Dataset Information

Investigation of an airport-associated cluster of falciparum malaria in Frankfurt, Germany, 2022.

ABSTRACT: Airport malaria is uncommon but increasing in Europe and often difficult to diagnose. We describe the clinical, epidemiological and environmental investigations of a cluster of airport malaria cases and measures taken in response. Three Frankfurt International Airport employees without travel histories to malaria-endemic areas were diagnosed with Plasmodium falciparum malaria in Germany in 2022. Two cases were diagnosed within 1 week, and the third one after 10 weeks. Two cases had severe disease, all three recovered fully. The cases worked in separate areas and no specific location for the transmissions could be identified. No additional cases were detected among airport employees. In June and July, direct flights from Equatorial Guinea, Nigeria and Angola and one parcel originating in Ghana arrived at Frankfurt airport. No vector-competent mosquitoes could be trapped to identify the source of the outbreak. Whole genome sequencing of P. falciparum genomes showed a high genetic relatedness between samples of the three cases and suggested the geographical origin closest to Ghana. A diagnosis of airport malaria should prompt appropriate and comprehensive outbreak investigations to identify the source and to prevent severe forms of falciparum malaria.

SUBMITTER: Kessel J

PROVIDER: S-EPMC10835754 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Investigation of an airport-associated cluster of falciparum malaria in Frankfurt, Germany, 2022.

Kessel Johanna J Rosanas-Urgell Anna A Dingwerth Tobias T Goetsch Udo U Haller Jonas J Huits Ralph R Kattenberg Johanna H JH Meinecke Anna A Monsieurs Pieter P Sroka Michael M Witte Torsten T Wolf Timo T

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 20240201 5

Airport malaria is uncommon but increasing in Europe and often difficult to diagnose. We describe the clinical, epidemiological and environmental investigations of a cluster of airport malaria cases and measures taken in response. Three Frankfurt International Airport employees without travel histories to malaria-endemic areas were diagnosed with <i>Plasmodium falciparum</i> malaria in Germany in 2022. Two cases were diagnosed within 1 week, and the third one after 10 weeks. Two cases had severe ...[more]

PMID: 38304950

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Project description:This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n = 20), vivax malaria (VM) (n = 17) and healthy controls (HC) (n = 20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.

Project description:BackgroundMalaria continues to be one of the most severe global infectious diseases, responsible for 1-2 million deaths yearly. The rapid evolution and spread of drug resistance in parasites has led to an urgent need for the development of novel antimalarial targets. Proteases are a group of enzymes that play essential roles in parasite growth and invasion. The possibility of designing specific inhibitors for proteases makes them promising drug targets. Previously, combining a comparative genomics approach and a machine learning approach, we identified the complement of proteases (degradome) in the malaria parasite Plasmodium falciparum and its sibling species 123, providing a catalog of targets for functional characterization and rational inhibitor design. Network analysis represents another route to revealing the role of proteins in the biology of parasites and we use this approach here to expand our understanding of the systems involving the proteases of P. falciparum.ResultsWe investigated the roles of proteases in the parasite life cycle by constructing a network using protein-protein association data from the STRING database 4, and analyzing these data, in conjunction with the data from protein-protein interaction assays using the yeast 2-hybrid (Y2H) system 5, blood stage microarray experiments 678, proteomics 9101112, literature text mining, and sequence homology analysis. Seventy-seven (77) out of 124 predicted proteases were associated with at least one other protein, constituting 2,431 protein-protein interactions (PPIs). These proteases appear to play diverse roles in metabolism, cell cycle regulation, invasion and infection. Their degrees of connectivity (i.e., connections to other proteins), range from one to 143. The largest protease-associated sub-network is the ubiquitin-proteasome system which is crucial for protein recycling and stress response. Proteases are also implicated in heat shock response, signal peptide processing, cell cycle progression, transcriptional regulation, and signal transduction networks.ConclusionsOur network analysis of proteases from P. falciparum uses a so-called guilt-by-association approach to extract sets of proteins from the proteome that are candidates for further study. Novel protease targets and previously unrecognized members of the protease-associated sub-systems provide new insights into the mechanisms underlying parasitism, pathogenesis and virulence.

Dataset Information

Investigation of an airport-associated cluster of falciparum malaria in Frankfurt, Germany, 2022.

Publications

Investigation of an airport-associated cluster of falciparum malaria in Frankfurt, Germany, 2022.

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