Session: Session 2

Deciphering the P. falciparum malaria host response by plasma proteomics

Clemens DIERKS^1,2, Pinkus TOBER-LAU², Florian KURTH², Markus RALSER¹

¹Institute of Biochemistry, Charité Universitätsmedizin , Berlin, Germany
²Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité Universitätsmedizin Berlin, Berlin, Germany

I:

Malaria presents with a wide range of disease severities, from asymptomatic to nfection to multi organ failure and death. High-throughput plasma proteomics helps in understanding host responses to malaria, identifying risk factors, and discovering novel biomarkers. This study aims to use the plasma proteome to identify disease severity and immunity signatures in malaria patients from endemic regions and non-immune travelers.

M:

We subjected plasma samples of 482 patients to MS-based measurement resulting in a dataset of over 330 high-abundant quantified proteins. Our cohort consists of patients with varying disease severities (asymptomatic, uncomplicated, severe), different immunity status and backgrounds (Germany, Gabon), and includes longitudinal measurements as well as complementing routine clinical diagnostics.

R:

We identified 146 proteins that were differentially regulated in infected patients compared to healthy controls. These proteins primarily reflected acute phase responses (e.g. AHSG, SAA1, CD14 ), immune reactions (e.g., immunoglobulins), and tissue reconstitution (e.g. GSN, EFEMP1). In contrast, asymptomatic infections showed markedly less pronounced alterations to the proteome with similar levels of many proteins as healthy controls. However, employing machine learning classifiers, we could robustly distinguish between different severity levels of infection (). Longitudinal analysis of the plasma proteome revealed distinct patterns: some proteins, like acute-phase proteins, responded quickly with progressing disease, while others, involved in the adaptive immune system, showed slower response patterns.

C:

Integrating proteomic data with routine clinical data and machine learning techniques enhances the understanding of host responses to Plasmodium falciparum malaria in populations with varying immunity levels. This is crucial as effective malaria management reduces exposure, leading to waning semi-immunity in previously endemic regions.