Successional changes in the chicken cecal microbiome during 42 days of growth are independent of organic acid feed additives

Abstract

Background: Poultry remains a major source of foodborne bacterial infections. A variety of additives with presumed anti-microbial and/or growth-promoting effects are commonly added to poultry feed during commercial grow-out, yet the effects of these additives on the gastrointestinal microbial community (the GI microbiome) as the bird matures remain largely unknown. Here we compared temporal changes in the cecal microbiome to the effects of formic acid, propionic acid, and medium-chain fatty acids (MCFA) added to feed and/or drinking water.

Results: Cecal bacterial communities at day of hatch (n = 5 birds), 7d (n = 32), 21d (n = 27), and 42d (n = 36) post-hatch were surveyed using direct 454 sequencing of 16S rRNA gene amplicons from each bird in combination with cultivation-based recovery of a Salmonella Typhimurium marker strain and quantitative-PCR targeting Clostridium perfringens. Treatment effects on specific pathogens were generally non-significant. S. Typhimurium introduced by oral gavage at day of hatch was recovered by cultivation from nearly all birds sampled across treatments at 7d and 21d, but by 42d, S. Typhimurium was only recovered from ca. 25% of birds, regardless of treatment. Sequencing data also revealed non-significant treatment effects on genera containing known pathogens and on the cecal microbiome as a whole. In contrast, temporal changes in the cecal microbiome were dramatic, highly significant, and consistent across treatments. At 7d, the cecal community was dominated by three genera (Flavonifractor, Pseudoflavonifractor, and a Lachnospiracea sequence type) that accounted for more than half of sequences. By 21d post-hatch, a single genus (Faecalibacterium) accounted for 23-55% of sequences, and the number of Clostridium 16S rRNA gene copies detected by quantitative-PCR reached a maximum.

Conclusions: Over the 42 d experiment, the cecal bacterial community changed significantly as measured by a variety of ecological metrics and increases in the complexity of co-occurrence networks. Management of poultry to improve animal health, nutrition, or food safety may need to consider the interactive effects of any treatments with the dramatic temporal shifts in the taxonomic composition of the cecal microbiome as described here.

Figure 1

Clustering of the cecal microbiome by treatment and time. Clustering was performed by canonical correspondence analysis as described in the text. Each point represents a single bird with sequences clustered independent of taxonomic assignments according to operational taxonomic units (OTUs) defined at a 97% similarity cutoff as described in the text. Data from day-of-hatch birds group off of the axes and are excluded for clarity. Clustering based on classification of sequences to the genus or species level gave equivalent results. Treatment designations are Ctl, control; FO, feed-only; WO, water-only; and FW, feed and water as described in the text.

Figure 2

Relative abundance at the genus level for sequences by treatment and time with taxonomic classifications performed with the RDP classifier as described in the text. Only sequences with a total relative abundance greater than 5% are shown. For day-of-hatch birds and each subsequent time point (7d, 21d, and 42 d post-hatch), the relative proportions are shown for each treatment. Day-of-hatch birds were proportionally high in Clostridium but low quantitatively as shown in Figure 3. Treatment designations are Ctl, control; FO, feed-only; WO, water-only; and FW, feed and water as described in the text.

Figure 3

Changes in relative abundance of putative pathogens by treatment and time. A) For each time point (7d, 21d, and 42 d post-hatch), the relative proportions are shown for each of the four treatments. Putative pathogens were defined using the intersection of independent taxonomic classifications with the RDP classifier and the Silva database as described in the methods. Sequences classified as Escherichia or Shigella by Silva are shown separately but not distinguished by RDP. Treatment designations are Ctl, control; FO, feed-only; WO, water-only; and FW, feed and water as described in the text. Note scale of Y axis. B) Number of gene copies of Clostridium as determined by quantitative-PCR for each time point. Treatments for each time point are grouped due to the non-significant effect of treatment as shown in Table 1. Quantitative loads of Clostridium were significantly higher at 21 d than 7d or 42 (p < 0.0001, one-sided t-tests),

Figure 4

Taxonomic richness and diversity of the cecal microbiome at the genus level through time. A) Richness and diversity statistics calculated at the species and OTU-level showed essentially similar patterns through time. B) Network complexity of the cecal microbiome through time as measured by the numbers of nodes and edges in network. Nodes represent genera with significant network connections to other genera and edges represent the total number of significant networks connections calculated as described in the text.