https://orcid.org/0000-0003-1511-7503
Figures
Abstract
Malaria diagnosis has progressed significantly with the introduction of Rapid Diagnostic Tests (RDTs) targeting Histidine-Rich Protein 2 (HRP2), Lactate dehydrogenase (LDH), and aldolase. However, challenges remain, such as the presence of Plasmodium falciparum histidine-rich protein 2 (pfhrp2) gene deletions and reduced sensitivity in regions with low parasitemia. This study evaluates antibody responses to novel biomarkers—glutamate dehydrogenase (GDH) and Heam detoxification protein (HDP)—in comparison to conventional antigens (HRP2, LDH, and aldolase) across different infection, transmission settings, and parasitemia conditions. We analyzed plasma samples from 928 participants, including 886 P. falciparum-positive cases, using enzyme-linked immunosorbent assay. Our results revealed antibody levels against all studied peptides clear differentiation between malaria-positive and negative samples, with Receiver Operating Characteristic (ROC) curve analysis showing high diagnostic accuracy (Area under the ROC curve > 96%) for detecting P. falciparum infection. However, the sensitivity and specificity for differentiating between endemicity or parasitemia groups were limited for certain peptides. Specifically, GDH2 and HRP2 effectively distinguished parasitemia levels, while GDH2, HDP2, and LDH showed promise in distinguishing between varying endemicity levels. These findings suggest that GDH and HDP have significant potential as reliable serological biomarkers for malaria detection. However, further studies are needed to refine their application in categorizing endemicity and parasitemia. This research highlights the need for adaptable diagnostic tools to address the complex challenges of malaria in endemic regions.
Citation: Ahmad A, Nema S, Arora K, Sikka R, Singh A, Krishna S, et al. (2025) Evaluation of GDH and HDP as novel serological biomarkers for Plasmodium falciparum malaria diagnosis across varying parasitemia and transmission settings in India. PLoS One 20(10): e0334313. https://doi.org/10.1371/journal.pone.0334313
Editor: Hassen Mamo, Addis Ababa University, ETHIOPIA
Received: March 27, 2025; Accepted: September 25, 2025; Published: October 15, 2025
Copyright: © 2025 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All findings have been thoroughly reported in the manuscript. Patient information sheets are available upon request from the Director of ICMR-NIMR at director-nimr@icmr.gov.in or from the corresponding author at ICMR-NIMR, New Delhi, India.
Funding: The Indian council of Medical Research, New Delhi, India, supported this investigation reported in this publication. The authors bear full responsibility for the content, which does not necessarily reflect the official views of the funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Malaria remains a significant global health challenge, especially in tropical and subtropical regions. In 2022, India accounted for 66% of South-east Asia’s malaria cases, with over half caused by Plasmodium falciparum [1], and approximately 2 million cases were estimated in 2023 [2]. Effective control and elimination strategies rely on accurate diagnostics; however, the limitations of existing methods, particularly in resource-limited settings, hinder these efforts [3,4]. While in microscopy, Giemsa-stained blood smear remains the ‘gold standard’ method [5], Rapid Diagnostic Tests (RDTs) based on P. falciparum histidine-rich protein 2 (PfHRP2) antigen are widely used due to their simplicity. However, pfhrp2 gene deletion and antigen persistence post-infection undermine RDT accuracy [6], complicating P. falciparum malaria detection. The World Health Organization (WHO) recommends using non-HRP2-based RDTs, such as those detecting P. falciparum lactate dehydrogenase (PfLDH) or aldolase, in regions with reported 5% pfhrp2 deletions [7]. Aldolase-based tests can be used in combination with PfHRP2 for detecting falciparum and non-falciparum species, while PfLDH and Pan-Plasmodium (pLDH) based tests are widely used to detect P. falciparum and other Plasmodium species [8]. However, these tests have lower sensitivity and specificity [9] and are less effective at detecting low parasite levels compared to HRP2-based RDTs [10]. Thus, identifying new diagnostic targets is crucial to address these limitations.
