what controls the initial dynamics of influenza and hiv
DESCRIPTION
What controls the initial dynamics of influenza and HIV. Alan S. Perelson Theoretical Biology and Biophysics Los Alamos National Laboratory Los Alamos, NM [email protected]. Acute HIV Infection: Target Cell Limited?. 78 of 102 pts had single founder virus. Keele et al PNAS 105: 7552 (2008) - PowerPoint PPT PresentationTRANSCRIPT
What controls the initial dynamics of influenza and HIV
Alan S. Perelson
Theoretical Biology and Biophysics
Los Alamos National Laboratory
Los Alamos, NM
Acute HIV Infection: Target Cell Limited?
Keele et alPNAS 105:7552 (2008)
Salazar-GonzalezJ Exp Med 206:1273 (2009)
78 of 102 pts had single founder virus
Keele et alJ Exp Med 206:1117 (2009)
Viral stock diverse
10-5
10-4
10-3
10-2
10-1
0
101
102
103
104
105
106
107
108
Transmission
Vir
us
Conce
ntr
ati
on in E
xtr
ace
llula
r Fl
uid
or
Pla
sma (
Copie
s/m
l)
Acute HIV-1 Infection
Time Post Exposure (days)
0 5 10 15 20 30 3525 40 45 50 55 60 65 70
Reservoir
Virus dissemination Transi
t
eclipse?
T0
Acute Phase ReactantsDays -5 to-7
Ab-virus Immune ComplexesDay 9
Onset cytokinesapoptosis, Day 7 Free IgM anti-gp41 Ab, Day 13 (non-neutralizing)
CD8 T CellResponses
CTL Escape
AutologousNeutralizing Antibody
AutologousNeutralizing
Antibody Escape
II TT
k Infection Rate
c
Clearance
pVirions/d
Target Cell
InfectedCell
Death
Model of HIV Infection
+
Model of Viral Infection
cVpIdt
dV
IkVTdt
dI
kVTdTdt
dT
Basic Target Cell-Limited Model T, target CD4 cellsI, infected CellsV, virus
λ, T cell productiond, normal T cell deathk, infectivity constantp, viral productionc, viral clearance, infected cell death
λ = 10,000 cells/μL/dayd = 0.01/dayc = 23/dayδ, k, and p are fit.
Stafford et al. JTB203: 285 (2000)
HIV PrimaryInfection
Target cell limitedmodel can fit HIV RNAdata during early AHI
Stafford et al. JTB203: 285 (2000)
At loner times the target cell limited model mayshow oscillations that arenot in the data and mayoverestimate the HIV RNA level if immune responsesare playing a role
Anti-HIV Antibodies• Plasma samples obtained by CHAVI from blood
bank donors have been analyzed for the presence of HIV RNA as well IgG, IgM and IgA antibody levels. G. Tomaras et al. JVI 82: 12449 (2008) has shown that the earliest antibodies are anti-gp41 and that immune complexes form between these antibodies and HIV.
• The question we want to address is whether the presence of anti-env antibodies impacts viral dynamics.
We Study Three Effects of Antibody
• Opsonize viral particles and increase their rate of clearance.
• Neutralize HIV and decrease rate of infection of cells.
• Bind to infected cells and hasten their destruction either through complement mediated lysis or antibody-dependent cellular cytotoxicity (ADCC).
Results: Fits to the Target Cell Limited Model (First 40 days
after virus detected - T100)
Target Cell Limited Model
Model of Antibody Enhanced Virion Clearance
VtIgGcpIdt
dV
IkVTdt
dI
kVTdTdt
dT
))(1(
T, target CD4 cellsI, infected CellsV, virus
λ, T cell productionδ, normal T cell deathk, infectivity constantp, viral productionc, viral clearance
λ = 10,000 cells/μL/dayd = 0.01/dayc = 23/dayδ, k, p, and α are fit.
The viral clearance term, c, in the target cell-limited modelis assumed to be increased proportional to the Ab conc.
Anti-gp41 IgG antibody term added to c
Model of Antibody Mediated Viral Neutralization
1 ( )
1 ( )
dT kdT VT
dt IgG t
dI kVT I
dt IgG t
dVpI cV
dt
The infectivity term, k, of the target cell-limited model is reduced by Ab
T, target CD4 cellsI, infected CellsV, virus
λ, T cell productiond, normal T cell deathk, infectivity constantp, viral productionc, viral clearance
No improvement in fit is seen except for 9032
Anti-gp41 IgG antibody term added to k
SSR increases as β increases, with k, p, and δ set to the best-fit parameters of the target cell-limited model.
Model of Antibody Enhanced Infected Cell Death
(1 ( ))
dTdT kVT
dtdI
kVT IgG t IdtdV
pI cVdt
T, target CD4 cellsI, infected CellsV, virus
λ, T cell productionδ, normal T cell deathk, infectivity constantp, viral productionc, viral clearance
λ = 10,000 cells/μL/dayd = 0.01/dayc = 23/dayδ, k, p, and α are fit.
The infected cell death rate, , in the target cell-limited model is assumed to be increased proportional to the anti-gp41 concentration.
