MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Plasmodium knowlesi Cytoadhesion Involves SICA Variant Proteins.

Plasmodium knowlesi poses a health threat throughout Southeast Asian communities and currently causes most cases of malaria in Malaysia. This zoonotic parasite species has been studied in Macaca mulatta (rhesus monkeys) as a model for severe malarial infections, chronicity, and antigenic variation. The phenomenon of Plasmodium antigenic variation was first recognized during rhesus monkey infections. Plasmodium-encoded variant proteins were first discovered in this species and found to be expressed at the surface of infected erythrocytes, and then named the Schizont-Infected Cell Agglutination (SICA) antigens. SICA expression was shown to be spleen dependent, as SICA expression is lost after P. knowlesi is passaged in splenectomized rhesus. Here we present data from longitudinal P. knowlesi infections in rhesus with the most comprehensive analysis to date of clinical parameters and infected red blood cell sequestration in the vasculature of tissues from 22 organs. Based on the histopathological analysis of 22 tissue types from 11 rhesus monkeys, we show a comparative distribution of parasitized erythrocytes and the degree of margination of the infected erythrocytes with the endothelium. Interestingly, there was a significantly higher burden of parasites in the gastrointestinal tissues, and extensive margination of the parasites along the endothelium, which may help explain gastrointestinal symptoms frequently reported by patients with P. knowlesi malarial infections. Moreover, this margination was not observed in splenectomized rhesus that were infected with parasites not expressing the SICA proteins. This work provides data that directly supports the view that a subpopulation of P. knowlesi parasites cytoadheres and sequesters, likely via SICA variant antigens acting as ligands. This process is akin to the cytoadhesive function of the related variant antigen proteins, namely Erythrocyte Membrane Protein-1, expressed by Plasmodium falciparum.

Malaria disrupts the rhesus macaque gut microbiome.

Previous studies have suggested that a relationship exists between severity and transmissibility of malaria and variations in the gut microbiome, yet only limited information exists on the temporal dynamics of the gut microbial community during a malarial infection. Here, using a rhesus macaque model of relapsing malaria, we investigate how malaria affects the gut microbiome. In this study, we performed 16S sequencing on DNA isolated from rectal swabs of rhesus macaques over the course of an experimental malarial infection with Plasmodium cynomolgi and analyzed gut bacterial taxa abundance across primary and relapsing infections. We also performed metabolomics on blood plasma from the animals at the same timepoints and investigated changes in metabolic pathways over time. Members of Proteobacteria (family Helicobacteraceae) increased dramatically in relative abundance in the animal's gut microbiome during peak infection while Firmicutes (family Lactobacillaceae and Ruminococcaceae), Bacteroidetes (family Prevotellaceae) and Spirochaetes amongst others decreased compared to baseline levels. Alpha diversity metrics indicated decreased microbiome diversity at the peak of parasitemia, followed by restoration of diversity post-treatment. Comparison with healthy subjects suggested that the rectal microbiome during acute malaria is enriched with commensal bacteria typically found in the healthy animal's mucosa. Significant changes in the tryptophan-kynurenine immunomodulatory pathway were detected at peak infection with P. cynomolgi, a finding that has been described previously in the context of P. vivax infections in humans. During relapses, which have been shown to be associated with less inflammation and clinical severity, we observed minimal disruption to the gut microbiome, despite parasites being present. Altogether, these data suggest that the metabolic shift occurring during acute infection is associated with a concomitant shift in the gut microbiome, which is reversed post-treatment.

Clinical recovery of Macaca fascicularis infected with Plasmodium knowlesi.

