https://orcid.org/0000-0002-7629-6843
Figures
Abstract
Introduction
Immunoinformatic tools can be used to predict schistosome-specific B-cell epitopes with little sequence identity to human proteins and antigens other than the target. This study reports an approach for identifying schistosome peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray immunoassay validation.
Method
Firstly, a comprehensive literature search was conducted to obtain published schistosome-specific peptides and recombinant proteins with the best overall diagnostic performances. For novel peptides, linear B-cell epitopes were predicted from target recombinant proteins using ABCpred, Bcepred and BepiPred 2.0 in-silico tools. Together with the published peptides, predicted peptides with the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected. Antibodies against the peptides were measured in sera, using peptide microarray immunoassays. Area under the ROC curve was calculated to assess the overall diagnostic performances of the peptides.
Results
Peptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminating S. mansoni and S. haematobium positives from healthy controls, with AUC values of 0.8043 and 0.7326 respectively for IgG. Peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI-177-193, SmSPI-379-388, MS3-10186-40-49 and SmS-197-214 had acceptable diagnostic performances for discriminating S. mansoni positives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG. Peptides SmSPI-359-372, Smp126160-438-452 and MS3 10186-25-41 had acceptable diagnostic performances for discriminating S. mansoni positives from S. mansoni negatives with AUC values of 0.7124, 0.7156 and 0.7115 respectively for IgG. Peptide MS3-10186-40-49 had an acceptable diagnostic performance for discriminating S. mansoni positives from healthy controls, with an AUC value of 0.7413 for IgM.
Conclusion
One peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation of the peptides with true negatives and a good standard positive reference.
Author summary
Schistosomiasis, commonly known as bilharzia, is the third most significant tropical disease after malaria and soil-transmitted helminthiases. Like other neglected tropical diseases common in Zimbabwe, schistosomiasis remains mostly undiagnosed or undetected. This is partly due to the fact that reliable identification of parasites requires expertise for specimen preparation, and microscopic examination. Unfortunately, this level of expertise is unavailable in most rural clinics. This limitation is further compounded by the fact that the recommended microscopy-based methods for schistosomiasis diagnosis lack sensitivity, especially in infections of low intensity. To overcome some of the problems associated with microscopy-based methods, highly sensitive serological tests have been utilized. Unfortunately, currently available serological tests have low specificity and show cross-reactivity with other helminthic infections. One way to mitigate cross-reactivity and increase the specificity, is to use immunoinformatic tools and immunoassays to identify schistosomiasis species-specific immunogenic peptides mimicking B-cell epitopes (short amino acid sequences of the antigen that reacts with antibodies). Utilizing immunoinformatic tools coupled with peptide microarray immunoassay validation approach several peptides that can be used to develop diagnostic tools for showing exposure to infection for people living in non-endemic or low-transmission areas were identified in the current study.
Citation: Vengesai A, Manuwa M, Midzi H, Mandeya M, Muleya V, Mujeni K, et al. (2024) Identification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technology. PLoS Negl Trop Dis 18(8): e0011887. https://doi.org/10.1371/journal.pntd.0011887
Editor: W. Evan Secor, Centers for Disease Control and Prevention, UNITED STATES OF AMERICA
Received: December 27, 2023; Accepted: August 6, 2024; Published: August 22, 2024
Copyright: © 2024 Vengesai 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 relevant data are within the manuscript and its Supporting Information files.
Funding: This work received financial support from the Merck Schistosomiasis Research Grant through funding from ARES Trading S.A., an affiliate of Merck KGaA, Darmstadt, Germany, to A.V. The views expressed in this publication are those of the author(s) and not necessarily those of ARES Trading S.A., an affiliate of Merck KGaA, Darmstadt, Germany. 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.
1 Introduction
Schistosomiasis is a neglected tropical disease caused by blood flukes from the genus Schistosoma [1]. Zimbabwe is endemic to urogenital schistosomiasis caused by Schistosoma haematobium and intestinal schistosomiasis caused by Schistosoma mansoni [2–4]. Besides effective implementation of mass drug administration (MDA) campaigns, access to safe water, improved sanitation and snail control, diagnostic tests are important tools for achieving and sustaining schistosomiasis elimination [1,5,6]. Diagnostic tests are important for schistosomiasis surveillance and control. They play a vital role in guiding the distribution of current program resources and the implementation and evaluation of schistosomiasis intervention strategies [5,6].
