Microbiome and Metabolome Analyses of Milk From Dairy Cows With Subclinical Streptococcus agalactiae Mastitis-Potential Biomarkers

Abstract

The microbial ecosystem in the udders of dairy cows directly influences the flavor and quality of milk. However, to our knowledge, no published research has analyzed the complex relationship between the udder microbiome and its associated metabolism in animals with subclinical mastitis. We identified the bacterial species and measured relative population numbers in the milk of cows with subclinical Streptococcus agalactiae mastitis (GBS) and compared this information to that from the milk of healthy cows. Metabolite profiles were determined to investigate correlations between the milk microbiota and metabolic factors in healthy vs. GBS dairy cows. Six milk samples from GBS cows and six from healthy cows were subjected to 16S rRNA gene sequencing to identify the microbial species using a MiSeq high-throughput sequencing apparatus. The metabolites present in the milk were identified by gas chromatography time-of-flight mass spectrometry. Both principal component analysis and orthogonal partial least squares discriminant analysis indicated that the metabolites were well-separated from each other in the milk samples from the two groups. GBS dramatically altered microbial diversity, and the GBS group had significantly fewer Proteobacteria, Actinobacteria, and Acidobacteria than the CON group, with greater relative abundance of Firmicutes (p < 0.01). Several bacterial genera, such as Streptococcus, were significantly more abundant in milk from the GBS group than in milk from the CON group, and there was a tendency for greater abundance of Turicibacter (p = 0.07) and Enterococcus spp. (p = 0.07) in the GBS group. The levels of five milk metabolites were significantly higher in the GBS group than in the CON group: phenylpyruvic acid, the homogentisic acid: 4-hydroxyphenylpyruvic acid ratio, the xanthine: guanine ratio, uridine and glycerol. Metabolic pathway analysis of the different metabolites revealed that the following were enriched in both groups: galactose metabolism; pentose and glucuronate interconversion; starch and sucrose metabolism; alanine, aspartate and glutamate metabolism; arginine biosynthesis; citrate cycle (TCA cycle); D-glutamine and D-glutamate metabolism; and the neomycin, kanamycin, and gentamicin biosynthesis pathways. Several typical metabolites were highly correlated with specific ruminal bacteria, such as Streptococcaceae, Lachnospiraceae, Lactobacillaceae and Corynebacteriaceae, demonstrating the functional correlations between the milk microbiome and associated metabolites. These findings revealed that the milk microbiota and metabolite profiles were significantly different between the two groups of cows, raising the question of whether the microbiota associated with the bovine mammary gland could be related to mammary gland health. There was also a relationship between milk quality and the presence of spoilage bacteria. Other bacterial taxa should be investigated, as related information may provide insights into how perturbations in milk metabolomics profiles relate to differences in milk synthesis between healthy cows and those with subclinical mastitis.

Keywords: Streptococcus agalactiae; dairy cows; mastitis; metabolomics; milk microbiome.

FIGURE 1

Principal coordinate analysis (PCoA) of bacterial communities in milk from healthy dairy cows (CON) and cows with subclinical Streptococcus agalactiae mastitis (GBS), n = 6.

FIGURE 2

Composition of the predominant bacterial phyla identified in milk samples from healthy cows (CON) and cows with S. agalactiae mastitis (GBS), n = 6.

FIGURE 3

Differences between the relative abundances of five predominant bacterial phyla in milk samples from healthy cows (CON) and cows with S. agalactiae mastitis (GBS), n = 6. The extended error bar plot was generated using STAMP software. Welch’s two-sided test was used, and Welch’s inverted test was 0.95.

FIGURE 4

Differences between the relative abundance of the 10 predominant bacterial genera in milk samples from healthy cows (CON) and cows with S. agalactiae mastitis (GBS), n = 6. The extended error bar plot was generated using STAMP software. Welch’s two-sided test was used, and Welch’s inverted test was 0.95.

FIGURE 5

3D PCA score map (A), corresponding PLS-DA validation plots (B), and OPLS-DA score plots (C) derived from the GC-TOFMS metabolite profiles of milk from the control group (green circles) compared to the S. agalactiae group (blue circles).

FIGURE 6

Metabolome map of changes in the common metabolites identified between healthy controls and S. agalactiae-infected cows. The x-axis represents the pathway impact, and the y-axis shows the pathway enrichment. Larger symbol sizes and darker colors indicate greater metabolite numbers and higher pathway impact values, respectively.

FIGURE 7

Correlation analyses between milk bacteria families and altered metabolite levels in healthy cows and those with S. agalactiae mastitis ^∗0.01 < p < 0.05, ^∗∗0.001 < p ≤ 0.01, ^∗∗∗p ≤ 0.001.