Serological Profiling for Malaria Surveillance Using a Standard ELISA Protocol

A dot-blot ELISA preliminary evaluation using PvMSP1-42 recombinant protein as antigen for serological diagnosis of Plasmodium vivax infection in Thailand

Plasmodium vivax is the most prevalent cause of malaria in Thailand and is predominant in malarial endemic areas worldwide. P. vivax infection is characterized by low parasitemia, latent liver-stage parasites, or asymptomatic infections leading to underreported P. vivax cases. These are significant challenges for controlling and eliminating P. vivax from endemic countries. This study developed and evaluated a dot-blot enzyme-linked immunosorbent assay (ELISA) using PvMSP1-42 recombinant antigen for serological diagnosis based on the detection of antibodies against P. vivax. The optimal PvMSP1-42 concentration and dilutions of anti-human IgG horseradish peroxidase (HRP)-conjugated antiserum were tested on 88 serum samples from P. vivax, Plasmodium falciparum and bacterial infection, including healthy individuals. A cut-off titer of 1:800 produced optimal values for sensitivity and specificity of 90.9 and 98.2%, respectively, with an accuracy of 95.5%. The positive and negative predictive values were 96.8 and 94.7% respectively. The results from microscopic examination and dot-blot ELISA showed strong agreement with the 0.902 kappa index. Thus, the dot-blot ELISA using PvMSP1-42 antigen provided high sensitivity and specificity suitable for serodiagnosis of P. vivax infection. The test is a simple and quick diagnostic assay suitable for field testing as it does not require specific equipment or particular skills.

Antibody response to malaria vaccine candidates in pregnant women with Plasmodium falciparum and Schistosoma haematobium infections

Malaria in pregnancy has severe consequences for the mother and foetus. Antibody response to specific malaria vaccine candidates (MVC) has been associated with a decreased risk of clinical malaria and its outcomes. We studied Plasmodium falciparum (Pf) and Schistosoma haematobium (Sh) infections and factors that could influence antibody responses to MVC in pregnant women. A total of 337 pregnant women receiving antenatal care (ANC) and 139 for delivery participated in this study. Pf infection was detected by qPCR and Sh infection using urine filtration method. Antibody levels against CSP, AMA-1, GLURP-R0, VAR2CSA and Pfs48/45 MVC were quantified by ELISA. Multivariable linear regression models identified factors associated with the modulation of antibody responses. The prevalence of Pf and Sh infections was 27% and 4% at ANC and 7% and 4% at delivery. Pf infection, residing in Adidome and multigravidae were positively associated with specific IgG response to CSP, AMA-1, GLURP-R0 and VAR2CSA. ITN use and IPTp were negatively associated with specific IgG response to GLURP-R0 and Pfs48/45. There was no association between Sh infection and antibody response to MVC at ANC or delivery. Pf infections in pregnant women were positively associated with antibody response to CSP, GLURP-R0 and AMA-1. Antibody response to GLURP-R0 and Pfs48/45 was low for IPTp and ITN users. This could indicate a lower exposure to Pf infection and low malaria prevalence observed at delivery.

Serological surveillance on potential Plasmodium vivax exposure risk in a post-elimination setting

China was declared malaria free in June of 2021. In the post-elimination setting, vigilant surveillance is essential to sustain malaria free status. Serological surveillance has been recognized as an efficient tool for assessing the immunity levels and exposure risk in a population. In this study, a cross-sectional serological survey was conducted in Yingjiang County, China, in August–September, 2021. The study sites were villages along the borders with Myanmar, which have no local transmission since the last indigenous case registered in 2016. A total of 923 participants from six villages were enrolled. The majority was aged > 36 years (56.12%) and 12.46% (115/923) participants had experienced malaria infection at least once. A magnetic- bead-based assay was used to test antibodies against Plasmodium vivax antigen PvMSP-119 to evaluate the prevalence of antibody positive subjects. A reversible catalytic model was used to assess the risk of exposure. The prevalence of anti-PvMSP-119 IgG was 12.84% [95% confidence interval (CI): 9.22%–16.47%], 13.93% (95% CI: 10.11%–17.74%), and 3.57% (95% CI: 1.40%–5.75%) in three different line-of-defense areas, which differed significantly (P < 0.0001). The prevalence of anti-PvMSP-119 IgG increased with age and no statistically significant difference was detected between the sexes. The reversible catalytic model indicated that the seropositive conversion rate and seronegative reversion rate were 0.0042, 0.0034, 0.0032 and 0.0024, 0.0004, 0.0065 in the first-, second-line-of-defense area and total areas, respectively, and the fitted value did not differ significantly from the observed value (P > 0.1). Although this study found the prevalence of antibody-positive subjects and the seroconversion rate in this post-elimination setting were lower than that in transmission setting, the population still had an exposure risk. Serological surveillance should be considered in post-elimination settings to provide valuable information with which to evaluate the risk of malaria re-establishment.

