Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle

Abstract

Exploring compositional and functional characteristics of the rumen microbiome can improve the understanding of its role in rumen function and cattle feed efficiency. In this study, we applied metatranscriptomics to characterize the active rumen microbiomes of beef cattle with different feed efficiencies (efficient, n = 10; inefficient, n = 10) using total RNA sequencing. Active bacterial and archaeal compositions were estimated based on 16S rRNAs, and active microbial metabolic functions including carbohydrate-active enzymes (CAZymes) were assessed based on mRNAs from the same metatranscriptomic data sets. In total, six bacterial phyla (Proteobacteria, Firmicutes, Bacteroidetes, Spirochaetes, Cyanobacteria, and Synergistetes), eight bacterial families (Succinivibrionaceae, Prevotellaceae, Ruminococcaceae, Lachnospiraceae, Veillonellaceae, Spirochaetaceae, Dethiosulfovibrionaceae, and Mogibacteriaceae), four archaeal clades (Methanomassiliicoccales, Methanobrevibacter ruminantium, Methanobrevibacter gottschalkii, and Methanosphaera), 112 metabolic pathways, and 126 CAZymes were identified as core components of the active rumen microbiome. As determined by comparative analysis, three bacterial families (Lachnospiraceae, Lactobacillaceae, and Veillonellaceae) tended to be more abundant in low-feed-efficiency (inefficient) animals (P < 0.10), and one archaeal taxon (Methanomassiliicoccales) tended to be more abundant in high-feed-efficiency (efficient) cattle (P < 0.10). Meanwhile, 32 microbial metabolic pathways and 12 CAZymes were differentially abundant (linear discriminant analysis score of >2 with a P value of <0.05) between two groups. Among them, 30 metabolic pathways and 11 CAZymes were more abundant in the rumen of inefficient cattle, while 2 metabolic pathways and 1 CAZyme were more abundant in efficient animals. These findings suggest that the rumen microbiomes of inefficient cattle have more diverse activities than those of efficient cattle, which may be related to the host feed efficiency variation.IMPORTANCE This study applied total RNA-based metatranscriptomics and showed the linkage between the active rumen microbiome and feed efficiency (residual feed intake) in beef cattle. The data generated from the current study provide fundamental information on active rumen microbiome at both compositional and functional levels, which serve as a foundation to study rumen function and its role in cattle feed efficiency. The findings that the active rumen microbiome may contribute to variations in feed efficiency of beef cattle highlight the possibility of enhancing nutrient utilization and improve cattle feed efficiency through modification of rumen microbial functions.

Keywords: RNA-seq; beef cattle; feed efficiency; metatranscriptome; rumen microbiome.

FIG 1

Profiles of bovine rumen microbiome. (A) Microbial metabolic pathways based on their first- and second-level functions in the KEGG hierarchy. (B) Carbohydrate-active enzymes (CAZymes). These graphs were generated using the program Krona (79).

FIG 2

Microbial compositional profiles of H- and L-RFI animals visualized using principal-coordinate analysis (PCoA). The first two PCoAs were plotted, and they were calculated based on the Bray-Curtis dissimilarity matrices at the bacterial family level (A) and archaeal mixed taxonomic level (B).

FIG 3

Differential rumen microbial metabolic pathways and carbohydrate-active enzymes (CAZymes) between H- and L-RFI cattle in metatranscriptomic data sets. (A) PCA based on microbial metabolic pathways. (B) PCA based on CAZymes. (C and E) Histograms of linear discriminant analysis (LDA) for the differential metabolic pathways (C) and CAZymes (E). (D and F) Abundances of differential pathways (D) and CAZymes (F). Pathways and CAZymes with an LDA score of >2 and P value of <0.05 were considered significantly differential features.

FIG 3

Differential rumen microbial metabolic pathways and carbohydrate-active enzymes (CAZymes) between H- and L-RFI cattle in metatranscriptomic data sets. (A) PCA based on microbial metabolic pathways. (B) PCA based on CAZymes. (C and E) Histograms of linear discriminant analysis (LDA) for the differential metabolic pathways (C) and CAZymes (E). (D and F) Abundances of differential pathways (D) and CAZymes (F). Pathways and CAZymes with an LDA score of >2 and P value of <0.05 were considered significantly differential features.

FIG 4

Correlation patterns showing associations between active microbial taxa and metabolic pathways. (A) Correlation patterns based on all 20 animals. (B) Correlation patterns within the H-RFI group. (C) Correlation patterns within the L-RFI group. Correlation analyses were conducted using Spearman's rank correlation. Only strong (correlation coefficient [ρ] of >0.5 or <−0.5) and significant (P < 0.05) correlations were chosen to be displayed. The scale ranged from −1 (red) to 1 (blue).