Increasing buffering capacity enhances rumen fermentation characteristics and alters rumen microbiota composition of high-concentrate fed Hanwoo steers

Abstract

The buffering capacity of buffer agents and their effects on in vitro and in vivo rumen fermentation characteristics, and bacterial composition of a high-concentrate fed Hanwoo steers were investigated in this study. Treatments were comprised of CON (no buffer added), BC_0.3% (low buffering capacity, 0.3% buffer), BC_0.5% (medium buffering capacity, 0.5% buffer), and BC_0.9% (high buffering capacity, 0.9% buffer). Four Hanwoo steers in a 4 × 4 Latin square design were used for the in vivo trial to assess the effect of treatments. Results on in vitro experiment showed that buffering capacity, pH, and ammonia-nitrogen concentration (NH₃-N) were significantly higher in BC_0.9% and BC_0.5% than the other treatments after 24 h incubation. Individual and total volatile fatty acids (VFA) concentration of CON were lowest compared to treatment groups. Meanwhile, in vivo experiment revealed that Bacteroidetes were dominant for all treatments followed by Firmicutes and Proteobacteria. The abundances of Barnesiella intestinihominis, Treponema porcinum, and Vibrio marisflavi were relatively highest under BC_0.9%, Ruminoccocus bromii and Succiniclasticum ruminis under BC_0.5%, and Bacteroides massiliensis under BC_0.3%. The normalized data of relative abundance of observed OTUs' representative families have grouped the CON with BC_0.3% in the same cluster, whereas BC_0.5% and BC_0.9% were clustered separately which indicates the effect of varying buffering capacity of buffer agents. Principal coordinate analysis (PCoA) on unweighted UniFrac distances revealed close similarity of bacterial community structures within and between treatments and control, in which BC_0.9% and BC_0.3% groups showed dispersed community distribution. Overall, increasing the buffering capacity by supplementation of BC_0.5% and and BC_0.9% buffer agents enhanced rumen fermentation characteristics and altered the rumen bacterial community, which could help prevent ruminal acidosis during a high-concentrate diet.

Figure 1

Boxplot representation of alpha diversity indices: (a) chao1, (b) Shannon, and (c) observed OTUs, between treatment groups. Alpha-diversity metrics visualization were done in MetaCOMET and computed using QIIME. CON (no buffer added); BC_0.3% (0.3% buffer); BC_0.5% (0.5% buffer); BC_0.9% (0.9% buffer).

Figure 2

Relative abundance of the observed (a) phyla, (b) genera, and (c) species from the four different treatments. Mean relative abundances of bacterial phyla and genera are presented in supplementary Table 2. Relative abundance was computed using QIIME. CON (no buffer added); BC_0.3% (0.3% buffer); BC_0.5% (0.5% buffer); BC_0.9% (0.9% buffer); asterisk (*): represents significant differences (P < 0.05).

Figure 3

(a) Membership-based representation of unique, shared and core bacterial community of rumen microbiome after treatment supplementation with varying level of buffering capacity, and the total size of observed species per treatment. Venn diagram was generated in MetaCOMET using jvenn. (b) Principal Coordinate Analysis (PCoA) of all samples using Bray–Curtis distance derived from the subset of identified OTUs. PCoA plot was generated using EMPeror. CON (no buffer added); BC_0.3% (0.3% buffer); BC_0.5% (0.5% buffer); BC_0.9% (0.9% buffer).

Figure 4

Heatmap presentation of relative abundance of representative families of observed OTUs. Treatments (columns) and families (rows) are clustered using Bray–Curtis dissimilarity test and Ward linkage. Normalized relative abundance are plotted from low (red), mid (peach), and high (blue). Heatmap clustering was generated in MetaCOMET utilizing the InCHlib application. CON (no buffer added); BC_0.3% (0.3% buffer); BC_0.5% (0.5% buffer); BC_0.9% (0.9% buffer); asterisk (*): represents significant differences (P < 0.05).