The Route of HIV Escape from Immune Response Targeting Multiple Sites Is Determined by the Cost-Benefit Tradeoff of Escape Mutations

Cytotoxic T lymphocytes (CTL) are a major factor in the control of HIV replication. CTL arise in acute infection causing escape mutations to spread rapidly through the population of infected cells. As a result, the virus develops partial resistance to the immune response. The factors controlling the order of mutating epitope sites are currently unknown and would provide a valuable tool for predicting conserved epitopes. In this work, we adapt a well-established mathematical model of HIV evolution under dynamical selection pressure from multiple CTL clones to include partial impairment of CTL recognition, ∆r, as well as cost to viral replication, ∆f. The process of escape is described in terms of the cost-benefit tradeoff of escape mutations and predicts a trajectory in the cost-benefit plane connecting sequentially escaped sites, which moves from high recognition loss/low fitness cost to low recognition loss/high fitness cost and has a larger slope for early escapes than for late escapes. The slope of the trajectory offers an interpretation of positive correlation between fitness costs and HLA binding impairment to HLA-A molecules and a protective subset of HLA-B molecules that was observed for clinically relevant escape mutations in the Pol gene. We estimate the value of ∆r from published experimental studies to be in the range (0.01-0.86) and show that the assumption of complete recognition loss (∆r = 1) leads to an overestimate of mutation cost. Our analysis offers a consistent interpretation of the commonly observed pattern of escape, in which several escape mutations are observed transiently in an epitope. This non-nested pattern is a combined effect of temporal changes in selection pressure and partial recognition loss. We conclude that partial recognition loss is as important as fitness loss for predicting the order of escapes and, ultimately, for predicting conserved epitopes that can be targeted by vaccines.Other Research Uni

Single cell transcriptomic analysis in a mouse model of Barth syndrome reveals cell-specific alterations in gene expression and intercellular communication

Barth Syndrome, a rare X-linked disorder affecting 1:300,000 live births, results from defects in Tafazzin, an acyltransferase that remodels cardiolipin and is essential for mitochondrial respiration. Barth Syndrome patients develop cardiomyopathy, muscular hypotonia and cyclic neutropenia during childhood, rarely surviving to middle age. At present, no effective therapy exists, and downstream transcriptional effects of Tafazzin dysfunction are incompletely understood. To identify novel, cell-specific, pathological pathways that mediate heart dysfunction, we performed single-nucleus RNA-sequencing (snRNA-seq) on wild-type (WT) and Tafazzin-knockout (Taz-KO) mouse hearts. We determined differentially expressed genes (DEGs) and inferred predicted cell–cell communication networks from these data. Surprisingly, DEGs were distributed heterogeneously across the cell types, with fibroblasts, cardiomyocytes, endothelial cells, macrophages, adipocytes and pericytes exhibiting the greatest number of DEGs between genotypes. One differentially expressed gene was detected for the lymphatic endothelial and mesothelial cell types, while no significant DEGs were found in the lymphocytes. A Gene Ontology (GO) analysis of these DEGs showed cell-specific effects on biological processes such as fatty acid metabolism in adipocytes and cardiomyocytes, increased translation in cardiomyocytes, endothelial cells and fibroblasts, in addition to other cell-specific processes. Analysis of ligand–receptor pair expression, to infer intercellular communication patterns, revealed the strongest dysregulated communication involved adipocytes and cardiomyocytes. For the knockout hearts, there was a strong loss of ligand–receptor pair expression involving adipocytes, and cardiomyocyte expression of ligand–receptor pairs underwent reorganization. These findings suggest that adipocyte and cardiomyocyte mitochondria may be most sensitive to mitochondrial Tafazzin deficiency and that rescuing adipocyte mitochondrial dysfunction, in addition to cardiomyocyte mitochondrial dysfunction, may provide therapeutic benefit in Barth Syndrome patients

Rapid HIV disease progression following superinfection in an HLA-B*27:05/B*57:01-positive transmission recipient.

BACKGROUND: The factors determining differential HIV disease outcome among individuals expressing protective HLA alleles such as HLA-B*27:05 and HLA-B*57:01 remain unknown. We here analyse two HIV-infected subjects expressing both HLA-B*27:05 and HLA-B*57:01. One subject maintained low-to-undetectable viral loads for more than a decade of follow up. The other progressed to AIDS in < 3 years. RESULTS: The rapid progressor was the recipient within a known transmission pair, enabling virus sequences to be tracked from transmission. Progression was associated with a 12% Gag sequence change and 26% Nef sequence change at the amino acid level within 2 years. Although next generation sequencing from early timepoints indicated that multiple CD8+ cytotoxic T lymphocyte (CTL) escape mutants were being selected prior to superinfection, < 4% of the amino acid changes arising from superinfection could be ascribed to CTL escape. Analysis of an HLA-B*27:05/B*57:01 non-progressor, in contrast, demonstrated minimal virus sequence diversification (1.1% Gag amino acid sequence change over 10 years), and dominant HIV-specific CTL responses previously shown to be effective in control of viraemia were maintained. Clonal sequencing demonstrated that escape variants were generated within the non-progressor, but in many cases were not selected. In the rapid progressor, progression occurred despite substantial reductions in viral replicative capacity (VRC), and non-progression in the elite controller despite relatively high VRC. CONCLUSIONS: These data are consistent with previous studies demonstrating rapid progression in association with superinfection and that rapid disease progression can occur despite the relatively the low VRC that is typically observed in the setting of multiple CTL escape mutants

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

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

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