Molina-Par?s C., Lythe G. (editors) Mathematical Models and Immune Cell Biology
Подождите немного. Документ загружается.
18 CTL in HIV Infection 373
escape mutants back to the wildtype form in hosts who do not mount an immune
response to the epitope [41, 42] (Fig.19.5b). This would be most probable when
an escape mutant selected in one host is transmitted to a different host who does
not carry the selecting HLA and so cannot recognise the epitope. Longitudinal data
on the reversion of escape mutants is therefore able to provide information on fit-
ness costs, given that it is again intuitive that the rate at which reversion occurs in
this setting is an indication of the fitness cost of the mutant–faster reversion implies
higher cost.
Asquith et al. [13] recently formulised these ideas into a mathematical model.
The model enables a quantitative estimate to be made from longitudinal CTL escape
and reversion data on the proportion of infected cell lysis that can be attributed to
CTLs. Here we describe the model and show how it can be used to derive this
important result.
A Within-Host Model of Outgrowth of Escape Mutants
The model encapsulates the dynamics of outgrowth of an escape mutant at a sin-
gle CTL epitope and includes two different types of productively infected CD4
C
T
cells – those that are infected with a wildtype viral strain, y, and those that are in-
fected with a viral strain that has an escape mutation in the epitope, x. Cells infected
with the wildtype strain replicate at rate a, (the net contribution of all factors except
CTL-mediated death), are killed by CTLs specific for epitopes other that the epi-
tope of interest at rate b, and are killed by CTLs specific for the epitope of interest
at rate c. For hosts who target the epitope c has a positive value (c>0), whereas for
hosts who do not target the epitope, c equals zero (c D 0). Cells infected with the
mutant strain replicate at rate a
0
and are killed by CTLs specific for epitopes other
than the epitope of interest at rate b.SeeFig.18.6. The pair of first-order ordinary
differential equations that define the model are
Py D ay by cy
Px D a
0
x bx (18.4)
It is therefore assumed that in any given host, the number of CD4 cells infected
with the wildtype strain changes in an exponential manner at rate a b c
(y D y.0/e
.abc/t
) and that the number of CD4 cells infected with the mutant
strain change in an exponential manner at rate a
0
b (x D x.0/e
.a
0
b/t
); note
that these growth rates can be negative. In order to understand how effective CTLs
are, the first aim is to estimate an average value for c, the rate of CTL-mediated
lysis directed against an epitope. This quantity cannot be measured directly, but
can be estimated indirectly from longitudinal escape and reversion data using two
expressions derived from the model. These are the rate of escape of a mutant (given
by k) and the fitness cost of a mutant (given by ). To estimate these values, the
model must first be manipulated to extract an expression for the quantity that can
be measured in hosts, namely the proportion of virus that has the escape mutant,
p.t/ D x.t/=.x.t/ C y.t//.
374 H. Fryer and A. McLean
Fig. 18.6 A model of the dynamics of productively infected CD4
C
T cells. Cells can either be
infected with virus that has an escape mutant at the epitope of interest, x, or with virus that is
wildtype at the epitope, y. Cells infected with an escape mutant replicate at rate a
0
per cell and are
killed at rate b per cell. Cells infected with wildtype virus replicate at rate a per cell and are killed
at a faster rate, b C c per cell
The Rate of Escape
In hosts who mount an immune response to the epitope (c>0), p.t/ is able to
increase over time as the mutant strain outgrows the wildtype strain. In the context
of the model this can be expressed as
p.t/
D
1
ge
kt
C 1
(18.5)
where g D y.0/=x.0/ and k D c a C a
0
(the rate of escape) is the difference
between the growth rate of the escape variant, a
0
b, and growth rate of the wild-
type variant, a b c. The rate of escape, k, can thus be estimated by fitting the
longitudinal escape mutant outgrowth data to the function 1=.ge
kt
C1/ (Fig. 19.5).
Using this method Asquith et al. [14] derived estimates of the rate of escape, k,
for 21 reported CTL escape variants. The rates were consistently slow across the
different escape variants: in 20 of the 21 datasets the rate of escape was less than
1day
1
and the median rate was 0.01day
1
.
The Fitness Cost of an Escape Mutant ()
In hosts who are infected with an escape mutant strain but do not mount a CTL
response to the epitope, the proportion of virus that is mutant, p.t/, can decline over
time as the fitness-compromised mutant strain is replaced by the wildtype strain.
