Investigating the Reciprocal Interrelationships among the Ruminal Microbiota, Metabolome, and Mastitis in Early Lactating Holstein Dairy Cows

Abstract

Mastitis in dairy cow significantly affects animal performance, ultimately reducing profitability. The reciprocal interrelationships among ruminal microbiota, metabolome, and mastitis combining early inflammatory factors (serum proinflammatory cytokines) in lactating dairy cows has not been explored, thus, this study evaluated these reciprocal interrelationships in early lactating Holstein dairy cows to identify potential microbial biomarkers and their relationship with ruminal metabolites. The ruminal fluid was sampled from 8 healthy and 8 mastitis cows for the microbiota and metabolite analyses. The critical ruminal microbial biomarkers and metabolites related to somatic cell counts (SCC) and serum proinflammatory cytokines were identified by the linear discriminant analysis effect size (LEfSe) algorithm and Spearman's correlation analysis, respectively. The SCC level and proinflammatory cytokines positively correlated with Sharpea and negatively correlated with Ruminococcaceae UCG-014, Ruminococcus flavefaciens, and Treponema saccharophilum. Furthermore, the metabolites xanthurenic acid, and 1-(1H-benzo[d]imidazol-2-yl) ethan-1-ol positively correlated with microbial biomarkers of healthy cows, whereas, xanthine, pantothenic acid, and anacardic acid were negatively correlated with the microbial biomarkers of mastitis cows. In conclusion, Ruminococcus flavefaciens and Treponema saccharophilum are potential strains for improving the health of dairy cows. The current study provides a novel perspective to assist in targeting the ruminal microbiota with preventive/therapeutic strategies against inflammatory diseases in the future.

Keywords: Holstein dairy cows; mastitis; metabolome; ruminal microbiota.

Figure 1

Somatic cell counts and serum cytokines of healthy and mastitic cows. (A) Log-transformed somatic cell counts and (B) serum cytokines (TNF-α and IL-6) were significantly higher in mastitic cows than in healthy cows (*** p < 0.001).

Figure 2

Ruminal bacteria and archaea composition identified by 16S rRNA sequencing of healthy and mastitic cows. (A) Venn diagram illustrating 2979 OTU of core microbiota identified by 16S rRNA sequencing of healthy (H) and mastitic (M) cows. (B) The Chao1 richness estimator and (C) Shannon’s diversity index. (D) Partial least squares discriminant analysis (PLS-DA) plot based on the relative abundance of OTUs indicates a significantly different composition of healthy versus mastitic cows. Ellipses represent 95% confidence intervals for each group. The top 10 (E) families and (F) genera identified in cow ruminal fluid, each bar refers to an individual cow.

Figure 3

Ruminal protozoa and fungi composition were identified by 18S rRNA sequencing. (A) The Chao1 richness estimator and Shannon’s diversity index. (B) Partial least squares discriminant analysis (PLS-DA) plot based on the relative abundance of OTUs indicates a significantly different composition of healthy versus mastitis groups. Ellipses represent 95% confidence intervals for each group. The top 10 (C) genera and (D) species identified in cow ruminal fluid, each bar refers to an individual cow.

Figure 4

Significant differential biomarkers were identified using the linear discriminant analysis (LDA) effect size (LEfSe) algorithm. (A) LEfSe cladogram and log-transformed LDA scores illustrate differential enrichment of biomarkers between groups. Bacterial networks of ruminal microbiota show the correlations between the seven genus and three species with (B) SCC and (C) IL-6. Each node represents a biomarker and the size corresponds to its relative abundance. Yellow and blue nodes show significant positive and negative correlations with somatic cell count/IL-6 (p < 0.05), respectively. Red and green edges indicate significant positive and negative correlations between biomarkers (p < 0.05), respectively. (D) The significant relative abundance of differential biomarkers. Symbols indicate significantly different relative abundances between groups using the Mann–Whitney U test (* p < 0.05; ** p < 0.01).

Figure 5

Different compositions of ruminal metabolites of the healthy and mastitic cows. (A) Volcano plot of 1336 compounds with log-transformed adjusted p values and fold change. The green and red dots indicate significantly higher metabolites in the healthy and mastitic groups, respectively. (B) Orthogonal partial least squares discriminant analysis (oPLS-DA) plot based on the 1336 compounds indicates significantly different metabolite compositions of the healthy and mastitic groups. Ellipses represent 95% confidence intervals for each group. Every dot represents a single individual cow.

Figure 6

Reciprocal interrelationships between the ruminal microbiota and metabolome by Spearman’s correlation test shown at the genus and species levels. Orange and blue colors indicate positive and negative correlation coefficients, respectively. Symbols indicate the significant correlation between metabolites and biomarkers (* p < 0.05; ** p < 0.01).