Molecular Epidemiology of Neisseria meningitidis Serogroup B in Brazil

Background: Neisseria meningitidis serogroup B has been predominant in Brazil, but no broadly effective vaccine is available to prevent endemic meningococcal disease. To understand genetic diversity among serogroup B strains in Brazil, we selected a nationally representative sample of clinical disease isolates from 2004, and a temporally representative sample for the state of São Paulo (1988-2006) for study (n = 372). Methods: We performed multi-locus sequence typing (MLST) and sequence analysis of five outer membrane protein (OMP) genes, including novel vaccine targets fHbp and nadA. Results: In 2004, strain B:4:P1.15,19 clonal complex ST-32/ET-5 (cc32) predominated throughout Brazil; regional variation in MLST sequence type (ST), fetA, and porB was significant but diversity was limited for nadA and fHbp. Between 1988 and 1996, the São Paulo isolates shifted from clonal complex ST-41/44/Lineage 3 (cc41/44) to cc32. OMP variation was associated with but not predicted by cc or ST. Overall, fHbp variant 1/subfamily B was present in 80% of isolates and showed little diversity. The majority of nadA were similar to reference allele 1. Conclusions: A predominant serogroup B lineage has circulated in Brazil for over a decade with significant regional and temporal diversity in ST, fetA, and porB, but not in nadA and fHbp

Characterisation of the immunomodulatory effects of meningococcal Opa proteins on human peripheral blood mononuclear cells and CD4+ T cells

Opa proteins are major surface-expressed proteins located in the Neisseria meningitidis outer membrane, and are potential meningococcal vaccine candidates. Although Opa proteins elicit high levels of bactericidal antibodies following immunisation in mice, progress towards human clinical trials has been delayed due to previous findings that Opa inhibits T cell proliferation in some in vitro assays. However, results from previous studies are conflicting, with different Opa preparations and culture conditions being used. We investigated the effects of various Opa+ and Opa- antigens from N. meningitidis strain H44/76 in a range of in vitro conditions using peripheral blood mononuclear cells (PBMCs) and purified CD4+ T cells, measuring T cell proliferation by CFSE dilution using flow cytometry. Wild type recombinant and liposomal Opa proteins inhibited CD4+ T cell proliferation after stimulation with IL-2, anti-CD3 and anti-CD28, and these effects were reduced by mutation of the CEACAM1-binding region of Opa. These effects were not observed in culture with ex vivo PBMCs. Opa+ and Opa- OMVs did not consistently exert a stimulatory or inhibitory effect across different culture conditions. These data do not support a hypothesis that Opa proteins would be inhibitory to T cells if given as a vaccine component, and T cell immune responses to OMV vaccines are unlikely to be significantly affected by the presence of Opa proteins

Methods to estimate breeding values in honey bees

Background Efficient methodologies based on animal models are widely used to estimate breeding values in farm animals. These methods are not applicable in honey bees because of their mode of reproduction. Observations are recorded on colonies, which consist of a single queen and thousands of workers that descended from the queen mated to 10 to 20 drones. Drones are haploid and sperms are copies of a drone’s genotype. As a consequence, Mendelian sampling terms of full-sibs are correlated, such that the covariance matrix of Mendelian sampling terms is not diagonal. Results In this paper, we show how the numerator relationship matrix and its inverse can be obtained for honey bee populations. We present algorithms to derive the covariance matrix of Mendelian sampling terms that accounts for correlated terms. The resulting matrix is a block-diagonal matrix, with a small block for each full-sib family, and is easy to invert numerically. The method allows incorporating the within-colony distribution of progeny from drone-producing queens and drones, such that estimates of breeding values weigh information from relatives appropriately. Simulation shows that the resulting estimated breeding values are unbiased predictors of true breeding values. Benefits for response to selection, compared to an existing approximate method, appear to be limited (~5%). Benefits may however be greater when estimating genetic parameters. Conclusions This work shows how the relationship matrix and its inverse can be developed for honey bee populations, and used to estimate breeding values and variance components