Microbial diversity and community composition of caecal microbiota in commercial and indigenous Indian chickens determined using 16s rDNA amplicon sequencing

Abstract

BackgroundThe caecal microbiota plays a key role in chicken health and performance, influencing digestion and absorption of nutrients, and contributing to defence against colonisation by invading pathogens. Measures of productivity and resistance to pathogen colonisation are directly influenced by chicken genotype, but host driven variation in microbiome structure is also likely to exert a considerable indirect influence.MethodsHere, we define the caecal microbiome of indigenous Indian Aseel and Kadaknath chicken breeds and compare them with the global commercial broiler Cobb400 and Ross 308 lines using 16S rDNA V3-V4 hypervariable amplicon sequencing.ResultsEach caecal microbiome was dominated by the genera Bacteroides, unclassified bacteria, unclassified Clostridiales, Clostridium, Alistipes, Faecalibacterium, Eubacterium and Blautia. Geographic location (a measure recognised to include variation in environmental and climatic factors, but also likely to feature varied management practices) and chicken line/breed were both found to exert significant impacts (p < 0.05) on caecal microbiome composition. Linear discriminant analysis effect size (LEfSe) revealed 42 breed-specific biomarkers in the chicken lines reared under controlled conditions at two different locations.ConclusionChicken breed-specific variation in bacterial occurrence, correlation between genera and clustering of operational taxonomic units indicate scope for quantitative genetic analysis and the possibility of selective breeding of chickens for defined enteric microbiota.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0501-9) contains supplementary material, which is available to authorized users.

Assembly of hundreds of novel bacterial genomes from the chicken caecum

“…When analysing the abundance of MAGs between birds from different lines, consuming either a vegetable diet or a diet containing fish meal, we found significant differences in the microbial communities based on both line and diet. This agrees with previous studies where significant differences have been described in the intestinal microbiota of chickens from different lines, including those from faster and slower growing lines [73][74][75]. Differences have also previously been observed in the microbiota when feeding chickens a diet supplemented with fish meal [34,35].…”

“…We also compared the abundance of our MAGs and MAG-derived CAZymes. It should be noted that care should be taken when generalising our findings, as the composition of the microbiota can vary significantly between chicken flocks [70,73]. When analysing the abundance of MAGs between birds from different lines, consuming either a vegetable diet or a diet containing fish meal, we found significant differences in the microbial communities based on both line and diet.…”

Assembly of hundreds of novel bacterial genomes from the chicken caecum

“…When analysing the abundance of MAGs between birds from different lines, consuming either a vegetable diet or a diet containing fish meal, we found significant differences in the microbial communities based on both line and diet. This agrees with previous studies where significant differences have been described in the intestinal microbiota of chickens from different lines, including those from faster and slower growing lines (89–91). Differences have also previously been observed in the microbiota when feeding chickens a diet supplemented with fish meal (31, 32).…”

16S rRNA gene sequencing reveals an altered composition of the gut microbiota in chickens infected with a nephropathogenic infectious bronchitis virus

“…Therefore, the optimal choice of the hypervariable region(s) and primer combination vary between various ecological communities. Previous research proved that the highest bacterial diversity and species richness in chicken gut microbiomes were obtained using the primer set corresponding to the V3-V4 region 34,35 . Based on these investigations, in our study, we selected primers targeting the V3-V4 regions of the 16S rRNA gene.…”