Variations of Phosphorous Accessibility Causing Changes in Microbiome Functions in the Gastrointestinal Tract of Chickens

<div><p>The chicken gastrointestinal tract (GIT) harbours a complex microbial community, involved in several physiological processes such as host immunomodulation and feed digestion. For the first time, the present study analysed dietary effects on the protein inventory of the microbiome in crop and ceca of broilers. We performed quantitative label-free metaproteomics by using 1-D-gel electrophoresis coupled with LC-MS/MS to identify the structural and functional changes triggered by diets supplied with varying amount of mineral phosphorous (P) and microbial phytase (MP). Phylogenetic assessment based on label-free quantification (LFQ) values of the proteins identified <i>Lactobacillaceae</i> as the major family in the crop section regardless of the diet, whereas proteins belonging to the family <i>Veillonellaceae</i> increased with the P supplementation. Within the ceca section, proteins of <i>Bacteroidaceae</i> were more abundant in the P-supplied diets, whereas proteins of <i>Eubacteriaceae</i> decreased with the P-addition. Proteins of the <i>Ruminococcaceae</i> increased with the amount of MP while proteins of <i>Lactobacillaceae</i> were more abundant in the MP-lacking diets. Classification of the identified proteins indicated a thriving microbial community in the case of P and MP supplementation, and stressed microbial community when no P and MP were supplied. Data are available via ProteomeXchange with identifier PXD003805.</p></div

Relevant biochemical pathways between experimental treatments.

<p>Comparison of different dietary treatments: <b>(A)</b> BD+ and BD–. <b>(B)</b> MP500 and MP0. <b>(C)</b> MP12500 and MP0 based on the log2 of the ratios between the cumulative intensities of the statistically significant pairs of KOs. Each of the graph´s bars represent a KEGG biochemical pathway. Only pathways with a cumulative abundance greater than 1% of the total are considered in the graph.</p

Functional classification of cecal proteins into COG categories.

<p>Heat-Map is drawn on the basis of the relative percentages of the proteins of each statistically different treatment. COG classification of crop samples proteins is available in Figure D in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164735#pone.0164735.s002" target="_blank">S2 File</a>.</p

Dietary effect on phylogenetic composition of the chicken´s crop (A) and cecal (B) microbiome.

<p>Cladograms of both sections show a comparative evaluation of the experimental treatments effects on the structure of the chicken´s GIT microbiome. Effects are calculated through LDA Effect Size [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164735#pone.0164735.ref053" target="_blank">53</a>], a two-module algorithm. In the first module, technical triplicates of each dietary group (<i>n</i> = 2) were subjected to non-parametric Kruskal-Wallis test to detect features with significant differential abundance with respect to the experimental treatments. In the second module, tabular abundance data formatted in the previous module are subjected to Linear Discriminant Analysis [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164735#pone.0164735.ref021" target="_blank">21</a>] to estimate the effect size of each differentially abundant feature. The only diets and bacterial families showing statistical significance (p<0.05) in the previous statistical tests are visualized in the figures. Yellow dots refer to bacterial specimens whose protein pattern and abundance did not score a statistical significant effect (p>0.05) in any of the experimental diets. Bacterial families legend: <b>(A)</b> a: <i>Lactobacillaceae</i>; b: <i>Veillonellaceae</i>; c: <i>Other families</i>; d: <i>Bradyrhizobiaceae</i>. <b>(B)</b> a: <i>Bacteroidaceae</i>; b: <i>Clostridiaceae</i>; c: <i>Eubacteriaceae</i>; d: <i>Lachnospiraceae</i>; e: <i>Ruminococcaceae</i>; f: <i>Lactobacillaceae</i>; g: <i>Other families</i>; h: <i>Helicobacteraceae</i>.</p

Principal coordinate analysis of the crop (A) and ceca (B) microbiome at different dietary treatments.

<p>Open shapes refer to diet without P supplementation, full shapes concern diets with P addition. Black, blue and red colors refer to MP0, MP500, and MP12500, respectively.</p

Changes in Rumen Microbial Community Composition during Adaption to an In Vitro System and the Impact of Different Forages.

This study examined ruminal microbial community composition alterations during initial adaption to and following incubation in a rumen simulation system (Rusitec) using grass or corn silage as substrates. Samples were collected from fermenter liquids at 0, 2, 4, 12, 24, and 48 h and from feed residues at 0, 24, and 48 h after initiation of incubation (period 1) and on day 13 (period 2). Microbial DNA was extracted and real-time qPCR was used to quantify differences in the abundance of protozoa, methanogens, total bacteria, Fibrobacter succinogenes, Ruminococcus albus, Ruminobacter amylophilus, Prevotella bryantii, Selenomonas ruminantium, and Clostridium aminophilum. We found that forage source and sampling time significantly influenced the ruminal microbial community. The gene copy numbers of most microbial species (except C. aminophilum) decreased in period 1; however, adaption continued through period 2 for several species. The addition of fresh substrate in period 2 led to increasing copy numbers of all microbial species during the first 2-4 h in the fermenter liquid except protozoa, which showed a postprandial decrease. Corn silage enhanced the growth of R. amylophilus and F. succinogenes, and grass silage enhanced R. albus, P. bryantii, and C. aminophilum. No effect of forage source was detected on total bacteria, protozoa, S. ruminantium, or methanogens or on total gas production, although grass silage enhanced methane production. This study showed that the Rusitec provides a stable system after an adaption phase that should last longer than 48 h, and that the forage source influenced several microbial species

Total gas and methane production, degradation of nutrients after 48 h of incubation, ammonia-N, and short-chain fatty acids (SCFA) in the effluent, and efficiency of microbial crude protein synthesis.

<p>Total gas and methane production, degradation of nutrients after 48 h of incubation, ammonia-N, and short-chain fatty acids (SCFA) in the effluent, and efficiency of microbial crude protein synthesis.</p

Chemical composition of the silages used for incubation.

<p>Chemical composition of the silages used for incubation.</p