Dataset Information

Development of a predictive model for bovine leukemia virus proviral load.

ABSTRACT:

Background

There is currently no commercially available method in Canada to identify bovine leukemia virus (BLV)-positive cows with high proviral load (PVL).

Objectives

First, develop a model to predict PVL using common, commercially available, cost-effective diagnostic tests. Second, investigate the relationship between lymphocyte count and PVL in BLV-positive cows.

Animals

A total of 339 BLV-positive and 62 BLV-seronegative cows on 15 dairy farms.

Methods

Cross-sectional study. Blood and milk samples were collected from all lactating BLV-positive cows on each farm and 5 to 10 BLV-seronegative cows depending on herd size. Blood and milk samples were tested for anti-BLV antibodies using enzyme-linked immunosorbent assay (ELISA). Complete blood counts were performed on blood samples, and standard components analyses were obtained for milk samples. Proviral load was determined by quantitative polymerase chain reaction for each cow.

Results

The inverse of lymphocyte count, the square of the inverse of lymphocyte count, and milk ELISA percent positivity were positively associated with increasing PVL in BLV-positive cows. For BLV-positive cows, lymphocyte count >5.2 × 10⁹ /L predicted a high PVL (BLV:Bovine DNA of >1 in blood) with a sensitivity of 92.4% and a specificity of 79.8%. For BLV-positive cows, white blood cell count >10.8 × 10⁹ /L predicted a high PVL, with a sensitivity of 85.5% and a specificity of 83.6%.

Conclusions and clinical importance

Based on these results, producers can implement commonly available diagnostic tests to identify cows with high probability of having high PVL, which may help in designing effective disease control strategies for BLV-positive herds.

SUBMITTER: John EE

PROVIDER: S-EPMC9511096 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Development of a predictive model for bovine leukemia virus proviral load.

John Emily E EE Droscha Casey C Cameron Marguerite M Stryhn Henrik H Keefe Greg G McClure J Trenton JT

Journal of veterinary internal medicine 20220811 5

<h4>Background</h4>There is currently no commercially available method in Canada to identify bovine leukemia virus (BLV)-positive cows with high proviral load (PVL).<h4>Objectives</h4>First, develop a model to predict PVL using common, commercially available, cost-effective diagnostic tests. Second, investigate the relationship between lymphocyte count and PVL in BLV-positive cows.<h4>Animals</h4>A total of 339 BLV-positive and 62 BLV-seronegative cows on 15 dairy farms.<h4>Methods</h4>Cross-sec ...[more]

PMID: 35950569

Similar Datasets

Project description:BACKGROUND: Bovine leukemia virus (BLV) is closely related to human T-cell leukemia virus (HTLV) and is the etiological agent of enzootic bovine leukosis, a disease characterized by a highly extended course that often involves persistent lymphocytosis and culminates in B-cell lymphomas. BLV provirus remains integrated in cellular genomes, even in the absence of detectable BLV antibodies. Therefore, to understand the mechanism of BLV-induced leukemogenesis and carry out the selection of BLV-infected animals, a detailed evaluation of changes in proviral load throughout the course of disease in BLV-infected cattle is required. The aim of this study was to develop a new quantitative real-time polymerase chain reaction (PCR) method using Coordination of Common Motifs (CoCoMo) primers to measure the proviral load of known and novel BLV variants in clinical animals. RESULTS: Degenerate primers were designed from 52 individual BLV long terminal repeat (LTR) sequences identified from 356 BLV sequences in GenBank using the CoCoMo algorithm, which has been developed specifically for the detection of multiple virus species. Among 72 primer sets from 49 candidate primers, the most specific primer set was selected for detection of BLV LTR by melting curve analysis after real-time PCR amplification. An internal BLV TaqMan probe was used to enhance the specificity and sensitivity of the assay, and a parallel amplification of a single-copy host gene (the bovine leukocyte antigen DRA gene) was used to normalize genomic DNA. The assay is highly specific, sensitive, quantitative and reproducible, and was able to detect BLV in a number of samples that were negative using the previously developed nested PCR assay. The assay was also highly effective in detecting BLV in cattle from a range of international locations. Finally, this assay enabled us to demonstrate that proviral load correlates not only with BLV infection capacity as assessed by syncytium formation, but also with BLV disease progression. CONCLUSIONS: Using our newly developed BLV-CoCoMo-qPCR assay, we were able to detect a wide range of mutated BLV viruses. CoCoMo algorithm may be a useful tool to design degenerate primers for quantification of proviral load for other retroviruses including HTLV and human immunodeficiency virus type 1.

Dataset Information

Development of a predictive model for bovine leukemia virus proviral load.

Background

Objectives

Animals

Methods

Results

Conclusions and clinical importance

Publications

Development of a predictive model for bovine leukemia virus proviral load.

Similar Datasets

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