Differentiation of African Components of Ancestry to Stratify Groups in a Case-Control Study of a Brazilian Urban Population

Background: Balancing the subject composition of case and control groups to create homogenous ancestries between each group is essential for medical association studies. Methods: We explored the applicability of single-tube 34-plex ancestry informative markers (AIM) single nucleotide polymorphisms (SNPs) to estimate the African Component of Ancestry (ACA) to design a future case-control association study of a Brazilian urban sample. Results: One hundred eighty individuals (107 case group; 73 control group) self-described as white, brown-intermediate or black were selected. The proportions of the relative contribution of a variable number of ancestral population components were similar between case and control groups. Moreover, the case and control groups demonstrated similar distributions for ACA <0.25 and >0.50 categories. Notably a high number of outlier values (23 samples) were observed among individuals with ACA <0.25. These individuals presented a high probability of Native American and East Asian ancestral components; however, no individuals originally giving these self-described ancestries were observed in this study. Conclusions: The strategy proposed for the assessment of ancestry and adjustment of case and control groups for an association study is an important step for the proper construction of the study, particularly when subjects are taken from a complex urban population. This can be achieved using a straight forward multiplexed AIM-SNPs assay of highly discriminatory ancestry markers.

Structure-based drug design studies on a series of aldolase inhibitors

Human African trypanosomiasis, also known as sleeping sickness, is a major cause of death in Africa, and for which there are no safe and effective treatments available. The enzyme aldolase from Trypanosoma brucei is an attractive, validated target for drug development. A series of alkyl‑glycolamido and alkyl-monoglycolate derivatives was studied employing a combination of drug design approaches. Three-dimensional quantitative structure-activity relationships (3D QSAR) models were generated using the comparative molecular field analysis (CoMFA). Significant results were obtained for the best QSAR model (r2 = 0.95, non-cross-validated correlation coefficient, and q2 = 0.80, cross-validated correlation coefficient), indicating its predictive ability for untested compounds. The model was then used to predict values of the dependent variables (pKi) of an external test set,the predicted values were in good agreement with the experimental results. The integration of 3D QSAR, molecular docking and molecular dynamics simulations provided further insight into the structural basis for selective inhibition of the target enzyme.