Optimisation of the immune response

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

2. Age-dependant Intensity

3. 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

4. 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

5. 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

6. 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

7. 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)

8. 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

9. 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

10. Trickle Exposure:  Dose

11. Natural Exposure:  Duration

12. Maternal Exposure

13. 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

14. 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

15. 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

16. 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)

17. 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

18. 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

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

20. 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

21. Effect of control parameter, 

22. 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…

23. Dependence on 

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

25. 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

26. R=0.5,1,1.5,2

27. R=0.5,1,1.5,2

28. =5,25,50,100

29. Age-Related RV =5,25,50,100

30. =5,25,50,100

31. =5,25,50,100

32. Age-dependant Intensity

33. 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

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

35. Peak Shift

37. 500 hosts with uniform random R,  and β; (constant )

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

39. 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

40. 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 2001:355 11311137

41. 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

42. 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