Given the limitations of current RDTs, there is a pressing need to identify novel, reliable diagnostic markers for P. falciparum. In this context, glutamate dehydrogenase (GDH) and Heam detoxification protein (HDP) have emerged as promising candidates based on their roles in parasite biology and stage-specific expression for P. falciparum diagnosis [11]. GDH plays a key role in glutamate’s catabolism and ammonium’s assimilation, serving as the primary NAD(P)+ source when parasites invade erythrocytes [12]. Importantly, GDH is exclusively expressed in the parasite’s erythrocytic stage and localized in the cytosol [13]. P. falciparum has three gdh genes, which encode the following proteins: pGDH1 and pGDH2 on chromosome 14, and pGDH3 on chromosome 8. HDP, which is involved in hemoglobin degradation and neutralizing heme toxicity essential for the parasite’s survival [14,15], is encoded by a single gene expressed during the erythrocytic stages. It shows limited genetic diversity among Indian and African isolates and is highly conserved across Plasmodium species [11].
This study compares immune responses to GDH and HDP with current RDT antigens (HRP2, LDH, aldolase). Previous findings indicated strong immune responses to PfGDH [13] and PfHDP [11] among P. falciparum patients from a single site. Here, antibody levels against all selected antigens were assessed in malaria patients and compared to malaria-slide negative individuals collected from 12 endemic Indian sites. Antibody levels were also compared among patients with parasite densities ranging from ≤200 (PD1), > 200 to <1000 (PD2), and >1000 (PD3) parasites/µL. Additionally, levels were compared across regions with high, moderate, and low transmission intensities. These findings aim to improve diagnostic strategies.
Materials and methods
Study site and sample collection
Plasma samples (n = 928) were obtained from a previous study [16] conducted across 12 sites in nine malaria-endemic states of India (Fig 1). In brief, microscopically positive P. falciparum samples, along with the malaria-slide negative controls, were collected from July to December 2014 across multiple sites during the rainy and peak transmission season. The study covered Odisha, Chhattisgarh, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan, Gujarat, Assam, and Tripura, selecting two community health centers (CHCs) per state representing high and low endemic regions.
Febrile patients (axillary temperature ≥ 37.5°C or fever in the past 48 h) visiting CHCs for malaria diagnosis were screened, and those positive for P. falciparum mono-infection (confirmed by microscopy) were enrolled. Blood smears were stained with Jaswant Singh–Bhattacharji [17] stain, examined by two independent microscopists, and discrepancies were resolved by a third expert. Parasite densities were calculated using standard methods (Parasites/μL = no. of asexual parasites X 8000/no. of WBC counted) [18]. Venous blood samples (1–2 mL) were collected, plasma separated, and stored under freezing conditions for molecular studies. Treatment followed the NVBDCP guidelines, and pregnant women were excluded from the study. Based on the annual parasite incidence (API), these sites were classified as high (API > 5), moderate (API 2–5), and low (API < 2) transmission settings during the sample collection.
Ethical considerations
Ethical clearance was obtained from the Institutional Ethics Committee of Indian Council of Medical Research-National Institute of Research in Tribal Health (ICMR-NIRTH), Jabalpur (RMRCT/IEC/575/2012, dated: 08/05/2012). The approval remained valid for the period of sample collection, and the study was conducted in accordance with the ethical guidelines for biomedical research on human participants of Indian Council of Medical Research (ICMR) published in 2006.
Written informed consent was obtained from all adult participants, and from the parents/guardians in the case of children. The consent form was also provided and explained in Hindi as well as local language, with the help of a local translator when needed. All individuals diagnosed with malaria were treated as per the national guidelines for diagnosis and treatment of malaria 2013 by the National Center for Vector Borne Diseases Control, (NCVBDC, 2013). The copies of the informed consent form and ethics approval letter are provided as supporting documents.
Identification of linear B cell epitopes and amino acid repeats
Linear B-cell epitopes in PfGDH, PfHDP, PfHRP2, PfLDH, and PfAldolase from the P. falciparum 3D7 strain were identified using the Bepipred 2.0 Linear Epitope Prediction software [19]. This tool, integrated with the Immune Epitope Database and Analysis Resource (IEDB-AR), applies parameters such as a center position of 4 and a threshold value of 0.500. Amino acid repeats in the protein sequence of Pf34 from P. falciparum 3D7 (PF3D7_0419700) were identified both manually and using the ProtRepeatsDB- Direp server [20].
Peptide designing
Peptides were designed to target different regions, as detailed in Table 1. For the three GDH proteins, distinct regions were selected. Two peptides were designed for both HDP and aldolase, targeting the N terminal and mid-regions. An additional alanine was added to the N-terminus of each peptide to improve binding efficiency to ELISA plates. For both HRP2 and LDH peptides, the N terminal regions were selected. The peptide designs were analyzed using synthesis and analysis tools from Thermofisher Scientific. All peptides were commercially synthesized by Sigma-Aldrich with a purity exceeding 95%.
