USE OF MOLECULAR MARKERS TO STUDY GENETIC VARIABILITY IN THREE POPULATIONS OF TRIATOMA INFESTANS.

SALDANIA, G., V. GARCIA, P. DIEZ, G. MURUA, M. BAIGORÍ & R. RIVERA POMAR

Minas Gerais, Brasil

Otro; XXX Anual Meeting on Basic Research in Chagas, Disease &XIX Meeting of the Brazilian Society of Protozoology; 2003

The Triatominae is a subfamily of the Reduviidae that is composed of 14 genera and 118 known species. All of them are obligated bloodsuckers, regardless of age and sex. More than half of them were proven to have ability to carry Trypanosoma cruzi, the flagellate that causes Chagas disease. The most important vectors of Chagas disease are Triatoma infestans,Triatoma brasiliensis, Triatoma dimidiata, Triatoma sordida, Rhodnius prolixus and Panstrongylus megistus (Schofield 1994). They are distributed from eastern and southern Brazil and from the southern half of Bolivia, down to the Argentinean province of Chubut. They are also present in Paraguay and in the largest part of Uruguay. At the east of the Andes, they can be found in northern Chile and Southern Peru. The species are almost exclusively domestic and per domestic. Sylva tic colonies are only reported from Bolivia. In the past decade there has been a remarkable increase in the use of genetic markers to characterize genetic diversity in different species. Some of these genetic markers have a different molecular basis, but all of them are focused to understand the organization of genetic structure of natural and cultivated populations. In additions, these markers have been used to determine the genetics similarity among and within population avoiding environmental influence. In this work we show the results of a genetic diversity study on intra an inter-population of Triatoma infestans, collected from three different cities in the Argentinean provinces of Cordoba, Catamarca and Mendoza. To obtain fingerprints of each population a total of 38 RAPD primers belonging to the OPA, OPI and OPB series were assayed. Bands were recorded in the binary form i.e (1) =presence and (0)=absence, and assemble in a data matrix table. The UPGMA algorithm was used for hierarchical cluster analysis. Pair wise comparisons were calculated using Simple Matching (SM) coefficient. In addition a dendrogram was buit using NTSYS-pc package (Rolf 1990).