Blood and Urinary Metabotyping Reveals Potential Predictive Biomarkers for Identifying Dairy Cows at Risk of Subclinical Mastitis During the Dry-Off Period

Abstract

Subclinical mastitis (SCM) remains one of the most important infectious diseases of dairy cows as it is associated with considerable losses in milk production and financial revenue. Currently, most SCM research and practices focus on diagnosing this intramammary infection (IMI) by counting somatic cells (SCC) in milk throughout lactation. Therefore, this study aimed to identify metabolic alterations in the serum and urine of pre-SCM cows during the dry period, along with developing panels of screening biomarkers for lab-based and pen-side tests. Early identification of susceptible cows will enable better preventative and management strategies for SCM. A combination of flow injection and liquid chromatography coupled with tandem mass spectrometry (FIA/LC-MS/MS) analysis were used to characterize 580 blood and urine samples collected from 145 Holstein cows at –8 and –4 wks before the expected date of calving. Cows enrolled in this nested-case control study were then monitored for the development of postpartum diseases. Fifteen cows were free of any condition (CON), and just 10 cows presented with only SCM (characterized by high SCC) after calving and were free of other diseases. Metabolomics identified 126 serum metabolites from which 59 at –8 wks and 47 at –4 wks were found altered (P ≤ 0.05) in pre-SCM cows compared to CON cows. Using FDR adjusted P values, 32 metabolites at –8 wks and 17 at –4 wks were in the range of q < 0.005. The main metabolite classes that were altered were related to lipid metabolism, such as acylcarnitines (ACs), lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs) and sphingomyelins (SMs). Others were amino acids (AAs), methyl donor compounds, organic acids (OAs), and several carbohydrate species. Univariate, multivariate, and machine learning analysis indicated that a panel of 4 serum metabolites including alanine, leucine, betaine, and ornithine (AUC = 0.92; P < 0.001) at –8 wks and alanine, pyruvate, methylmalonate, and lactate (AUC = 0.92, P < 0.01) at –4 wks before parturition might serve as the best predictive serum biomarkers for SCM for a pen-side test. On the other hand, a total of 82 metabolites were found in the urine samples, and only 27 compounds (P ≤ 0.05) were different at each sampling period. At q < 0.005 only 4 metabolites were altered from each week. The most discriminating metabolites were ACs, several AAs and their derivatives, glucose, and OAs. Further regression analysis showed that four metabolites: ADMA, proline, leucine, and homovanillate (AUC=0.88; P = 0.02) at –8 wks and another four metabolites: ADMA, spermidine, methylmalonic acid and citrate (AUC = 0.88, P = 0.03) at –4 wks as specific urinary biomarkers for SCM. Overall, these data indicated systemic metabolic alterations occur in pre-SCM cows. They also showed that differentiation of pre-SCM cows against CON cows is possible and the data provided more information on the pathobiology of SCM. These predictive biomarkers also offer the potential to develop lab-based and pen-side tests to identify cows at risk of SCM during the dry period. The health status dataset for all the cows enrolled in this study demonstrated that several other cows were positive for SCM and at least one or more other diseases, including ketosis, leukosis, retained placenta, lameness, and milk fever. This complicates the development of lab and pen-side tests and warrants more research to explore the possibility of identifying specific metabolites for SCM alone that can separate SCM cows from the other diseases.