Global sequence diversity of the lactate dehydrogenase gene in Plasmodium falciparum

Rapid diagnostic tests for malaria diagnosis in Cameroon: impact of histidine rich protein 2/3 deletions and lactate dehydrogenase gene polymorphism

Malaria rapid diagnostic tests (mRDT) play a vital role in malaria control in endemic areas. In this study, histidine-rich protein (hrp) and lactate dehydrogenase (ldh) genes were genotyped in Plasmodium falciparum (Pf) and Plasmodium ovale (Po) spp. isolates. Deletions in P. falciparum hrp2/3 (pfhrp2/3) proteins and single nucleotide polymorphisms (SNPs) were analyzed. Twenty-four samples were analyzed for pfhrp2/3 gene deletions and 25 for SNPs in ldh gene (18 Pf and 7 Po spp.). Deletions in pfhrp2/3 genes were observed in 1.9% malaria positive isolates. The pfldh gene sequences showed one SNP at codon 272 (D272N) in 22.2% of samples while in Po spp., sequences were 100% similar to P. ovale curtisi but when compared to P. ovale wallikeri reference sequence, SNPs at positions 143 (P143S), 168 (K168N), 204 (V204I) were found. Findings suggest low prevalence in pfhrp2/3 genes and highlight the circulation of P. ovale curtisi in the studies areas.

Ultrasensitive electrochemical genosensors for species-specific diagnosis of malaria

The absence of reliable species-specific diagnostic tools for malaria at point-of-care (POC) remains a major setback towards effective disease management. This is partly due to the limited sensitivity and specificity of the current malaria POC diagnostic kits especially in cases of low-density parasitaemia and mixed species infections. In this study, we describe the first label-free DNA-based genosensors based on electrochemical impedance spectroscopy (EIS) for species-specific detection of P. falciparum, P. malariae and P. ovale. The limits of detection (LOD) for the three species-specific genosensors were down in attomolar concentrations ranging from 18.7 aM to 43.6 aM, which is below the detection limits of previously reported malaria genosensors. More importantly, the diagnostic performance of the three genosensors were compared to quantitative real-time polymerase chain reaction (qPCR) assays using purified genomic DNA and the paired whole blood lysates from clinical samples. Remarkably, all the qPCR-positive purified genomic DNA samples were correctly identified by the genosensors indicating 100% sensitivity for each of the three malaria species. The specificities of the three genosensors ranged from 66.7% to 100.0% with a Therapeutic Turnaround Time (TTAT) within 30 min, which is comparable to the TTAT of current POC diagnostic tools for malaria. This work represents a significant step towards the development of accurate and rapid species-specific nucleic acid-based toolkits for the diagnosis of malaria at the POC.

Predicting Plasmodium falciparum infection status in blood using a multiplexed bead-based antigen detection assay and machine learning approaches

Background

Plasmodium blood-stage infections can be identified by assaying for protein products expressed by the parasites. While the binary result of an antigen test is sufficient for a clinical result, greater nuance can be gathered for malaria infection status based on quantitative and sensitive detection of Plasmodium antigens and machine learning analytical approaches.

Methods

Three independent malaria studies performed in Angola and Haiti enrolled persons at health facilities and collected a blood sample. Presence and parasite density of P. falciparum infection was determined by microscopy for a study in Angola in 2015 (n = 193), by qRT-PCR for a 2016 study in Angola (n = 208), and by qPCR for a 2012–2013 Haiti study (n = 425). All samples also had bead-based detection and quantification of three Plasmodium antigens: pAldolase, pLDH, and HRP2. Decision trees and principal component analysis (PCA) were conducted in attempt to categorize P. falciparum parasitemia density status based on continuous antigen concentrations.

Results

Conditional inference trees were trained using the known P. falciparum infection status and corresponding antigen concentrations, and PCR infection status was predicted with accuracies ranging from 73–96%, while level of parasite density was predicted with accuracies ranging from 59–72%. Multiple decision nodes were created for both pAldolase and HRP2 antigens. For all datasets, dichotomous infectious status was more accurately predicted when compared to categorization of different levels of parasite densities. PCA was able to account for a high level of variance (>80%), and distinct clustering was found in both dichotomous and categorical infection status.

Conclusions

This pilot study offers a proof-of-principle of the utility of machine learning approaches to assess P. falciparum infection status based on continuous concentrations of multiple Plasmodium antigens.

Development of machine learning model for diagnostic disease prediction based on laboratory tests

The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

Genetic diversity of Plasmodium vivax and Plasmodium falciparum lactate dehydrogenases in Myanmar isolates

Background

Plasmodium lactate dehydrogenase (pLDH) is a major target in diagnosing the erythrocytic stage of malaria parasites because it is highly expressed during blood-stage parasites and is distinguished from human LDH. Rapid diagnostic tests (RDTs) for malaria use pLDH as a target antigen; however, genetic variations in pLDH within the natural population threaten the efficacy of pLDH-based RDTs.

Methods

Genetic polymorphisms of Plasmodium vivax LDH (PvLDH) and Plasmodium falciparum LDH (PfLDH) in Myanmar isolates were analysed by nucleotide sequencing analysis. Genetic polymorphisms and the natural selection of PvLDH and PfLDH were analysed using DNASTAR, MEGA6, and DnaSP ver. 5.10.00 programs. The genetic diversity and natural selection of global PvLDH and PfLDH were also analysed. The haplotype network of global PvLDH and PfLDH was constructed using NETWORK ver. 5.0.0.3. Three-dimensional structures of PvLDH and PfLDH were built with YASARA Structure ver. 18.4.24 and the impact of mutations on structural change and stability was evaluated with SDM ver. 2, CUPSAT and MAESTROweb.

Results

Forty-nine PvLDH and 52 PfLDH sequences were obtained from Myanmar P. vivax and P. falciparum isolates. Non-synonymous nucleotide substitutions resulting in amino acid changes were identified in both Myanmar PvLDH and PfLDH. Amino acid changes were also identified in the global PvLDH and PfLDH populations, but they did not produce structural alterations in either protein. Low genetic diversity was observed in global PvLDH and PfLDH, which may be maintained by a strong purifying selection.

Conclusion

This study extends knowledge for genetic diversity and natural selection of global PvLDH and PfLDH. Although amino acid changes were observed in global PvLDH and PfLDH, they did not alter the conformational structures of the proteins. These suggest that PvLDH and PfLDH are genetically well-conserved in global populations, which indicates that they are suitable antigens for diagnostic purpose and attractive targets for drug development.

Diagnosis of Malaria Parasites Plasmodium spp. in Endemic Areas: Current Strategies for an Ancient Disease

Fast and effective detection of the causative agent of malaria in humans, protozoan Plasmodium parasites, is of crucial importance for increasing the effectiveness of treatment and to control a devastating disease that affects millions of people living in endemic areas. The microscopic examination of Giemsa-stained blood films still remains the gold-standard in Plasmodium detection today. However, there is a high demand for alternative diagnostic methods that are simple, fast, highly sensitive, ideally do not rely on blood-drawing and can potentially be conducted by the patients themselves. Here, the history of Plasmodium detection is discussed, and advantages and disadvantages of diagnostic methods that are currently being applied are assessed.

Plasmodium falciparum glutamate dehydrogenase is genetically conserved across eight malaria endemic states of India: Exploring new avenues of malaria elimination

Accurate and timely diagnosis is very critical for management, control and elimination of the malaria. Malaria rapid diagnostic tests (RDTs) have improved the diagnosis and management of malaria in remote areas, community and places where microscopy is not available for diagnosis. According to WHO report 2018, Plasmodium falciparum malaria constitutes more than 50% of malaria cases in India. Most of the RDTs used for diagnosis of falciparum malaria today employ HRP2 as a target antigen. However, low density parasitemia and deletion of hrp-2 gene in P. falciparum leads to false negative results and necessitates the development of alternative/ new or improved RDT for malaria diagnosis. We have analysed the genetic diversity and homology modelling of Pfgdh (glutamate dehydrogenase), ldh (lactate dehydrogenase) and aldolase genes in P. falciparum isolates from the eight endemic states of India to assess their potential as antigen for RDT development. We observed negligible sequence diversity in Pfgdh in comparison to the low level of diversity in ldh and aldolase gene. No structural or functional changes were observed in modelling studies and all three genes were under negative purifying selection pressure. The highly conserved nature of pfgdh gene suggests that GDH could be a potential target molecule for Pan/Pf diagnostic test for malaria.

Assessment of false negative rates of lactate dehydrogenase-based malaria rapid diagnostic tests for Plasmodium ovale detection

Currently, malaria rapid diagnostic tests (RDTs) are widely used for malaria diagnosis, but test performance and the factors that lead to failure of Plasmodium ovale detection are not well understood. In this study, three pLDH-based RDTs were evaluated using cases in China that originated in Africa. The sensitivity of Wondfo Pf/Pan, CareStart pLDH PAN and SD BIOLINE Pf/Pan in P. ovale detection was 70, 55 and 18%, respectively. CareStart was worse at detecting P. o. curtisi (36.5%) than at detecting P. o. wallikeri (75.0%), and SD could not detect P. o. curtisi. The overall detection ratio of all three RDTs decreased with parasite density and pLDH concentration. Wondfo, CareStart and SD detected only 75.0, 78.1 and 46.9% of the P. ovale cases, respectively, even when the parasitemia were higher than 5000 parasites/μL. Subspecies of P. ovale should be considered while to improve RDT quality for P. ovale diagnosis to achieve the goal of malaria elimination.

Plasmodium ovale (P. ovale) are under-estimated and overshadowed by other malaria parasites in tropical countries, which can cause chronic infections that last from months to years. The chronic infection caused by P. ovale should be of concern in the context of the long-term goal of eliminating malaria. Rapid diagnostic tests (RDTs) is one of the WHO recommended tools to confirm the infection of plasmodium parasites, which can distinguish Plasmodium falciparum and non-falciparum species as well. However, little is known about their performance detecting P. ovale, and the factors that affect the efficiency of RDTs in the detection of P. ovale have not been systemically investigated. This study suggested that the performance of the three pLDH-based RDTs for P. ovale detection was not optimal, the low parasite density and pLDH concentration contributed to the failure of the RDT test for P. ovale. It provided information for the application of malaria RDTs in the field and for research and development to improve RDTs for malaria diagnosis.