Assuming c D 0 (no CTL directed against epitope), p.t/ can be expressed as
18 CTL in HIV Infection 375
p.t/ D
1
ge
t
C 1
(18.6)
where D a a
0
(the fitness cost or reversion rate of the escape mutant) is the
difference in the replication rate between the wildtype strain, a,andtheescapemu-
tant strain, a
0
. The fitness cost can thus be estimated by fitting the longitudinal
reversion data to the function 1=.ge
t
C 1/ (Fig. 19.5b).
To see if the slow escape rates could be explained by high fitness costs, Asquith
et al. estimated the fitness costs from seven reversion datasets using this method.
Their analysis revealed that the fitness cost of an average CTL escape mutation is
very low – approximately 0.005 day
1
. This slow rate is consistent both with data
from in vitro competition assays [43] and with the observation that many escape
mutants accumulate in the population over time [44–46].
The Rate of Killing of Productively Infected Cells by a CTL
Response Against a Single CTL Epitope (c)
Since k D c a Ca
0
and D a a
0
, the rate of escape can be simplified to equal
k D c . The rate of lysis of infected cells by CTLs against a particular epitope, c,
can therefore be derived directly from estimates of the escape rate, k, and the fitness
cost, :
c D k C:
Asquith et al. found that the estimated escape rates and fitness costs are consistent
across different individuals. It is therefore meaningful to use the average estimate for
each of these quantities to estimate the rate of lysis of infected cells by a single CTL
response. Given that the escape rate is estimated to be 0.01 day
1
and the fitness cost
is estimated to be 0.005 day
1
, the rate of lysis by CTLs specific for a single epitope
is estimated to be 0.02 day
1
. This means that an infected cell would have a lifespan
of approximately 50 days if it were subject only to lysis by CTLs specific for one
epitope. The estimated average lifespan of a productively infected cell is consider-
ably shorter at approximately 1 day [35–38,40]. Taken together these figures imply
that only about 2% of infected cell death is attributable to the CTL response directed
against a single epitope.
The Rate of Killing of Productively Infected Cells by CTL:
Estimating the Total Response Against Different Epitopes
Most infected individuals do not just elicit a CTL response against a single epitope,
therefore the total degree of cell death attributable to CTL-mediated lysis within
any individual is likely to be more than 2%. To extrapolate the single-epitope figure
376 H. Fryer and A. McLean
to understand the total strength of the CTL response within a typical individual, an
estimate is needed for the number of epitopes targeted by CTLs within a typical
host. Comprehensive epitope analysis put this average at between 14 and 19 epi-
topes [27,47], however, typically less than five of these epitopes will be sufficiently
strong to drive CTL escape [48]. Given that the data upon which the single-epitope
killing rate was based related to CTL responses strong enough to drive escape – in
fact most were the patient’s immunodomiant response – Asquith et al. used a range
of between 5 and 14 epitopes to estimate that CTL-mediated lysis of infected cells
contributes to only about 10–20% of all infected cell death.
Comparing the Strength of the CTL Response in Primary
Infection and Chronic Infection
It is often assumed that the strength of CTL response in HIV infection is consis-
tent between primary and chronic infection. For each of the 21 datasets, Asquith et
al. estimated the epitope-specific rate of the killing by CTL. For this calculation a
uniform fitness cost of 0.005 day
1
was assumed for each dataset. The results show
that the rate of killing by a single CTL response is markedly slower in chronic in-
fection compared to primary infection. This is consistent with the findings derived
from analysing viral load dynamics using the target-cell limited model; namely that
CTLs have some impact during acute infection, but there is no explicit evidence that
they are important during chronic infection.
Comparing the Strength of the CTL Response in Humans
and Macaques
Before HIV vaccine candidates are tested in humans, they undergo efficacy tests
in non-human primates – typically macaques – challenged with SIV or SHIV. It
is clear that, like humans, they produce a CTL response against the virus, though
it is not clear how the strength of this response compares to the strength of the
CTL response in humans. This is a pertinent question in view of the recent (and
unequivocal) failure in humans of the Merck vaccine [11] that had previously proved
to be effective in SHIV challenged macaques [12].
