Award details

Cytokine network ecology: towards a dynamic understanding of immune responses to co-infection

Reference	BB/D01977X/1
Principal Investigator / Supervisor	Dr Andrea Graham
Co-Investigators / Co-Supervisors
Institution	University of Edinburgh
Department	Inst for Immunology and Infection Resrch
Funding type	Research
Value (£)	1,060,453
Status	Completed
Type	Fellowships
Start date	01/08/2006
End date	31/12/2010
Duration	53 months

Abstract

Cytokines, key determinants of immune response efficacy, are signalling molecules that interact dynamically during infection. For example, in response to replication of protozoa, mammalian hosts often must make pro-inflammatory cytokines to enable suitable parasite-killing mechanisms. These cytokines must in turn be closely regulated by anti-inflammatory cytokines, so that hosts do not suffer the immunopathology that can result from excessive inflammation. Pro- and anti- inflammatory cytokines thus must be produced in appropriate quantities at appropriate times in order to maximise host health or fitness. I propose to study these dynamics in a multi-cytokine network context, using probabilistic network analyses & optimality hypothesis testing. I will focus upon cytokine responses to protozoan infection in the presence and absence of network-perturbing helminth co-infection. Modern 'multiplex' methods allow immunologists to measure many cytokines at once, in small sample volumes. I propose a series of experiments that will quantify an array of cytokines and receptors from mouse plasma and cultured lymphocytes at daily intervals following malaria infection. Dynamic optimality analysis will reveal the time lags between expression of each molecule and the fulfilment of its biological effect: either control of parasitemia or of immunopathology. A cumulative Bayesian network analysis of whole-organism cytokine biology will then reveal synergies among measured cytokines and will also clarify the qualitative and quantitative alterations in the cytokine network that are induced by nematode co-infection. The project will culminate in a timelag-wise and network-wise test of the evolutionary prediction that immune responses against virulent pathogens should be prioritised during co-infection.

Summary

Cytokines are important molecules for the organisation of immune responses: they activate cells to divide and to attack infectious agents, and they sometimes act directly against pathogens. Cytokines thus strongly influence how (& how quickly) parasites are killed. Multiple cytokine signals are involved in every immune response -- an intricate network of positive and negative feedback loops that determine how well the host manages to fight infection. These feedback loops, and the time lags inherent in such a signalling system, can make the immune system difficult to study. Data collected at any one time point or analysed only one variable at a time simply cannot reveal the way that different cytokines must interact to generate an observed system-wide immune response. With this project, I propose to treat the host as a closed ecological system in which ecological and evolutionary analyses can identify how cytokines work together to generate effective versus pathological immune responses. Why study cytokines to learn about the whole immune system? The appeal of cytokines is three-fold: - cytokines are immunologically relevant -- e.g., nearly all immunologists measure cytokines to help them infer the function of cells, no matter which host cell type nor which infection or autoimmune condition is under study. - cytokines are analytically tractable -- the same <10 cytokines are implicated in all described infectious and autoimmune diseases. Details of which cells and membrane bound molecules (& less abundant or less well described cytokines) are involved does change across these systems, but the fact that the same cytokines are always important is striking. Better to model these <10 than to model the many cell populations, for example, that vary across context. - and (for evolutionary studies), because cytokines choose parasite-killing mechanisms, they strongly influence host health and survival. The proposed project aims to integrate real data on multiplecytokines of co-infected mice into a network framework, making use of optimality-based and probabilistic mathematical methods. Optimality methods are appropriate because we expect that there has been natural selection on immune systems, particularly to optimise their ability to multi-task / for example, to optimally manage protozoan-helminth co-infection. At the same time, probabilistic methods are also appropriate because cytokines form a probabilistic network, with a lot of variability and chance events. The probabilistic statistical methods that I propose to use have been successfully applied to environmental science (for example, to predict damage to coral reefs given multiple interacting environmental factors). Testing optimality predictions has greatly deepened our understanding of the evolutionary biology of everything from bird song to the development of antibiotic resistance. The combined application of these predictive analytical methods to immunological molecules will also bear fruit: an integrated understanding of immune system functioning that links to the health of hosts.

Committee	Closed Committee - Engineering & Biological Systems (EBS)
Research Topics	Animal Health, Immunology, Microbiology
Research Priority	X – Research Priority information not available
Research Initiative	Fellowship - David Phillips Fellowship (DF) [1995-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

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