BACKGROUND: Kra monkeys (Macaca fascicularis), a natural host of Plasmodium knowlesi, control parasitaemia caused by this parasite species and escape death without treatment. Knowledge of the disease progression and resilience in kra monkeys will aid the effective use of this species to study mechanisms of resilience to malaria. This longitudinal study aimed to define clinical, physiological and pathological changes in kra monkeys infected with P. knowlesi, which could explain their resilient phenotype. METHODS: Kra monkeys (n = 15, male, young adults) were infected intravenously with cryopreserved P. knowlesi sporozoites and the resulting parasitaemias were monitored daily. Complete blood counts, reticulocyte counts, blood chemistry and physiological telemetry data (n = 7) were acquired as described prior to infection to establish baseline values and then daily after inoculation for up to 50 days. Bone marrow aspirates, plasma samples, and 22 tissue samples were collected at specific time points to evaluate longitudinal clinical, physiological and pathological effects of P. knowlesi infections during acute and chronic infections. RESULTS: As expected, the kra monkeys controlled acute infections and remained with low-level, persistent parasitaemias without anti-malarial intervention. Unexpectedly, early in the infection, fevers developed, which ultimately returned to baseline, as well as mild to moderate thrombocytopenia, and moderate to severe anaemia. Mathematical modelling and the reticulocyte production index indicated that the anaemia was largely due to the removal of uninfected erythrocytes and not impaired production of erythrocytes. Mild tissue damage was observed, and tissue parasite load was associated with tissue damage even though parasite accumulation in the tissues was generally low. CONCLUSIONS: Kra monkeys experimentally infected with P. knowlesi sporozoites presented with multiple clinical signs of malaria that varied in severity among individuals. Overall, the animals shared common mechanisms of resilience characterized by controlling parasitaemia 3-5 days after patency, and controlling fever, coupled with physiological and bone marrow responses to compensate for anaemia. Together, these responses likely minimized tissue damage while supporting the establishment of chronic infections, which may be important for transmission in natural endemic settings. These results provide new foundational insights into malaria pathogenesis and resilience in kra monkeys, which may improve understanding of human infections.

Humoral immunity prevents clinical malaria during Plasmodium relapses without eliminating gametocytes.

Plasmodium relapses are attributed to the activation of dormant liver-stage parasites and are responsible for a significant number of recurring malaria blood-stage infections. While characteristic of human infections caused by P. vivax and P. ovale, their relative contribution to malaria disease burden and transmission remains poorly understood. This is largely because it is difficult to identify 'bona fide' relapse infections due to ongoing transmission in most endemic areas. Here, we use the P. cynomolgi-rhesus macaque model of relapsing malaria to demonstrate that clinical immunity can form after a single sporozoite-initiated blood-stage infection and prevent illness during relapses and homologous reinfections. By integrating data from whole blood RNA-sequencing, flow cytometry, P. cynomolgi-specific ELISAs, and opsonic phagocytosis assays, we demonstrate that this immunity is associated with a rapid recall response by memory B cells that expand and produce anti-parasite IgG1 that can mediate parasite clearance of relapsing parasites. The reduction in parasitemia during relapses was mirrored by a reduction in the total number of circulating gametocytes, but importantly, the cumulative proportion of gametocytes increased during relapses. Overall, this study reveals that P. cynomolgi relapse infections can be clinically silent in macaques due to rapid memory B cell responses that help to clear asexual-stage parasites but still carry gametocytes.

Correction to: Integrative analysis associates monocytes with insufficient erythropoiesis during acute Plasmodium cynomolgi malaria in rhesus macaques.

After publication of the article [1], it was brought to our attention that several symbols were missing from Fig. 1, including some cited in the figure's key. The correct version of the figure is shown below and has now been updated in the original article.

Integrative analysis associates monocytes with insufficient erythropoiesis during acute Plasmodium cynomolgi malaria in rhesus macaques.

BACKGROUND: Mild to severe anaemia is a common complication of malaria that is caused in part by insufficient erythropoiesis in the bone marrow. This study used systems biology to evaluate the transcriptional and alterations in cell populations in the bone marrow during Plasmodium cynomolgi infection of rhesus macaques (a model of Plasmodium vivax malaria) that may affect erythropoiesis. RESULTS: An appropriate erythropoietic response did not occur to compensate for anaemia during acute cynomolgi malaria despite an increase in erythropoietin levels. During this period, there were significant perturbations in the bone marrow transcriptome. In contrast, relapses did not induce anaemia and minimal changes in the bone marrow transcriptome were detected. The differentially expressed genes during acute infection were primarily related to ongoing inflammatory responses with significant contributions from Type I and Type II Interferon transcriptional signatures. These were associated with increased frequency of intermediate and non-classical monocytes. Recruitment and/or expansion of these populations was correlated with a decrease in the erythroid progenitor population during acute infection, suggesting that monocyte-associated inflammation may have contributed to anaemia. The decrease in erythroid progenitors was associated with downregulation of genes regulated by GATA1 and GATA2, two master regulators of erythropoiesis, providing a potential molecular basis for these findings. CONCLUSIONS: These data suggest the possibility that malarial anaemia may be driven by monocyte-associated disruption of GATA1/GATA2 function in erythroid progenitors resulting in insufficient erythropoiesis during acute infection.

Automated Predictive Big Data Analytics Using Ontology Based Semantics.

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.