To compare the strength of the CTL response between humans and macaques,
longitudinal CTL escape and reversion data from macaques can be analysed in
exactly the same way as has been described for the human data. Asquith et al.
[14] presented a summary of 35 escape events and 9 reversion events observed in
macaques that were used for this comparison. In the majority (23/35) of the escape
events, the outgrowth of the CTL escape variant was so rapid in comparison to the
sampling interval that it was only possible to put a lower bound on the rate of es-
cape. In the humans the same was true for 10/21 escape events. Across all of these
18 CTL in HIV Infection 377
Fig. 18.7 A comparison of escape and reversion rates in humans and macaques. (a) To compare
escape rates the data is stratified according to the average time interval between observations. For
each grouping the rate of escape is faster in macaques than in humans. (b) A direct comparison of
reversion rates reveals that reversion is faster in macaques than in humans [13]
events (amongst both human and macaque), a strong negative correlation was found
between the rate of escape and the average sampling interval. To compare the rate
of escape in humans with the rate of escape in macaques, Asquith et al. controlled
for the length of the sampling interval by dividing the dataset into four groups with
similar average sampling intervals. For each group the rate of escape in humans was
compared to the corresponding rate of escape in macaques (Fig. 18.7). The rate in
macaques was found to be significantly faster than the rate in humans. This result
held true even when data relating to acutely infected macaques were excluded. In
fact, unlike in humans, there was no statistical difference between the rate of escape
in acute and chronic infection.
To determine whether the faster escape rates observed in macaques could simply
be explained by lower fitness costs, fitness costs for nine reversion events were es-
timated in the same way as described for humans. For the majority of events (6/9)
the fitness cost could be determined precisely, therefore a direct comparison was
made between the macaque and human estimates (Fig.18.7). This analysis revealed
that fitness costs of escape mutants are significantly higher in macaques than in hu-
mans. The faster escape rates observed in macaques therefore cannot be explained
by differences in fitness costs.
Since both the escape rate, k, and fitness cost, , are higher in macaques than
in humans, the rate at which a CTL response to one epitope kills productively in-
fected cells (c D k C ) is also higher in macaques. The mean difference in the
escape rate between macaques and humans is estimated to be 0.02 day
1
. The mean
difference in fitness cost is estimated to be 0.2 day
1
. The difference in the rate of
CTL-mediated lysis can therefore be estimated to be 0.22 day
1
(0:02C0:2), mean-
ing that infected cells are killed almost ten times more rapidly in macaques than in
humans.
378 H. Fryer and A. McLean
In summary, this model has been used to estimate the selection pressure exerted
by CTL responses that drive the emergence of immune escape variants, thereby en-
abling the efficiency of HIV-1 specific CTLs to be quantified. Firstly, this analysis
has revealed that only around 2% of productively infected cell death is attributable
to CTLs recognising a single epitope. When this figure is scaled up to consider the
total CTL response by an individual, the implication is that CTLs are not respon-
sible for the majority of infected cell death, thus the basis upon which hopes of a
CTL-based vaccine are pinned, may be flawed. Secondly, the analysis has revealed
that the killing rate is significantly faster during acute infection than during chronic
infection. The CTL response therefore may be functionally important during acute
HIV infection, but not during chronic infection. Finally, the analysis has shown that
infected cells are killed by CTLs almost ten times more rapidly in macaques than
in humans, highlighting that SIV/SHIV infected macaques are a less than perfect
model for testing HIV vaccines. This may explain the recent failure of the CTL-
based Merck vaccine that was efficacious in macaques, but entirely ineffective in
humans.
Discussion
The combination of mathematical modelling and data analysis to elucidate various
aspects of the within-host dynamics of HIV infections has been one of the great
success stories of mathematical biology in the last decade. A number of factors have
contributed to this success. First, data on the within host dynamics of HIV is amply
available. HIV circulates in the blood and there are reliable quantitative methods
of detecting viral load in the blood. Thus longitudinal measures of viral abundance
in the blood of infected individuals are quite common. Second, the models that
have been developed to interrogate that data have remained really rather simple and
transparent with many of their important parameters accessible from observed data.
Despite this simplicity these models have had substantial explanatory power for
the observed data. In combination these good data and good models have yielded
new insights that would not have been available unless both models and data were
brought to bear upon the questions. Because of this, the study of ‘viral dynamics’
in HIV infection (often meaning mathematical modelling of immune system-virus
interactions) is very widely accepted in a broad church of HIV biologists.
