Low in vitro permeability of the cyanotoxin microcystin-LR across a Caco-2 monolayer: With identification of the limiting factors using modelling - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Toxicon Année : 2014

Low in vitro permeability of the cyanotoxin microcystin-LR across a Caco-2 monolayer: With identification of the limiting factors using modelling

Résumé

Microcystins (MCs) are toxins produced by several cyanobacteria species found worldwide. MC-LR is the most frequent. Here, we used the human Caco-2 cell line grown on semi-permeable filter supports as an in vitro model for determining MC-LR intestinal bidirectional transport. In this study, there was very low and time-dependent apparent permeability of MC-LR. To identify the limiting factors involved in the low permeability of MC-LR, a mathematical model was constructed to get physiologically relevant and informative parameters. The apical-to-basolateral transport was characterised by a rapid and substantial decrease in apical MC-LR concentrations (24–40% of the initial amount). In the basolateral compartment, the concentrations increased slowly after a lag time, but represented only a small fraction of the loaded concentrations (0.3–1.3%) after 24 h. This weak permeability was mainly due to a low clearance of efflux (from the cellular to the basolateral compartment) and effective secretion (from the cellular to the apical compartment). During the basolateral-to-apical transport, we observed a slow decrease in basolateral concentrations and a rapid increase in apical concentrations. In conclusion, modelling has the potential to highlight the key mechanisms involved in the complex kinetics of toxin transport.

Domaines

Toxicologie
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Dates et versions

hal-01076975 , version 1 (23-10-2014)

Identifiants

Citer

Jérôme Henri, Antoine Huguet, Jean-Michel Delmas, Aurore Besson, Pascal Sanders, et al.. Low in vitro permeability of the cyanotoxin microcystin-LR across a Caco-2 monolayer: With identification of the limiting factors using modelling. Toxicon, 2014, pp.1-11. ⟨10.1016/j.toxicon.2014.07.023⟩. ⟨hal-01076975⟩

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