Anti-merozoite antibodies induce natural killer cell effector function and are associated with immunity against malaria

Natural killer (NK) cells are potent immune effectors that can be activated via antibody-mediated Fc receptor engagement. Using multiparameter flow cytometry, we found that NK cells degranulate and release IFN-γ upon stimulation with antibody-opsonized Plasmodium falciparum merozoites. Antibody-dependent NK (Ab-NK) activity was largely strain transcending and enhanced invasion inhibition into erythrocytes. Ab-NK was associated with the successful control of parasitemia after experimental malaria challenge in African adults. In an independent cohort study in children, Ab-NK increased with age, was boosted by concurrent P. falciparum infections, and was associated with a lower risk of clinical episodes of malaria. Nine of the 14 vaccine candidates tested induced Ab-NK, including some less well-characterized antigens: P41, P113, MSP11, RHOPH3, and Pf_11363200. These data highlight an important role of Ab-NK activity in immunity against malaria and provide a potential mechanism for evaluating vaccine candidates.

Characterization of a novel Plasmodium falciparum merozoite surface antigen and potential vaccine target

Introduction

Detailed analyses of genetic diversity, antigenic variability, protein localization and immunological responses are vital for the prioritization of novel malaria vaccine candidates. Comprehensive approaches to determine the most appropriate antigen variants needed to provide broad protection are challenging and consequently rarely undertaken.

Methods

Here, we characterized PF3D7_1136200, which we named Asparagine-Rich Merozoite Antigen (ARMA) based on the analysis of its sequence, localization and immunogenicity. We analyzed IgG and IgM responses against the common variants of ARMA in independent prospective cohort studies in Burkina Faso (N = 228), Kenya (N = 252) and Mali (N = 195) using a custom microarray, Div-KILCHIP.

Results

We found a marked population structure between parasites from Africa and Asia. African isolates shared 34 common haplotypes, including a dominant pair although the overall selection pressure was directional (Tajima's D = -2.57; Fu and Li's F = -9.69; P < 0.02). ARMA was localized to the merozoite surface, IgG antibodies induced Fc-mediated degranulation of natural killer cells and strongly inhibited parasite growth in vitro. We found profound serological diversity, but IgG and IgM responses were highly correlated and a hierarchical clustering analysis identified only three major serogroups. Protective IgG and IgM antibodies appeared to target both cross-reactive and distinct epitopes across variants. However, combinations of IgG and IgM antibodies against selected variants were associated with complete protection against clinical episodes of malaria.

Discussion

Our systematic strategy exploits genomic data to deduce the handful of antigen variants with the strongest potential to induce broad protection and may be broadly applicable to other complex pathogens for which effective vaccines remain elusive.

Characterization of Anopheles gambiae D7 salivary proteins as markers of human-mosquito bite contact

BACKGROUND

Malaria is transmitted when infected Anopheles mosquitoes take a blood meal. During this process, the mosquitoes inject a cocktail of bioactive proteins that elicit antibody responses in humans and could be used as biomarkers of exposure to mosquito bites. This study evaluated the utility of IgG responses to members of the Anopheles gambiae D7 protein family as serological markers of human-vector contact.

METHODS

The D7L2, D7r1, D7r2, D7r3, D7r4 and SG6 salivary proteins from An. gambiae were expressed as recombinant antigens in Escherichia coli. Antibody responses to the salivary proteins were compared in Europeans with no prior exposure to malaria and lifelong residents of Junju in Kenya and Kitgum in Uganda where the intensity of malaria transmission is moderate and high, respectively. In addition, to evaluate the feasibility of using anti-D7 IgG responses as a tool to evaluate the impact of vector control interventions, we compared responses between individuals using insecticide-treated bednets to those who did not in Junju, Kenya where bednet data were available.

RESULTS

We show that both the long and short forms of the D7 salivary gland antigens elicit a strong antibody response in humans. IgG responses against the D7 antigens reflected the transmission intensities of the three study areas, with the highest to lowest responses observed in Kitgum (northern Uganda), Junju (Kenya) and malaria-naïve Europeans, respectively. Specifically, the long form D7L2 induced an IgG antibody response that increased with age and that was lower in individuals who slept under a bednet, indicating its potential as a serological tool for estimating human-vector contact and monitoring the effectiveness of vector control interventions.

CONCLUSIONS

This study reveals that D7L2 salivary antigen has great potential as a biomarker of exposure to mosquito bites and as a tool for assessing the efficacy of vector control strategies such as bednet use.

Convolutional neural networks to automate the screening of malaria in low-resource countries

Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.