Probably the first new insight generated by these interdisciplinary interactions
was that, during HIV infection, infected cells turnover on a very rapid timescale,
most of them not surviving beyond 1 day after infection. At the time this was a
big surprise to many researchers in the field. With time, repeated observation of
the same phenomena and further corroborating data this once novel insight is now
widely accepted.
The mathematical models presented here offer three insights about the role of
CTLs in immunodeficiency virus infections:
1. In chronically infected humans fewer than 20% of cells are killed by specific
CTL responses.
18 CTL in HIV Infection 379
2. In acutely infected humans CTL killing is more efficient, but not dramatically so.
3. In simian infections CTL efficacy is very dramatically higher.
The insights described in this chapter, although also based on simple models and
abundant, repeatable data, are yet to gain widespread acceptance. This is despite
the fact that their major prediction (that CTL vaccines might work in macaques but
not in humans) was amply borne out by the STEP vaccine trial. Nevertheless we
contend that this is another example of modelling elucidating previously confusing
biological data that will, in time, gain wide acceptance.
References
1. UNAIDS (2008) 2008 Report on global AIDS epidemic.
2. Flynn NM, Forthal DN, Harro CD, Judson FN, Mayer KH, Para MF (2005) Placebo-controlled
phase 3 trial of a recombinant glycoprotein 120 vaccine to prevent HIV-1 infection. J Infect
Dis 191:654–665
3. Borrow P, Lewicki H, Hahn BH, Shaw GM, Oldstone MB (1994) Virus-specific CD8
C
cytotoxic T-lymphocyte activity associated with control of viremia in primary human immun-
odeficiency virus type 1 infection. J Virol 68:6103–6110
4. Koup RA, Safrit JT, Cao Y, Andrews CA, McLeod G, Borkowsky W, Farthing C, Ho DD
(1994) Temporal association of cellular immune responses with the initial control of viremia
in primary human immunodeficiency virus type 1 syndrome. J Virol 68:4650–4655
5. Borrow P, Lewicki H, Wei X, Horwitz MS, Peffer N, Meyers H, Nelson JA, Gairin JE, Hahn
BH, Oldstone MB, Shaw GM (1997) Antiviral pressure exerted by HIV-1-specific cytotoxic T
lymphocytes (CTLs) during primary infection demonstrated by rapid selection of CTL escape
virus. Nature 3:205–211
6. Price DA, Goulder PJ, Klenerman P, Sewell AK, Easterbrook PJ, Troop M, Bangham CR,
Phillips RE (1997) Positive selection of HIV-1 cytotoxic T lymphocyte escape variants during
primary infection. Proc Natl Acad Sci USA 94:1890–1895
7. Carrington M, Nelson GW, Martin MP, Kissner T, Vlahov D, Goedert JJ, Kaslow
R, Buchbinder S, Hoots K, O’Brien SJ (1999) HLA and HIV-1: heterozygote advantage and
b*35-cw*04 disadvantage. Science 283:1748–1752
8. Gao X, Nelson GW, Karacki P, Martin MP, Phair J, Kaslow R, Goedert JJ, Buchbinder S,
Hoots K, Vlahov D, O’Brien SJ, Carrington M (2001) Effect of a single amino acid change in
MHC class I molecules on the rate of progression to AIDS. N Engl J Med 344:1668–1675
9. Carrington M, O’Brien SJ (2003) The influence of HLA genotype on AIDS. Annu Rev Med
54:535–551
10. Cohen J (2007) AIDS research. Did Merck’s failed HIV vaccine cause harm? Science
318:1048–1049
11. Cohen J (2007) AIDS research. Promising AIDS vaccine’s failure leaves field reeling. Science
318:28–29
12. Shiver JW, Fu TM, Chen L, Casimiro DR, Davies ME, Evans RK, Zhang ZQ, Simon AJ,
