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Abstract

Function of duck (Anas platyrhynchos) major histocompatibility complex class I (Anpl-MHC I) molecules in binding peptides is through the peptide binding groove (PBG), which is thought to be influenced by the high polymorphism of α1 and α2 domains. However, little is known about the polymorphism of Anpl-MHC I peptide binding domain (PBD), especially in the domestic duck. Here, we analyzed the polymorphism of forty-eight Anpl-MHC I α1 and α2 domains from domestic duck breeds previously reported. All sequences were analyzed through multiple sequence alignment and a phylogenetic tree was constructed. The coefficient of variance of the peptide binding domains (PBDs) from WS, CV, JD, and SX duck breeds was estimated based on the Wu-Kabat variability index, followed by the location of the highly variable sites (HVSs) on reported crystal structure models. Analysis of α1 and α2 domains showed common features of classical MHC class I and high polymorphism, especially in α1 domain. The constructed phylogenetic tree showed that PBDs of domestic ducks did not segregate based on breeds and had a close phylogenetic relationship, even with wild ducks. In each breed, HVSs were mostly located in the PBG, suggesting that they might determine peptide-binding characteristics and subsequently influence peptide presentation and recognition. The combined results of sequence data and crystal structure provide novel valuable insights into the polymorphism and diversity of Anpl-MHC I PBDs that will facilitate further studies on disease resistance differences between duck breeds and the development of cytotoxic T-lymphocyte (CTL) epitope vaccines suited for preventing diseases in domestic ducks.
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Abstract

Despite the considerable progress that has recently been made in medicine, the treatment of viral infections is still a problem remaining to be solved. This especially concerns infections caused by newly emerging patogenes such as: human immunodeficiency virus, hepatitis C virus or SARS-coronavirus. There are several lines of evidence that the unusual genetic polymorphism of these viruses is responsible for the observed therapeutic difficulties. In order to determine whether some parameters describing a very complex and variable viral population can be used as prognostic factors during antiviral treatment computational methods were applied. To this end, the structure of the viral population and virus evolution in the organisms of two patients suffering from chronic hepatitis C were analyzed. Here we demonstrated that phylogenetic trees and Hamming distances best reflect the differences between virus populations present in the organisms of patients who responded positively and negatively to the applied therapy. Interestingly, the obtained results suggest that based on the elaborated method of virus population analysis one can predict the final outcome of the treatment even before it has started.
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