Malaria remains a significant public health concern and is responsible for more than 400,000 deaths annually, most of which occur in African children under the age of five. The majority of malaria infections remain asymptomatic; only a small percentage progress to clinical symptoms and an even smaller proportion of these progress to a life-threatening disease. The host and parasite factors leading to this clinical heterogeneity are not well described or well understood. Our hypothesis is that the speed with which a malaria infection develops and the total number of parasites present in the body are important determinants of disease severity. Variables related to parasite dynamics cannot currently be measured in the human host and ex vivo measurements do not reflect in vivo reality. This proposal will take advantage of a unique set of parasitological and patient specimens collected over the past several decades in Blantyre, Malawi to develop more robust and accurate estimates of two important measures of parasite dynamics: total body parasite load and parasite multiplication rate. ? Total body parasite load (TBPL): We will use tissue samples from children who died of cerebral malaria to quantify parasites in the most heavily parasitized organs and then extrapolate to the parasite load present in the entire body. This will create a new estimate of TBPL grounded in actual histological parasite counts. ? Parasite multiplication rate (PMR): Current estimates of PMR rely on removing the parasites from the host and testing their replication in culture, inevitably introducing culture-related artifact. We propose to use a novel method based on parasite-produced proteins with different half-lives to calculate PMR in vivo. We will then use clinical samples from well-characterized individuals at both extremes of the infection spectrum, asymptomatic parasitemia and cerebral malaria, in order to build a model to test the hypothesis that PMR and TBPL are important determinants of disease severity. If our hypothesis is correct and these parameters are related to disease severity, prognostic tests based on these metrics could lead to the real-time in vivo detection of high-risk malaria infections. Improved prognosis would lead, in turn, to earlier triage, and ultimately, a decrease in malaria morbidity and mortality. In addition, these measures of parasite dynamics would represent pathogenic processes that could be used to identify druggable targets. Should this hypothesis not hold true, we will still have generated novel, robust measures of in vivo parasite activity, which will be useful in characterizing the interactions of parasites with the host immune system as well as defining inter-clone competition within a host.

Public Health Relevance

Infection with Plasmodium falciparum, the causative agent of the severest form of malaria, results in a wide spectrum of disease states; asymptomatic carriers fall at one end of this spectrum, while those with coma and death fall at the other extreme and account for over 400,000 deaths annually. This proposal will develop new tests to evaluate whether the rate of growth of a malaria infection and the total number of parasites in the body determines the severity of the disease. If these new measures correlate with disease severity, they can be used to screen malaria patients early in the disease process, allowing resources and treatments to be directed to those most likely to progress to severe disease thereby decreasing malaria morbidity and mortality.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AI133094-01A1
Application #
9528181
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
O'Neil, Michael T
Project Start
2018-05-18
Project End
2020-04-30
Budget Start
2018-05-18
Budget End
2019-04-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Michigan State University
Department
Internal Medicine/Medicine
Type
Schools of Osteopathic Medicine
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824