Collaborators Virus dynamics ] Robert May (Oxford) ] Sebastian Bonhoeffer (Zurich) ] Dominik Wodarz (Seattle) ] Marc Lipsitch (Harvard) ] Alun Lloyd (Princeton) ] George Shaw (Birmingham, Alabama) Martin Nowak ] Andrew McMichael (Oxford) Institute for Advanced Study ] Charles Bangham (London) Princeton ] Jeff Lifson (Washington)
HIV is a retrovirus
Virus RNA How fast does HIV reproduce in vivo ?
Reverse transcription mRNA
Provirus DNA
1994: Treatment leads to a rapid protease decline in virus load inhibitors and quantitative PCR Anti-viral treatment Virus load
HIV T1/2=1-3 days
Time
George Shaw
1 Virus dynamics with Virus dynamics treatment
k k λ λ β + d u a d u a
Uninfected Virus Infected Uninfected Virus Infected target cell target cell target cell target cell
The basic model of virus dynamics Anti-viral treatment
Uninfected cells x& = λ − dx − β xv Uninfected cells x& = λ − dx − β xv Infected cells y& = β xv − ay Infected cells y& = β xv − ay Free virus v& = ky − uv Free virus v& = ky − uv
Micro-epidemiology within infected host
Virus declines as Latently infected cells
−at −ut Anti-viral treatment ue − ae Anti-viral treatment v(t) = v* Free virus half-life u − a Virus load T1/2=1-3 days Virus load Infected cell T1/2=10 days half-life: 1-3 days
Time Time
2 An extended model of virus dynamics HIV-1 half-lives
] Productively infected cells : 1-3 days Uninfected cells x& = λ − dx − β xv Productively ] Latently infected cells : 10 days infected cells y& 1 = q 1 β xv − a 1 y 1 + α y 2 ] Defective provirus : 100 days Latently infected cell y& 2 = q 2 β xv − a 2 y 2 − α y 2 ] Free virus : hours Cells with defective provirus y& 3 = q 3 β xv − a 3 y 3 Free virus v& = ky 1 − uv
HIV-1 half-lives HIV-1 half-lives
] Productively infected cells : 1-3 days ] Productively infected cells : 1-3 days ] Latently infected cells : 10 days ] Latently infected cells : 10-100 days ] Defective provirus : 100 days ] Defective provirus : 100 days ] Free virus : hours ] Free virus : hours
HIV eradication requires 1-3 years of HIV eradication requires >10 years of effective therapy. effective therapy and is most likely impossible.
What kills productively Comparing HIV and HBV infected cells? dynamics:
] viral cytopathicity ] Half-life of productively infected cells: ] CTL responses ] HIV: 1-3 days ] HBV: 10-100 days
Note that all patients have very similar decay slopes corresponding to half-lifes of 1-3 days.
3 Viral cytopathicity leads to a constant half-life despite different CTL activity
What is the mechanism of HIV disease progression? Infection Cell becomes Cell produces a target for CTL new virions and dies
The experiment is biased toward those cells that produce plasma virus. CTL can greatly reduce virus production without affecting the half-life.
Evolution of virulence HIV-1: clinical profile
] The closest relatives of HIV-1 and HIV-2 1000 <2 to >15 years are SIVs. CD4
] All SIVs appear to be apathogenic in their 0 natural hosts. ] SIV can be transferred to other species, Virus where it induces AIDS.
Primary phase Asymptomatic phase AIDS
Time
A mechanism of disease HIV-1: clinical profile progression
1000 <2 to >15 years ] .. has to explain why the steady state of CD4 virus dynamics (with a timescale of days)
0 shifts over many years. Why is there such a long and Virus variable asymptomatic phase? ] 2 possibilities: \ the immune system changes \ the virus changes Primary phase Asymptomatic phase AIDS
Time
4 HIV is a quasispecies Evolution toward disease
] Viral replication is error prone. ] Escape from immune responses ] HIV reverse transcriptase and RNA −4 ] Faster replicating, more aggressive strains polymerase have error rates of about 10 ] Broader cell tropism ] The virus population in any one patient is extremely heterogeneous. Virus ] HIV can escape from immune load responses. Diversity threshold
Time
Antigenic variation Antigenic variation virus mutant i v&i = rvi − pxivi Total virus load is proportional to antigenic diversity. i =1,...,n immune response against mutant i x&i = cvi − bxi br v := v = n ∑i i cp Each mutant goes to equilibrium: br r v = x = i cp i p
Add new mutants over time.
