly different from) 1 and b n.s.d. 0 (perfect agreement between observations and simulations), (ii) a n.s.d. 1 and b s.d. 0 (significant constant difference between observations and simulations) or a s.d. 1 and a s.d. 0 and b n.s.d. 0 (differences proportional to the values of the variable), (iii) a s.d. 1 and a s.d. 0 and b s.d. 0 (superimposition of a constant difference and a proportional difference), and (iv) a n.s.d. 0 (no relation between simulations and observations). The value of r 2 is used to rank two simulations pertaining to the same group. That classification of the quality of the simulations is applied to a real-data example: a simulation of the temporal change in chlorophyll a in a high-rate algal pond.

Reporter Cell Lines to Monitor Steroid and Antisteroid Potential of Environmental Samples

In order to monitor the (anti)steroid activity of environmental samples, we established stable cell lines expressing luciferase under the control of estrogens, androgens, progestives and glucocorticoids. The breast cancer MCF-7 cells which express the estrogen receptor α (ERα), the glucocorticoid receptor (GR) and the progesterone receptor (PR) were transfected by an estrogen (ERE-βGlob-Luc) or a glucocorticoid/progestin/androgen (MMTV-Luc) regulated luciferase plasmid in order to enable the detection of compounds which bind both ERα, PR and GR (MELN and MMLN cells). Human prostatic cells PC3 were stably transfected by both an androgen receptor gene and the MMTV-luciferase plasmid (PALM cells). These three cellular models were validated as tools to check the estrogenic, progestive, glucocorticoid and androgenic activities using several potential xenohormones and environmental samples. As these environmental samples were fractionated after solid phase extraction to isolate active compounds, we used these cellular models to monitor the different fractions. In the estrogenic model mid-polar fractions of environmental samples were found active while in the androgenic model, the same fractions had antagonist activity.

A model for predicting the quantities of dissolved inorganic nitrogen released in effluents from a sea bass (Dicentrarchus labrax) recirculating water system

Fish excretions and the transformation of nitrogen by bacteria in the nitrifying biofilter are two of the main sources of dissolved inorganic nitrogen (DIN) in fish farms that use recirculating water systems. In this study, the DIN concentration in an experimental Dicentrarchus labrax aquaculture system was calculated using empirical sub-models for fish growth, ingested food and water replacement. The specific growth rate (SGR) (% day(-1)) and the daily feeding rate (DFR) (% day(-1)) both depend on the average weight, W (g), of the fish: Y = aW(b), where Y may be SGR or DFR, and a and b are empirical constants. The DIN discharge rate, Gamma(N) (% of ingested nitrogen), in the experimental aquaculture system was expressed as a function of increasing replacement water flow rate, theta (day(-1)): DIN = c theta(d), where c and d are empirical constants. Only three variables (the number of fish, the initial fish weight and the replacement water flow rate) are required to run the general model, which was tested over a period of 12 months (June 1997-June 1998). This model, calibrated and validated on independent sets of data obtained from the same experimental system, accurately predicted the concentration of DIN in the effluent (r(2) = 0.92).

Modelling orthophosphate evolution in a high rate algal pond

High rate algal ponds (HRAPs) are an efficient treatment for controlling wastewater pollution by reducing the organic matter and the inorganic nutrient content. An experimental HRAP was set up in Meze (France) and sampled over 24 months. A model simulating orthophosphate (PO4) evolution was constructed using Stella II software. It was thought that deterministic modelling of the temporal evolution of PO4 might provide a rational basis for pond management policies. In hardwater, two processes have to be taken into consideration: PO4 uptake by algae and PO4 precipitation. The model also has to take into account the fact that the latter process is very sensitive to nycthemeral variations in pH. Simulated PO4 concentrations over a two-year period show that about 10% of total input is removed by precipitation whereas about 30% is removed by absorption. These results are of critical importance for the management of this type of wastewater treatment system.

Some difficulties in modelling chlorophyll a evolution in a high rate algal pond ecosystem

Abstract The High Rate Algal Pond (HRAP) is an efficient treatment for controlling wastewater pollution by reducing the organic matter and the inorganic nutrient content. Deterministic modelling of temporal evolution of algae could provide a rational basis for pond management. An experimental HRAP was set up in Meze (France) and sampled weekly over 24 months. Models simulating the evolution of chlorophyll a concentration and nutrients (N and P) were constructed using Stella II software. The seasonal pattern of chlorophyll a concentrations results from the annual cycle of solar irradiance and temperature, whereas shorter trends (1 to 4 weeks) are dependent on the evolution of zooplankton groups. The first difficulty is to determine the functional relationships of the phytoplankton and zooplankton groups. In the model the evolution of the phytoplankton taxa is considered to be dependent on (i) inherent parameters of phytoplankton taxa (mortality rate, growth rate, saturating light intensity, etc.) and on (ii) parameters of zooplankton taxa (filtration rate, size selectivity, etc.). To take all these taxa as state variables, and all the associated parameters, into account is impossible: to solve such a problem, we forced the evolution of the biomass of the phytoplankton and zooplankton taxa. This approach improves the agreement between the simulated and observed chlorophyll a concentration values. The second difficulty concerns the determinism of appearance of the phytoplankton and zooplankton taxa used in the model: up to now we are only able to force these appearances. Thus, even in quite simple ecosystems, using deterministic modelling as a predictive tool requires a full understanding of the exact biological succession and interaction processes.

Temporal study of estrogenic responses of mussel (Mytilus galloprovinciallis) extracts applied to reporter cell lines

In vitro bioassays would facilitate monitoring of estrogen-like compounds in mussels (Mytilus galloprovincialis) since they tend to accumulate lipophilic compounds in their fat and muscle tissue. However, estradiol (E2) steroid (already identified in mussels) could induce false positive responses. This study focuses on temporal variability in estrogenic responses of mussel extracts and the possible relation of this response with E2 levels. The reproductive cycle and growth were thus monitored for six months in sexed mussels. E2 levels were similar between genders and these levels varied with reproductive stages. human estrogen receptor (hER) activities were similar at all stages except in February. E2 present in male and female extracts accounted for part of the hER activation observed, but no correlation was found between E2 levels and hER activities.