An in vitro rumen incubation method to study exsheathment kinetics of Haemonchus contortus third-stage infective larvae

This study developed and evaluated an in vitro rumen incubation (IVRI) method to describe the exsheathment kinetics of Haemonchus contortus third-stage infective larvae (L-3) in ruminal liquor (RL). The specific objectives were (i) to standardize the IVRI method to facilitate the contact between L-3 and RL as well as the larval recovery, and (ii) to apply the IVRI method to describe the exsheathment kinetics of H. contortus and to select the best fitting nonlinear model. Incubation devices containing H. contortus larvae were incubated according to the IVRI technique in cattle RL or PBS. The incubation conditions included RL mixed with a nitrogen-rich media, maintained at 39 degrees C, with pH = 7.0, vented with CO2 and manual agitation. The larvae were recovered after 0, 1, 3, 6, 9, 12, and 24 h. The exsheathed and ensheathed larvae were counted to estimate the exsheathment (%) in RL or PBS. Exsheathment in RL was analyzed with nonlinear regression models: Exponential, Gompertz, Logistic, Log-Logistic, and Weibull. The models' fit was compared to select the one that best described the exsheathment kinetics. The exsheathment in RL reached 6.52%, 20.65%, 58.22%, 69.24%, 73.08%, and 77.20% in 1, 3, 6, 9, 12, and 24 h, respectively. Although the Gompertz, Weibull, and Logistic models were adequate to describe the observed exsheathment, the Log-Logistic model had the best fit. The IVRI method using bovine RL represents a suitable tool for the study of the in vitro exsheathment kinetics of H. contortus L-3.

Automated summary

This study developed and evaluated an in vitro rumen incubation method to describe the exsheathment kinetics of Haemonchus contortus third-stage infective larvae in ruminal liquor. The aim was to standardize the method and select the best fitting nonlinear model to describe the exsheathment kinetics. The exsheathment in ruminal liquor reached 6.52%, 20.65%, 58.22%, 69.24%, 73.08%, and 77.20% in 1, 3, 6, 9, 12, and 24 hours, respectively. The Log-Logistic model had the best fit among the tested models.