Trigona WL, Dubey SA, Huang L, Harris VA, Long RS, Liang X, Handt L, Schleif WA, Zhu L,
Freed DC, Persaud NV, Guan L, Punt KS, Tang A, Chen M, Wilson KA, Collins KB, Heidecker
GJ,FernandezVR,PerryHC,JoyceJG,GrimmKM,CookJC,KellerPM,KresockDS,Mach
H, Troutman RD, Isopi LA, Williams DM, Xu Z, Bohannon KE, Volkin DB, Montefiori DC,
Miura A, Krivulka GR, Lifton MA, Kuroda MJ, Schmitz JE, Letvin NL, Caulfield MJ, Bett
AJ, Youil R, Kaslow DC, Emini EA (2002) Replication-incompetent adenoviral vaccine vector
elicits effective anti-immunodeficiency-virus immunity. Nature 415:331–335
13. Asquith B, Edwards CT, Lipsitch M, McLean AR (2006) Inefficient cytotoxic T lymphocyte-
mediated killing of HIV-1-infected cells in vivo. PLoS Biol 4:e90
380 H. Fryer and A. McLean
14. Asquith B, McLean AR (2007) In vivo CD8
C
T cell control of immunodeficiency virus infec-
tion in humans and macaques. Proc Natl Acad Sci USA 104:6365–6370
15. HVTN (2007) The step study. Summary. Press briefing. November 7, 2007
16. Walker BD, Chakrabarti S, Moss B, Paradis TJ, Flynn T, Durno AG, Blumberg RS, Kaplan
JC, Hirsch MS, Schooley RT (1987) HIV-specific cytotoxic T lymphocytes in seropositive
individuals. Nature 328:345–348
17. Buseyne F, Fevrier M, Garcia S, Gougeon ML, Riviere Y (1996) Dual function of a hu-
man immunodeficiency virus (HIV)-specific cytotoxic T-lymphocyte clone: inhibition of HIV
replication by noncytolytic mechanisms and lysis of HIV-infected CD4
C
cells. Virology
225:248–253
18. Yang OO, Kalams SA, Trocha A, Cao H, Luster A, Johnson RP, Walker BD (1997) Suppression
of human immunodeficiency virus type 1 replication by CD8
C
cells: evidence for HLA class
i-restricted triggering of cytolytic and noncytolytic mechanisms. J Virol 71:3120–3128
19. Van Baalen CA, Schutten M, Huisman RC, Boers PH, Gruters RA, Osterhaus AD (1998)
Kinetics of antiviral activity by human immunodeficiency virus type 1-specific cytotoxic T
lymphocytes (CTL) and rapid selection of CTL escape virus in vitro. J Virol 72:6851–6857
20. Matano T, Shibata R, Siemon C, Connors M, Lane HC, Martin MA (1998) Administration
of an anti-CD8 monoclonal antibody interferes with the clearance of chimeric simian/human
immunodeficiency virus during primary infections of rhesus macaques. J Virol 72:164–169
21. Jin X, Bauer DE, Tuttleton SE, Lewin S, Gettie A, Blanchard J, Irwin CE, Safrit JT, Mittler J,
Weinberger L, Kostrikis LG, Zhang L, Perelson AS, Ho DD (1999) Dramatic rise in plasma
viremia after CD8
C
T cell depletion in simian immunodeficiency virus-infected macaques.
J Exp Med 189:991–998
22. Schmitz JE, Kuroda MJ, Santra S, Sasseville VG, Simon MA, Lifton MA, Racz P, Tenner-Racz
K, Dalesandro M, Scallon BJ, Ghrayeb J, Forman MA, Montefiori DC, Rieber EP, Letvin NL,
Reimann KA (1999) Control of viremia in simian immunodeficiency virus infection by CD8
C
lymphocytes. Science 283:857–860
23. Harrer T, Harrer E, Kalams SA, Elbeik T, Staprans SI, Feinberg MB, Cao Y, Ho DD, Yilma
T, Caliendo AM, Johnson RP, Buchbinder SP, Walker BD (1996) Strong cytotoxic T cell and
weak neutralizing antibody responses in a subset of persons with stable nonprogressing HIV
type 1 infection. AIDS Res Hum Retroviruses 12:585–592
24. Ogg GS, Jin X, Bonhoeffer S, Dunbar PR, Nowak MA, Monard S, Segal JP, Cao Y, Rowland-
Jones SL, Cerundolo V, Hurley A, Markowitz M, Ho DD, Nixon DF, McMichael AJ (1998)
Quantitation of HIV-1-specific cytotoxic T lymphocytes and plasma load of viral RNA. Sci-
ence 279:2103–2106