Enzyme-linked immunosorbent assay
Lyophilized synthetic peptides were dissolved in Milli Q water and diluted to a final concentration of 2 µg/mL in a coating buffer containing 50mM sodium carbonate and 50mM sodium bicarbonate (pH 9.6). Maxisorp ELISA plates (NUNC, Denmark) were coated with 200ng of peptide per well and incubated overnight at 4°C. Next morning, the plates were blocked with 200µl of 5% non-fat dry milk (Sigma-Aldrich) in phosphate buffer saline (PBS) for 1 hour at 37°C. After blocking, plates were washed three times with PBS containing 0.1% Tween 20 (PBST). Plasma samples were diluted at a ratio of 1:200 in an assay diluent consisting of PBST with 5% non-fat dry milk. A volume of 100 µl of the diluted plasma samples was added in duplicate and incubated for 1 hour at 37°C.The plates were washed four times in PBST and incubated for 1 hour at 37°C following the addition of horseradish peroxidase-conjugated goat-antihuman-IgG (Genei, Bangalore; 1:4000 in PBS-5% skimmed milk, 0.1% Tweeen-20). The assay was developed by adding the tetra-methylbenzidine enzyme substrate (Genei, Bangalore) and incubated for 30 minutes at 37°C. The reaction was stopped with 2M H2SO4. The optical density (OD) was measured at 450 nm using an ELISA plate reader [21].
To determine the specificity of the assay, plasma samples from 16 individuals from a non-malaria endemic area (not exposed to malaria) were used as negative controls. Prevalence of total IgG immune response for the different antigens were considered positive if their OD values were higher than the mean plus two standard deviations of the negative control after subtraction [21].
Statistical analysis
Principal component analysis (PCA) was performed using the R package prcomp, with visualization done via the ggbiplot package. Antibody levels for each peptide were compared between groups using the Mann-Whitney U test. Receiver Operating Characteristic (ROC) curves were used to evaluate the accuracy of antibody levels in detecting P. falciparum patients, distinguishing parasitemia levels, and categorizing transmission conditions. The area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity were calculated to determine the discriminatory power of the studied peptides. A two-sided p < 0.05 was considered statistically significant. All statistical analyses were performed using R 4.3.3 (R Foundation for Statistical Computing) and GraphPad Prism 10 (GraphPad Software, Inc.).
Results
Patient characteristics
We conducted an immunological analysis on samples from 928 participants. Among these, 886 tested positives for P. falciparum, while 42 were malaria-slide negative. Based on parasite density, 12.6% (n = 112) of the patients had a density of less than 200 parasites/µl (PD1), 42.1% (n = 373) had a density between 200 and 1000 parasites/µl (PD2), and 45.3% (n = 401) had a density exceeding 1000 parasites/µl (PD3). When categorized by transmission intensity, 12.6% of patients (n = 112) were from low-transmission regions, 22.8% (n = 202) from moderate-transmission regions, and 64.6% (n = 572) from high-transmission regions.
PCA analysis of IgG antibody levels across sample classifications
PCA was performed to assess patterns in antibody levels against nine peptides (GDH1, GDH2, GDH3, HDP1, HDP2, ALD1, ALD2, HRP2, and LDH) under three classification methods: infection status (P. falciparum-positive vs. malaria-slide negative), three endemicity levels, and varying parasitemia levels. Fig 2 illustrates the PCA plot for the case-control classification, where the first two PCs (PC1 and PC2) accounted for 83.1% and 4.7% of the total variance, respectively. The PCA plot revealed two well-defined clusters, with P. falciparum positive samples (red) and malaria-slide negative samples (blue) distinctly separated, indicating robust differentiation based on infection status. S1 and S2 Fig present the PCA results for classifications based on endemicity levels and parasitemia levels, respectively. In both cases, PC1 and PC2 explained similar proportions of the variance (82.1% and 5.0%). However, no distinct clustering patterns were observed for either endemicity or parasitemia levels.
Antibody levels obtained against nine studied peptides were included in the PCA analysis. Ellipse shapes are showing clustering of P. falciparum-positive and malaria-slide negative group samples.
