Detection of positive selection eliminating effects of structural constraints in hemagglutinin of H3N2 human influenza A virus

The role of natural selection in shaping genetic variation in a promising Chagas disease drug target: Trypanosoma cruzi trans-sialidase

Rational drug design creates innovative therapeutics based on knowledge of the biological target to provide more effective and responsible therapeutics. Chagas disease, endemic throughout Latin America, is caused by Trypanosoma cruzi, a protozoan parasite. Current therapeutics are problematic with widespread calls for new approaches. Researchers are using rational drug design for Chagas disease and one target receiving considerable attention is the T. cruzi trans-sialidase protein (TcTS). In T. cruzi, trans-sialidase catalyzes the transfer of sialic acid from a mammalian host to coat the parasite surface membrane and avoid immuno-detection. However, the role of TcTS in pathology variance among and within genetic variants of the parasite is not well understood despite numerous studies. Previous studies reported the crystalline structure of TcTS and the TS protein structure in other trypanosomes where the enzyme is often inactive. However, no study has examined the role of natural selection in genetic variation in TcTS. To understand the role of natural selection in TcTS DNA sequence and protein variation, we examined a 471 bp portion of the TcTS gene from 48 T. cruzi samples isolated from insect vectors. Because there may be multiple parasite genotypes infecting one insect and there are multiple copies of TcTS per parasite genome, all 48 sequences had multiple polymorphic bases. To resolve these polymorphisms, we examined cloned sequences from two insect vectors. The data are analyzed to understand the role of natural selection in shaping genetic variation in TcTS and interpreted in light of the possible role of TcTS as a drug target. The analysis highlights negative or purifying selection on three amino acids previously shown to be important in TcTS transfer activity. One amino acid in particular, Tyr342, is a strong candidate for a drug target because it is under negative selection and amino acid substitutions inactivate TcTS transfer activity. AUTHOR SUMMARY: Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted to humans and other mammals primarily by Triatomine insects. Being endemic in many South and Central American countries and affecting millions of people the need for new more effective and safe therapies is evident. Here, we examine genetic variation and natural selection on DNA (471 bp) and amino acid (157 aa) sequence data of the T. cruzi trans-sialdiase (TcTS) protein, often suggested as a candidate for rational drug design. In our surveyed region of the protein there were five amino acid residues that have been shown to be integral to the function of TcTS. We found that three were under strong negative selection making them ideal candidates for drug design; however, one was under balancing selection and should be avoided as a drug target. Our study provides new information into identifying potential targets for a new Chagas drug.

Profile comparison revealed deviation from structural constraint at the positively selected sites

The amino acid substitutions at a site are affected by mixture of various constraints. It is also known that the amino acid substitutions are accelerated at sites under positive selection. However, the relationship between the substitutions at positively selected sites and the constraints has not been thoroughly examined. The advances in computational biology have enabled us to divide the mixture of the constraints into the structural constraint and the remainings by using the amino acid sequences and the tertiary structures, which is expressed as the deviation of the mixture of constraints from the structural constraint. Here, two types of profiles, or matrices with the size of 20 x (site length), are compared. One of the profiles represents the mixture of constraints, and is generated from a multiple amino acid sequence alignment, whereas the other is designed to represent the structural constraints. We applied the profile comparison method to proteins under positive selection to examine the relationship between the positive selection and constraints. The results suggested that the constraint at a site under positive selection tends to be deviated from the structural constraint at the site.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Predictability of antigenic evolution for H3N2 human influenza A virus

Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.