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The two main causes of child mortality and morbidity in Africa are malaria and invasive bacterial diseases. In addition, co-infections in sub-Saharan Africa are the rule rather than the exception. However, not much is known about the host-pathogen interaction during a concomitant infection or how it affects the outcome of disease.

In order to study the immunological responses during malaria and bacterial co-infections, we established a co-infection mouse model. In these studies we used two pathogenic bacteria found in malaria co-infected patients: Streptococcus pneumoniae and Relapsing fever Borrelia duttonii.

Hosts co-infected with malaria and Borrelia showed greatly increased spirochetal growth but low parasite densities. In addition, the co-infected hosts presented symptoms of experimental-cerebral malaria, in an otherwise unsusceptible mouse model. This was found to be a consequence of a dysregulated immune response due to loss of timing and control over regulatory mechanisms in antigen presenting cells thus locking the host in an inflammatory response. This results in inflammation, severe anemia, internal organ damage and pathology of experimental cerebral malaria.

On the other hand, in the malaria - S. pneumoniae co-infection model we found that co-infected hosts cleared the bacterium much more efficiently than the single infected counterpart. This efficiency of clearance showed to be neutrophil dependent. Furthermore, in vitro studies revealed that neutrophils isolated from malaria-infected hosts present an altered migratory effect together with a significantly increased capacity to kill S. pneumoniae. This suggests that a malaria infection primes neutrophils to kill S. pneumoniae more efficiently.

Furthermore, a study was carried out on plasma samples from Rwandan children under the age of five, on which a full metabolomics profile was performed. We showed that these children could be divided in different disease categories based on their metabolomics profile and independent of clinical information. Additionally, the mild malaria group could further be divided in two sub-groups, in which one had a metabolomic profile resembling that of severe malaria infected patients. Based on this, metabolite profiling could be used as a diagnostic tool to determine the distinct phase, or severity of a malaria infection, identify risk patients and provide helpful and correct therapy.