Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome

Abstract

Antimicrobial growth promoters (AGP) have played a decisive role in animal agriculture for over half a century. Despite mounting concerns about antimicrobial resistance and demand for antibiotic alternatives, a thorough understanding of how these compounds drive performance is missing. Here we investigate the functional footprint of microbial communities in the cecum of chickens fed four distinct AGP. We find relatively few taxa, metabolic or antimicrobial resistance genes similarly altered across treatments, with those changes often driven by the abundances of core microbiome members. Constraints-based modeling of 25 core bacterial genera associated increased performance with fewer metabolite demands for microbial growth, pointing to altered nitrogen utilization as a potential mechanism of narasin, the AGP with the largest performance increase in our study. Untargeted metabolomics of narasin treated birds aligned with model predictions, suggesting that the core cecum microbiome might be targeted for enhanced performance via its contribution to host-microbiota metabolic crosstalk.

Fig. 1. Effects of four different AGP on bird performance and cecal microbiome composition.

a) Box plot showing the average daily gain as a function of bird age. ADG was calculated separately for each week of the experiment for each of the treatment groups. b) Like a, but for the feed conversion ratio as a function of bird age. n = 5 pens per treatment in a and b. c) Principal coordinates analysis using the Bray-Curtis dissimilarity among 16 S microbiome profiles. Each symbol represents a sample. The numbers in parenthesis indicate the percentage of the dissimilarity variance explained by each of the first two principal components. d) ANOSIM analysis for samples from birds of different ages and AGP treatment groups. In the heatmap, values below the diagonal represent the ANOSIM R-score. Lower values indicate more similar microbial communities. Values above the diagonal indicate the corresponding p-value for the null hypothesis of similar communities. e) Amplicon sequence variant (ASV) richness for cecal samples from birds of different ages and AGP treatment groups quantified with the Chao1 index. f) Like e but for ASV diversity quantified using the Simpson index. n = 15 birds per treatment in e and f. The boxes in a, b, e and f represent the median and interquartile range; whiskers indicate the range of the distribution excluding outliers. Outliers are at least 1.5 times the interquartile range below or above the first and third quartile, respectively. *: One-sided Mann-Whitney U p-value < 0.05; X: Two-sided p-value < 0.05.

Fig. 2. Major functional categories enriched or depleted in response to AGP relative to controls in the cecal microbiome.

Each dot represents a set of functional roles significantly enriched or depleted relative to controls (FDR < 0.15). Blue lines connect AGP to significantly enriched processes according to Gene Set Enrichment Analysis (GSEA). Red lines connect AGP to significantly depleted processes. Labels capture the common functionality in select clusters of enriched terms (see Supplementary Data 3 for the full list of terms). AVI avilamycin, VIR virginiamycin, NAR narasin, AA amino acid, ED Entner Duodoroff, Mo molybdenum.

Fig. 3. Correlation between gene and relative abundance contributions of core genera.

Rows in the figure indicate functional terms (subsystems) significantly enriched by different treatments when considering only genes assigned to core genera of the cecum microbiome. Each dot represents a core microbial genus and its corresponding value on the x-axis shows the spearman correlation between the relative abundance of the genus and its contribution to the abundance of the corresponding functional term across all samples (n = 74 birds). The functional contribution is calculated as the mean copy number contributed by the genus across all the genes in the subsystem. Dot colors indicate the Spearman correlation p-value. BMD bacitracin methylene disalicylate, AVI avilamycin, VIR virginiamycin, NAR narasin.

Fig. 4. Antimicrobial resistance gene abundance in the cecal microbiome of birds treated with different AGP.

a) Total AMR gene abundance (RPKM) as a function of treatment group. b) Number of distinct AMR genes detected as a function of treatment group. * one-sided Mann-Whitney p-value < 0.05. n = 15 birds per group. The boxes in a, and b represent the median and interquartile range; whiskers indicate the range of the distribution excluding outliers. C control, B BMD, A avilamycin, V virginiamycin, N narasin. c) ANOSIM analysis of the Bray-Curtis dissimilarities between samples in different treatments based on their AMR profiles. In the heatmap, values below the diagonal represent the ANOSIM R-score. Lower values indicate more similar AMR profiles between treatments. Values above the diagonal indicate the corresponding p-values for the null hypothesis that AMR profiles are similar between treatments. d) Abundance of AMR genes of distinct classes for samples in different treatment groups (colors). Classes are sorted according to their mean AMR gene abundance across samples. Color arrows indicate a significant (p-value < 0.05) increase (pointing up) or decrease (pointing down) in the abundance of AMRs in the corresponding color treatments compared to control samples. MLS macrolides, lincosamides, and streptrogramin A and B.

Fig. 5. Predicted metabolic properties of the core cecum microbiome.

a) The predicted ability of each core genera to use individual compounds as main carbon sources for biomass synthesis. Core genera are shown in the columns grouped by taxonomic family. Each row represents a distinct carbon-containing compound. b) Like a but for the ability to use individual compounds as main nitrogen sources. c) The number of carbon sources with significantly (one-sided Mann-Whitney U p-value < 0.05) higher or lower utilization potential in samples from each of the AGP treatments compared to control. d) Like c but for compounds used as nitrogen sources. e) The total metabolic demand for core microbiome biomass synthesis across treatments. The total metabolic demand represents the likelihood that any given metabolite is essential for the growth of a random member of the core cecum microbiome. Boxes represent the median and interquartile range; whiskers indicate the range of the distribution excluding outliers. * two-sided Mann-Whitney U p-value < 5 × 10⁻³. n = 15 birds per treatment. C control, B BMD, A avilamycin, V virginiamycin, N narasin.

Fig. 6. Cecum and serum metabolites altered by narasin treatment.

a) The median-normalized ion counts (scaled intensity) of 20 amino acids in the cecum of control and narasin treated birds. *Welch’s t p-value < 0.05, n = 15 birds per treatment. b) The scaled intensity of uric acid in the cecum and serum of control and narasin treated birds. c) The scaled intensity of urea in the serum control and narasin treated birds. The boxes in a, b, and c represent the median and interquartile range; whiskers indicate the range of the distribution excluding outliers. d) The correlation between the abundance of dipeptides and amino acids in the cecum and bird weight at day 35 for control and narasin treated birds. Dashed red lines represent the correlation threshold for p-values < 0.05. e) The correlation between the abundance of amino acids in the serum and bird weight at day 35 for control and narasin treated birds. In panels d and e, the x-axis values show the Pearson correlation coefficient between the logarithm of the scaled intensities and weight.