The Value of Gut Microbiota to Predict Feed Efficiency and Growth of Rabbits Under Different Feeding Regimes

Abstract

Background: Feed efficiency is a paramount concept for the environmental and economical sustainability of rabbit production. In this sense, identifying all the components involved in its determinism is highly desirable. Microbial communities inhabiting the intestinal tract play an important role in nutrient absorption and could also impact rabbit growth and feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual and cage phenotypes related to growth and feed efficiency.Results: Cecal microbiota was assessed in 425 meat rabbits raised under two feeding regimes (ad libitum or restricted). The dataset under study comprised individual average daily gain, and cage-average daily feed intake, and feed efficiency records from these kits and their cage mates. The consideration of pedigree relationships in different mixed models allowed to accomplish the study of cage-average traits even though cecal microbiota was not measured in all the animals within a cage. When microbial information was fitted into certain mixed animal models, their predictive ability increased up to 20% for cage-average feed efficiency traits and up to 46% for individual growth traits. These gains in the predictive ability of the models were associated with large microbiability estimates and with reductions, with respect to those from the models not fitting the microbial effect, on the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability of the studied traits. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and FE traits, although specific OTUs have a higher weight.Conclusions: Rabbit growth and feed efficiency are influenced by host cecal microbiota and considering microbial information in models improve the prediction of these complex phenotypes. Nonetheless, the prior assumptions for the microbial effects and the method used condition the quality of the predictions.