Rhinovirus Genome Variation during Chronic Upper and Lower Respiratory Tract Infections.

Routine screening of lung transplant recipients and hospital patients for respiratory virus infections allowed to identify human rhinovirus (HRV) in the upper and lower respiratory tracts, including immunocompromised hosts chronically infected with the same strain over weeks or months. Phylogenetic analysis of 144 HRV-positive samples showed no apparent correlation between a given viral genotype or species and their ability to invade the lower respiratory tract or lead to protracted infection. By contrast, protracted infections were found almost exclusively in immunocompromised patients, thus suggesting that host factors rather than the virus genotype modulate disease outcome, in particular the immune response. Complete genome sequencing of five chronic cases to study rhinovirus genome adaptation showed that the calculated mutation frequency was in the range observed during acute human infections. Analysis of mutation hot spot regions between specimens collected at different times or in different body sites revealed that non-synonymous changes were mostly concentrated in the viral capsid genes VP1, VP2 and VP3, independent of the HRV type. In an immunosuppressed lung transplant recipient infected with the same HRV strain for more than two years, both classical and ultra-deep sequencing of samples collected at different time points in the upper and lower respiratory tracts showed that these virus populations were phylogenetically indistinguishable over the course of infection, except for the last month. Specific signatures were found in the last two lower respiratory tract populations, including changes in the 5'UTR polypyrimidine tract and the VP2 immunogenic site 2. These results highlight for the first time the ability of a given rhinovirus to evolve in the course of a natural infection in immunocompromised patients and complement data obtained from previous experimental inoculation studies in immunocompetent volunteers.

Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms.

BACKGROUND: The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. METHODS: To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. RESULTS: The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men. CONCLUSIONS: Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption.

Bristle pattern formation in the thorax formation of Drosophila: a systems level approach

A fundamental question in developmental biology is how tissues are patterned to give rise to differentiated body structures with distinct morphologies. The Drosophila wing disc offers an accessible model to understand epithelial spatial patterning. It has been studied extensively using genetic and molecular approaches. Bristle patterns on the thorax, which arise from the medial part of the wing disc, are a classical model of pattern formation, dependent on a pre-pattern of trans-activators and –repressors. Despite of decades of molecular studies, we still only know a subset of the factors that determine the pre-pattern. We are applying a novel and interdisciplinary approach to predict regulatory interactions in this system. It is based on the description of expression patterns by simple logical relations (addition, subtraction, intersection and union) between simple shapes (graphical primitives). Similarities and relations between primitives have been shown to be predictive of regulatory relationships between the corresponding regulatory factors in other Systems, such as the Drosophila egg. Furthermore, they provide the basis for dynamical models of the bristle-patterning network, which enable us to make even more detailed predictions on gene regulation and expression dynamics. We have obtained a data-set of wing disc expression patterns which we are now processing to obtain average expression patterns for each gene. Through triangulation of the images we can transform the expression patterns into vectors which can easily be analysed by Standard clustering methods. These analyses will allow us to identify primitives and regulatory interactions. We expect to identify new regulatory interactions and to understand the basic Dynamics of the regulatory network responsible for thorax patterning. These results will provide us with a better understanding of the rules governing gene regulatory networks in general, and provide the basis for future studies of the evolution of the thorax-patterning network in particular.

Do antibody responses to malaria vaccine candidates influenced by the level of malaria transmission protect from malaria?

OBJECTIVES: To examine whether the humoural response to malaria vaccine candidate antigens, Plasmodium falciparum [circumsporozoite repetitive sequence (NANP)(5) GLURP fragments (R0 and R2) and MSP3] varies with the level of malaria transmission and to determine whether the antibodies (IgG) present at the beginning of the malaria transmission season protect against clinical malaria. METHODS: Cross-sectional surveys were conducted to measure antibody response before, at the peak and at the end of the transmission season in children aged 6 months to 10 years in two villages with different levels of malaria transmission. A cohort study was performed to estimate the incidence of clinical malaria. RESULTS: Antibodies to these antigens showed different seasonal patterns. IgG concentrations to any of the four antigens were higher in the village with high entomological inoculation rate. Multivariate analysis of combined data from the two villages indicated that children who were classified as responders to the selected antigens were at lower risk of clinical malaria than children classified as non-responders [(NANP)(5) (incidence rate ratio (IRR) = 0.65, 95% CI: 0.46-0.92; P = 0.016), R0 (IRR = 0.69, 95% CI: 0.48-0.97; P = 0.032), R2 (IRR = 0.73, 95% CI: 0.50-1.06; P = 0.09), MSP3 (IRR = 0.52, 95% CI: 0.32-0.85; P = 0.009)]. Fitting a model with all four antibody responses showed that MSP3 looked the best malaria vaccine candidate (IRR = 0.63; 95% CI: 0.38-1.05; P = 0.08). CONCLUSION: Antibody levels to the four antigens are affected by the intensity of malaria transmission and associated with protection against clinical malaria. It is worthwhile investing in the development of these antigens as potential malaria vaccine candidates.