Can we improve QTL mapping in the thymic development?

Duarte and Penha-Gonçalves (2001) have previously identified the MHC locus in the chromosome 17 as the major quantatitive trait locus (QTL) that controls the variations in the proportions of CD4 thymocytes. To improve previous QTL mapping, we extended previous analysis to the proportions of CD4-CD8-, CD4+CD8+, CD4+CD8- and CD4-CD8+ thymocytes. Using techniques of compositional data and principal component analysis coupled with traditional interval QTL mapping methodology, we detected not only the same MHC locus, but also other two loci in chromosomes 11 and 19. The first previously undetected locus maps to the T-cell transcription factor-1 gene which when mutated seems to block thymocytes at imature stages of development. The other locus contains no clear candidate genes to it, and can be considered as a spurious result of QTL mapping. Our statistical analysis shows also that these three locus have a pleiotropic effect on the different proportions of thymocytes. The MHC locus has a strong effect on the proportion of CD4+, but moderate on the other thymocyte compartments. The remaining loci seem to have a moderate effect on every thymocyte population. These results remain to be experimentally confirmed.

From genomotyping up to social networks: characterizing the epidemiological profiles of Streptococcus pneumoniae strains.

Integration of data from the molecular to the population level (both host and microbial populations) is a necessary step towards a deeper understanding of Streptococcus pneumoniae epidemiological behaviour. The combined analysis of genomotyping and carriage/infection surveillance studies will guide the construction, interpretation and the questions addressed by theoretical models describing the epidemiological spread of S. pneumoniae strains in host populations with structured social contact interactions (e.g. small world or scale-free type of networks). Evaluating the relative contribution of molecular determinants and social network characteristics to the overall epidemiological behaviour will become the main accomplishment of this proposal.

Modelling malaria: The interplay between transmission and clinical immunity.

I will report the analysis of 8 datasets from different Malaria endemic areas of Sub-Saharan Africa. We developed a model of malaria transmission incorporating immunological aspects onto an epidemiological background and fitted its output to the data, using different forces of infection for each dataset but assuming all the other parameters to be exactly the same for all locations. As such, we hoped to obtain an estimate for all the parameters that are intrinsic to malaria and to those that may vary between regions. Using the fitted forces of infection for each region, we calculated the coefficients of transmission at equilibrium, by dividing the force of infection by the sum of all infections. This coefficient of transmission was then used in an age-independent time model to obtain a severe disease output at equilibrium, with which we could ascertain the variation of severe disease in terms of transmission. The model structure combined with the values of the estimated parameters produced bistable equilibria that can serve as a basis for catastrophic dynamics.

No seminar due to Carnival holidays.