Dataset Information

Cecal microbiome profile of Hawaiian feral chickens and pasture-raised broiler (commercial) chickens determined using 16S rRNA amplicon sequencing.

ABSTRACT: This study investigated the taxonomic profile and abundance distribution of the bacterial community in the ceca of feral and pasture-raised broiler (commercial) chickens. Cecal content from feral and commercial chickens (n = 7 each) was collected, and total DNA was isolated. Next-Generation Sequencing (Illumina MiSeq) was performed to characterize the cecal microbiota. Specific bacteria explored were: Bacteroides, Bifidobacterium, Lactobacillus, Enterococcus, Escherichia, and Clostridium. At the phylum level, 92% of the bacteria belonged to Firmicutes, Bacteroidetes, and Proteobacteria for both feral and commercial chickens. The proportional abundance of Firmicutes was 55.3% and 63.3%, Bacteroidetes was 32.5% and 24.4%, and Proteobacteria was 7.0% and 5.9% in the feral and commercial chickens, respectively. The alpha-diversity Shannon index (P = 0.017) and Simpson index (P = 0.038) were significantly higher for commercial than for feral chickens. Predictive functional profiling by PICRUSt showed enriched microbial metabolic pathways for L-proline biosynthesis in the feral group (P < 0.01). There were a greater percentage of specific bacteria in the feral than commercial chickens, albeit with lower diversity but a more functional microbiota. In conclusion, feral birds have distinguished microbial communities, and further microbiome analysis is mandated to know the specific functional role of individual microbiota. The difference in microbiota level between feral and commercial birds could be accounted to the scavenging nature, diverse feed ingredients, and distinct rearing localities.

SUBMITTER: Yadav S

PROVIDER: S-EPMC8182230 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Hatcheries, where eggs from multiple breeder farms are incubated and hatched before being sent to different broiler farms, represent a nexus point in the commercial production of broilers in the United States. Considering all downstream microbial quality and safety aspects of broiler production (live production, processing, consumer use) can be potentially affected by the hatchery, a better understanding of microbial ecology within commercial hatcheries is essential. Therefore, a commercial broiler hatchery was biomapped using 16S rRNA amplicon-based microbiome analyses of four sample type categories (Air, Egg, Water, Facility) across five different places in the pre-hatch, hatch, and post-hatch areas. While distinct microbiota were found for each sample type category and hatchery area, microbial community analyses revealed that Egg microbiota trended towards clustering with the facility-related samples when moving from the prehatch to post-hatch areas, highlighting the potential effect of the hatchery environment in shaping the pre-harvest broiler-related microbiota. Prevalence analyses revealed 20 ASVs (Core20) present in the core microbiota of all sample types and areas, with each ASV possessing a unique distribution throughout the hatchery. Interestingly, three Enterobacteriaceae ASVs were in the Core20, including Salmonella. Subsequent analyses showed that Salmonella, while a minor prehatch and hatch Core20ASV, dominated the Enterobacteriaceae niche and total microbiota in the chick pad feces in the post-hatch area of the hatchery, and the presence of this Salmonella ASV in the post-hatch feces was associated with swabs of breakroom tables. These findings highlight the complexity of commercial hatchery microbiota, including identifying chick pad feces and breakroom tables as potentially important sampling or disinfection targets for hatchery managers to focus their Salmonella mitigation efforts to reduce loads entering live production farms.

Project description:Gut microbiota play an important role in animal health. For livestock, an understanding of the effect of husbandry interventions on gut microbiota helps develop methods that increase sustainable productivity, animal welfare, and food safety. Poultry microbiota of the mid-gut and hind-gut can only be investigated postmortem; however, samples from the terminal cloaca may be collected from live animals. This study tests whether cloacal microbiota reflect cecal microbiota in European broiler poultry by evaluating total and paired cecal and cloacal microbiomes from 47 animals. 16S amplicon libraries were constructed and sequenced with a MiSeq 250 bp PE read metric. The composition of cloacal and cecal microbiomes were significantly affected by the age and location of animals, but the effect was very small. Bacilli were relatively more abundant in ceca and Clostridia in cloaca. There was an overlap of 99.5% for the abundances and 59% for the types of taxa between cloacal and cecal communities, but the small fraction of rare nonshared taxa were sufficient to produce a signal for differentiation between cecal and cloacal communities. There was a significant positive correlation between specific taxa abundances in cloacal and cecal communities (Rho = 0.66, P = 2 × 10-16). Paired analyses revealed that cloacal communities were more closely related to cecal communities from the same individual than expected by chance. This study is in line with the only other study to evaluate the relationship between cecal and cloacal microbiomes in broiler poultry, but it extends previous findings by analyzing paired cecal-cloacal samples from the same birds and reveals that abundant bacterial taxa in ceca may be reasonably inferred by sampling cloaca. Together, the findings from Europe and Australasia demonstrate that sampling cloaca shows promise as a method to estimate cecal microbiota, and especially abundant taxa, from live broiler poultry in a manner which reduces cost and increases welfare for husbandry and research purposes.

Project description:BackgroundApicomplexan protozoans of the genus Eimeria cause coccidiosis, one of the most economically relevant parasitic diseases in chickens. The lack of a complete understanding of molecular mechanisms in the host-parasite interaction limits the development of effective control measures. In the present study, RNA sequencing (RNA-Seq) was applied to investigate the host mRNA profiles of the cecal mucosa collected at day 5 post-infection with Eimeria maxima (EM).ResultsTotal RNA from cecal samples of the uninfected naïve control and the EM groups was used to make libraries, generating 354,924,372 and 356,229,250 usable reads, respectively, which were assembled into a total of 386,088 high-quality unigenes (transcripts) in Trinity software. RNA-Seq analysis of cecal samples in the two groups revealed 332 upregulated and 363 downregulated genes with significant differences (P ≤ 0.05), including several significant immune-related gene families, such as the major histocompatibility complex (MHC) class I alpha chain, granzyme A and immunoglobulin subtype genes among upregulated differentially expressed genes. In addition, a total of 60 clusters of differentiation (CD) molecular genes and 570 novel genes were found. The completeness of the assembled transcriptome was further assessed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Gene ontology (GO), eggNOG and CAZy for gene annotation. The broad gene categories represented by the highly differentiated host genes suggested enrichment in immune responses, and downregulation in the metabolic pathway, MARK signaling pathway, vascular smooth muscle contraction, and proteins processing in endoplasmic reticulum after EM infection.ConclusionsEimeria maxima induced statistically significant differences in the cecal mucosal gene expression of infected chickens. These findings provide new insights into the host-parasite interaction and enhance our understanding of the molecular mechanism of avian coccidiosis.

Dataset Information

Cecal microbiome profile of Hawaiian feral chickens and pasture-raised broiler (commercial) chickens determined using 16S rRNA amplicon sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets