Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A, Margolick J, Vlahov D, Quinn T, Farzadegan H, Yu XF (1998) Proc Natl Acad Sci USA 95: 12568-12573 Michael Pina, Salomon Garcia Journal Club Presentation BIOL398-01/S10: Bioinformatics Laboratory March 2, 2010

Outline Introduction to the Markham et al. paper Our question about the article Methods Results Conclusion

CD4 T-cell count associated with diversity and divergence The evolution of the HIV-1 gene was studied in 15 seroconverting participants (intravenous drug users) They were selected for differences in the rate of their CD4 T-cell decline Rates of diversity and divergence both showed a pattern of increase among the progressor groups

Evolution of the HIV-1 virus in the participants Viral evolution among the progressor groups showed a selection for nonsynonymous mutants Nonprogressors with low viral loads selected against nonsynonymous mutations For the progressor groups, this may have resulted in higher reproduction rates of the virus

HIV-1 variants over the course of the study No single variant was dominant across all participants Evolution away from a variant was followed by evolution towards a variant This may show selection against a predominant strain or the product of independent evolutions within different host environments

The importance of CD4 T-cells Differences of CD4 T-cell count reflect not only the quantity of mutations, but differences in the mutations that may be best suited to the host environment

CD4 T cell trajectory, diversity, and divergence over time since first seropositive visit (t = 0) in each of the 15 subjects

Taking a closer look at the paper We wanted to know how the amino acid changes are affecting the interaction between the virus and host environment “The overall pattern is one in which viral strains from nonprogressors showed possible selection against amino acid change, while those from progressors showed selection for such change (or against the absence of change).”

Our question regarding the paper What can the dS-dN value tell us about the selection for/against amino acid change? Our hypothesis is that subject 10 will show more nonsynonymous changes and subject 13 will show more synonymous changes.

Methods to find an answer Subjects 10 and 13 were chosen because of their quintessential qualities as “rapid progressor” and “nonprogressor”, respectively They have nearly the same amount of data points collected over a similar time span All of their DNA sequences were obtained from the Bedrock website, in addition to other data from the study The online Biology Workbench tools were used for a variety of tasks

Statistical calculations Subject # clones S Theta Min Diff Max Diff 10 49 74 16.3 1.14 20.0 13 26 24 6.43 9.12

Using the Biology Workbench CLUSTALW was used for multiple sequences alignments for all available sequences of subjects 10 and 13 Phylogenetic trees were also generated CLUSALDIST was used to generate a distance matrix

Phylogenetic trees for subject 10 and 13

dS-dN values Converted to log scale for consistency among data values 10 13 .5 - n/a 1 -0.118 -0.861 2 0.530 3 -0.333 4 -0.133 1.724 5 -0.447 0.479 6 0.785 7 8 9 11 12 Converted to log scale for consistency among data values Negative value indicates synonymous mutations Positive value indicates nonsynonymous mutations

An answer to our question It was determined that subjects 10 and 13 do indeed differ in their diversity and divergence as represented visually in their phylogenetic trees and also in our statistical calculations The dS-dN values are so similar and limited that it is difficult to say whether or not progressors show a selection for amino acid change

A more recent article Functional diversity of HIV-1 envelope proteins expressed by contemporaneous plasma viruses. “…clones carrying unique mutations in V3 often displayed low infectivity” No correlation was observed between viral infectivity and sensitivity to entry inhibitors