25. Edwards BH, Bansal A, Sabbaj S, Bakari J, Mulligan MJ, Goepfert PA (2002) Magnitude of
functional CD8
C
T-cell responses to the gag protein of human immunodeficiency virus type 1
correlates inversely with viral load in plasma. J Virol 76:2298–2305
26. Oxenius A, McLean AR, Fischer M, Price DA, Dawson SJ, Hafner R, Schneider C, Joller
H, Hirschel B, Phillips RE, Weber R, Gunthard HF (2002) Human immunodeficiency virus-
specific CD8
C
t-cell responses do not predict viral growth and clearance rates during structured
intermittent antiretroviral therapy. J Virol 76:10169–10176
27. Addo MM, Yu XG, Rathod A, Cohen D, Eldridge RL, Strick D, Johnston MN, Corcoran C,
Wurcel AG, Fitzpatrick CA, Feeney ME, Rodriguez WR, Basgoz N, Draenert R, Stone DR,
Brander C, Goulder PJ, Rosenberg ES, Altfeld M, Walker BD (2003) Comprehensive epitope
analysis of human immunodeficiency virus type 1 (HIV-1)-specific T-cell responses directed
against the entire expressed HIV-1 genome demonstrate broadly directed responses, but no
correlation to viral load. J Virol 77:2081–2092
28. Kiepiela P, Ngumbela K, Thobakgale C, Ramduth D, Honeyborne I, Moodley E, Reddy S,
de Pierres C, Mncube Z, Mkhwanazi N, Bishop K, van der Stok M, Nair K, Khan N, Crawford
H, Payne R, Leslie A, Prado J, Prendergast A, Frater J, McCarthy N, Brander C, Learn GH,
Nickle D, Rousseau C, Coovadia H, Mullins JI, Heckerman D, Walker BD, Goulder P (2007)
CD8
C
t-cell responses to different HIV proteins have discordant associations with viral load.
Nature 13:46–53
18 CTL in HIV Infection 381
29. Betts MR, Nason MC, West SM, De Rosa SC, Migueles SA, Abraham J, Lederman MM,
Benito JM, Goepfert PA, Connors M, Roederer M, Koup RA (2006) HIV nonprogressors pref-
erentially maintain highly functional HIV-specific CD8
C
T cells. Blood 107:4781–4789
30. Daucher M, Price DA, Brenchley JM, Lamoreaux L, Metcalf JA, Rehm C, Nies-Kraske E,
Urban E, Yoder C, Rock D, Gumkowski J, Betts MR, Dybul MR, Douek DC (2008) Virological
outcome after structured interruption of antiretroviral therapy for human immunodeficiency
virus infection is associated with the functional profile of virus-specific CD8
C
T cells. J Virol
82:4102–4114
31. McMichael AJ, Phillips RE (1997) Escape of human immunodeficiency virus from immune
control. Annu Rev Immunol 15:271–296
32. Perelson AS (2002) Modelling viral and immune system dynamics. Nature 2:28–36
33. Phillips AN (1996) Reduction of HIV concentration during acute infection: independence from
a specific immune response. Science 271:497–499
34. Stafford MA, Corey L, Cao Y, Daar ES, Ho DD, Perelson AS (2000) Modeling plasma virus
concentration during primary HIV infection. J Theor Biol 203:285–301
35. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD (1996) HIV-1 dynam-
ics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science
271:1582–1586
36. Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M (1995) Rapid
turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373:123–126
37. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA, Deutsch P, Lifson JD, Bonhoeffer S,
Nowak MA, Hahn BH, et al (1995) Viral dynamics in human immunodeficiency virus type 1
infection. Nature 373:117–122
38. Rodrigo AG, Shpaer EG, Delwart EL, Iversen AK, Gallo MV, Brojatsch J, Hirsch MS, Walker
BD, Mullins JI (1999) Coalescent estimates of HIV-1 generation time in vivo. Proc Natl Acad
Sci USA 96:2187–2191
