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Metabolic signature profiling as a diagnostic and prognostic tool in paediatric Plasmodium falciparum malaria
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster)
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
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2015 (English)In: Open Forum Infectious Diseases, ISSN 2328-8957, Vol. 2, no 2Article in journal (Refereed) Published
Abstract [en]

Background: Accuracy in malaria diagnosis and staging is vital in order to reduce mortality and post infectious sequelae. Herein we present a metabolomics approach to diagnostic staging of malaria infection, specifically Plasmodium falciparum infection in children. Methods: A group of 421 patients between six months and six years of age with mild and severe states of malaria with age-matched controls were included in the study, 107, 192 and 122 individuals respectively. A multivariate design was used as basis for representative selection of twenty patients in each category. Patient plasma was subjected to Gas Chromatography-Mass Spectrometry analysis and a full metabolite profile was produced from each patient. In addition, a proof-of-concept model was tested in a Plasmodium berghei in-vivo model where metabolic profiles were discernible over time of infection. Results: A two-component principal component analysis (PCA) revealed that the patients could be separated into disease categories according to metabolite profiles, independently of any clinical information. Furthermore, two sub-groups could be identified in the mild malaria cohort who we believe represent patients with divergent prognoses. Conclusion: Metabolite signature profiling could be used both for decision support in disease staging and prognostication.

Place, publisher, year, edition, pages
Oxford University Press, 2015. Vol. 2, no 2
Keyword [en]
disease staging, malaria, metabolomics
National Category
Bioinformatics (Computational Biology) Infectious Medicine
URN: urn:nbn:se:umu:diva-102800DOI: 10.1093/ofid/ofv062ISI: 000365786200047OAI: diva2:809971
Available from: 2015-05-05 Created: 2015-05-05 Last updated: 2015-12-28Bibliographically approved
In thesis
1. Host responses to malaria and bacterial co-­infections
Open this publication in new window or tab >>Host responses to malaria and bacterial co-­infections
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Umeå Universitet: Umeå universitet, 2015. 59 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1720
Plasmodium, Malaria, Borrelia, S. pneumoniae, Co-infection, Immunology, Metabolomics
National Category
Cell and Molecular Biology
urn:nbn:se:umu:diva-102795 (URN)978-91-7601-276-5 (ISBN)
Public defence
2015-05-29, Major Grove, Building J1, Department of Molecular Biology, Umeå, 09:00 (English)
Available from: 2015-05-08 Created: 2015-05-05 Last updated: 2015-05-08Bibliographically approved

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Surowiec, IzabellaKarlsson, ElisabethNelson, MariaBonde, MariBergström, SvenTrygg, JohanNormark, Johan
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Department of ChemistryDepartment of Molecular Biology (Faculty of Medicine)Umeå Centre for Microbial Research (UCMR)Molecular Infection Medicine Sweden (MIMS)Infectious Diseases
Bioinformatics (Computational Biology)Infectious Medicine

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