Composition and Function of Chicken Gut Microbiota

Abstract

Studies analyzing the composition of gut microbiota are quite common at present, mainly due to the rapid development of DNA sequencing technologies within the last decade. This is valid also for chickens and their gut microbiota. However, chickens represent a specific model for host-microbiota interactions since contact between parents and offspring has been completely interrupted in domesticated chickens. Nearly all studies describe microbiota of chicks from hatcheries and these chickens are considered as references and controls. In reality, such chickens represent an extreme experimental group since control chicks should be, by nature, hatched in nests in contact with the parent hen. Not properly realising this fact and utilising only 16S rRNA sequencing results means that many conclusions are of questionable biological relevance. The specifics of chicken-related gut microbiota are therefore stressed in this review together with current knowledge of the biological role of selected microbiota members. These microbiota members are then evaluated for their intended use as a form of next-generation probiotics.

Keywords: Bacteroidetes; Firmicutes; caecum; chicken; development; faecal; gut microbiota; ileum.

Figure 1

Microbiota composition along the digestive tract of 3 different adult hens. Proximal parts of the digestive tract are dominated by Lactobacilli. Microbiota diversity increases considerably in the caecum. Colonic microbiota can be a mixture of caecal and ileal microbiota, depending on the time of sampling related to the time of voiding the caecal content into colon. Hens (a,b) were sacrificed and sampled shortly after voiding the caecal content in the colon while hen (c) was sampled a longer time after voiding of the caecal content into the colon, at the time when the caecal microbiota was “washed out” and replaced with digesta originating from the ileum. Data in this figure originate from Videnska et al. [8] complemented with recent laboratory results.

Figure 2

Major bacterial species colonising the chicken caecum. Genomic GC content and genome sizes are shown using a colour gradient. Families are highlighted with background colors. Presence or absence of genes for outer membrane biosynthesis (OM), succinate-methylmaloneta-propinionate pathway (MMal), spore formation (Spor), flagellar (Fla) motility and butyrate production from acetyl-CoA (ACoA), lysine (Lys) or succinate (Succ) is shown by full symbols. See Supplementary Figure S1 to zoom in. Data in this figure originate from Medvecky et al. [30] complemented with recent laboratory results.

Figure 3

Bacterial genera of environmental or parental origin in microbiota of one-week-old chicks. Forty-five genera were more abundant in microbiota of contact chicks. These genera belonged to Bacteroidetes, Firmicutes/Veillonellaceae and Proteobacteria different from Enterobacteriaceae (left pie chart). Genera equally represented in contact and control chicks belonged mainly to Firmicutes including family Lactobacillaceae. Out of eight genera which were less abundant in contact than in control chicks, three belonged to family Enterobacteriaceae, phylum Proteobacteria. Data in this figure originate Kubasova et al. [53] complemented with recent laboratory results.

Figure 4

Principle Coordinate analysis (PCoA) of caecal microbiota composition in 17 groups of one-week-old control chickens. Microbiota composition was determined by sequencing of V3/V4 variable region of 16S rRNA genes as described [4]. Each dot represents one 7-day-old chick positioned in the figure based on its caecal microbiota composition. Each color represents chickens from different experiments. Both unweighted (a) and weighted (b) PCoA show experiment-dependent development of caecal microbiota. Data in this figure originate from reference [46] complemented with recent laboratory results.

Figure 5

Enrichment of genomes of selected chicken gut microbiota members by genes belonging to specific functional categories. Whole genome sequences were automatically annotated by RAST (Rapid Annotation using Subsystem Technology) and predicted genes were assigned into specific functional categories. Data in this figure originate from Medvecky et al. [30] complemented with recent laboratory results.