Interação entre patógenos: abordagens computacionais na busca por padrões em genomas filogeneticamente distantes

Abstract

Considered an emerging area, the study of Pathogen-Pathogen Interaction has received considerable attention over the recent years because of the health implications it poses to the human population. At the beginning of this research project, biologists from the Department of Bio-logy from the Federal Rural University of Pernambuco conducted an analysis on the genes and proteins of Human papillomavirus type 16 (HPV 16) contained in the National Center for Bi-otechnology Information (NCBI) sequences databases. The initial analysis resulted in similar alignments and in synteny with the genome of Chlamydia trachomatis. As these pathogens are phylogenetically distant, little is known about their history of interaction and evolution at the genetic level.The analysis of evolutionary events between phylogenetically distant genomes involves lo-o king for patterns that are not previously known in conserved regions of the genomes, taking in to account their specific characteristics. Considering the non availability of computational methods to deal with this problem and its specificities, the present research project intends to study current approaches to similar problems and to implement a heuristic using classical computational methods for motif fiding and specific biological knowledge in order to investigate possible evolutionary relationships and interactions between the speciesAlphapapilomavirus9andChlamydia trachomatis through the application of computational techniques and comparative genomics.The implementation of the heuristics involved gathering information about genome homogenization, codon usage, physiochemical properties of amino acids, and finding motifs com-mon to the sequences through exhaustive searching. Since the results obtained from the implementation of the heuristic were bulky, it was necessary to cluster them through a statistical method. The method chosen was correspondence analysis, which helps with data visualization and allows the view of relationships between the variables of the analysis and the results obtained. This clustering of the data gathered in the process provided clues that support the hypothesis initially raised by the biologists, allowing for the formulation of new interpretations as of how these organisms interact.