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

2. Outline
Recap: HIV escape from CTL
Aim
Results
Summary

3. HIV-I escape
from CTL

4. 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

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

6. Aim

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

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

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

10. 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

11. B*1402 : Gag AADTGNSSQ

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

13. 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

14. Estimating the selective advantage
where k is the selective advantage 14

15. Quantify selective advantage of CTL escape variants

16. y = 8.53x 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 yrs
Increase in selective advantage from day-1 to day-1 => decrease in the AIDS-free period of 1.2yrs

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

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

19. 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

20. Epitopes where variant has a weak selective advantage more likely to be recognised
y = -7.69x 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

21. Less sequence variation in epitopes associated with good prognosis
y = 0.55x 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

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

24. 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%

25. 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

26. 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

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

28. 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

29. 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

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

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

34. 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

35. rs=0.6 p<10-16

36. Predict: Increase in log(vl) of about 0.08 per escape event
IQ:
Observe: Increase in log(vl) of about 0.09 per escape event

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

38. 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 day-1 to 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

39. Contradictory Conclusions?
viral load
HLA
escape

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

41. 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

42. 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%.