Personalised Medicine Global Challenges: Research Taps into Cerebral Malaria Gene

Image shows an unconscious comatose child diagnosed with life-threathening cerebral malaria. With permission from Department of Paediatrics, University College Hospital Ibadan, Nigeria

Towards Understanding Individual Predisposition to Life-Threatening Cerebral Malaria

A multi-site multidisciplinary team led by D. Fernandez-Reyes (UCL Department of Computer Science); S. Marquet and A. Dessein (Aix-Marseille University, Marseille, France); O. Doumbo (Faculty of Medicine, Bamako, Mali) and B.J. Brown (College of Medicine, University of Ibadan, Ibadan, Nigeria) have identified a variant in a key immune-regulatory-network gene (IL-22) that predisposes to childhood life-threatening cerebral malaria. The findings are published in the 31^st January issue of Nature Scientific Reports: “A Functional IL22 Polymorphism (rs2227473) Is Associated with Predisposition to Childhood Cerebral Malaria. Sci. Rep. 7, 41636; doi: 10.1038/srep41636 (2017)”. http://rdcu.be/pla1

Malaria is endemic in more than 90 countries and together with HIV and Tuberculosis continues to be a major Global Health Challenge. Most deaths occur among children under the age of five-years living in sub-Saharan Africa as a result of severe clinical complications such as cerebral malaria and severe malarial anaemia. Because of the high mortality associated with these severe complications, a key challenge is to identify individual risk factors that discriminate between children with uncomplicated malaria and those who will develop severe complications.

Our multidisciplinary work brings together our expertise in the computational and data sciences with that of the clinical, life and population sciences, across academic partners in Africa, Europe and the US and it is contributing to discover functional associations that underpin predisposition to life-threatening malaria in children living in regions of the World under large burden of endemic malaria. These biomedical data-driven validated discoveries, together with our current work on robotic fast-automated microscopic malaria diagnosis strengthen our strategies to harness machine learning, artificial intelligence, digital health and novel computational approaches to tackle global childhood mortality due to malaria.