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

Abstract

Background: The 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.

Methods: Here, 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.

Results: Each 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.

Conclusion: Chicken 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.

Keywords: 16S rRNA gene; Amplicon sequencing; Chickens; Microbiome; Pathogens.

Fig. 1

Rarefaction curve and PCoA for each breed and location sampled, created by combining data from all three primer pairs. a Rarefaction and b PCoA for each individual chicken breed or line was generated using QIIME where sequences were clustered at 97% similarity and rarified at > 10,000 sequences per sample. c PCoA based on location was generated using the Bray-Curtis distance method using PAST. A-, C-, R- and K- represent the Aseel, Cobb400, Ross 308 and Kadaknath chicken breeds or lines respectively. -A sampled at location 1 in Anand, -T sampled at location 2 in Tamil Nadu

Fig. 2

A rarefaction curve (a), box plot (b) and PCoA (c) for each primer pair. For analysis, respective sequences of each primer pair were clustered at 97% similarity using QIIME. For rarefaction plots, sequences were rarefied with 10,000 sequences per sample and the Chao1 index was plotted. PCoA was generated using unweighted unifrac metrics. Box plots were generated using BoxplotR

Fig. 3

PCoA and class and genus level classification of caecal microbiomes from chicken breeds and lines reared at locations 1 and 2 (Anand and Tamil Nadu). Only sequencing reads produced using primer P2 were used for this analysis. a PCoA using the Bray-Curtis method in PAST. b Box plot indicating differences in the ranked distances in each group (see Additional file 10 for the pairwise comparison of P values by ANOSIM). c, d abundance of bacteria in the caeca of chicken lines at class and genus level respectively. Only classes and genera with abundance > 1.0% in any of the chicken lines was plotted. A-, C-, R- and K- represent the Aseel, Cobb400, Ross 308 and Kadaknath chicken breeds/lines respectively. -A sampled at location 1 in Anand, -T sampled at location 2 in Tamil Nadu

Fig. 4

Chicken breed and line-specific biomarkers. a LEfSe analysis shows differentially abundant genera as biomarkers determined using Kruskal-Wallis test (P < 0.05) with LDA score > 3.5. b Cladogram representation of the differentially abundant families and genera (only top 50% are plotted here). The root of the cladogram denotes the domain bacteria. The taxonomic levels of phylum and class are labelled, while family and genus are abbreviated, with the colours indicating the breed/line hosting the greatest abundance. The size of each node represents their relative abundance

Fig. 5

Correlation among the bacterial genera detected in the caeca of different chicken breeds. Sequencing reads produced using primer pair P2 were pooled into a single pool for each breed, combining samples from different farm locations. A Pearson’s r correlation was expressed using METAGENassist. The breeds represented are a Aseel, b Cobb400, c Ross 308 and d Kadaknath