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Palarea-Albaladejo, J.

Publications and source records attributed to Palarea-Albaladejo, J..

2 recordsLinked to original sources

Characterisation of protective vaccine antigens from the thiol-containing components of excretory/secretory material of Ostertagia ostertagi

Previous vaccination trials have demonstrated that thiol proteins affinity purified from Ostertagia ostertagi excretory-secretory products (O. ostertagi ES-thiol) are protective against homologous challenge. Here we have shown that protection induced by this vaccine was consistent across four independent vaccine-challenge experiments. Protection is associated with reduced cumulative faecal egg counts across the duration of the trials, relative to control animals. To better understand the diversity of antigens in O. ostertagi ES-thiol we used high-resolution shotgun proteomics to identify 490 unique proteins in the vaccine preparation. The most numerous ES-thiol proteins, with 91 proteins identified, belong to the sperm-coating protein/Tpx/antigen 5/pathogenesis-related protein 1 (SCP/TAPS) family. This family includes previously identified O. ostertagi vaccine antigens O. ostertagi ASP-1 and ASP-2. The ES-thiol fraction also has numerous proteinases, representing three distinct classes, including: metallo-; aspartyl- and cysteine proteinases. In terms of number of family members, the M12 astacin-like metalloproteinases, with 33 proteins, are the most abundant family in O. ostertagi ES-thiol. The O. ostertagi ES-thiol proteome provides a comprehensive database of proteins present in this vaccine preparation and will guide future vaccine antigen discovery projects.

zoology↗

A microRNA-based Johne's disease diagnostic predictive system: preliminary results

BackgroundJohnes disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic enteritis impacting welfare and productivity in cattle. Screening and animal removal are common for disease management, but efforts are hindered by low diagnostic sensitivity. Expression levels of small non-coding RNA molecules involved in gene regulation (microRNAs) altered during mycobacterial infection may present an alternative diagnostic method. MethodsLevels of 24 microRNAs affected by mycobacterial infection were measured in sera from MAP-positive (n=66) and MAP-negative samples (n=65). They were used to train a collection of statistical and machine learning models to identify an optimal classifier for diagnosis. ResultsThe best-performing model provided 72% accuracy, 78% AUC, 73% sensitivity and 71% specificity on average. LimitationsAlthough control samples were collected from farms nominally MAP-free, low sensitivity in current diagnostics means animals may be misclassified. ConclusionMicroRNA profiling combined with advanced predictive modelling techniques accurately diagnosed Johnes disease in cattle.

bioinformatics↗