The recommended method for schistosomiasis diagnosis is the detection of schistosome-specific eggs in stool or urine specimens by microscopy. For urogenital schistosomiasis, the urine filtration technique is the standard diagnostic method and for intestinal schistosomiasis, the Kato-Katz technique is the standard diagnostic method [6–8]. In addition to high specificity [7,9], both techniques have minimal operational costs (the test kits are inexpensive), low complexity and both tests are relatively easy to perform in resource-limited field settings [6,8,9]. Moreover, prepared Kato-Katz slides can be stored for months at room temperature for later microscopic examination.
Nevertheless, both techniques have significant disadvantages including, the need for qualified personnel to prepare and examine slides, poor reproducibility and most importantly, low sensitivity [7,8,10,11]. The sensitivity of the techniques is limited by the host infection intensity, daily variation of schistosome egg excretion and uneven distribution of eggs within stool specimens [11]. Additionally, both methods are unable to diagnose recent infections (for instance, in cases where worms have not yet started to produce eggs) single-sex and non-fecund worm infections. The Kato-Katz technique is also unable to analyse watery stool specimens [9]. The lack of sensitivity especially in low endemic areas and after successful control interventions lead to the underestimation of true schistosomiasis prevalence in such settings [7–10]. Moreover, undetected and untreated individuals may continue schistosomiasis transmission by contaminating fresh water sources with urine and faeces containing schistosome eggs [1,9]. Due to the numerous disadvantages associated with the microscope based techniques, the use of other methods like molecular detection, circulating cathodic antigen (CCA), circulating anodic antigen (CAA) has gathered pace [8]. These alternative methods for schistosomiasis diagnosis have their own advantages and limitations too.
Molecular methods targeting DNA of the parasite are more sensitive and specific and can detect early stage schistosome infections [8]. However, molecular methods require skilled laboratory personnel, expensive and fragile equipment and they are time consuming, thus impeding their use as point-of-care tools (POC) in remote resource-limited endemic areas [5]. Although CCA and CAA allow for rapid schistosomiasis diagnosis, these methods have their own shortcomings [3,7,8]. The CCA test is sensitive for moderate to high level S. mansoni infections but not for S. haematobium infections, and its widespread use in poor rural endemic areas may be limited by its cost, currently around US $1.75 per test [3,12,13]. While the CAA test is more sensitive than the CCA, it is labour intensive and riddled with a complicated assay procedure. This major drawback is further compounded by the fact that there has not been any commercially available CAA tests to date [8].
To overcome some of the drawbacks associated with microscopy; molecular, CCA and CAA based diagnostic tests serological tests have been utilized [14]. The use of serological tests has gained traction due to higher sensitivities compared to microscopy based techniques [8]. However, currently available serological tests exhibit cross-reactivity with other helminthic infections due to shared antigenic epitopes [14,15]. Additional crude antigens and recombinant are of limited application in poor resource regions due to high costs of production (List 2010). One way to mitigate the cross-reactivity and increase specificity is to use bioinformatic and proteomics tools to predict schistosome specific immunodominant B-cell epitopes with little or no sequence identity to proteins other than the target [4,14,16]. However, to date, only a limited number of linear B-cell epitopes have been identified for the serological diagnosis of S. haematobium and S. mansoni [17–19]. It is against this background that we present an approach for predicting schistosome specific peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray technology. Several methods can be used for the identification and prediction of linear B-cell epitopes. As previously described, two methods in-silico prediction and identification of published peptides reduce the burden and costs associated with epitope mapping by decreasing the list of potential targets for experimental testing [20–24]. Therefore, in the present study a comprehensive literature search was conducted to identify published schistosome-specific peptides for inclusion in the peptide microarray immunoassays.
