Using various antigen preparations to produce monoclonal antibodies against bovine leukaemia virus (BLV) gp51SU

The objective of this study was to compare different antigen preparations to produce monoclonal antibodies against bovine leukaemia virus gp51SU. The four antigen preparations for immunization of BALB/c mice were: CL: BLV-FLK cell lysate, UF: a fraction of CL (between 30 and 100 kDa), WVP: whole virus particles and SP: with ion exchange chromatography, gp51SU was semipurified. A total of nine successful fusions were performed which resulted in production of 23 monoclonal antibodies (mAbs) specific against gp51SU. The highest ratio of specific hybridoma colonies in each fusion was with SP preparation. Based on the reactivity of the mAbs in Western blotting, mAbs were classified into four groups: anti-gp51SU (23 mAbs), anti-gp30TM (8 mAbs), anti-Pr72 (5 mAbs) and antibodies against other viral proteins (7 mAbs). Some of the anti-gp51SU mAbs reacted with more than one band in Western blotting, suggesting that these colonies recognized not only gp51SU but also its precursors.Jafari Jozani, R.; Taheri, M.; Khazraiinia, P. and Hemmatzadeh, F

Production and characterization of monoclonal antibody to bovine leukemia virus gp51-SU

Enzootic bovine leukemia (EBL) is a disease of cattle with worldwide distribution. The bovine leukemia virus (BLV), is a typical type C retrovirus, has four genes: gag, pol, pro and env, flanked by long terminal repeats (LTR) and lacking oncogenes. Gene env encodes a precursor protein named Pr72 env which undergoes glycosylation and lysis, giving rise to an envelope glycoprotein gp51-SU with a total of 12 epitopes. The objective of the present study was to produce a panel of monoclonal antibodies (mAbs) against BLV-gp51-SU following the standard immunization protocols. The antigen preparations included cellular lysates, a fraction of cellular lysates to study antibodies against BLV proteins precursors, whole virus particles and semipurified gp51 -SU. Both route of injection (SC and IP) resulted in hyperimmunized mice. As indicated by indirect-ELISA and also yielded high fusion efficiency. The hybridoma colonies were stable and had a tendency to save their ability to secrete antibodies. Fusions produced five positive colonies in screening tests, two of which were against gp51-SU. The targeted hydrophobic domain of the gp51-SU molecule was detected by one of the colonies of mAbs. The two interested mAbs were used for immunohistochemistry on lymph node sections of a cow with lymphosarcoma which can detect immunoreactive materials in sections and also in H&E staining. We conclude the produced panel of mAbs is able to be used in detection of gp51-SU and a new epitope identified on this molecule could be used in IHC method for diagnosis of BLV infection. © IDOSI Publications, 2011.Raziallah Jafari Joozani, Parvaneh Khazrai Nia, Mohammad Taheri, Farhid Hemmatzadeh and Javad Ashrafihela

High-resolution melting curve analysis: a novel method for identification of Mycoplasma species isolated from clinical cases of bovine and porcine respiratory disease

Mycoplasma species cause wide ranges of infectious diseases in human and animals. The aim of the present study was to evaluate a real-time polymerase chain reaction (RT-PCR) followed by a high-resolution melting curve assay (HRM) for rapid differentiation of Mycoplasma species isolated from clinical cases of bovine and porcine respiratory disease. Lung samples from suspected cases to respiratory infections from cows and pigs were cultured on specific media, and the extracted DNA were tested by conventional polymerase chain reaction (PCR) assays for Mycoplasma. A set of universal primers specific for the 16S ribosomal RNA gene was designed and used for RT-PCR and HRM. The HRM analysis was able to differentiate between five different species of Mycoplasmas, namely, M. hyopneumoniae, M. bovis, M. hyorhinis, M. hyosynoviae and other uncultured Mycoplasma. All results were confirmed based on 16S rRNA gene sequencing. This rapid and reliable assay was as a simple alternative to PCR and sequencing, differentiating bovine and porcine mycoplasmas in species level.Ania Ahani Azari, Reza Amanollahi, Razi Jafari Jozani, Darren J. Trott, Farhid Hemmatzade

Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images

Background: lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training.Methods: training and testing of model performance of the convolutional neural network (CNN) algorithm for automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines), with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial infarction or ischemic cerebrovascular disease ascertained from linked healthcare data.Findings: the average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012 images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate 14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment, moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31–1.80 & HR 2.06, 95% CI 1.75–2.42, respectively), compared to those with low ML-AAC-24.Interpretation: the ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be readily implemented into these clinical settings to improve identification of people at high CVD risk