Optimisation of the immune response Graham Medley Ecology & Epidemiology group Warwick, UK

Age-dependant Intensity

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

Also applies to other chronic infections, e.g. malaria. As intensity of transmission (immigration rate) increases: >> the overall intensity of infection increases >> the age at peak intensity decreases >> there is a “change” at sexual maturity Lusingu et al., Malaria Journal 2004, 3:26

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

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

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)

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

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

Trickle Exposure:  Dose

Natural Exposure:  Duration

Maternal Exposure

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

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

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

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)

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

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

Model Structure Differential equations –Three equations ( g, p, s ) –Solved & maximised numerically IBM stochastic simulations Unscaled –Redundancy: pathogenicity ~ immigration –Quantitatively meaningless

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

Effect of control parameter, 

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…

Dependence on 

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

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

R=0.5,1,1.5,2

 =5,25,50,100

Age-Related RV  =5,25,50,100

Age-dependant Intensity

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

Mutapi et al. – S.haematobium BMC Infectious Diseases 2006, 6:96

Peak Shift

500 hosts with uniform random R,  and β; (constant  )

Conclusions IR in host context –Reproduces observed phenomena: Age-related intensity Peak shift Heterogeneity Predisposition

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

Years since infection Proportion surviving Survival against age at HIV seroconversion 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 2001:  1137

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

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