2 Methods
2.1. Ethical approval
Approval to conduct the study was obtained from Medical Research Council of Zimbabwe (MRCZ/A/2571). Provincial Medical directors, District Medical officers, councillors, and headmen provided the permission to carry out the study in the districts. Before enrolment, the aims and procedures of the study were explained to all participants and their parents or guardians in English or Shona. Adults were included into the study after signing informed consent forms. Children aged 1–11 years were enrolled into the study after their verbal assent and/or submission of signed consent forms by their parents or legal guardians. Children aged 12–18 years were recruited into the study after signing assent forms and submitting consent forms signed by their parents or legal guardians. Enrolment into the study was completely voluntary and parent or guardians were free to withdraw their children at any time with no need of explanation.
2.2. Study design, area and population
Village health workers and environmental health technicians who resided in the same communities and were familiar with the study participants were engaged in the research and involved in the recruitment of participants for inclusion in the cross-sectional study. Urine and stool specimens for the diagnosis of schistosomiasis, and sera for the peptide microarray immunoassays were collected at local clinics in the community-based study. S. haematobium was diagnosed by the urine filtration technique and S. mansoni was diagnosed by the Kato-Katz and formal ether concentration techniques. To evaluate the diagnostic performance of peptides to detect S. mansoni and S. haematobium IgG and IgM antibodies, sera from 135 participants (130 pre-school aged and primary school aged children aged 1–12 years and 5 participants older than 16) with a median age of 9 (IQR: 4–12) were purposively selected. Among the 135 participants 63% (n = 85) were females, 37% (n = 50) were males, 93% (n = 125) were from Mashonaland central a high schistosomiasis endemic area [2] and 7% (n = 10) which were designated the control group were from Harare a low schistosomiasis endemic area [2]. Participants from Mashonaland central (located 31°40′0” E longitude and 17°10′0” S latitude Northeast of Zimbabwe) were permanent residents of Mount Darwin and Shamva rural districts. Participants from Harare the capital city of Zimbabwe designated the control group were permanent residents of Glaudina a high-density urban area and they had no known history of Schistosoma infection or exposure to infested water sources.
2.3. Blood collection
Experienced local nurses collected approximately 5 ml of blood from each participant. The 5ml blood limit was within the guidelines for children issued by MRCZ research ethics committee. Blood samples were collected into red top serum tubes, stored overnight at 4°C and centrifuged for 10 minutes at 1000X to obtain sera that was used for the peptide microarray serological assays.
2.4. Parasitology examination
Urine and stool specimens were collected between 10: 00 am and 14:00 pm for optimal egg passage necessary for diagnosis of schistosomiasis. The specimens were stored away from direct sunlight until processing. The urine filtration technique was performed using 10 ml of urine to detect S. haematobium egg. The technique was repeated for three consecutive days to minimise misdiagnosis due to day-to-day egg variation. Infection intensity for S. haematobium was calculated as the arithmetic mean of the number of eggs per 10 ml of urine across all available samples. Participants were then classified into three infection intensity categories: negative (0 eggs per 10 ml urine), light (1–49 eggs per 10 ml of urine), and heavy (≥50 eggs per 10 ml of urine) [25].
The Kato-Katz technique was used to detect S. mansoni eggs from one stool sample using one standard 41.7 mg template for each participant. To determine the infection intensity the number of eggs were multiplied by a factor of 24 to scale the measurement to eggs per 1 g (EPG) of stool. Participants were grouped into four S. mansoni infection intensity categories: negative (0 EPG), light (1–99 EPG), moderate (100–399 EPG), and heavy (≥400 EPG) [25]. Additionally, S. mansoni was diagnosed using the formal ether concentration technique to improve accuracy. Participants were classified as infected if at least one parasitic egg was detected by either technique for both S. haematobium and S. mansoni.
2.5. Identification and selection of linear-cell epitopes
Two methods were used for the identification and selection of linear B-cell epitopes (peptides). These were a comprehensive literature search for published synthetic peptides and in silico prediction of novel peptides. For novel peptides, a literature search was conducted to identify recombinant proteins with good diagnostic candidate properties (excellent to outstanding diagnostic performances), ability to detect early and single worm infections, single-sex and non-fecund worm infections.
