A systems biology approach to understand immunity and pathogenesis of malaria in children

Malaria is a major health problem, with approximately half of the world's population at risk. Most malaria cases and deaths occur in sub-Saharan Africa and are caused by the species "Plasmodium falciparum", with an annual mortality rate of up to 700,000, mostly among children.. Despite many years of work and investment, we still do not have a highly effective vaccine. One reason for this is that we have an incomplete understanding of the interaction between malaria parasites and immunity. Immune responses to Plasmodium are complex, and protective immunity in malaria endemic areas develops only after several years of exposure. We know from our long-term studies of longitudinal cohorts that the outcome of malaria infection in children is extremely variable such that some children have two or three clinical episodes while others have very frequent clinical episodes or severe episodes without apparently becoming immune. Malaria can have a profound suppressive impact on immune responses. Our hypothesis is that these children with frequent and symptomatic malaria infections are caught in a causal "loop" (or "vicious cycle") whereby malaria episodes lead to impaired immunity to malaria, which in turn leads to further episodes of malaria.

In our first phase of analysis, we will investigate immune responses in a cohort of children developing immunity to P.falciparum malaria, for whom we have detailed life histories of malaria exposure. We will compare those immune responses of children with a history of repeated and clinical malaria episodes with those of children with a normal number of clinical malaria episodes (matching age, location and malaria exposure). We will also examine responses in a third group of children who live nearby, but are not exposed to malaria. This first phase of analysis will establish the analytical methodology, determine patterns of responses, and generate models and hypotheses that can be tested in the second phase (see below). We will combine transcriptomic, flow-cytometric, cytokine analyses, and malaria-specific B cell/antibody responses to compare as comprehensively as possible the features of the host response in the two groups.

A single "snap shot" of immune responses may represent only the endpoint of immune processes and may not reflect those that are causally related to differences in malaria outcome. Therefore, in a second phase of testing, we will collect and store samples from the whole cohort prior to surveillance for clinical malaria episodes. Samples will be stored, and then tested and analysed when parallel groups to those describe for the first phase above can be identified (i.e. multiple episodes vs normal episodes). These samples will be analysed by the laboratory and analytical teams blinded to epidemiological data, and the association between the signatures identified in the first phase and malaria episodes in prospect will be examined. We will undertake a further cross-sectional assessment of the children in this cohort in the second year, in order to assess the stability of these markers over time.

Our studies will identify immune response patterns that can be used as predictive markers of the child's ability to develop immunity to clinical malaria, and will be of great value in informing rational vaccine design, and monitoring vaccine efficacy and therapeutics. We will establish a set of robust analytical and modeling tools, which can be used in further studies, which will combine genetic variation in parasite and host, and which will inform mechanistic studies in animal models. Our unique dataset will be available for other researchers, and the expertise acquired will help establish a systems immunology expertise in the UK.

We will examine immune response profiles of children developing immunity to Plasmodium falciparum malaria, in order to define a response pattern that distinguishes children who have suffered very frequent clinical malaria episodes from those who have an average number of clinical. We will then sample a cohort prior to surveillance, store the samples, and then test once clinical data are available to examine whether these profiles can prospectively predict multiple clinical episodes and failure to acquire immunity.
Using RNA sequencing we will determine the transcriptome profile of the immune response. This will be combined with flow cytometric analyses on leukocytes in whole blood, and measurements of cytokines, chemokines and P. falciparum specific B cell/antibody responses.
At each analytical step we will identify potentially important 'predictors', which could be individual up- or down-regulated genes, gene clusters, regulatory pathways etc. These will be collated and iteratively used to build up our predictive model. We will concentrate on the most significant pathways for more in depth analysis by developing mathematical descriptions for pathways and then predict the effects of perturbations in the study population. Detailed cytometric analyses (CyTOF and Flow) and functional B-cell and T-cell assays will be performed, and will inform mechanistic animal model studies. During this study we will develop novel analytical tools and train scientists in systems immunology, build up large databases of childrens' immune responses, and define biomarkers that will predict frequent malaria episodes in children.

This project seeks to use a systems approach to define the characteristics of protective immunity to the malaria parasite, Plasmodium falciparum, and to understand how the host response contributes to severe malaria in children. We believe our studies will be the first comprehensive dataset obtained from children in an endemic area of P. falciparum transmission that will link epidemiological and clinical data on malaria to a large range of immune parameters such as the gene expression of immune-related genes, cytokine and chemokine production, the breadth of B-cell/antibody responses and functional capacity of immune cells.
Our data set will be of great value, and will be available to other Malaria immunologists, epidemiologists, and vaccine developers, who will be able to interrogate the data for their particular areas of immunological or other interest. The data set can also be used by any interested parties to define pathways, cells and molecules of interest that can be then investigated in mechanistic studies in experimental models. This would be an excellent way to bridge the gap between experimental models and human infection and disease by focussing on those responses that are directly relevant to human malaria.
The plasma remaining from this study will be available to colleagues, who are already in the process of developing various P. falciparum proteomes for screening for vaccine candidate antigens and developing protein micro-arrays targeting blood-stage merozoite and variable surface antigens, respectively, and will be able to link the resultant data sets with the results of this study.
We will collect samples such as P. falciparum DNA and RNA as well as human DNA in addition to the human RNA, peripheral blood cells and plasma to be used in our immunological studies. These samples will then be integrated into existing and planned large-scale studies funded by other sources such as Malariagen (www.malariagen.net), a network that integrates malaria epidemiology with genome studies on both the host and the malaria. This will enable us to expand our systems approach to bring together genetics with gene expression, functional immune responses and parasite variation.
This has huge potential for identifying markers for protective immunity and for identifying risk factors for severe disease. The data from this study will identify potential immune modulatory pathways that can be interrogated by interested immunologists from anywhere in the world. Furthermore the studies will provide important novel information on the relationships between genetic variability, gene expression and functional responses. Within the time scale of this project we will train young postgraduate and postdoctoral scientists in bioinformatics, predictive modeling and systems approaches to study human immunology and thereby establish a cohort of scientists who will further and expand this type of research in the UK and in Africa.
Identification of patterns of immune responses that can be used as predictive markers of the child's ability or not to develop immunity will be of great value in informing rational vaccine design, monitoring vaccine efficacy and determining indicators for children at risk of severe disease. Our findings will aid in designing vaccines to target the most protective responses can be used by those organisations such as WHO, MVI, Gates Foundation, MRC, Wellcome Trust and pharmaceutical companies involved in furthering development of vaccines and therapeutic interventions.
Information that would accelerate malaria vaccine development against an infection to which half of the world's population are exposed would have a significant impact on the educational and economic development of those affected countries, and therefore in the longer term also on the global economy and British economy.