Transcriptomic analysis of intestinal organoids, derived from pigs divergent in feed efficiency, and their response to Escherichia coli.

BACKGROUND: There is increasing interest in using intestinal organoids to study complex traits like feed efficiency (FE) and host-microbe interactions. The aim of this study was to investigate differences in the molecular phenotype of organoids derived from pigs divergent for FE as well as their responses to challenge with adherent and invasive Escherichia coli (E. coli). RESULTS: Colon and ileum tissue from low and high FE pigs was used to generate 3D organoids and two dimensional (2D) monolayers of organoid cells for E. coli challenge. Genome-wide gene expression was used to investigate molecular differences between pigs that were phenotypically divergent for FE and to study the difference in gene expression after challenge with E. coli. We showed, (1) minor differences in gene expression of colon organoids from pigs with low and high FE phenotypes, (2) that an E. coli challenge results in a strong innate immune gene response in both colon and ileum organoids, (3) that the immune response seems to be less pronounced in the colon organoids of high FE pigs and (4) a slightly stronger immune response was observed in ileum than in colon organoids. CONCLUSIONS: These findings demonstrate the potential for using organoids to gain insights into complex biological mechanisms such as FE.

Predicting dairy herd resilience on farms with conventional milking systems.

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.