Quantifying the Impact of HIV Escape from CTL Becca Asquith & Ulrich Kadolsky Department of Immunology, Imperial College

Outline Recap: HIV escape from CTL Aim Results Summary

HIV-I escape from CTL

HIV mutations can reduce CTL killing via Reduction of MHC-peptide binding Disruption of proteasomal cleavage Disruption of TCR recognition wild type escape variant Phillips et al. Nature 1995

Obvious (?) that HIV escape should have a detrimental impact but this has not been convincingly shown

Aim

To quantify the impact of HIV escape from CTL HLA-associated rate of progression to AIDS viral load

Results 1 Aim: Quantify the impact of HIV escape on HLA-associated rate of progression

HLA molecules determine (in part) the outcome of HIV infection Gao et al. N Eng J Med 2001

HLA-associated rate of progression is quantified as the relative hazard 1.25 A*02 0.91 A*03 0.97 A*11 0.73 A*23 1.24 A*24 1.15 A*25 A*26 0.57 … Carrington/ O’ Brien/ Gao et al

B*1402 : Gag 119-127 AADTGNSSQ

Evolutionary selective advantage: Rate at which variant replaces wild type from first appearance of variant

Estimating the selective advantage a and a’ replication rates b and b’ death rates Selective advantage = net growth rate variant- net growth rate wild type = a’ - b’ - (a - b) 13

Estimating the selective advantage where k is the selective advantage 14

Quantify selective advantage of CTL escape variants

y = 8.53x + 0.76 R 2 = 0.34 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 -0.01 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Selective advantage of the escape variant (day-1) Relative hazard of the presenting HLA molecule p = 0.008 Progress to AIDS in approx. 6.5 yrs Progress to AIDS in approx. 12.5 yrs Increase in selective advantage from 0.028 day-1 to 0.056 day-1 => decrease in the AIDS-free period of 1.2yrs

Hypothesis Variants with weak selective advantage Escape late, infrequently & slowly CTL surveillance maintained for longer associated with better prognosis

Predictions Epitopes where variant has a weak selective advantage more likely to be recognised Less sequence variation in epitopes associated with good prognosis

Epitopes where variant has a weak selective advantage more likely to be recognised Selective advantage: already measured CTL recognition : 150 HIV-infected individuals, IFNγ ELIspot

Epitopes where variant has a weak selective advantage more likely to be recognised y = -7.69x + 0.57 R 2 = 0.22 0.2 0.4 0.6 0.8 1 1.2 -0.01 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Selective advantage (day -1 ) Proportion of individuals with a CTL response to this epitope p = 0.017

Less sequence variation in epitopes associated with good prognosis y = 0.55x + 0.88 R 2 = 0.24 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0.0 0.1 0.3 0.5 0.7 Average Shannon Entropy of CTL Epitope P=0.006 Relative Hazard of the Presenting HLA p=0.006 Entropy at anchor residue v non-anchor residue p=0.014

Ulrich Kadolsky Results 2 Aim: To quantify the impact of HIV escape on viral load Ulrich Kadolsky

Cohort of 160 HIV+ untreated individuals Definition of escape: 1) HLA-associated amino acid variation 2) HLA-associated amino acid variation + drop in predicted binding score of ≥50%

Causality Moore et al Science 2002 Brumme et al PLoS Path 2007 low CD4 count - frequent escape Frequent escape caused low CD4 count Long infection period caused low CD4 count & variants accumulated for longer Lemey et al PLoS Med 2007

Multiple linear regression Number of synonymous changes To correct for frequency of mutation calculated synonymous changes in epitopes Multiple linear regression Number of synonymous changes Number of non synonymous changes

Number of escape events Partial residual plot Number of escape events MLR p=0.009

Impact of escape on viral load is very small 0-2 escape events: 47,850 copies per ml 9-11 escape events: 72,100 copies per ml Increase in log(vl) of about 0.09 per escape event

No single protein drives the effect gag 0.332 rev 0.309 nef 0.436 vif 0.411 env 0.066 vpr 0.515 pol 0.035

Why doesn’t HIV escape matter? underpowered? CTL flexible? escape variant small advantage? [CTL ineffective/ variant attenuated]

new CTL escape escape CTL flexible (impact of escape transient) vl small escape escape vl small Variant small advantage (impact of escape always small)

Min Max λ 5 30 d 0.0133 0.0775  0.001 0.01 ’ 0.01  b 0.5 1 c 0.05 h 20 200 h' 0.01h u 3 300

rs=0.6 p<10-16

Predict: Increase in log(vl) of about 0.08 per escape event IQ: 0.05- 0.13 Observe: Increase in log(vl) of about 0.09 per escape event

new CTL escape escape CTL flexible (impact of escape transient) vl small escape escape vl small Variant small advantage (impact of escape always small)

Conclusions HLA-associated rate of progression Viral load Good HLA molecules present epitopes where escape is slow & infrequent 30% of variation in HLA-associated rate of progression “explained” by escape Increase in selective advantage from 0.028 day-1 to 0.056 day-1 => decrease in the AIDS-free period of 1.2yrs Viral load Escape significantly associated with a small increase in viral load Impact of escape is independent of viral gene Impact small because variant growth rate only slightly higher than wt

Contradictory Conclusions? viral load HLA escape

Acknowledgements Ulrich Kadolsky Wellcome Trust, RCUK & MRC Aidan MacNamara Charles Bangham Angela McLean Los Alamos National Lab databases Wellcome Trust, RCUK & MRC

Selective advantage of 0.08 0.07 p = 0.009 0.06 0.05 Selective advantage of escape variant (d-1) 0.04 0.03 0.02 0.01 non-Gag epitopes Gag epitopes -0.01

2D v 5D model P<0.005 Pearson correlation two tailed. 95% CI for intercept (-0.002, 0.001); gradient (1.008, 1.014). Median absolute error was 1.1%, the maximum was 8.6%.