Discovery of an expanded set of avian leukosis subgroup E proviruses in chickens using Vermillion, a novel sequence capture and analysis pipeline [corrected].

Transposable elements (TEs), such as endogenous retroviruses (ERVs), are common in the genomes of vertebrates. ERVs result from retroviral infections of germ-line cells, and once integrated into host DNA they become part of the host's heritable genetic material. ERVs have been ascribed positive effects on host physiology such as the generation of novel, adaptive genetic variation and resistance to infection, as well as negative effects as agents of tumorigenesis and disease. The avian leukosis virus subgroup E family (ALVE) of endogenous viruses of chickens has been used as a model system for studying the effects of ERVs on host physiology, and approximately 30 distinct ALVE proviruses have been described in the Gallus gallus genome. In this report we describe the development of a software tool, which we call Vermillion, and the use of this tool in combination with targeted next-generation sequencing (NGS) to increase the number of known proviruses belonging to the ALVE family of ERVs in the chicken genome by 4-fold, including expanding the number of known ALVE elements on chromosome 1 (Gga1) from the current 9 to a total of 40. Although we focused on the discovery of ALVE elements in chickens, with appropriate selection of target sequences Vermillion can be used to develop profiles of other families of ERVs and TEs in chickens as well as in species other than the chicken.

Monitoring associations between clade-level variation, overall community structure and ecosystem function in enhanced biological phosphorus removal (EBPR) systems using terminal-restriction fragment length polymorphism (T-RFLP).

The role of Candidatus "Accumulibacter phosphatis" (Accumulibacter) in enhanced biological phosphorus removal (EBPR) is well established but the relevance of different Accumulibacter clades to the performance of EBPR systems is unknown. We developed a terminal-restriction fragment length polymorphism (T-RFLP) technique to monitor changes in the relative abundance of key members of the bacterial community, including Accumulibacter clades, in four replicate mini-sequencing batch reactors (mSBRs) operated for EBPR over a 35-day period. The ability of the T-RFLP technique to detect trends was confirmed using fluorescence in situ hybridisation (FISH). EBPR performance varied between reactors and over time; by day 35, performance was maintained in mSBR2 whilst it had deteriorated in mSBR1. However, reproducible trends in structure-function relationships were detected in the mSBRs. EBPR performance was strongly associated with the relative abundance of total Accumulibacter. A shift in the ratio of the dominant Accumulibacter clades was also detected, with Type IA associated with good EBPR performance and Type IIC associated with poor EBPR performance. Changes in ecosystem function of the mSBRs in the early stages of the experiment were more closely associated with changes in the abundance of (unknown) members of the flanking community than of either Accumulibacter or Candidatus "Competibacter phosphatis". This study therefore reveals a hitherto unrecorded and complex relationship between Accumulibacter clades, the flanking community and ecosystem function of laboratory-scale EBPR systems.

Genes for the majority of group a streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease.

BACKGROUND: The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. METHODS: In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. RESULTS: Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. CONCLUSIONS: Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/or host factors.