Evaluating the Effect of Dietary Protein-Energy Ratios on Yak Intestinal Microbiota Using High-Throughput 16S rRNA Gene Sequencing

Abstract

This study investigated the impact of varying dietary protein-energy ratios on the intestinal microbiota composition in postpartum weaned female yak. For this study, forty yaks were divided into four groups and provided with different dietary treatments (group FA: high-energy high-protein, FB: high-energy low-protein, FC: low-energy high-protein, and FD: control group, provided with 48% alfalfa hay, 48% oat grass, and 4% premix) to investigate the variations in microflora profiles and metabolic responses. Rectal fecal samples (n = 24 × 2) were collected at day 15 and 30, from all four groups, and total DNA was extracted to estimate microbial heterogeneity and community structures by 16S rRNA sequencing focusing V3-V4 regions, using the Illumina Nova Seq 6000 platform. The results revealed a total of 5,669,645 raw data sequences (3,189,115 and 2,480,530 from day 15 and day 30, respectively). Results showed that groups FA and FB had enhanced protein metabolism and microbial diversity, which was marked by a significant increase (p < 0.05) in abundance of Ruminococcus. Conversely, the FD group showed a low level of microbial diversity with a significant (p < 0.05) predominance of Clostridium and Proteobacteria, indicating microbial dysbiosis and metabolic stress. It was concluded that imbalanced diets (groups FC and FD) upregulated the stress-related pathways with no favorable microbial shifts, whereas, dietary treatments in group FA and FB significantly (p < 0.05) supported the pathways involved in amino acids and carbohydrate metabolism and beneficially shifted the gut microbiota. These findings emphasize the importance of postpartum supplementation with appropriate proportions of protein and energy feed to promote optimal microbial health and metabolic functioning, particularly for yaks inhabiting high-altitude regions, which is a challenging environment.

Keywords: 16S rRNA sequencing; microbial diversity; protein–energy ratio; yak intestinal microbiota.

Figure 1

Rarefaction curves and Venn diagrams for samples at day 15 (a–d) and day 30 (e–h).

Figure 2

Alpha diversity analysis: rank abundances and other α-diversity indices at day 15 (a,b) and day 30 (c,d).

Figure 3

Beta diversity analysis at day 15 (a,b) and at day 30 (c,d).

Figure 4

Chart of relative species abundance at the rank of each group in different taxa on day 15 and day 30. This figure shows the top 10 phyla in group EA, EB, and EC (a–c), and the top 10 genera in group EA, EB, and EC (d–f) on the 15th day and 10 phyla in group EA, EB and EC (g–i) and top 10 genera in group EA, EB and EC (j–l) at 30th day compared to the control group.

Figure 5

Cluster heat map of species abundance at the level of genus at day 15 (a) and day 30 (b).

Figure 6

Significance analysis of gut microbiota in different groups at the phylum level at day 15 (a) and day 30 (b). Data are represented as means ± SD. * p < 0.05, ** p < 0.01.

Figure 7

Significance analysis of gut microbiota in different groups at the genus level (15 d). Data are represented as means ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 8

Significance analysis of gut microbiota in different groups at the genus level (30 d). Data are represented as means ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Figure 9

LEfSe analysis of gut microbiota in different groups at day 15 (a,b) and day 30 (c,d).

Figure 10

Function analysis of gut microbiota in different groups: (a,b) PCA analysis; (c,d) heatmaps; (e–i) LEfSe analysis.