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What controls the initial dynamics of influenza and HIV Alan S. Perelson Theoretical Biology and Biophysics Los Alamos National Laboratory Los Alamos,

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Presentation on theme: "What controls the initial dynamics of influenza and HIV Alan S. Perelson Theoretical Biology and Biophysics Los Alamos National Laboratory Los Alamos,"— Presentation transcript:

1 What controls the initial dynamics of influenza and HIV Alan S. Perelson Theoretical Biology and Biophysics Los Alamos National Laboratory Los Alamos, NM

2 Acute HIV Infection: Target Cell Limited?

3 Keele et al PNAS 105: 7552 (2008) Salazar-Gonzalez J Exp Med 206: 1273 (2009) 78 of 102 pts had single founder virus

4 Keele et al J Exp Med 206: 1117 (2009)

5 Viral stock diverse

6

7 Transmission Virus Concentration in Extracellular Fluid or Plasma (Copies/ml) Acute HIV-1 Infection Time Post Exposure (days) Reservoir Virus dissemination Transit eclipse ? T0T0 Acute Phase Reactants Days -5 to-7 Ab-virus Immune Complexes Day 9 Onset cytokines apoptosis, Day 7 Free IgM anti-gp41 Ab, Day 13 (non-neutralizing) CD8 T Cell Responses CTL Escape Autologous Neutralizing Antibody Autologous Neutralizing Antibody Escape

8 IT k Infection Rate c Clearance p Virions/d Target Cell Infected Cell Death  Model of HIV Infection +

9 Model of Viral Infection Basic Target Cell-Limited Model T, target CD4 cells I, infected Cells V, virus λ, T cell production d, normal T cell death k, infectivity constant p, viral production c, viral clearance , infected cell death λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, k, and p are fit.

10 Stafford et al. JTB 203: 285 (2000) HIV Primary Infection Target cell limited model can fit HIV RNA data during early AHI

11 Stafford et al. JTB 203: 285 (2000) At loner times the target cell limited model may show oscillations that are not in the data and may overestimate the HIV RNA level if immune responses are playing a role

12 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: (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.

13 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).

14 Results: Fits to the Target Cell Limited Model (First 40 days after virus detected - T 100 )

15 Target Cell Limited Model

16 Model of Antibody Enhanced Virion Clearance T, target CD4 cells I, infected Cells V, virus λ, T cell production δ, normal T cell death k, infectivity constant p, viral production c, viral clearance λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, k, p, and α are fit. The viral clearance term, c, in the target cell-limited model is assumed to be increased proportional to the Ab conc.

17 Anti-gp41 IgG antibody term added to c

18 Model of Antibody Mediated Viral Neutralization The infectivity term, k, of the target cell-limited model is reduced by Ab T, target CD4 cells I, infected Cells V, virus λ, T cell production d, normal T cell death k, infectivity constant p, viral production c, viral clearance No improvement in fit is seen except for 9032

19 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.

20 Model of Antibody Enhanced Infected Cell Death T, target CD4 cells I, infected Cells V, virus λ, T cell production δ, normal T cell death k, infectivity constant p, viral production c, viral clearance λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, 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

21 Patient Clearance 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 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.

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23 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.

24 Viral infectivity may vary with time May be due to host or virus; Ma et al J Virol. 83:

25 SIV Infection

26 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.

27 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

28 Goonetilleke et al. J Exp Med. 2009

29 Escape from CTL pressure Wildtype virus (infected cells) Escape mutant (infected cells) CTL killing Replication rate a’=a(1-c) Fitness cost, c=0 no cost, c=1 maximal cost For WT,  =d+k = total rate of killing Note, k/  is fraction of killing attributed to CTL

30 Time course of escape variants Proportion of mutant virus over time We can use this equation to fit McMichael’s data and estimate the rate of escape r.

31 Model Fits to Kinetics of HIV-1 Escape from CTL Responses in Acute Infection TW10

32 Escape rates and epitopes Elispot / IFN g staining 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.

33 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).

34 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.

35 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: Exponential growth of virus, peaks day 2-3, then declines and is cleared.

36 Data

37 MODEL TargetsInfected Virus p   c

38 Model with Infection Delay

39 DELAY is dashed Baccam et al. JVI 2006

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41 Double Peaks

42 Interferon Response

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44 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.


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