• Analysis of DRB1 exon 2 genotyping by STR size analysis in Suffolk and Texel sheep breeds

      Sayers, Gearoid; Mitchel, S; Ryan, Marion T; Stear, Michael J.; Hanrahan, James P; Sweeney, Torres; Department of Agriculture, Food and the Marine; Wellcome Trust; RSF16; 061354 (Teagasc (Agriculture and Food Development Authority), Ireland, 2004)
      Alleles of the DRB1 exon 2 locus of the major histocompatibility complex have recently been associated with genetic resistance to gastrointestinal nematodes in sheep. While sequence-based typing is the standard method for allele discrimination, a rapid, high throughput method for DRB1 exon 2 genotyping is required if such information is to be incorporated into national breeding programmes. Previous studies have highlighted a simple tandem repeat (STR) located within intron 2 of the DRB1 gene, which could potentially be used to accurately assess the allele present within the adjacent exon 2. The aims of this study were firstly to compare two methods of STR analysis, Genescan™ and autoradiography, and secondly to investigate if STR analysis of DRB1 intron 2 could be used to accurately assess the profile of DRB1 exon 2. Six DRB1 exon 2 alleles were identified by sequence-based typing in Suffolk (n = 31) and eight in Texel (n = 60) sheep. The results indicated that Genescan™ was a more accurate method of STR analysis than autoradiography. The expected 1:1 correspondence between STR size, analysed by Genescan™ and DRB1 exon 2 allele, determined by sequence-based typing, was not observed. However, the correspondence was found to be degenerate, whereby some alleles were associated with two STR sizes. Thus, irrespective of the STR size identified, STR analysis by Genescan™ identified the correct allele in all cases within both populations of animals studied. However, the Genescan™ method of allele identification cannot be used for Suffolk × Texel crossbred progeny or in other breeds where the relationship between STR size and DRB1 exon 2 allele is not known.
    • Good animal welfare makes economic sense: potential of pig abattoir meat inspection as a welfare surveillance tool

      Harley, Sarah; More, Simon J; Boyle, Laura; O'Connell, Niamh E.; Hanlon, A.; Wellcome Trust (Biomed Central, 2012-06-27)
      During abattoir meat inspection pig carcasses are partially or fully condemned upon detection of disease that poses a risk to public health or welfare conditions that cause animal suffering e.g. fractures. This incurs direct financial losses to producers and processors. Other health and welfare-related conditions may not result in condemnation but can necessitate ‘trimming’ of the carcass e.g. bruising, and result in financial losses to the processor. Since animal health is a component of animal welfare these represent a clear link between suboptimal pig welfare and financial losses to the pig industry. Meat inspection data can be used to inform herd health programmes, thereby reducing the risk of injury and disease and improving production efficiency. Furthermore, meat inspection has the potential to contribute to surveillance of animal welfare. Such data could contribute to reduced losses to producers and processors through lower rates of carcass condemnations, trimming and downgrading in conjunction with higher pig welfare standards on farm. Currently meat inspection data are under-utilised in the EU, even as a means of informing herd health programmes. This includes the island of Ireland but particularly the Republic. This review describes the current situation with regard to meat inspection regulation, method, data capture and utilisation across the EU, with special reference to the island of Ireland. It also describes the financial losses arising from poor animal welfare (and health) on farms. This review seeks to contribute to efforts to evaluate the role of meat inspection as a surveillance tool for animal welfare on-farm, using pigs as a case example.
    • Whole blood gene expression profiling of neonates with confirmed bacterial sepsis

      Dickinson, Paul; Smith, Claire L.; Forster, Thorsten; Craigon, Marie; Ross, Alan J.; Khondoker, Mizan R; Ivens, Alasdair; Lynn, David J.; Orme, Judith; Jackson, Allan; et al. (ElsevierDickinson, P., Smith, C., Forster, T., Craigon, M., Ross, A., Khondoker, M., Ivens, A., Lynn, D., Orme, J., Jackson, A., Lacaze, P., Flanagan, K., Stenson, B. and Ghazal, P. Whole blood gene expression profiling of neonates with confirmed bacterial sepsis. Genomics Data, [online] 3, pp.41-48. Available at: https://dx.doi.org/10.1016/j.gdata.2014.11.003 [Accessed 1 Aug. 2019]., 2014-11-15)
      Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.
    • Whole blood gene expression profiling of neonates with confirmed bacterial sepsis

      Dickinson, Paul; Smith, Claire L.; Forster, Thorsten; Craigon, Marie; Ross, Alan J.; Khondoker, Mizan R.; Ivens, Alasdair; Lynn, David J.; Orme, Judith; Jackson, Allan; et al. (Elsevier BV, 2014-11-15)
      Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.