2.5.1. Literature search.
Five data bases PubMed, EMBASE, PsycInfo, CINAHL and the Cochrane library were systematically searched to identify S. haematobium and S. mansoni published peptides and recombinant proteins as previous described [26]. The databases were searched using variations and combinations of the following keywords: recombinant proteins, peptides, and schistosomiasis. The literature search was conducted from January 2000 to February 2022 without any language restrictions. The search strategy and results are shown in S1 File. Additionally, preprint databases MedRxiv and BioRxiv, websites of the WHO, the Schistosomiasis Control Initiative (SCI), and the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) were also searched.
2.5.2. In silico prediction and selection of novel peptides.
The sequences and the alpha fold predicted structures of the identified recombinant proteins were obtained from Uniprot (https://www.uniprot.org/). SignalP 6.0 (https://services.healthtech.dtu.dk/service.php?SignalP-6.0) was used to identify the presence of signal peptides. Transmembrane domains were predicted using SOSUI (https://harrier.nagahama-i-bio.ac.jp/sosui/) and cellular localisation was predicted using WoLF Psort II (https://wolfpsort.hgc.jp/). DeepView/Swiss-PDB Viewer (www.expasy.org/spdbv/) was used for spatial location of the candidate peptides on the recombinant protein crystal structures.
Linear B cell epitopes were predicted using three different programs namely ABCpred (http://www.imtech.res.in/raghava/abcpred/), Bcepred (http://crdd.osdd.net/raghava/bcepred) and BepiPred 2.0 (www.cbs.dtu.dk/services/BepiPred/). ABCpred uses an artificial neural network, Bcepred predicts using physico-chemical properties of amino acids and BepiPred 2.0 uses a random forest algorithm. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were then selected using the NCBI Protein BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to minimize potential cross reactivity. Peptides that had the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected for inclusion on the peptide microarrays.
2.6. Peptide microarray content
The peptide microarrays were fabricated by PEPperPRINT GmbH (Heidelberg, Germany) (https://www.pepperprint.com/). The peptide microarrays contained 16 identical sub-arrays (copies). Each sub-array contained 122 S. haematobium and S. mansoni 9–16 amino acids long peptides printed randomly in duplicate. The peptides on each subarray were framed by HA (YPYDVPDYAG, 5 spots) and polio (YPYDVPDYAG, 3 spots) control peptides. Additionally, each sub-array was framed by glycine spacers (G spots).
2.7. Peptide microarrays immunoassay
Immunoassays were performed by PEPperPRINT GmbH (Heidelberg, Germany) as previously described [27,28]. Briefly, the immunoassays involved two steps on the same microarray. The first step was incubation with secondary antibody to identify false positive signals. The second step was incubation with serum and the secondary antibodies.
2.8. Identifying schistosome-specific antigenic peptides
The negative cut-off was determined by averaging the negative control readings (10 sera obtained from schistosomiasis unexposed and uninfected individuals with no prior history of Schistosoma infections) and adding 3 standard deviations. A positive response was defined as fluorescence intensity above the negative cut-off for each specific peptide for both IgG and IgM. Peptides for which at least one infected individual was positive were selected for further analysis. Statistical comparison between groups was done by the Kruskal-Wallis equality-of-populations rank test and p-values less 0.05 were considered statistically significant. Diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was calculated to assess the overall diagnostic performance of peptides that were able to distinguish schistosome positives from schistosome negatives or healthy controls. Data curation and analyses were performed with Microsoft excel and Stata v17 (Stata, College Station, Texas, USA) respectively.
3 Results
3.1. Parasitology
Sera from ten participants from Harare confirmed to be egg negative for both Schistosoma infections by the urine filtration, Kato-Katz and formal ether techniques and with no known history of Schistosome infections or contact with contaminated water were considered the negative control. To evaluate the diagnostic performance of peptides to detect S. haematobium patient serum IgG and IgM antibodies, 43 (40 light infections and 3 heavy infections) serum samples collected from S. haematobium egg positive and 36 serum samples collected form S. haematobium egg negative patients were considered (S2 File). To evaluate the diagnostic performance of peptides to detect S. mansoni patient serum IgG and IgM antibodies, 46 and 37 serum samples collected from S. mansoni egg positive and negative patients respectively were considered. Among the serum samples collected positive patients 34 (15 light infections, 12 moderate infections and 7 heavy infections) were diagnosed by the Kato-Katz methods and 12 by the formal ether concentration technique (S2 File).
