Optimisation of the immune response

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Optimisation of the immune response

• Graham Medley
• Ecology Epidemiology group
• Warwick, UK

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Age-dependant Intensity
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Macroparasite Immunity Models

• Immune response is a function of history of
  exposure
• Memory, M(a)
• Immunity is a non-linear, increasing function of
  M(a)
• But why?
• If it takes hours to respond to a virus, why does
  it take years to respond to macroparasites?
• Hosts should be more concerned with present
  future than past

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Also applies to other chronic infections, e.g.
malaria. As intensity of transmission
(immigration rate) increases gtgt the overall
intensity of infection increases gtgt the age at
peak intensity decreases gtgt there is a change
at sexual maturity
Lusingu et al. , Malaria Journal 2004, 326
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What is the Immune System for?

• Hosts use their IS to maximise survival and
  reproduction
• Possibly tautological, but true
• The IS does not have the sole aim of killing
  parasites
• IS is constrained by other physiology
• Persistence of infection does not immediately
  imply parasite cunning or immunity failure
• Generate questions about the functions of
  immunity
• and therefore the mechanisms that might be
  expected

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Constraints to Immunity

• IS is expensive in terms of limited resources
  (energy protein)
• Other processes that enhance fitness
• E.g. growth reproduction
• Many physiological processes constrained by
  minimum energy or minimum protein
• IS is dangerous
• Autoimmune disease

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• Hosts may choose to devote resources to things
  other than immunity
• especially if infection is rarely immediately
  lethal and continuous (macroparasites)
• not if infection will be lethal if uncontrolled
  (viruses)

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Immunopathology

• For many infections, the immune response causes
  the disease
• Respiratory syncytial virus
• Eosinophilia creates the clinical disease
• Ablate eosinophilia mice die without symptoms
• Schistosomiasis
• Circulatory failure due to granuloma formation
  around eggs embedded in liver
• Ascaris suum
• Single large dose leads to explusion
• Same dose trickled leads to establishment
  little pathology

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Adaptive Immunity

• Adaptive to overcome pathogen adaptation
• Adaptive to host requirements protein energy
• Also adaptive to survival / reproduction context
• Nutrition (resources)
• Malnourished hosts experience more disease
• Gender Social Status
• Males females do not have same priorities
• Hormonal influence (effect of testosterone)
• Age
• Priorities change
• Immuno-modulation of parasite burden

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Trickle Exposure ? Dose
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Natural Exposure ? Duration
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Maternal Exposure
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Adaptive Immunity

• Exposure modulates infection so that prevalence
  increases and maximum burdens decrease
• Variability is decreased
• Immune system is the modulator
• Exposure results in shuffling of individual
  burdens within a group of hosts
• No expulsion

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Model of Resource Allocation

• How should hosts devote resources between
  immunity and other functions as they age?
• Simple model of infection, immunity and fitness
• Single host over age
• Constrained optimisation problem

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Macroparasites

• Within-host parasite population, p
• Immigration-death process
• Parasites do not reproduce within the host
• Immigration death rates of parasite depend on
  level of immunity

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Simple Model Immunity

• Resource input is constant R
• Partitioned into immunity (I), growth
  reproduction
• Resources devoted to immunity are dependent on
• parasite population
• individual host dependent parameter, ?(a)

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Simple Model Host

• Fixed age at maturity, w
• Investment in growth during immaturity to
  increase size, g
• Survival to any age is dependent on relative size
  and current parasite burden determine survival, s
• Reproduction is dependent on size and resources
  available

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Reproductive Value, RV

• Maximum age, L
• Expected future reproductive success
• survival is related to size and parasite burden
• reproductive effort is related to size and
  resources (not used for parasite resistance)
• Maximise fitness as a trade-off between
• reducing parasites now
• less likely to die
• and growing to be bigger
• less likely to die in the future reproduce more

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Model Structure

• Differential equations
• Three equations ( g, p, s )
• Solved maximised numerically
• IBM stochastic simulations
• Unscaled
• Redundancy pathogenicity immigration
• Quantitatively meaningless

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Optimisation Problem

• Aim is to optimise the host fitness by varying
  proportion of resources devoted to immunity, ?(a)
• Initially assume ? constant throughout life
• RV at birth maximised

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Effect of control parameter, ?
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Immunity is always sub-optimal

• Reproductive value is optimised at when resources
  devoted to immunity are intermediate
• There is an optimal parasite burden
• Given continuous (constant) immigration and
  constant resources
• Optimised values change with conditions
• Changing immigration resource level

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Dependence on ?
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Dependence on resources
Medley, G.F. (2002) Parasitology 125 (7), S61-S70
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Age-related immunity

• Allow ?(a)
• Linear segments
• RV calculated throughout life
• Amounts to maximising at each age
• Dynamic programming approach each ?(a) depends
  on the others
• All other parameters (R, ?) constant with age

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R0.5,1,1.5,2
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R0.5,1,1.5,2
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?5,25,50,100
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Age-Related RV
?5,25,50,100
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?5,25,50,100
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?5,25,50,100
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Age-dependant Intensity
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Results

• Maximum age span (30)
• Immunity reduced as death approaches
• No value in compromising reproduction for
  survival
• Reproductive maturity
• Big change in immunity
• Emphasise growth during immaturity
• Emphasise survival in maturity
• Optimal strategy is to increase risk of death in
  order to be fitter when older

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Mutapi et al. S.haematobium BMC Infectious
Diseases 2006, 696
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Peak Shift
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(No Transcript)
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500 hosts with uniform random R, ? and ß
(constant ?)
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Conclusions

• IR in host context
• Reproduces observed phenomena
• Age-related intensity
• Peak shift
• Heterogeneity
• Predisposition

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Speculations

• What we can expect the IS to do
• Dynamic
• Mechanisms for continual monitoring of damage,
  changes in parasite population size,
  physiological state
• Effectiveness (e.g. B-cell affinity maturation)
• Defined by host context (age, nutrition etc)
• Mechanisms for interaction with remainder of
  physiology
• Molecules that operate in both, e.g. leptin
• Learning
• Adaptive immunity is a sensory system
• Controls innate immunity
• Determines immune response in context, e.g.
  effects of age vs HLA in HIV

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Survival against age at HIV seroconversion
Proportion surviving
Years since infection
Time from HIV-1 seroconversion to AIDS and death
before widespread use of highly-active
anti-retroviral therapy A collaborative
re-analysis. Cascade Collaboration. Lancet
2001355 1131?1137
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Is Death a Failure?

• Death does not immediately imply immune system
  failure
• Risking death to be bigger
• Apoptosis
• Cell death to kill intracellular parasites
• Do eusocial insects die to kill their parasites
  protect their sisters?
• Since infection transmits least some
  immuno-modulation is not optimal for individual
• Hand-waving arguments involving inclusive fitness

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Individuals ? Populations

• Infection rate depends on sum of individual
  parasite burdens
• Resources are limiting
• Competition for resources dependent on size?
• Dynamic game
• Individual strategies determine others (and own)
  conditions
• Real time optimisation of individual IR
• High discount rate (e.g. random death) will
  emphasise current immunity
• Immuno-ecology