Big Data To The Rescue Of Malaria – Why Do Some People Get Infected But Not Sick?

In recent years, the study of human immunology has been fostered by the development and rapid advances of high throughput technologies used to profile genetic, transcriptional, translational, and metabolic phenotypes during good health and disease. They have been particularly important because many of the standard manipulations using animal models are not suited for human research due ethical considerations. Therefore, methodologies such as genome-wide gene expression profiling (transcriptomics) and global metabolite screening (metabolomics) offer rich information from often limited clinical samples, such as blood.

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Diverse studies have taken advantage of these methodologies to investigate host-parasite interactions in the lethal parasitic disease, Malaria. Caused by protozoans from the Plasmodium genus, 216 million cases have been recorded by the World Health Organization in 2016, with 445,000 fatal outcomes. P. vivax is the most widespread human malaria parasite and is particularly characterized by its ability to cause relapsing malaria episodes due to dormant liver stages. Besides that, frequent exposure to Plasmodium parasites in regions of high malaria transmission results in a development of tolerance to clinical symptoms (reduced intensity of symptoms) or asymptomatic infections (no evident symptoms), but relatively high levels of the parasite in the blood. This means that control of symptoms is not necessarily or exclusively related to the control of the parasite. Why this happens and, most importantly, how these subjects acquire resistance to symptoms but not the parasite are long-dated questions. Therefore, a detailed understanding of the pathophysiology of this disease is of extreme importance to the development of novel management strategies and vaccines.

To gather insights into human response to P. vivax infection, a clinical trial was set up at the Malaria Vaccine and Drug Development Center in Cali, Colombia. Of the 100 subjects assessed for eligibility, 16 individuals were allocated in two groups: one with subjects that have never been in contact with Plasmodium and never suffered from Malaria; and another with subjects that had at least one previous episode of Malaria caused by P. vivax. These voluntary subjects, who were fully aware of the risks and agreed with participating of this clinical trial, were then submitted to an experimental infection with P. vivax by the exposure to bites of infected Anopheles albimanus, the transmitting mosquito of Central America.

The subjects were followed during the whole infection period and received curative doses of antimalarial drugs, chloroquine, and primaquine. Blood samples were collected before the infection, during the peak of blood parasitemia (between 10-13 days), and 3 weeks after the treatment. Symptoms were monitored and demonstrated that previous exposure to malaria parasites indeed results in clinical tolerance. To evaluate different layers of the system, whole blood RNA was used to investigate genome-wide gene expression by RNA sequencing, whereas plasma samples were obtained to profile global metabolite abundance using mass spectrometry coupled with liquid chromatography. Furthermore, cutting-edge computational methods were used to analyze, interpret and integrate different types of data.

With this approach, clear distinction patterns were observed in which groups of study differed in gene expression related to interferon signature, neutrophils and B cells before infection. During the peak of parasitemia, subjects that were more tolerant to symptoms showed higher activation of genes related to the inflammatory response involving myeloid cells, such as neutrophils and monocytes or even blood coagulation. Interestingly, metabolomics analyses revealed differences in amino acid pathways and lipid metabolism, including methionine and cysteine metabolism, fatty acid metabolism and linoleate metabolism. The integration of these distinct layers of the same system uncovered unknown relationships between genes and metabolites involved in molecular events such as platelet activation, innate immunity, and T cell signaling.

To validate these associations identified by unbiased, data-driven analyses, additional metabolomics experiments were performed with platelets from different healthy subjects. These investigations revealed that plasma metabolites associated with platelet-related genes from malaria subjects were highly enriched in platelets from healthy subjects, indicating that the recorded associations between genes and metabolites were not merely artifacts, but rather reflect the activation of platelets during P. vivax malaria. Importantly, platelet counts also differed between groups, with naïve subjects presenting the greatest drop in blood platelets after infection. These findings suggest that platelets contribute directly to the control of clinical symptoms during infection with P. vivax and might thus provide a novel target or approach for treating symptoms during severe disease.

These findings are described in the article entitled Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling, recently published in the journal Redox BiologyThis work was conducted by Luiz G. Gardinassi, Regina J. Cordy, ViLinh Tran, Matthew R. Smith, Ken H. Liu, Young-Mi Go, Dean P. Jones, Mary R. Galinski, and Shuzhao Li from Emory UniversityMyriam Arévalo-Herrera from the Malaria Vaccine and Drug Development Center and the Universidad del ValleSócrates Herrera from the Malaria Vaccine and Drug Development Center and Caucaseco Scientific Research CenterMichelle S. Johnson, Balu Chacko, and Victor M. Darley-Usmar from the University of Alabama at Birmingham, and the MaHPIC Consortium

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Cite this article as:
Luiz G. Gardinassi. Big Data To The Rescue Of Malaria – Why Do Some People Get Infected But Not Sick?, Science Trends, 2018.
DOI: 10.31988/SciTrends.33209
*Note, DOIs are registered Friday weekly and therefore may not work until then.

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