Malaria can quickly progress from an uncomplicated infection into a life-threatening severe disease. However, the unspecificity of early symptoms often makes it difficult to identify patients at high risk of developing severe disease. Additionally, one of the most feared malaria complications — cerebral malaria — is challenging to diagnose, often resulting in treatment delays that can lead to adverse outcomes. To identify candidate biomarkers for the prognosis and/or diagnosis of severe and cerebral malaria, we have analyzed the transcriptomic response of human brain microvascular endothelial cells to erythrocytes infected with Plasmodium falciparum. Candidates were validated in plasma samples from a cohort of pediatric patients with malaria from Mozambique, resulting in the identification of several markers with capacity to distinguish uncomplicated from severe malaria, the most potent being the metallopeptidase ADAMTS18. Two other biomarkers, Angiopoietin-like-4 and Inhibin-βE were able to differentiate children with cerebral malaria within the severe malaria group, showing increased sensitivity after combination in a biomarker signature. The validation of the predicted candidate biomarkers in plasma of children with severe and cerebral malaria underscores the power of this transcriptomic approach and indicates that a specific endothelial response to P. falciparum–infected erythrocytes is linked to the pathophysiology of severe malaria.
Cláudia Gomes, Rosauro Varo, Miquel Duran-Frigola, Antonio Sitoe, Rubão Bila, Sonia Machevo, Alfredo Mayor, Quique Bassat, Ana Rodriguez
Usage data is cumulative from October 2023 through June 2024.
Usage | JCI | PMC |
---|---|---|
Text version | 1,357 | 150 |
333 | 64 | |
Figure | 141 | 1 |
Table | 49 | 0 |
Supplemental data | 104 | 4 |
Citation downloads | 38 | 0 |
Totals | 2,022 | 219 |
Total Views | 2,241 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.