Infection with Plasmodium falciparum can lead to a range of severe to minimal symptoms, occasionally resulting in death in young children or nonimmune adults. In areas of high transmission, older children and adults generally suffer only mild or asymptomatic malaria infections and rarely develop severe disease. The immune features underlying this apparent immunity to severe disease remain elusive. To gain insight into host responses associated with severe and mild malaria, we conducted a longitudinal study of five children who first presented with severe malaria and, 1 month later, with mild malaria. Employing peripheral blood whole-genome profiling, we identified 68 genes that were associated with mild malaria compared to their expression in the severe malaria episode (paired Students t test, P < 0.05). These genes reflect the interferon (IFN) pathway and T cell biology and include IFN-induced protein transcripts 1 to 3, oligoadenylate synthetases 1 and 3, and the T cell markers cathepsin W and perforin. Gene set enrichment analysis identified Gene Ontology (GO) pathways associated with mild malaria to include the type I interferon-mediated signaling pathway (GO 0060337), T cell activation (GO 0042110), and other GO pathways representing many aspects of immune activation. In contrast, only six genes were associated with severe malaria, including thymidine kinase 1, which was recently found to be a biomarker of cerebral malaria susceptibility in the murine model, and carbonic anhydrase, reflecting the blood9s abnormal acid base environment during severe disease. These data may provide potential insights to inform pathogenesis models and the development of therapeutics to reduce severe disease outcomes due to P. falciparum infection.

Cerebral malaria (CM) presents as an encephalopathy and is due to infection with Plasmodium falciparum. Patients are comatose, often with fever, recurrent seizures and this condition is associated with a high mortality rate. The etiology of the coma and seizures are poorly understood. Circulating small molecules and lipids have bioactive functions and alterations in their concentrations have been implicated in seizure disorders and other forms of encephalopathy. We carried out a comprehensive analysis of blood metabolites during CM to explore a biochemical basis of this encephalopathy. A paired metabolomics analysis was performed on the plasma samples of Malawian children (n = 11) during CM and at convalescence thirty days later, to identify differentially abundant molecules associated with CM. We also report plasma molecules associated with CM mortality (n = 4) compared to survival (n = 19). Plasma metabolites were identified through ultra high performance liquid chromatography/tandem mass spectrometry and gas chromatography/mass spectrometry to maximize compound detection and accuracy and then compared to a library for identification. We detected a total of 432 small molecules in the plasma and 247 metabolites were significantly differentially abundant between CM and convalescence (p < 0.05, FDR < 0.10). These represented global changes across many classes of molecules including lipids, amino acids and hemoglobin metabolites. We observed significant changes in molecules that could impact neurologic function during CM; these include increased levels of kynurenate and decreased indolepropionate, glutamate, arginine and glutamine. Moreover, 1-methylimidazoleacetate, kyurenate, arachidonic acid and dimethylarginine were associated with mortality (p < 0.05, fold change > 1.2). These results highlight the broad changes in blood chemistry during CM. We have identified metabolites that may impact central nervous system physiology and disease outcomes and can be further explored for their mechanistic roles into the pathophysiology of CM.

HIV infected individuals in malaria endemic areas experience more frequent and severe malaria episodes compared to non HIV infected. This clinical observation has been linked to a deficiency in antibody responses to Plasmodium falciparum antigens; however, prior studies have only focused on the antibody response to <0.5% of P. falciparum proteins. To obtain a broader and less-biased view of the effect of HIV on antibody responses to malaria we compared antibody profiles of HIV positive (HIV+) and negative (HIV-) Rwandan adults with symptomatic malaria using a microarray containing 824 P. falciparum proteins. We also investigated the cellular basis of the antibody response in the two groups by analyzing B and T cell subsets by flow cytometry. Although HIV malaria co-infected individuals generated antibodies to a large number of P. falciparum antigens, including potential vaccine candidates, the breadth and magnitude of their response was reduced compared to HIV- individuals. HIV malaria co-infection was also associated with a higher percentage of atypical memory B cells (MBC) (CD19+CD10-CD21-CD27-) compared to malaria infection alone. Among HIV+ individuals the CD4⁺ T cell count and HIV viral load only partially explained variability in the breadth of P. falciparum-specific antibody responses. Taken together, these data indicate that HIV malaria co-infection is associated with an expansion of atypical MBCs and a diminished antibody response to a diverse array of P. falciparum antigens, thus offering mechanistic insight into the higher risk of malaria in HIV+ individuals.

Objectives Despite the availability of effective antimalarial prophylaxis, imported adult and pediatric malaria occurs in the United States, and this can pose diagnostic issues. We examined the clinical characteristics and diagnostic challenges of imported malaria requiring adult or pediatric inpatient admission at Montefiore Medical Center in the Bronx which provides care for a large population of immigrants from malaria endemic areas. Study Design We conducted a retrospective single center review of patients admitted with a diagnosis of malaria at Montefiore Medical Center from 2005 through 2012. We extracted historical, clinical, and laboratory values from the electronic medical record and patient charts. Results We identified 95 patients who were diagnosed and hospitalized with malaria from 2005 to 2012, 33 (35%) of them were children and 17 (18%) were with severe malaria. Most patients contracted malaria while visiting friends and relatives in West Africa. Only 38% of travelers took prophylaxis, and fewer than half reported taking it as prescribed. Misdiagnosis by emergency room or primary care doctors was observed in almost one quarter of all of the patients. Misdiagnosis occurred significantly more frequently in children (43%) compared to adults (13%) (P = 0.002). Pediatric patients were more likely to present with abdominal pain (42% vs. 15%; P = 0.005). Conclusions Pediatric patients admitted for imported malaria at Montefiore Medical Center had a higher rate of misdiagnosis and presented with more gastrointestinal symptoms than hospitalized adults. By describing the clinical characteristics of patients with imported malaria, we hope to improve diagnostic accuracy by health care workers and raise awareness that friends and relatives may require more intensive pretravel counseling.

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.