3.2. Characterisation of the native forms for schistosome recombinant proteins selected for novel peptide prediction
The literature search conducted to retrieve articles published between January 2000 and February 2022 identified a total of 18 S. haematobium and 10 S. mansoni recombinant proteins (S3 File). Due to limited funding only four were selected for the prediction of novel peptides, two for each Schistosome species. Table 1 summarizes the characteristics of the proteins selected. MS3_10385 a serine protease inhibitor (SERPIN) and MS3_10186 a tetraspanin were selected for S. haematobium because they had excellent diagnostic performance across three platforms (urine IgG protein microarray, serum IgG ELISA and serum IgG protein microarray) with AUC values ranging from 0.86 to 0.93 (see S3 File). The native forms for the two recombinant proteins were predicted to have a signal peptide attached to them. Signal peptides are short N-terminal amino acid sequences that target proteins to the secretory pathways indicating that native forms of MS3_10385 and MS3_10186 are secretory proteins [29]. Native forms for both MS3_10385 and MS3_10186 were predicted to be soluble proteins indicating that they are not part of the transmembrane helix. Finally, WOLF Psort predicted the native forms of MS3_10385 and MS3_10186 to be extracellular or secreted proteins which are most likely to be involved in immunological.
For S. mansoni one protein SmSPI a SERPIN which had a sensitivity of 91.7% and a specificity of 93.3% on a serum IgG protein microarray was selected based on its diagnostic performance (S3 File). SmSPI was also selected because SAPLIPs have the potential to elicit a strong immune response as they may be continuously released into the host circulatory system in schistosome worm vomitus [30,31]. The second protein AAB81008 a saposin-like protein (SAPLIP) was selected due to its ability to detect early schistosome infections [32] and detect single worm infections which are missed by the egg detection methods. The native form of AAB81008 was predicted to have a signal peptide attached to it and the form of SmSPI was predicted not to possess a signal peptide. The native forms of AAB81008 and SmSPI were predicted to be membrane and soluble proteins respectively. Lastly, the native form of AAB81008 was predicted to be an extracellular protein and that of SmSPI was predicted to be located in the mitochondria.
3.3. Linear B-cell epitopes/peptides
Out of the 122 peptides S. haematobium and S. mansoni peptides selected for the peptide microarray immunoassays; 40.98% (n = 50), 22.95% (n = 28) and 21.31% (n = 26) were predicted with ABCpred, Bepi Pred 2 and Bcepred respectively and the remaining 14.75% (n = 18) obtained from literature (S2 File). The recombinant proteins AAB81008, SmSPI, 10385 and MS3_10186 were used to predict novel peptides. Tables 2 and 3, show that from the 122 peptides, only 14.75% (n = 18) were able to distinguish between schistosome positives, schistosome negatives and healthy controls. However, according to ROC curve analysis some peptides were inaccurate with AUC values less than 0.50.
3.4. Diagnostic performance for discriminating S. mansoni positives from healthy controls
Out of the 16 peptides that were able to distinguish S. mansoni positive sera from schistosome negative or healthy controls sera, only peptide AA81008-19-30 had an excellent diagnostic performance for discriminating S. mansoni positives from healthy controls with an AUC value of 0.8043 for IgG peptide microarray (Fig 1). Six peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI_177–193, SmSPI_379–388, MS3_10186-40-49 and SmS_197–214 had acceptable diagnostic performances for discriminating S. mansoni positives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG peptide microarray (S3 File). For IgM peptide microarray the ROC curve analysis for discriminating S. mansoni positives from the healthy controls yielded AUC values of 0.602 and 0.7413 for MS3 10186_105–121 and MS3 10186_40–49 respectively (S3 File). Peptide MS3_10186-40-49 had an acceptable diagnostic performance for discriminating S. mansoni positives from healthy controls for both IgG and IgM (Fig 2).
