Publications

Antimalarial clinical trials use genotyping techniques to distinguish new infection from recrudescence. In areas of high transmission, the accuracy of genotyping may be compromised due to the high number of infecting parasite strains. We compared the accuracies of genotyping methods, using up to six genotyping markers, to assign outcomes for two large antimalarial trials performed in areas of Africa with different transmission intensities. We then estimated the probability of genotyping misclassification and its effect on trial results. At a moderate-transmission site, three genotyping markers were sufficient to generate accurate estimates of treatment failure. At a high-transmission site, even with six markers, estimates of treatment failure were 20% for amodiaquine plus artesunate and 17% for artemether-lumefantrine, regimens expected to be highly efficacious. Of the observed treatment failures for these two regimens, we estimated that at least 45% and 35%, respectively, were new infections misclassified as recrudescences. Increasing the number of genotyping markers improved the ability to distinguish new infection from recrudescence at a moderate-transmission site, but using six markers appeared inadequate at a high-transmission site. Genotyping-adjusted estimates of treatment failure from high-transmission sites may represent substantial overestimates of the true risk of treatment failure.

We co-immunoprecipitated the Ca(2+)-sensing receptor (CaR) and type B gamma-aminobutyric acid receptor (GABA-B-R) from human embryonic kidney (HEK)-293 cells expressing these receptors and from brain lysates where both receptors are present. CaRs extensively co-localized with the two subunits of the GABA-B-R (R1 and R2) in HEK-293 cell membranes and intracellular organelles. Coexpressing CaRs and GABA-B-R1s in HEK-293 cells suppressed the total cellular and cell surface expression of CaRs and inhibited phospholipase C activation in response to high extracellular [Ca(2+)] ([Ca(2+)](e)). In contrast, coexpressing CaRs and GABA-B-R2s enhanced CaR expression and signaling responses to raising [Ca(2+)](e). The latter effects of the GABA-B-R2 on the CaR were blunted by coexpressing the GABA-B-R1. Coexpressing the CaR with GABA-B-R1 or R2 enhanced the total cellular and cell surface expression of the GABA-B-R1 or R2, respectively. Studies with truncated CaRs indicated that the N-terminal extracellular domain of the CaR participated in the interaction of the CaR with the GABA-B-R1 and R2. In cultured mouse hippocampal neurons, CaRs co-localized with the GABA-B-R1 and R2. CaRs and GABA-B-R1s also co-immunoprecipitated from brain lysates. The expression of the CaR was increased in lysates from GABA-B-R1 knock-out mouse brains and in cultured hippocampal neurons with their GABA-B-R1 genes deleted in vitro. Thus, CaRs and GABA-B-R subunits can form heteromeric complexes in cells, and their interactions affect cell surface expression and signaling of CaR, which may contribute to extracellular Ca(2+)-dependent receptor activation in target tissues.

BACKGROUND

Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.

RESULTS

To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages.

CONCLUSION

GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms.