The Plasmodium falciparum protein PfMRP1 functions as an influx ABC transporter

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters play an important role in mediating solute or drug transport across cellular membranes. Although this class of transporters has been well characterized in diverse organisms little is known about the physiological roles in Plasmodium falciparum, the deadliest malaria parasite species. We studied the Plasmodium falciparum Multidrug Resistance-associated Protein 1 (PfMRP1; PF3D7_0112200), an ABC transporter localized to the parasite plasma membrane, generating genetic disrupted parasites. We demonstrate that parasites with disrupted pfmrp1 are resistant to folate analogs, methotrexate and aminopterin, with antimalarial activity. This phenotype occurs due to reduction in compound accumulation in the parasite cytoplasm. Phylogenetic analysis supports pfmrp1 being distantly related to ABC transporters in other eukaryotes, suggesting an unusual function. We propose that PfMRP1 can act as a solute importer, a function not previously observed in this organism.

C-type lectin 4 regulates broad-spectrum melanization-based refractoriness to malaria parasites

Anopheles gambiae melanization-based refractoriness to the human malaria parasite Plasmodium falciparum has rarely been observed in either laboratory or natural conditions, in contrast to the rodent model malaria parasite Plasmodium berghei that can become completely melanized by a TEP1 complement-like system-dependent mechanism. Multiple studies have shown that the rodent parasite evades this defense by recruiting the C-type lectins CTL4 and CTLMA2, while permissiveness to the human malaria parasite was not affected by partial depletion of these factors by RNAi silencing. Using CRISPR/Cas9-based CTL4 knockout, we show that A. gambiae can mount melanization-based refractoriness to the human malaria parasite, which is independent of the TEP1 complement-like system and the major anti-Plasmodium immune pathway Imd. Our study indicates a hierarchical specificity in the control of Plasmodium melanization and proves CTL4 as an essential host factor for P. falciparum transmission and one of the most potent mosquito-encoded malaria transmission-blocking targets.

Contribution of Transcriptome to Elucidate the Biology of Plasmodium spp

In the present review, we discuss some of the new technologies that have been applied to elucidate how Plasmodium spp escape from the immune system and subvert the host physiology to orchestrate the regulation of its biological pathways. Our manuscript describes how techniques such as microarray approaches, RNA-Seq and single-cell RNA sequencing have contributed to the discovery of transcripts and changed the concept of gene expression regulation in closely related malaria parasite species. Moreover, the text highlights the contributions of high-throughput RNA sequencing for the current knowledge of malaria parasite biology, physiology, vaccine target and the revelation of new players in parasite signaling.

Identification of Plasmodium secreted proteins based on monoDiKGap and distance-based Top-n-gram methods

Background: Many malaria infections are caused by Plasmodium falciparum. Accurate classification of the proteins secreted by the malaria parasite, which are essential for the development of anti-malarial drugs, is essential. Objective: To accurately classify the proteins secreted by the malaria parasite. Methods: Therefore, in order to improve the accuracy of the prediction of plasmodium secreted proteins, we established a classification model MGAP-SGD. MonodikGap features (k=7) of the secreted proteins were extracted, and then the optimal features were selected by the AdaBoost method. Finally, based on the optimal set of secreted proteins, the model was used to predict the secreted proteins using the stochastic gradient descent (SGD) algorithm. Results: Our model uses a 10-fold cross-validation set and independent test set in the stochastic gradient descent (SGD) classifier to validate the model, and the accuracy rates are 98.5859% and 97.973%, respectively. Conclusion: This also fully proves that the effectiveness and robustness of the prediction results of the MGAP-SGD model can meet the prediction needs of the secreted proteins of plasmodium.

Malaria Parasite Detection in Thick Blood Smears using Deep Learning

Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.

Distinguishing gene flow between malaria parasite populations

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report examines the use of population-level summaries of genetic divergence (FST) and relatedness (identity-by-descent) to distinguish levels of gene flow between malaria populations, focused on field-relevant questions about data size, sampling, and interpretability of observations from genomic surveillance studies. To do this, we use P. falciparum whole genome sequence data and simulated sequence data approximating malaria populations evolving under different current and historical epidemiological conditions. We employ mobile-phone associated mobility data to estimate parasite migration rates over different spatial scales and use this to inform our analysis. This analysis underscores the complementary nature of divergence- and relatedness-based metrics for distinguishing gene flow over different temporal and spatial scales and characterizes the data requirements for using these metrics in different contexts. Our results have implications for the design and implementation of malaria genomic surveillance studies.