Effect of Mannan-rich fraction supplementation on commercial broiler intestinum tenue and cecum microbiota

Abstract

Abstract Background The broiler gastrointestinal microbiome is a potent flock performance modulator yet may also serve as a reservoir for pathogen entry into the food chain. The goal of this project was to characterise the effect of mannan rich fraction (MRF) supplementation on microbiome diversity and composition of the intestinum tenue and cecum of commercial broilers. This study also aimed to address some of the intrinsic biases that exist in microbiome studies which arise due to the extensive disparity in 16S rRNA gene copy numbers between bacterial species and due to large intersample variation. Results We observed a divergent yet rich microbiome structure between different anatomical sites and observed the explicit effect MRF supplementation had on community structure, diversity, and pathogen modulation. Birds supplemented with MRF displayed significantly higher species richness in the cecum and significantly different bacterial community composition in each gastrointestinal (GI) tract section. Supplemented birds had lower levels of the zoonotic pathogens Escherichia coli and Clostridioides difficile across all three intestinum tenue sites highlighting the potential of MRF supplementation in maintaining food chain integrity. Higher levels of probiotic genera (eg. Lactobacillus and Blautia) were also noted in the MRF supplemented birds. Following MRF supplementation, the cecum displayed higher relative abundances of both short chain fatty acid (SFCA) synthesising bacteria and SCFA concentrations. Conclusions Mannan rich fraction addition has been observed to reduce the bioburden of pathogens in broilers and to promote greater intestinal tract microbial biodiversity. This study is the first, to our knowledge, to investigate the effect of mannan-rich fraction supplementation on the microbiome associated with different GI tract anatomical geographies. In addition to this novelty, this study also exploited machine learning and biostatistical techniques to correct the intrinsic biases associated with microbiome community studies to enable a more robust understanding of community structure.