Fitness loss and recognition losses of escape mutations are predicted to correlate positively, with a slope that deceases in time.

<p>Sites are randomly sampled from the simulation described in the caption to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g002" target="_blank">Figure 2</a> in order to simulate the effect of acute samples (high ranking sites), late chronic samples (low ranking sites) or patient samples from random times (random ranks). (A) No CTL decay. The slope of the correlation from acute (blue) or late chronic (red) sampled escape mutations is the same, however, it is lower for escape mutations sampled at random times (green). (B) CTL decay causes the slope of the correlation to decrease in time, due to decrease of CTL selection pressure. CTL decay is introduced as described in the caption to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g002" target="_blank">Figure 2</a> and <i>Model</i>.</p

The pattern of emergence of escape variants in a single epitope contains information about the fraction of recognition and fitness lost by single-site mutations in the epitope.

<p>Using simulation of the model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g001" target="_blank">Figure 1A</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e114" target="_blank">Equations 6</a> to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e116" target="_blank">8</a>) with two sites per epitope, , the pattern of escape is calculated for a range of recognition and fitness losses. The pattern that is obtained is plotted as a function of the parameters of recognition loss at the first and second site ( and , respectively). In each panel, certain parameters are fixed in order to focus on the effect of recognition loss. Fixed parameters are: the escape rate of the first haplotype () and the number of targeted epitopes (), values which correspond to escape mutations that occur in acute infection (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.s003" target="_blank">Figure S3</a> for parameters that correspond to later in infection). Fitness costs are chosen such that the second site is less costly than the first: equal to 3 (A) or much less costly than the first, (B). Other parameters given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-t001" target="_blank">Table 1</a>. Mostowy: 2012iv Equations S6 (red line) and S9 (blue line) determine the region where the leapfrog pattern can be observed. Regions that require are not allowed by definition (magenta line). The shaded regions between these three lines correspond to regions of parameter space where both sites escape. The corresponding patterns are: “leapfrog” (, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g004" target="_blank">Figure 4C</a>), “nested” (, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g004" target="_blank">Figure 4E</a>), “nested leapfrog” (). Observation of the leapfrog pattern in an epitope tightly constrains the fraction of CTL recognition loss conferred by sites in an epitope. The inset shows the length of time during which haplotype 01 is dominant in the escaping epitope.</p

Model parameters.

<p>Model parameters for the model of escape from multiple CTL shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g001" target="_blank">Figure 1</a>.</p><p>Model parameters.</p

A computational model of the interaction between HIV and multiple CTL clones.

<p>(A) The model given by <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e114" target="_blank">Equations 6</a> to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e116" target="_blank">8</a> comprises three interacting cell compartments: target cells (T), infected cells (I) and multiple CTL clones (E). Viral genomes contain multiple epitopes, which can mutate to partially abrogate CTL recognition. An escape mutation is denoted by an X. Each CTL clone recognizes a single viral epitope and is stimulated to divide at a rate proportional to the number of infected cells with recognizable epitopes. The model is designed to study the rate of escape in epitopes when CTL pressure is distributed across multiple epitopes, as well as study intra-epitope escape patterns when CTL respond dynamically to the infected cells that they recognize. Black arrows: flux of cells from one compartment to another. Blue arrows: dependence of the rate of flux from one compartment on another. Dotted lines represent attenuation of the interaction strength. (B) Simulation example showing three phases of HIV evolution. A single virus strain initiates the infection (transmitted strain, black). In response to the growing number of infected cells, multiple CTL clones are activated (colored lines), and the system reaches a steady state. Finally, virus strains with escape mutations (dashed, colored lines) replace the transmitted strain. In response to lowered activation signals, some CTL clones decline. The escape strains are colored to match the CTL against which an escape was most recently acquired. Model parameters: number of epitopes, ; number of sites per epitope, . Epitopes 1–3 have parameters that allow escape , epitopes 4–6 have parameters that prohibit escape, . Other parameters are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-t001" target="_blank">Table 1</a>.</p

The escape trajectory in the cost-benefit plane bends over time due to CTL decay.

<p>Fitness costs and recognition losses are randomly generated for 100 sites (10 epitopes with 10 sites per epitope) in order to study the sequence of escaped sites (black line) in the whole genome without CTL decay (A,B) or with CTL decay (C,D) for 1000 simulation runs. (A) For each site that escapes, the fractional fitness cost, , multiplied by the number of epitopes, , and fractional recognition loss, (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e044" target="_blank">Equation 1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-t001" target="_blank">Table 1</a>) is shown. Colors show the predicted rank of escape mutations, from early escape mutations (blue) to late escape mutations (red). The average trajectory over all runs (black) moves from high recognition loss, low fitness cost to low recognition loss, high fitness cost. Inset: The best-fit slope for each escape rank. A positive correlation is observed between the fitness and recognition losses for all epitopes that escape at a given rank. (B) The maximum escape rate of any epitope site for all 10 epitopes for a representative simulation run. (C–D) As in (A–B), except including CTL decay. CTL decay is simulated by reducing recognition losses for all epitope sites in epitopes that have partially escaped according to , summing over all <i>i</i> sites in the epitope that have escaped with per escape. When CTLs decay in response to an escape in an epitope, the immune pressure on all other sites in that epitope is decreased. The result is that the average trajectory in the cost-benefit plane bends towards the horizontal axis.</p

MOESM2 of Disease progression despite protective HLA expression in an HIV-infected transmission pair

Additional file 2: Figure S2. Sites of HLA-associated footprints in the donor from an HIV transmission pair at 8 months post-diagnosis aligned to B and CRF01_AE clade consensus sequence. Epitopes restricted by the favourable alleles expressed by the donor, A*3201, B*1302 and B*1401, are shown