Regression tree analysis of the relationship between the concentrations of antimicrobial components and the microbiota of normal milk from dairy cows.

The purpose of this study was to determine the concentrations of antimicrobial components (immunoglobulin A (IgA), lactoferrin (LF), lingual antimicrobial peptide (LAP), and S100A7) in normal milk and their relation to host factors (Age, somatic cell count (SCC), days in milk, richness, and alpha diversity of the milk microbiota) in dairy cows using multivariate regression tree analyses, and to clarify how the milk microbiota is related to the obtained results. Thirty normal milk samples were collected from a commercial dairy farm in June 2020. The thresholds that predicted the concentration of each antimicrobial component in milk were obtained by regression tree analysis, and the beta-diversity of the milk microbiota composition between groups divided according to each threshold was compared by an analysis of similarities test. The IgA and LF concentrations were mainly predicted by the SCC (177,500 and 70,000 cells/ml, respectively), and the LAP and S100A7 concentrations were predicted by Age (29.667 and 40.3 months, respectively). No relationship was observed between the concentration of IgA, LAP, or S100A7 and the milk microbiota composition between the groups divided by the threshold for prediction, but the milk microbiota composition was significantly different between the groups divided by the threshold for predicting the LF concentration. Our results indicated that the LF concentration in normal milk may be associated with the milk microbiota composition.

Examination of the microbiota of normal cow milk using MinIONTM nanopore sequencing.

The aim of this study was to evaluate the microbiota of normal milk in dairy cows and their relationship with host factors, such as the age of the cow (Age), somatic cell counts in milk (SCCs), and days in milk (DIM). We investigated 48 milk samples from 22 cows with no systemic or local clinical signs using MinIONTM nanopore sequencing for a 16S rRNA gene amplicon. Bacterial richness was positively correlated with the DIM (P=0.043), and both the Shannon-Wiener Index and Simpson's Index, which are metrics of alpha-diversity, were also significantly positively correlated with the SCC (P<0.001). The composition ratios of both Actinobacteria at the phylum level and Kocuria spp. at the genus level in the milk microbiota were significantly correlated with the SCC (P<0.001 and P<0.001, respectively). In the beta-diversity test, the one-way analysis of similarities test showed a significant difference (P=0.0051) between the low- and high-SCC groups. This study clarified that the composition of the normal milk microbiota in this herd was related to the SCC. It also raised the possibility of variations in bacterial genera in the normal milk microbiota between the low- and high-SCC groups. However, to clarify the actual condition of the milk microbiota and to elucidate the relationship with the SCC, it is necessary to perform further analyses taking into account not only the relative abundance, but also the absolute abundance of microbes.