Reina Arias, Ramsés

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https://academica-e.unavarra.es/handle/2454/49837

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Reina Arias

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Ramsés

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Instituto de Agrobiotecnología (IdAB)

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0000-0003-1265-9139

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5614

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2014 - 20202
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Author: Luján, Lluís × Subject: ELISA ×

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    PublicationOpen Access
    Lack of relationship between Visna/maedi infection and scrapie resistance genetic markers
    (Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA), 2014) Salazar, Eider; Berriatua, Eduardo; Pérez, Marta María; Marín, Belén; Acín, Cristina; Martín Burriel, Inmaculada; Reina Arias, Ramsés; Andrés Cara, Damián de; Amorena Zabalza, Beatriz; Badiola, Juan José; Luján, Lluís; IdAB. Instituto de Agrobiotecnología / Agrobioteknologiako Institutua
    The relationship between Visna/maedi virus (VMV) antibody status and scrapie genetic resistance of 10,611 Rasa Aragonesa sheep from 17 flocks in Aragón (Spain) was investigated. The fifteen most common PRNP gene haplotypes and genotypes were identified and the genotypes were classified into the corresponding scrapie risk groups (groups 1 to 5). ARQ (93.3%) and ARR (31.8%) were the most common haplotypes and ARQ/ARQ (56%) and ARR/ARQ (25.6%) were the most common genotypes. The frequencies of scrapie risk groups 1, 2, 3, 4 and 5 were 3.3%, 27.3%, 63.5%, 1.2% and 4.8%, respectively. Overall Visna/maedi seroprevalence was 53% and flock seroprevalence ranged between 21-86%. A random effects logistic regression model indicated that sheep VMV serological status (outcome variable) was not associated with any particular scrapie risk group. Instead, VMV seropositivity progressively increased with age, was signif icantly greater in females compared to males and varied between flocks. The absence of a relationship between VMV infection and scrapie genotypes is important for VMV control and specifically for sheep participating in an ELISA-based Visna/maedi control program.
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    PublicationOpen Access
    Multi-platform detection of small ruminant lentivirus antibodies and provirus as biomarkers of production losses
    (Frontiers Media, 2020) Echeverría Garín, Irache; Miguel, Ricardo de; Pablo Maiso, Lorena de; Glaría Ezquer, Idoia; Benito, Alfredo A.; Blas, Ignacio de; Andrés Cara, Damián de; Luján, Lluís; Reina Arias, Ramsés; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura
    Small ruminant lentiviruses (SRLVs) are endemic in most areas of Europe, causing a chronic infection and a multisystemic disease affecting the udder, carpal joints, lungs, and central nervous system. Due to the lack of treatments and protective vaccination strategies, infection control is focused on the identification of infected animals through serological or molecular techniques. However, antigenic and genetic heterogeneity of SRLVs represent a clear drawback for diagnosis. Infected animals may present lower animal production parameters such as birth weight or milk production and quality, depending on productive systems considered and, likely, to the diagnostic method applied. In this study, four sheep flocks dedicated to dairy or meat production were evaluated using three different ELISA and two PCR strategies to classify animal population according to SRLV infection status. Productive parameters were recorded along one whole lactation or reproductive period and compared between positive and negative animals. SRLV was present in 19% of the total population, being unequally distributed in the different flocks. Less than half of the infected animals were detected by a single diagnostic method, highlighting the importance of combining different diagnostic techniques. Statistical analysis employing animal classification using all the diagnostic methods associated lambing size, lamb weight at birth, and daily weight gain with SRLV infection status in meat flocks. Milk production, somatic cell count, fat, and protein content in the milk were associated with SRLV infection in dairy flocks, to a greater extent in the flock showing higher seroprevalence. A multi-platform SRLV diagnostic strategy was useful for ensuring correct animal classification, thus validating downstream studies investigating production traits.