Differentiation of sample groups using IgG antibody levels
Using the Mann-Whitney U test, the antibody levels against nine peptides were assessed to evaluate their ability to distinguish between P. falciparum-positive and malaria-slide negative groups, as well as across different endemicity and parasitemia levels. A significant difference in antibody levels was observed between P. falciparum-positive (n = 886) and malaria-slide negative (n = 42) groups (Fig 3). When comparing endemicity levels—low (n = 112), moderate (n = 202), and high (n = 572)—antibody levels were significantly different overall. However, responses to six peptides (GDH1, GDH3, HDP1, ALD1, ALD2, and HRP2) did not show significant differences between low and moderate endemicity groups (Fig 4). For parasitemia levels, the samples were grouped into PD1 (n = 112), PD2 (n = 373), and PD3 (n = 401). Antibody levels against GDH1, HDP2 and HRP2 differentiated between PD2 and PD3 (p < 0.05), while GDH2, GDH3 and HDP1 differentiated between PD1 and PD2 (p < 0.05). Additionally, GDH2 and HRP2 differentiated between PD1 and PD3 (p < 0.005). Antibody responses to the remaining peptides did not show significant variation across parasitemia levels (Fig 5).
Statistical differences were determined using the Mann-Whitney U test. T bars represent the median and interquartile ranges. (*p < 0.05, **p < 0.005, ***p < 0.0005, ****p < 0.00005).
Statistical differences were determined using the Mann-Whitney U test. T bars represent the median and interquartile ranges. (*p < 0.05, **p < 0.005, ***p < 0.0005, ****p < 0.00005).
Statistical differences were determined using the Mann-Whitney U test. T bars represent the median and interquartile ranges. (*p < 0.05, **p < 0.005, ***p < 0.0005, ****p < 0.00005).
Area under the ROC curve analysis of IgG antibody levels
The discriminatory performance of antibody levels against the nine peptides was evaluated using the area under the ROC curve to distinguish between the sample groups. As shown in Table 2, antibody levels against all peptides exhibited excellent performance in distinguishing P. falciparum-positive samples from malaria-slide negative samples, with AUC values exceeding 96% and p-values < 0.005. However, the AUC analysis did not perform as well when distinguishing between samples from different endemicity levels (Table 3) or parasitemia levels (Table 4). In both cases, the accuracy remained at or below 75%, with suboptimal sensitivity and specificity.
Discussion
Significant advances in malaria control stem from improved diagnostics and treatments. RDTs targeting HRP2, LDH, and aldolase have revolutionized malaria detection with their speed and simplicity [22]. However, HRP2-based tests fail in regions with pfhrp2 gene deletions, causing false negatives [6], while LDH and aldolase tests have reduced sensitivity at low parasitemia levels (10), necessitating alternative biomarkers. GDH and HDP, crucial for parasite metabolism and heme detoxification, respectively, show diagnostic promise due to broad Plasmodium species expression and functional conservation [23–25]. This study evaluates immune responses to GDH, HDP, and conventional markers (HRP2, LDH, and aldolase) under varying infection, endemicity, and parasitemia conditions to inform improved diagnostic strategies.
PCA of antibody responses demonstrated that the peptides effectively distinguished between malaria-slide negative and P. falciparum-positive samples. The clear separation between these groups highlights the robust discriminatory power of the evaluated peptides for infection status. However, PCA failed to reveal distinct clusters based on endemicity levels or parasitemia ranges, suggesting that these factors do not strongly influence the antibody profiles in this dataset. These findings suggest that while immune responses are sensitive to infection, they are less responsive to variations in malaria exposure intensity or parasitemia. This reduced responsiveness may be due to the plateauing of antibody levels following repeated exposure or chronic low-grade infections, which can blur immunological distinctions between individuals from areas of differing endemicity.
The Mann-Whitney U test confirmed significant differences in antibody levels between P. falciparum-positive and malaria-slide negative groups for all peptides (p < 0.005). However, comparisons across endemicity levels revealed that antibody responses to six peptides (GDH1, GDH3, HDP1, ALD1, ALD2, and HRP2) did not significantly differ between low and moderate endemicity. This pattern aligns with evidence that repeated malaria exposure induces sustained immune responses, reducing differentiation between closely related endemicity levels [26]. Interestingly, GDH2, HDP2, and LDH demonstrated significant potential in distinguishing between varying endemicity groups, providing valuable insights into their differential diagnostic capabilities.
Parasitemia classification also revealed specific distinctions. Antibody responses to GDH1 significantly differentiated between PD1 and PD3, while HRP2 distinguished between PD1 and PD2. These findings highlight the capacity of GDH1 and HRP2 to serve as parasitemia-dependent markers, contradicting earlier reports that suggested limited correlation between antigen load and immune response [27].