Receiver Operating Characteristic (ROC) curves for Peptide AAB81008-19-30 a. Peptide AAB81008-19-30 ROC curve and area under the ROC curve (AUC) for discrimination of S. mansoni positives from healthy controls for IgG. b. Peptide AAB81008-19-30 ROC curve and area under the ROC curve (AUC) for discrimination of S. haematobium positives from healthy controls for IgG.
Receiver Operating Characteristic (ROC) curves for Peptide MS3 10186-40-49 a. Peptide MS3_10186-40-49 ROC curve and area under the ROC curve (AUC) for discrimination of S. mansoni positives from healthy controls for IgG. b. Peptide MS3_10186-40-49 ROC curve and area under the ROC curve (AUC) for discrimination of S. haematobium positives from healthy controls for IgM.
3.5. Diagnostic performance for discriminating S. mansoni positives from S. mansoni negatives
Three peptides SmSPI-359-372, Smp 126160-438-452 and MS3_10186-25-41 had acceptable diagnostic performances for discriminating S. mansoni positives from S. mansoni negatives with AUC values of 0.7124, 0.7156, 0.7115 respectively for IgG peptide microarray (Fig 3). For IgM peptide microarray the ROC curve analysis for discriminating the S. mansoni positives from the S. mansoni negatives yielded AUC values of 0.6898 and 0.6387 for MS3_10186-105-121 and MS3_10186-40-49 respectively (S5 and S6 Files).
Receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) for discriminating S. mansoni positives from S. mansoni negatives for IgG peptide microarray. a. Peptide SmpSPI-359-372 b. Peptide Smp 126160-438-452 c. Peptide MS3_10186-25-41.
3.6. Diagnostic performance for discriminating S. haematobium positives from S. haematobium negatives and healthy controls
Peptides SmSPI-177-193, AAB81008-19-30, SmSP1-165-181 and AAB81008-19-30 had inaccurate diagnostic performances for discriminating S. haematobium positives from S. haematobium negatives for both IgG and IgM peptide microarray with AUC values under 0. 5 (S5 and 6Files). Peptides SmSPI-177-193 and SmSPI-165-181 had poor diagnostic performances for discriminating S. haematobium positives from healthy controls for IgG peptide microarray. Peptide AAB81008-19-30 had an acceptable diagnostic performance for discriminating S. haematobium positives from healthy controls with an AUC value of 0.7326 for IgG peptide microarray. Peptide AAB81008-19-30 was able to discriminate both S. haematobium and S. mansoni positives from healthy controls (Fig 1).
3.7. Spatial location of novel peptides with good and acceptable diagnostic performance on the recombinant proteins 3D structures
Understanding the spatial location of peptides within a protein crystal structure is crucial for unravelling the intricacies of molecular interactions and biological functions. In our study DeepView/Swiss-PDB Viewer (www.expasy.org/spdbv/) was used for spatial location of the candidate peptides on the protein crystal structures of MS3_10385; MS3_10186; AAB81008 and SmSPI (Fig 4). Peptides AAB1008-19-30, MS3_10186-123-131 and MS3_10385-359-372 were located at the exterior surface of their respective protein structures. Most of the target peptide amino acid sequence was located at the exterior surface of the protein with a few amino acid residues encapsulated within the protein for peptides SmSPI_197–214, MS3_10186-40-49 and MS3_10186-25-41. Lastly, most of the amino acid residues for peptides for SmSPI_177–193, SmSPI_378–388 and MS3_10385-339-354 were encapsulated within the protein structure and only a few amino acid residues were at the exterior surface of the protein.
DeepView/Swiss-PDB Viewer was used to determine the spatial location of the peptides on the crystal structure of recombinant proteins (MS3_10385; MS3_10186; AAB81008-19-30 and SmSPI).
4. Discussion
As countries target the elimination of schistosomiasis as a public health problem and as the prevalence of the disease decreases due to MDA campaigns, it has become apparently clear that more sensitive field-applicable diagnostics are needed for the effective management and surveillance of schistosome infections as advocated by the WHO [5,10,12]. This is particularly important in low endemicity areas, where microscope-based diagnostic methods may underestimate the true prevalence of the disease [11,33]. This background has provided an impetus for this study in the identification of peptides that can be employed in the development of antibody-based diagnostic tools using an immunoinformatic approach and peptide microarray immunoassay validation.
Ten peptides (one with an excellent diagnostic performance and nine with acceptable diagnostic performances) were identified using the approach. These findings validate studies by Carvalho and colleagues, 2022 and Lopes and colleagues, 2017 who previously used immunoinformatic approaches and immuno-assay validation to identify S. haematobium and S. mansoni peptides with diagnostic potential [18,19]. Eighteen previously published peptides were included on the peptide microarray, 10 from our previous study Vengesai and colleagues, 2022 [27] and 8 peptides from two studies by Carvalho and Colleagues, 2022 and Lopes and Colleagues, 2017 [18,19]. Despite the two studies showing that peptides Sm168240, Smp_136560 (1564–1578), Smp_126160(438–452), Sm140560, Sm041370, Smp_180240(339–353), Smp_150390.1(216–230), Smp_093840(219–233) had AUC values ranging from 0.76 (95% CI 0.6024–0.9213) to 0.99 (95% CI 0.987–100) results from the current study show that only one published peptide Smp 126160-438-452 had an acceptable diagnostic performance with an AUC value of 0.7115 (95% CI 0.605–0.82218). These findings may be attributed to the fact that peptide microarray immunoassays were used in the determination of antibody reactivity against the peptides whilst in the other two studies [18,19] antibody reactivity was investigated using peptide based serum IgG ELISA. Pertaining to the other 10 published peptides XP_035587815.1-269-283, XP_012797374.1-78-92, AAZ29530.1-25-29, XP_012799745.1-16-30, P20287.1-58-72, AAA29903.1-222-237, P09841.3-6-20, AAA29900.1-145-159, P09792.1-29-43, XP_035588858.1-206-220 none of the peptides showed a clear discrimination between the schistosome infected and uninfected groups and these results are in agreement with findings from our previous study [27].
The peptide microarray immunoassay identified one peptide AAB81008-19-30 which showed cross reactivity with S. haematobium and S. mansoni IgG. The cross-reactivity of peptide AAB81008-19-30 may have important consequences for species-specific and species transcending immunity [34]. Furthermore, Peptide AAB81008-19-30 was derived from RP26 a SAPLIP which is expressed at the cercarial stages [32] thereby, making it a good vaccine candidate to fight both S. haematobium and S. mansoni early infections.
Despite, the egg detection methods being able to detect active infections and provide quantitative information on infection intensity and worm burden, they are not able to detect early, single, single-sex and non-fecund schistosome worm infections [35]. Undetected, and untreated cases pose as drivers for persistent hotspots in the case of early/recent infections, threatening the successful elimination of schistosomiasis by serving as infection reservoirs and re-infecting their communities [9,36,37]. The single-sex and non-fecund infections can be drivers for persistent morbidity. RP26 is also expressed at schistosomula and immature worms’ stages, and not in the egg stage [32] suggesting that peptide AA81008-19-30 has the ability to detect early infections and exposure to single worm, single-sex and non-fecund worm infections. Furthermore, peptide-based antibody/serological assays have been shown to be capable of simultaneous multiplex diagnosis of different pathogens with a single patient serum sample [38–40]. The possibility of testing the same serum sample simultaneously for the presence of antibodies against both S. haematobium and S. mansoni is an added value particularly in seroprevalence studies linked to control programs in endemic areas where these two parasites coexist.
4.1. Limitations and recommendations
Ideally, to properly evaluate the performance of the identified peptides their diagnostic accuracy needs to be compared to a reference standard test. The reference standard test should be able to discriminate the true positives and true negatives. The absence of a standard reference test or absolute knowledge of the true positives was a significant limitation in the present study [11,41–43]. Despite the inappropriateness of doing so, Kato-Katz and urine filtration techniques were used as the reference tests in the current study. The techniques lack sensitivity especially in low endemicity settings; hence it is expected that some positive individuals were misdiagnosed as false-negative [10,11]. This might have contributed to the low diagnostic performances of some identified peptides. To circumvent this limitation, we propose use of the latent class analysis previously described by Mesquita and colleagues, 2022, which combines multiple direct and indirect tests to construct a standard reference outcome [11]. However, if the LCA is based on the same principle, for example antibody detection assays, and only a weak contribution of parasitological and/or molecular exams, the results might be misleading. Therefore, we also propose inclusion of a composite reference standard based on the direct tests [43].
