Abrupt temporal fluctuations in the chicken fecal microbiota are explained by its gastrointestinal origin

Abstract

One of the main challenges in understanding the composition of fecal microbiota is that it can consist of microbial mixtures originating from different gastrointestinal (GI) segments. Here, we addressed this challenge for broiler chicken feces using a direct 16S rRNA gene-sequencing approach combined with multivariate statistical analyses. Broiler feces were chosen because of easy sampling and the importance for pathogen transmission to the human food chain. Feces were sampled daily for 16 days from chickens with and without a feed structure-induced stimulation of the gastric barrier function. Overall, we found four dominant microbial phylogroups in the feces. Two of the phylogroups were related to clostridia, one to lactobacilli, and one to Escherichia/Shigella. The relative composition of these phylogroups showed apparent stochastic temporal fluctuations in feces. Analyses of dissected chickens at the end of the experiment, however, showed that the two clostridial phylogroups were correlated to the microbiota in the cecum/colon and the small intestine, while the upper gut (crop and gizzard) microbiota was correlated to the lactobacillus phylogroup. In addition, chickens with a stimulated gizzard also showed less of the proximate GI dominating bacterial group in the feces, supporting the importance of the gastric barrier function. In conclusion, our results suggest that GI origin is a main determinant for the chicken fecal microbiota composition. This knowledge will be important for future understanding of factors affecting shedding of both harmful and beneficial gastrointestinal bacteria through feces.

Fig 1

A schematic representation of data analysis. Spectra (n = 625) obtained by direct sequencing on samples were divided into data subsets depending on their origin. The dominating spectral profiles (components) from 492 samples (242 duplicated and 8 single fecal samples) were extracted using the MCR-ALS method. The number of available duplicates for each set of samples is given in parentheses. The 16S rRNA gene clone library of 1,903 clones and their spectra was downsized to the 101 most unique spectra. Correlation coefficients and coefficients of determination (r²) were calculated between the extracted feces components and spectra from samples with different gut segment origins. The feces components were regressed to spectra from the most unique clones using PLSR. Sequences from unique clones that were significant in PLSR were identified using the closest match with the RDP10 database of 16S rRNA gene sequences.

Fig 2

Five components extracted from 492 feces samples using MCR-ALS. The four nucleotide spectra are plotted on top of each other marked with different colors. The overlapping peaks indicate the presence of different bacteria. x axis, migration time (ms); y axis, signal intensity (relative fluorescence units [rfu]).

Fig 3

Phylogenetic tree illustrating the relationship of selected bacterial sequences with significant and high regression coefficients in regression between pure bacterial spectra and MCR-ALS extracted spectral profiles (components) from mixed feces. The best match to RDP10 regarding the order/family level is indicated in the parentheses. The number of bootstrap replicates (>0.5) that support each branch (500 replicates in total) is shown on nodes.

Fig 4

A PCA score plot showing separation of samples isolated from 5 different gut segments in chicken. The main MCR-ALS component extracted from samples in each circle is highly correlated to one of the MCR-ALS components from feces samples. The latter component is indicated next to the circle.