Genetic diversity of Brucella ovis isolates from Rio Grande do Sul, Brazil, by MLVA16

BACKGROUND: Ovine epididymitis is predominantly associated with Brucella ovis infection. Molecular characterization of Brucella spp. achieved by multi-locus variable number of tandem repeats (VNTR) analyses (MLVA) have proved to be a powerful tool for epidemiological trace-back studies. Thus, the aim of this study was to evaluate the genetic diversity of Brucella ovis isolates from Rio Grande do Sul State, Brazil, by MLVA16. FINDINGS: MLVA16 genotyping identified thirteen distinct genotypes and a Hunter-Gaston diversity index of 0.989 among the fourteen B. ovis genotyped strains. All B. ovis MLVA16 genotypes observed in the present study represented non-previously described profiles. Analyses of the eight conserved loci included in panel 1 (MLVA8) showed three different genotypes, two new and one already described for B. ovis isolates. Among ten B. ovis isolates from same herd only two strains had identical pattern, whereas the four isolates with no epidemiologic information exhibited a single MLVA16 pattern each. Analysis of minimal spanning tree, constructed using the fourteen B. ovis strains typed in this study together with all nineteen B. ovis MLVA16 genotypes available in the MLVAbank 2014, revealed the existence of two clearly distinct major clonal complexes. CONCLUSIONS: In conclusion, the results of the present study showed a high genetic diversity among B. ovis field isolates from Rio Grande do Sul State, Brazil, by MLVA16

High sero-prevalence of caseous lymphadenitis identified in slaughterhouse samples as a consequence of deficiencies in sheep farm management in the state of Minas Gerais, Brazil

<p>Abstract</p> <p>Background</p> <p>Caseous lymphadenitis (CLA), caused by <it>Corynebacterium pseudotuberculosis</it>, is one of the most important diseases of sheep and goats, causing considerable economic losses for herd owners.</p> <p>Results</p> <p>We assessed the seroprevalence of infection with <it>C. pseudotuberculosis </it>in 805 sheep from 23 sheep farms that supply slaughterhouses in the state of Minas Gerais; we also analyzed management practices that could be associated with CLA occurrence, used on these and nearby farms that also supplied animals to the slaughterhouse (n = 60). The serum samples for assaying CLA infection were taken at the slaughterhouse. Frequency of infection with <it>C. pseudotuberculosis </it>was estimated at 43.7%, and farm frequency was estimated at 100%. Management practices were analyzed through a questionnaire. All farmers (60/60) had extensive/semi-extensive rearing system; 70.0% (42/60) identified sheep individually; 11.7% (7/60) had periodical technical assistance; 41.7% (25/60) disinfected the facilities; 86.7% (52/60) used barbed wire fences and did not implement adequate CLA control measures; only 11.7% (7/60) of breeders reported vaccination against <it>C. pseudotuberculosis</it>; 13.3% (8/60) took note of animals with clinical signs of CLA; 1.7% (1/60) opened and sanitized abscesses, and isolated the infected animals; 10.0% (6/60) knew the zoonotic potential of this disease and 1.7% (1/60) of the farmers culled animals in case of recurrence of abscesses.</p> <p>Conclusions</p> <p>It can be concluded that <it>C. pseudotuberculosis </it>infection is widely spread in sheep flocks in Minas Gerais state in Brazil and that there is a lack of good management measures and vaccination, allowing transmission of this infectious agent throughout the production network.</p

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Phylogenetic Group of Escherichia coli

The aim of the study was to determine the phylogenetic groups of E. coli strains isolated from seemingly healthy broiler and broiler condemned suspected of colibacillosis in a Brazilian slaughterhouse. Samples from respiratory tract and edible giblets (liver and heart) of broilers with and without macroscopic lesions of colibacillosis were collected at slaughter. There were 84 strains isolated from broilers condemned of which 11 were obtained from swabs of the heart, 7 from the liver, and 66 from the respiratory tract. Of the 53 E. coli strains isolated from broilers not condemned, 5 were isolated from the heart, 4 from the liver, and 44 from the respiratory tract. E coli strains were tested via PCR for phylogenetic groups A, B1, B2, C, D, E, and F. Phylogroups A and B1 were the most common phylogroups of E. coli obtained from healthy and sick-appearing broiler carcasses. The results of the study showed that phylogroups B2 and E were associated with the heart samples and phylogroup A was associated with respiratory tract samples, phylogroup B1 with not condemned carcass, and phylogroup D with liver samples

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods