African swine fever virus (ASFV) is the causal agent of African swine fever, a hemorrhagic and often lethal porcine disease causing enormous economical losses in affected countries. Endemic for decades in most of the sub-Saharan countries and Sardinia, the risk of ASFV-endemicity in Europe has increased since its last introduction into Europe in 2007. Live attenuated viruses have been demonstrated to induce very efficient protective immune responses, albeit most of the time protection was circumscribed to homologous ASFV challenges. However, their use in the field is still far from a reality, mainly due to safety concerns. In this study we compared the course of the in vivo infection caused by two homologous ASFV strains: the virulent E75 and the cell cultured adapted strain E75CV1, obtained from adapting E75 to grow in the CV1 cell-line. Interestingly, the kinetics of both viruses not only differed on the clinical signs that they caused and in the virus loads found, but also in the immunological pathways activated throughout the infections. Furthermore, E75CV1 confirmed its protective potential against the homologous E75 virus challenge and allowed the demonstration of poor cross-protection against BA71, thus defining it as heterologous. The in vitro specificity of the CD8+ T-cells present at the time of lethal challenge showed a clear activation against the homologous virus (E75) but not against BA71. These findings will be of utility for a better understanding of ASFV pathogenesis and for the rational designing of safe and efficient vaccines against this virus.

BackgroundOnce a list of differentially expressed genes has been identified from a microarray experiment, a subsequent post-analysis task is required in order to find the main biological processes associated to the experimental system. This paper describes two pathways analysis tools, ArrayUnlock and Ingenuity Pathways Analysis (IPA) to deal with the post-analyses of microarray data, in the context of the EADGENE and SABRE post-analysis workshop. Dataset employed in this study proceeded from an experimental chicken infection performed to study the host reactions after a homologous or heterologous secondary challenge with two species of Eimeria.ResultsAnalysis of the same microarray data source employing both commercial pathway analysis tools in parallel let to identify several biological and/or molecular functions altered in the chicken Eimeria maxima infection model, including several immune system related pathways. Biological functions differentially altered in the homologous and heterologous second infection were identified. Similarly, the effect of the timing in a homologous second infection was characterized by several biological functions.ConclusionFunctional analysis with ArrayUnlock and IPA provided information related to functional differences with the three comparisons of the chicken infection leading to similar conclusions. ArrayUnlock let an improvement of the annotations of the chicken genome adding InterPro annotations to the data set file. IPA provides two powerful tools to understand the pathway analysis results: the networks and canonical pathways that showed several pathways related to an adaptative immune response.

Salmonella is a major foodborne pathogen which successfully infects animal species for human consumption such as swine. The pathogen has a battery of virulence factors which it uses to colonise and persist within the host. The host microbiota may play a role in resistance to, and may also be indirectly responsible from some of the consequences of, Salmonella infection. To investigate this, we used 16S rRNA metagenomic sequencing to determine the changes in the gut microbiota of pigs in response to infection by Salmonella Typhimurium at three locations: ileum mucosa, ileum content and faeces. Early infection (2 days post-infection) impacted on the microbiome diversity at the mucosa, reflected in a decrease in representatives of the generally regarded as desirable genera (i.e., Bifidobacterium and Lactobacillus). Severe damage in the epithelium of the ileum mucosa correlated with an increase in synergistic (with respect to Salmonella infection; Akkermansia) or opportunistically pathogenic bacteria (Citrobacter) and a depletion in anaerobic bacteria (Clostridium spp., Ruminococcus, or Dialliser). Predictive functional analysis, together with metabolomic analysis revealed changes in glucose and lipid metabolism in infected pigs. The observed changes in commensal healthy microbiota, including the growth of synergistic or potentially pathogenic bacteria and depletion of beneficial or competing bacteria, could contribute to the pathogen’s ability to colonize the gut successfully. The findings from this study could be used to form the basis for further research aimed at creating intervention strategies to mitigate the effects of Salmonella infection.

BackgroundThe aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.ResultsSeveral conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.ConclusionIt is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.

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