The aim of this study was to evaluate the effect of chronic respiratory diseases in calves on the serum protein electrophoretic pattern. Twenty five calves of the Slovak Spotted breed, Lowland Black Spotted breed, and their crossbreeds with clinical symptoms of chronic respiratory diseases were included into this study. Blood serum was analyzed for the total serum protein concentrations, and the relative and absolute values of serum protein fractions. The results obtained in sick animals were compared with those in 29 clinically healthy calves of the same age and breed. In the calves suffering from chronic respiratory diseases, we found significantly (P < 0.001) higher total serum protein concentrations than in healthy calves. In sick calves a marked shift in the concentrations of the most of protein fractions was observed, with significantly higher values of α1-, β1-, β2and γ-globulins (P < 0.001, P < 0.05, P < 0.001, and P < 0.01, respectively). On the other hand, the concentrations of albumin in the calves with respiratory diseases were significantly (P < 0.001) lower than those measured in healthy ones. The presented results indicate a marked effect of chronic respiratory diseases in calves on the serum protein electrophoretic pattern, with a shift in the albumin and globulin concentrations, which could be useful for clinicians for better evaluation of the pathological changes in affected animals. Cattle, bronchopneumonia, electrophoresis, serum protein fractions Together with diarrhoea, respiratory tract diseases constitute the main health problem and overall the most common cause of morbidity and mortality in calves and young cattle (Svensson et al. 2006). In case of dairy calf pneumonia, diagnosis and treatment are mainly based on the observation of clinical symptoms. However, in many cases, the infected calves show only mild clinical symptoms that could be easily missed in a group of calves on a farm (Ganheim et al. 2003). Therefore, there is a need for objective indicators of health or disease in calf herds applicable to laboratory diagnosis of diseases. Several blood indicators (e.g. total leukocyte count) have been introduced to indicate inflammatory diseases including respiratory diseases (Fulton et al. 2002). However, the use of total leukocyte count to detect infection is not informative enough in cattle like in many other species (Kidd 1991). Objective indicators of animal health such as the measurement of serum protein fractions could be useful for identifying diseased animals. Serum protein electrophoresis is a laboratory technique used to separate serum proteins by size and electrical charge, thus allowing the identification and quantification of protein fractions (Ceron et al. 2011). Although it provides useful information on the pathological conditions associated with disorders of the protein profile, serum protein electrophoresis in cattle is a rarely used laboratory method. Indications for serum protein electrophoresis in humans include hyperproteinaemia and suspicion of plasma cell neoplasia as well as investigation of hepatic diseases (Vavricka et al. 2009). In veterinary medicine, previous reports described the serum protein electrophoretic pattern in small animals, goats, and horses, predominantly in hepatic, endocrine, and infectious diseases (Sevelius and Andersson 1995; Janků et al. 2011). However, identification of serum protein fractions ACTA VET. BRNO 2012, 81: 365–370; doi:10.2754/avb201281040365 Address for correspondence: Csilla Tothova, DVM, PhD. Clinic for Ruminants, University of Veterinary Medicine and Pharmacy Komenskeho 73, 041 81 Kosice, Slovak Republic Phone: +421 915 493 082 Fax: +421 55 67 11 674 E-mail: tothova@uvm.sk http://actavet.vfu.cz/ in cattle with various organ diseases is not so well documented, predominantly in chronic cases. Therefore, the aim of this study was to determine whether chronic respiratory diseases in calves cause changes in the serum protein electrophoretic pattern. Materials and Methods Animals and clinical examination Twenty-five calves with clinical signs of chronic respiratory diseases of varying intensity were included into this study. The calves were of the Slovak Spotted breed, Low-land Black Spotted breed, or their crossbreeds. The age of the calves ranged from 4 to 6 months, and their body weight from 85 to 140 kg. The animals were taken to the Clinic for Ruminants of the University of Veterinary Medicine and Pharmacy in Kosice by private veterinarians from three different conventional dairy farms. The same feeding and management regimes were applied to calves from these herds. At the clinic, the calves were housed individually, fed twice a day with free access to water. After the arrival to the clinic, all calves were thoroughly examined using standard clinical examination procedures focused on the general health state (body temperature, food intake, behaviour), and then especially on the respiratory system (Jackson and Cockcroft 2002). The respiratory system was examined by visual inspection (breathing rate, nasal discharges, dyspnoea and cough) and auscultation (increased or decreased loudness of the breathing sounds, bronchial sounds, abnormal breath sounds). This study included calves manifesting clinical signs of the disease for more than 2 weeks despite the antimicrobial, antiinflammatory, and supportive therapy done by private veterinarians on the farm. The chronicity of the disease was defined from the pacient history. The animals did not show any pathological lesions on the other organ systems. To compare the evaluated variables between sick and healthy animals, 29 clinically healthy calves from a conventional dairy farm were used as a control group. These calves were of the same age and breed like the calves with respiratory diseases. They were in good general health without any obvious signs of disease. Sample collection Blood samples were taken from both healthy and sick animals once after initial clinical examination. Blood samples were collected by a direct puncture of v. jugularis into serum gel separator tubes without anticoagulant. The separated serum was stored at -20 °C until analyzed for the total serum protein concentrations, and identification of serum protein fractions. Laboratory analyses Total protein (TP, g/l) concentrations in blood serum were assayed in the automated biochemical analyzer Alize (Lisabio, France) by the biuret method using commercial diagnostic kits (Randox, United Kingdom). Serum protein fractions were separated by zone electrophoresis on the buffered agarose gel at pH 8.8 on an automated electrophoresis system Hydrasys (Sebia Corporate, France) using commercial diagnostic kits Hydragel 7 Proteine (Sebia Corporate) according to the procedure described by the manufacturer. The electrophoretic migration was performed for 15 min at 20 °C constantly at 10 W, 40 mA, and 240 V. After migration, the gels were stained in amidoblack staining solution, and then destained by acidic solutions and dried completely. The electrophoretic gels were scanned, and the serum protein fractions were visualized and displayed on the densitometry system Epson Perfection V700 (Epson America Inc., California, USA) by light transmission and automatic convertion into an optical density curve presentation. Protein fractions were identified and quantified by computer software Phoresis version 5.50 (Sebia Corporate). Serum proteins were separated into the following fractions in the order from the fastest to the slowest mobilities: albumin, alpha1 (α1)-, alpha2 (α2)-, beta1 (β1)-, beta2 (β2)-, and gamma (γ)globulins. The relative concentrations (%) of protein fractions were determined as the percentage of the optical absorbance, and the absolute concentrations (g/l) were calculated from the total serum protein concentrations. Albumin: globulin ratios (A/G) were computed from the electrophoretic scan. Statistical analyses Arithmetic means (x) and standard deviations (SD) for the evaluated variables were calculated using the descriptive statistical procedures. Mann-Whitney non-parametric test was used to compare the results and to evaluate the significance of differences in the values measured between healthy and sick calves. All statistical analyses were performed using the computer programme GraphPad Prism V5.02 (GraphPad Software Inc.