Again no improvement in fit is seen
PatientClearance Enhanced,
IgG
Clearance Enhanced,
IgM
Clearance Enhanced, IgG+IgM
Infectivity Diminished,
IgG
Infectivity Diminished,
IgM
Infectivity Diminished,
IgG+IgM
Cell death enhanced,
IgG
Cell death enhanced,
IgM
Cell death enhanced, IgG+IgM
6240 0.320 0.326 0.266 0.975 0.977 0.975 0.975 0.975 1.000
6246 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
9032 0.004 0.006 0.001 0.005 0.006 0.006 0.89 0.011 0.923
9077 0.567 0.998 0.687 0.604 0.998 0.623 0.722 0.998 0.991
9079 0.992 0.994 0.960 0.998 0.649 0.992 0.964 0.070 0.598
12008 0.566 0.607 0.590 0.648 0.546 0.657 1.000 1.000 1.000
9077 0.567 0.998 0.687 0.604 0.998 0.623 0.722 0.998 0.991
9079 0.992 0.994 0.960 0.998 0.649 0.992 0.964 0.070 0.598
P-values from F-test comparison of target-cell limited model with antibody-including models for all patients, fitting through day 40. The value in bold indicates the lowest p-value for patient 9032, the only patient better fit by an antibody-including model.
Limitations• One possible limitation of our approach is
that the antibody data we used is the concentration of free anti-gp41 antibody.
• Antibody in immune complexes is being ignored as well as antibodies with other specificities.
• Only looked at first 40 days after virus is detectable.
Viral infectivity may vary with time
May be due to host or virus; Ma et al J Virol. 83: 3288 2009
SIV Infection
CTL Escape
• Gertrude Elion – “An antiviral is a drug that selects for resistance”.
• Similarly, one can assess the potency of a CTL response by asking if it selects for escape mutations.
Escape from CTL pressure
Asquith et al., PLoS Biol 2006
Idea: By examining rate of escape one can estimate the CTL pressure on the virus
Goonetilleke et al. J Exp Med. 2009
Escape from CTL pressure
'
dwaw dw kw
dtdm
a m dmdt
Wildtype virus (infected cells)
Escape mutant (infected cells)
CTL killing
Replication ratea’=a(1-c)
Fitness cost, c=0 no cost, c=1 maximal cost
For WT, =d+k = total rate of killingNote, k/is fraction of killing attributed to CTL
Time course of escape variants
(0)(0)
( ) 1( )
( ) ( ) 1w rtm
m tp t
w t m t e
Proportion of mutant virus over time
We can use this equation to fit McMichael’s data andestimate the rate of escape r.
Model Fits to Kinetics of HIV-1 Escape from CTL Responses in Acute Infection
TW10
TW10
Escape rates and epitopes
Elispot / IFN g staining
Patient Mutations Escape rate
(day-1) t50%
(days)
CD8 T cell
epitope
Confirmed T cell response (HXB2
position)
CH40 Nef S188R/N, R196Q 0.22 15 yes Yes, (Nef185-202)
Gag A120D 0.17 31 no no
Gag I401L, R403K 0.17 31 yes Yes (Gag389-406)
Vpr S84R 0.16 31 yes no
Vif T167I 0.37 38 yes no
Pol K76R 0.015 119 yes Yes, (Pol73-90)
CH77 Env R355K/S/N,T358A 0.36 9 yes Yes, (Env350-368)
Nef R21K/G, R22K/G/,TD23E/N, P25S 0.30 19 yes Yes, (Nef17-34)
Nef K82Q/T/P, A83G, L85H, L87I 0.29 24 yes Yes, (Nef81-98)
Gag I147L 0.035 101 yes Yes, (Gag140-157) IW9
Gag T242N,V247I, G248E/S 0.063 124 yes Yes, (Gag236-253) TW10
CH58 Env Y586H 0.10 21 yes Yes, (Env576-596)
Env F829V/S/L, I830M/L/F, A832T, V833I 0.12 27 no no
Gag T248N, G254E 0.085 60 yes Yes, (Gag236-253) TW10
Gag I147L 0.007 430 yes Yes , (Gag140-157) IW9
SUMA0874 Rev R48K, Q51H, Q53R, S54L, L55I 0.32 30 yes Yes, (Rev47-55)
Median 0.17 30
Results: Median rate of CTL escape = 0.17/d; Maximum rate of CTL escape = 0.37/d Avg death rate of productively-infected cells on HAART = 1/d.
Conclusions• Escapes measured here are faster than previously seen:
– Median r =0.17 day-1, max r =0.37 day-1
– Asquith et al. (2006), median r = 0.04 day-1
• Comparing rate of escape with the death rate of infected cells, = 1 day-1 (HAART data) one sees CTL pressure to one epitope is high and accounts for as much as 37% of the killing rate and on average 17%. However, virus rapidly escapes this pressure.
• Multiple simultaneous responses could account for even more of the loss rate of infected cells (new modeling work is examining this).
Influenza
• Unlike HIV, influenza is generally rapidly cleared.
• Clearance can be due to target-cell limitation as long as target cells do not replenish before the virus is eliminated.– If targets replenish, virus can resurge and an
immune response is needed to ultimately clear virus.
Data: Infection in Humans
Murphy, B. R et al., Evaluation of influenza A/Hong Kong/123/77 (H1N1) ts-1A2 and cold- adapted recombinant viruses in seronegative adult volunteers. Infect. Immun. 29:348-55 1980.
• Exponential growth of virus, peaks day 2-3, then declines and is cleared.
Data
MODEL
Targets Infected
Virus
dTVT
dt
dIVT I
dt
T IrT
dVpI cV
d
T
t
0
(1 )
p
c
11
21 2
2
(3 )
(3 )
(3 )
dTTV a
dtdI
TV kI bdtdI
kI I cdtdV
pI cVdt
Model with Infection Delay
DELAY is dashedBaccam et al. JVI 2006
Double Peaks
2 ( )dF
sI t Fdt
1
2
ˆ(6)
1
ˆ
1
F
pp
F
Interferon Response
Conclusions
• Target cell limited models work well in explaining viral load data obtained early in HIV and influenza infection. As infection progresses immune responses are seen and contribute to the late dynamics. Quantifying the precise contribution of both innate and adaptive responses is ongoing.