Abstract

Health and performance are important aspects in the broiler industry. Underlying complex traits like total mean weight and feed efficiency are polygenic and related to genetic background and an association of the microbiota with these traits has been identified. Whether this association is also reflected in the fecal samples of broilers is not extensively investigated. The objective of this study was to investigate to what extend diet, genetics, and environment influence the fecal microbiome composition during the life time of broilers.Two experiments were performed, in the first experiment the focus was on investigating if a European (EU) or United States (USA) diet effects the fecal microbiota in a commercial line (Cobb500). Whereas in the second diet (EU/USA) and lines with a genetic background (EU/USA) were investigated in relation to the fecal microbiota.In the first experiment we observed a significant effect in commercial broiler line (Cobb500) of the 3-way interaction for age by feed by sex on Total Mean Weight (TMW), and the 2-way interaction of age by feed for Feed Conversion Ratio (FCR). For the microbiota data, we observed differences in alpha-diversity for Age. When comparing the diets on a time-point, this resulted in significant differences for Observed species at day 21 and for Observed species, Shannon index, and Pielou's evenness at day 35. In the beta-diversity, a significant effect of age by feed interaction was observed. Two genera were significantly different in feces of broilers between diets, i.e. Streptococcus on day 7 and Bilophila on day 21.In the second experiment we observed only a significant effect for the main effect age on TMW. Alpha-diversity showed a significant increase for all three measures for age. Furthermore, a significant effect of environment was observed in the Observed species. This effect of environment was also observed in the beta-diversity, where a significant effect for age and environment was observed. This environmental effect was not expected, because here environment represents two different compartments within the same stable, unfortunately it was not possible to perform further down-stream analyses.This research shows the different aspects (feed, sex, genetics, and environment) influence complex traits, like TMW and FCR and are affecting the fecal microbiome. We have shown that interventions, like feed and the effect on microbiome, are reproducible between experiments. Moreover, these results with these two genetic divers chicken lines suggest that the succession of the fecal microbiota was independent of genetic background.

Highlights

  • Performance and health are important aspects of broiler production and has an economic impact on the broiler industry

  • The objective of this study was to investigate to what extend diet, genetics, and environment influence the fecal microbiome composition during the life time of broilers

  • We have conducted two experiments to investigate the effects of different aspects, i.e. feed and genetics that influence the fecal microbiota succession of broilers

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Summary

Introduction

Performance and health are important aspects of broiler production and has an economic impact on the broiler industry. Like body weight (or total mean weight) (Tarsani et al, 2019) and feed ef­ ficiency (Reyer et al, 2015), are polygenic of nature and are of biolog­ ical interest These traits have been studied solely from the host perspective (genetic markers / SNPs), in the last decade microbiome profile typing became more available. Recent studies have shown that different aspects can influence the microbiome composition in the small intestine (Feye et al, 2020), including age (Ballou et al, 2016a), sex (Lee et al, 2017), genetics (Schokker et al, 2015), feed (Engberg et al, 2002; Oakley et al, 2014), management (Wang et al, 2016), and environment, i.e. location (Siegerstetter et al, 2017) and housing system (Kers et al, 2018). After hatch, the microbiome is being established and succession of different bacterial species will occur (Apajalahti et al, 2004; Schokker et al, 2015; Ballou et al, 2016b, 2017; Jurburg et al, 2019)

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