Antigenic variation Antigenic variation of HIV virus mutant i virus mutant i v&i = vi (r − pxi − qz) v&i = vi (r − pxi − qz) specific specific i =1,...,n immune response x&i = cvi − bxi immune response x&i = cvi − bxi − uvxi i =1,...,n cross reactive z = kv − bz cross reactive z = kv − bz − uvz immune response & immune response &
Virus load: brn Virus load: brn v = v = cp + kqn cp − (ru − kq)n n
5 The ‘diversity threshold’ model Antigenic variation of HIV has 3 possible outcomes
Virus load: brn 1. Disease after long asymptomatic period. v = cp − (ru − kq)n kq < ru < kq + cp
Diversity threshold: 2. Indefinite virus control. cp ru < kq nc = ru − kq 3. Immediate disease. kq + cp < ru
Immune responses to Immune responses to multiple epitopes multiple epitopes
Immunodominance
Antigenic variation in Multiple epitope theory presence of multiple epitopes
v&ij = vij (rij − pi xi − q j y j )
x&i =η civi* + xi (civi* − b)
y& j =η k jv* j + y j (k jv* j − b)
6 Antigenic variation in Antigenic variation in presence of multiple epitopes presence of multiple epitopes
a new mutant arises
Diversification in the immunodominant epitope
Antigenic variation in Antigenic variation in presence of multiple epitopes presence of multiple epitopes
Partial shift in immunodominance, Partial shift in immunodominance, no response to new variant response to new variant loss of response to old variant
Antigenic variation in Antigenic variation in presence of multiple epitopes presence of multiple epitopes
Complete shift in immunodominance loss of old variant Immune response against variable epitope selects for viral diversity.
7 Antigenic variation in Immune responses to presence of multiple epitopes multiple epitopes
Immunodominance Immune response against conserved epitope breadth of the response is related to immune memory selects against viral diversity. Dominik Wodarz
HIV disease progression The virus will return if according to this model therapy is withdrawn
] There is a highly dynamic balance Anti-viral treatment between the virus and the immune system with rapid virus turnover. Virus load
] The evolutionary adaptation of the virus in Detection individual patients is the mechanism of limit disease progression. Time
A new theory of CTL memory
Is it possible to treat and help the patient’s immune ] Long lived CTL responses can eliminate system to gain control of virus infections or reduce virus load to low the virus? levels.
8 Cytotoxic T lymphocytes CTL HIV
] HIV kills CD4 cells which are needed for CTL Helper cells memory. CD4 ] Failure to establish a CTL memory response leads to persistent infection, high virus load and help rapid disease progression Killer cells ] A good CTL memory response leads to virus CD8 CTL memory CTL elimination (rare ?) or low virus load and slow is characterized by highly kill disease progression responsive, long-lived cells
Infected cell
HIV replication and HIV: rate of disease progression establishment of memory
Fast progressors: high virus load No CTL Memory
CTL Memory No CTL Memory CTL memory makes the difference. CTL Memory
Slow progressors: low virus load
Initial rate of viral spread
HIV replication and Treatment during establishment of memory primary infection
No CTL Memory No treatment Treatment
CTL Memory No CTL Memory CTL Memory
Vaccination or early treatment CTLp Virus Initial rate of viral spread SIV: Jeff Lifson HIV: Bruce Walker
9 SIV infection, no treatment 4 weeks of treatment ; re-challenge
Virus CD4 response
CD4 response
Virus
Time (weeks) Time (weeks)
SIV primary infection Treatment during without treatment chronic HIV infection
Virus Virus load in the first week of infection Treatment Treatment with drug holiday(s) is correlated with set-point set-point is correlated with survival.
Time CTLp Virus Jeff Lifson: 12 monkeys, 12 authors
Anti-viral treatment and immunotherapy HIV therapy
Immunotherapy ] For primary infection: Use vaccination and early treatment to reduce the initial viral growth rate and bring patients into a state of long term non-progression. ] For chronic infection: Use treatment and immunotherapy to switch patients into a state of long term non-progression. CTLp Virus
10 Summary Collaborators
] HIV dynamics ] Robert May (Oxford) ] Disease progression ] Sebastian Bonhoeffer (Zurich) ] Dominik Wodarz (Seattle) ] CTL memory / virus control ] Marc Lipsitch (Harvard) ] Alun Lloyd (Princeton) ] George Shaw (Birmingham, Alabama) ] Andrew McMichael (Oxford) ] Charles Bangham (London) ] Jeff Lifson (Washington)
Three possible mechanisms of HIV disease progression
] Evolution of the virus ] Slow break-down of the immune system ] Accumulation of opportunistic infections
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