The majority of the participants had light S. haematobium infections and light to moderate S. mansoni infections. The peptides generally had good specificities and poor sensitives against these light to moderate infections probably due to low antibody levels, ultimately making the peptides poor diagnostic candidates. However, the possibility of the construction of multi-epitope chimeric proteins could improve the sensitivity of these peptides [19,44]. A study by Oliveira and colleagues used a multi-epitope chimeric protein constructed from a pool of 5 epitopes/peptides derived from S. mansoni proteins in a serum-based IgG ELISA and demonstrated high diagnostic performances compared to the single peptides [17].
Unfortunately, like most currently available serological tests the peptides identified are not useful for monitoring and evaluation programmes because antibodies to schistosome infections remain detectable long after treatment [45]. This limits the clinical value of antibody detection for confirmation of the success of chemotherapy, since specific antibodies continue to be present long after the worms have disappeared [14,46]. There may be certain antigens (for example peptides) to which certain antibody isotype subclasses like IgG4, disappear more rapidly [45]. These antibodies can be targeted to circumvent the limitation associated with persisting antibodies. According to the WHO, serological and immunological tests are useful for showing exposure to schistosome infection in people living in non-endemic or low-transmission [47]. Alternatively, the identified peptides can be used to develop serological tools for showing exposure to infection for people living in non-endemic and low-transmission areas.
As urogenital and intestinal schistosomiasis are poverty related diseases prevalent in resource-limited settings, cost is a major factor for developing diagnostic tools for the diseases [48]. Therefore, we recommend that peptides discovered by peptide microarray technologies be transferred to point-of-care lateral flow platforms. Lastly, as previously described potential peptides should be validated with extremely well characterized samples with multiple infections, as well as true negative controls and a good positive reference (characterised with parasitological, molecular and antigen-based assays) [43,49].
4.2. Conclusion
In conclusion, one peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performance were identified using the immunoinformatic approach and peptide microarray validation. Identified peptides maybe be used to develop diagnostic tools for showing exposure to schistosome infection in people living in non-endemic or low-transmission areas. Peptide AA81008-19-30 may be used to develop a tool for diagnosis of early S. mansoni worm infections. However, there is need for validation of the findings with true negative controls from a non-endemic country and a good reference tool.
Supporting information
S1 File. Literature search strategies and results for the identification of S. haematobium and S. mansoni peptides and recombinant proteins published between 2000 and 2022.
https://doi.org/10.1371/journal.pntd.0011887.s001
(PDF)
S2 File. S. haematobium and S. mansoni demographics, parasitology and peptide microarray immunoassay data sets.
https://doi.org/10.1371/journal.pntd.0011887.s002
(XLSX)
S3 File. Diagnostic performance of recombinant proteins to detect S. haematobium and S. mansoni patient IgG and IgM antibodies.
https://doi.org/10.1371/journal.pntd.0011887.s003
(PDF)
S4 File. S. haematobium and S. mansoni linear B-cell epitopes.
https://doi.org/10.1371/journal.pntd.0011887.s004
(XLSX)
S5 File.
Receiver operating characteristics (ROC) curve and area under the ROC curve (AUC) to detect S. mansoni (a-q) and S. haematobium (r-t) patient serum IgG.
https://doi.org/10.1371/journal.pntd.0011887.s005
(PDF)
S6 File.
Receiver operating characteristics (ROC) curve and area under the ROC curve (AUC) to detect S. mansoni (a-b) and S. haematobium (c) patient serum IgM.
https://doi.org/10.1371/journal.pntd.0011887.s006
(PDF)
Acknowledgments
We thank the communities of Shamva, and Mount Darwin rural districts in the Mashonaland Central province of Zimbabwe for their participation, and support in the study. The authors would like to acknowledge the valuable input of laboratory technicians from the University of Zimbabwe who assisted in sample collection.
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