Mastitis detection in milk microbiomes

Mastitis is a complex inflammatory disease of the mammary gland and one of the biggest causes of monetary loss in dairy industry. On farm detection is usually done by a positive California mastitis test (CMC), which is often followed by a bacteriological investigation of mastitic milk samples. Yet the outcome often is culture negative or non-conclusive, since bacterial mastitis can be caused by a wide range of microorganisms. Further, as subclinical mastitis produces no symptoms and can remain undetected for a long time. An earlier detection of a developing or subclinical mastitis would not only reduce the risk of spreading within the herd, but also reduce economic loss due to reduced milk yield and quality or the risk of culling. Therefore, in this study we used a combination of 16S rRNA amplicon sequencing in combination with physiological parameters monitored by rumen bolus sensors to investigate the potential of detecting a mastitis prior to diagnosis by CMC, based on the hypothesis that the milk microbiome will change towards the diagnosis date. We monitored a herd of 130 cows in Brandenburg, Germany with samples being taken biweekly from January to April 2023 by an automated milking system. We could show that Streptococcus sp. was the major mastitis pathogen during that time. Further, we could predict a developing mastitis up to six days prior to diagnosis with an accuracy of 40-60% based only on the milk microbiome data. Mastitic microbiome samples did correlate with rumination activity and milk flow, which is helpful in further developing rumen bolus sensor system and putting its alarms into context. Lastly, predictor species analysis pointed out Clostridium sp. and Aerococcus sp., which could indicate a role as mastitis pathogens so far overlooked by routine diagnostics.