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Abstract Background Within complex microbial ecosystems, microbe-microbe interrelationships play crucial roles in determining functional properties such as metabolic potential, stability and colonization resistance. In dairy cows, microbes inhabiting different ecological niches of the udder may have the potential to interact with mastitis pathogens and therefore modulate susceptibility to intramammary infection. In the present study, we investigated the co-occurrence patterns of bacterial communities within and between different niches of the bovine mammary gland (teat canal vs. milk) in order to identify key bacterial taxa and evaluate their associations with udder health parameters and mastitis susceptibility. Results Overall, teat canal microbiota was more diverse, phylogenetically less dispersed, and compositionally distinct from milk microbiota. This, coupled with identification of a large number of bacterial taxa that were exclusive to the teat canal microbiota suggested that the intramammary ecosystem, represented by the milk microbiota, acts as a selective medium that disfavors the growth of certain environmental bacterial lineages. We further observed that the diversity of milk microbiota was negatively correlated with udder inflammation. By performing correlation network analysis, we identified two groups of phylogenetically distinct hub species that were either positively (unclassified Bacteroidaceae and Phascolarctobacterium) or negatively (Sphingobacterium) correlated with biodiversity metrics of the mammary gland (MG). The latter group of bacteria also showed positive associations with the future incidence of clinical mastitis. Conclusions Our results provide novel insights into the composition and structure of bacterial communities inhabiting different niches of the bovine MG. In particular, we identified hub species and candidate foundation taxa that were associated with the inflammatory status of the MG and/or future incidences of clinical mastitis. Further in vitro and in vivo interrogations of MG microbiota can shed light on different mechanisms by which commensal microbiota interact with mastitis pathogens and modulate udder homeostasis.

The aim of the present study was to: 1) check whether it would be possible to detect cows susceptible to mastitis at an early stage of their utilization based on selected genotypes and basic production traits in the first three lactations using ensemble data mining methods (boosted classification tress – BT and random forest – RF), 2) find out whether the inclusion of additional production variables for subsequent lactations will improve detection performance of the models, 3) identify the most significant predictors of susceptibility to mastitis, and 4) compare the results obtained by using BT and RF with those for the more traditional generalized linear model (GLZ). A total of 801 records for Polish Holstein-Friesian Black-and-White cows were analyzed. The maximum sensitivity, specificity and accuracy of the test set were 72.13%, 39.73%, 55.90% (BT), 86.89%, 17.81%, 59.49% (RF) and 90.16%, 8.22%, 58.97% (GLZ), respectively. Inclusion of additional variables did not have a significant effect on the model performance. The most significant predictors of susceptibility to mastitis were: milk yield, days in milk, sire’s rank, percentage of Holstein-Friesian genes, whereas calving season and genotypes (lactoferrin, tumor necrosis factor alpha, lysozyme and defensins) were ranked much lower. The applied models (both data mining ones and GLZ) showed low accuracy in detecting cows susceptible to mastitis and therefore some other more discriminating predictors should be used in future research.