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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, NM [email protected] Acute HIV Infection: Target Cell Limited? 78 of 102 pts had single founder virus Keele et al PNAS 105: 7552 (2008) Salazar-Gonzalez J Exp Med 206: 1273 (2009) Keele et al J Exp Med 206: 1117 (2009) Viral stock diverse Acute HIV-1 Infection Onset cytokines apoptosis, Day 7 Virus Concentration in Extracellular Fluid or Plasma (Copies/ml) Acute Phase Reactants Days -5 to-7 Free IgM anti-gp41 Ab, Day 13 (non-neutralizing) Ab-virus Immune Complexes Day 9 108 Autologous Neutralizing Antibody 107 106 105 ? 104 Reservoir eclipse 103 102 101 0 10-1 10-2 Transit 10-3 CTL Escape CD8 T Cell Virus Responses dissemination Autologous Neutralizing Antibody Escape T0 10-4 10-5 0 Transmission 5 10 15 20 25 30 35 40 Time Post Exposure (days) 45 50 55 60 65 70 Model of HIV Infection k Infection Rate p Virions/d I + Infected Cell T Target Cell d Death c Clearance Model of Viral Infection Basic Target Cell-Limited Model dT dT kVT dt dI kVT dI dt dV pI cV dt T, target CD4 cells I, infected Cells V, virus λ, d, k, p, c, d, T cell production normal T cell death infectivity constant viral production viral clearance infected cell death λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, k, and p are fit. HIV Primary Infection Target cell limited model can fit HIV RNA data during early AHI 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 Stafford et al. JTB 203: 285 (2000) 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 The viral clearance term, c, in the target cell-limited model is assumed to be increased proportional to the Ab conc. dT dT kVT dt dI kVT dI dt dV pI c(1 IgG (t ))V dt T, target CD4 cells I, infected Cells V, virus λ, δ, k, p, c, T cell production normal T cell death infectivity constant viral production viral clearance λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, k, p, and α are fit. Anti-gp41 IgG antibody term added to c Model of Antibody Mediated Viral Neutralization The infectivity term, k, of the target cell-limited model is reduced by Ab dT k dT VT dt 1 IgG (t ) dI k VT d I dt 1 IgG (t ) dV pI cV dt No improvement in fit is seen except for 9032 T, target CD4 cells I, infected Cells V, virus λ, d, k, p, c, T cell production normal T cell death infectivity constant viral production viral clearance 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 celllimited model. Model of Antibody Enhanced Infected Cell Death The infected cell death rate, d, in the target cell-limited model is assumed to be increased proportional to the anti-gp41 concentration. dT dT kVT dt dI kVT d (1 IgG (t )) I dt dV pI cV dt Again no improvement in fit is seen T, target CD4 cells I, infected Cells V, virus λ, δ, k, p, c, T cell production normal T cell death infectivity constant viral production viral clearance λ = 10,000 cells/μL/day d = 0.01/day c = 23/day δ, k, p, and α are fit. Patient Clearance Enhanced, IgG Clearance Enhanced, IgM Clearance Infectivity Infectivity Infectivity Cell death Cell death Enhanced, Diminished, Diminished, Diminished, enhanced, enhanced, IgG+IgM IgG IgM IgG+IgM IgG 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 Idea: By examining rate of escape one can estimate the CTL pressure on the virus Asquith et al., PLoS Biol 2006 Goonetilleke et al. J Exp Med. 2009 Escape from CTL pressure CTL killing dw aw dw kw dt dm a ' m dm dt Replication rate a’=a(1-c) Wildtype virus (infected cells) Escape mutant (infected cells) For WT, d =d+k = total rate of killing Note, k/d is fraction of killing attributed to CTL Fitness cost, c=0 no cost, c=1 maximal cost Time course of escape variants Proportion of mutant virus over time m(t ) p(t ) w(t ) m(t ) 1 w (0) rt 1 m (0) e We can use this equation to fit McMichael’s data and estimate 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 Mutations Patient CH40 CH77 CH58 SUMA0874 Median Escape rate (day-1) (days) CD8 T cell epitope t50% Confirmed T cell response (HXB2 position) 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) 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 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 Rev R48K, Q51H, Q53R, S54L, L55I 0.32 30 yes Yes, (Rev47-55) 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. Elispot / IFN g staining 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, d= 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 coldadapted 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 dT TI VT rT (1 ) dt T0 Targets Infected p c Virus d dI VT d I dt dV pI cV dt Model with Infection Delay dT dt dI1 dt dI 2 dt dV dt TV TV kI1 kI1 d I 2 pI 2 cV Baccam et al. JVI 2006 DELAY is dashed Double Peaks Interferon Response dF sI 2 (t ) F dt ˆ 1 1 F p pˆ 1 2F 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.