Biomarkers for Prediction of Bovine Respiratory Disease Outcome

Fatal bovine respiratory disease (BRD) is frequently the result of a primary viral and a secondary bacterial respiratory infection. In cattle, BRD causes more than half of feedlot deaths and has a major impact on financial losses in the cattle industry in North America. It is, therefore, very important to understand the mechanism of this complex disease process as well to predict and identify BRD susceptible cattle to enhance treatment efficacy. We recently established the value of using combinatorial omics approaches to identify candidate biomarkers associated with stress responses, a factor that can increase the severity of BRD. The objective of the present investigation was to experimentally recreate fatal BRD and to use a combinatorial analysis of proteomic, metabonomic, and elemental profiles in serum samples to determine if multimethod analysis of these biomarkers could predict disease outcome. The proteomic studies revealed that changes in the serum proteome were significant on day 4 postviral infection when compared to preinfection (day 0) serum samples. Proteomic studies identified a group of acute phase proteins (haptoglobin and apolipoprotein AI), which could be linked to a primary viral respiratory infection, but there was no significant association observed with fatal BRD. In contrast, metabonomic and elemental analyses identified candidate biomarkers for viral infection (glucose, LDL, valine, phosphorous, and iron) and disease outcome (lactate, glucose, iron). While multivariate analysis of proteomic and metabolite profiles did not discriminate between animals that survived or died postsynergic viral–bacterial infection by analyzing preinfection (day 0) serum samples, analysis of serum elemental profiles prior to infection was, however, predictive of BRD outcome. Furthermore, discriminant analyses of all three methodologies used to profile serum (collected on day 4 postviral but prior to bacterial infection) revealed differential trends between animals that survived or died following synergic viral–bacterial infection. Thus, a combinatorial approach using proteomic, metabonomic, and elemental analyses of serum samples revealed that multimethod analysis could discriminate between the complex biological responses to secondary bacterial respiratory infection and predict disease outcome.