ROC analysis further supported the diagnostic utility of the peptides. All evaluated peptides exhibited AUC values exceeding 96% for distinguishing P. falciparum-positive from malaria-slide negative samples, confirming their robustness as reliable biomarkers for malaria detection. However, the AUC values for endemicity and parasitemia classifications did not exceed 75%, indicating reduced sensitivity and specificity for these categorizations.
When compared to current diagnostics, these peptide-based markers offer notable advantages. Microscopy, while species-specific and able to quantify parasitemia, is labor-intensive and operator dependent. RDTs, though widely accessible, are increasingly undermined by pfhrp2 gene deletions and reduced performance at low parasitemia [6]. PCR-based methods provide high sensitivity but remain impractical for routine use due to infrastructure and cost constraints [28]. The peptides identified here demonstrated higher diagnostic accuracy under field-relevant conditions, positioning them as strong candidates for improved detection tools.
Remarkably, the antibody responses elicited by these peptides are well-suited for integration into Luminex-based multiplex assays, which enable simultaneous, high-throughput measurement of multiple immune markers with high sensitivity [29]. These platforms are ideal for sero-surveillance and large-scale screening, where cost-efficiency improves with scale and can support efforts to map transmission intensity, identify asymptomatic carriers, and monitor intervention impact [30].
This study provides a comprehensive immunological analysis of antibody responses to nine peptides in a large cohort of participants, offering valuable insights into their diagnostic potential for P. falciparum malaria. Our findings demonstrate that antibody levels against these peptides exhibit robust discriminatory performance, effectively differentiating P. falciparum-positive samples from malaria-slide negative samples. This is evidenced by PCA clustering, significant group differences, and AUC values exceeding 96%. These results highlight the potential for translating these findings into diagnostic tools that can accurately confirm infection with high sensitivity and specificity. However, there are several possibilities for future research to further validate and improve the clinical utility of these biomarkers. Future studies should focus on validating these biomarkers in diverse endemic regions, where varying transmission dynamics may influence their diagnostic performance. Additionally, integrating these biomarkers into multiplex RDTs could enable simultaneous detection of multiple antigens, improving diagnostic efficiency and reducing costs. Longitudinal studies assessing the utility of these biomarkers for tracking malaria exposure or reinfection over time could also provide valuable insights into their role in surveillance and monitoring of malaria control efforts. Expanding sample diversity, including individuals with mixed infections and non-malarial febrile illnesses, will be crucial in refining these biomarkers’ ability to distinguish malaria from other infectious diseases. A key limitation of this study is the inability to perform age-stratified analyses due to insufficient sample size within specific age groups, which may mask age-dependent variations in immune responses. Another limitation was using plasma instead of whole blood, which may affect field applicability. Plasma allowed controlled antibody quantification for early biomarker discovery, but whole blood is preferred for point-of-care use. Future work will optimize the assay for whole blood to enable rapid test deployment. The unequal sample sizes across transmission settings may have influenced comparative analyses, and the persistence of antibodies beyond the period of infection could result in false-positive findings. As diagnosis was based on microscopy, the possibility of undetected low-density or submicroscopic mixed infections cannot be fully excluded. Furthermore, the detection limit was not assessed using molecular methods, and the exclusion of submicroscopic and asymptomatic infections may limit the broader applicability of our findings for malaria elimination.
Conclusion
This study suggests that immunological markers such as GDH and HDP hold promise for the detection of P. falciparum infections. The observed antibody responses indicate that these markers may help distinguish P. falciparum-positive samples from malaria-slide negative samples. While further validation in diverse populations and epidemiological settings is needed, the findings provide a foundation for exploring their integration into serological tools that could strengthen malaria surveillance and diagnosis in endemic regions.
Supporting information
S1 Fig. Principal component Analysis of antibody levels obtained against nine studied peptides among different endemicities (low, moderate, and High).
https://doi.org/10.1371/journal.pone.0334313.s001
(TIF)
S2 Fig. Principal component Analysis of antibody levels obtained against nine studied peptides among different parasitemia groups including PD1(<200 parasites), PD2 (>200 to <1000 parasites) and PD3 (>1000 parasites).
https://doi.org/10.1371/journal.pone.0334313.s002
(TIF)
Acknowledgments
We would like to thank the study participants for their consent. We would also like to thank the paramedical/technical staff for their efforts during sample collection and enrolment of patients.
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