39. Macallan DC, Asquith B, Irvine AJ, Wallace DL, Worth A, Ghattas H, Zhang Y, Griffin GE,
Tough DF, Beverley PC (2003) Measurement and modeling of human T cell kinetics. Eur J
Immunol 33:2316–2326
40. Markowitz M, Louie M, Hurley A, Sun E, Di Mascio M, Perelson AS, Ho DD (2003) A novel
antiviral intervention results in more accurate assessment of human immunodeficiency virus
type 1 replication dynamics and t-cell decay in vivo. J Virol 77:5037–5038
41. Friedrich TC, Dodds EJ, Yant LJ, Vojnov L, Rudersdorf R, Cullen C, Evans DT, Desrosiers RC,
Mothe BR, Sidney J, Sette A, Kunstman K, Wolinsky S, Piatak M, Lifson J, Hughes AL, Wil-
son N, O’Connor DH, Watkins DI (2004) Reversion of CTL escape-variant immunodeficiency
viruses in vivo. Nature 10:275–281
42. Leslie AJ, Pfafferott KJ, Chetty P, Draenert R, Addo MM, Feeney M, Tang Y, Holmes EC,
Allen T, Prado JG, Altfeld M, Brander C, Dixon C, Ramduth D, Jeena P, Thomas SA, St John
A, Roach TA, Kupfer B, Luzzi G, Edwards A, Taylor G, Lyall H, Tudor-Williams G, Novelli
V, Martinez-Picado J, Kiepiela P, Walker BD, Goulder PJ (2004) HIV evolution: CTL escape
mutation and reversion after transmission. Nature 10:282–289
43. Draenert R, Le Gall S, Pfafferott KJ, Leslie AJ, Chetty P, Brander C, Holmes EC, Chang SC,
Feeney ME, Addo MM, Ruiz L, Ramduth D, Jeena P, Altfeld M, Thomas S, Tang Y, Verrill
CL, Dixon C, Prado JG, Kiepiela P, Martinez-Picado J, Walker BD, Goulder PJ (2004) Immune
selection for altered antigen processing leads to cytotoxic T lymphocyte escape in chronic HIV-
1 infection. J Exp Med 199:905–915
44. Goulder PJ, Brander C, Tang Y, Tremblay C, Colbert RA, Addo MM, Rosenberg ES, Nguyen
T, Allen R, Trocha A, Altfeld M, He S, Bunce M, Funkhouser R, Pelton SI, Burchett SK,
McIntosh K, Korber BT, Walker BD (2001) Evolution and transmission of stable CTL escape
mutations in HIV infection. Nature 412:334–338
45. Moore CB, John M, James IR, Christiansen FT, Witt CS, Mallal SA (2002) Evidence of HIV-1
adaptation to HLA-restricted immune responses at a population level. Science 296:1439–1443
46. Kawashima Y, Pfafferott K, Frater J, Matthews P, Payne R, Addo M, Gatanaga H, Fujiwara
M, Hachiya A, Koizumi H, Kuse N, Oka S, Duda A, Prendergast A, Crawford H, Leslie A,
382 H. Fryer and A. McLean
Brumme Z, Brumme C, Allen T, Brander C, Kaslow R, Tang J, Hunter E, Allen S, Mulenga J,
Branch S, Roach T, John M, Mallal S, Ogwu A, Shapiro R, Prado JG, Fidler S, Weber J, Pybus
OG, Klenerman P, Ndung’u T, Phillips R, Heckerman D, Harrigan PR, Walker BD, Takiguchi
M, Goulder P (2009) Adaptation of HIV-1 to human leukocyte antigen class i. Nature 458:
641–645
47. Frahm N, Korber BT, Adams CM, Szinger JJ, Draenert R, Addo MM, Feeney ME, Yusim K,
Sango K, Brown NV, SenGupta D, Piechocka-Trocha A, Simonis T, Marincola FM, Wurcel
AG, Stone DR, Russell CJ, Adolf P, Cohen D, Roach T, StJohn A, Khatri A, Davis K, Mullins
J, Goulder PJ, Walker BD, Brander C (2004) Consistent cytotoxic-t-lymphocyte targeting of
immunodominant regions in human immunodeficiency virus across multiple ethnicities. J Virol
78:2187–2200
48. Geels MJ, Cornelissen M, Schuitemaker H, Anderson K, Kwa D, Maas J, Dekker JT, Baan E,
Zorgdrager F, van den Burg R, van Beelen M, Lukashov VV, Fu TM, Paxton WA, van der Hoek
L, Dubey SA, Shiver JW, Goudsmit J (2003) Identification of sequential viral escape mutants
associated with altered t-cell responses in a human immunodeficiency virus type 1-infected
individual. J Virol 77:12430–12440