Peptide Microarray Immunoassay Research Articles

Identification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technology.

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. 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. 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. 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.

Peptide microarray analysis of in-silico predicted B-cell epitopes in SARS-CoV-2 sero-positive healthcare workers in Bulawayo, Zimbabwe

Immunogenic peptides that mimic linear B-cell epitopes coupled with immunoassay validation may improve serological tests for emerging diseases. This study reports a general approach for profiling linear B-cell epitopes derived from SARS-CoV-2 using an in-silico method and peptide microarray immunoassay, using healthcare workers’ SARS-CoV-2 sero-positive sera. SARS-CoV-2 was tested using rapid chromatographic immunoassays and real-time reverse-transcriptase polymerase chain reaction. Immunogenic peptides mimicking linear B-cell epitopes were predicted in-silico using ABCpred. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were selected using the NCBI Protein BLAST. IgG and IgM antibodies against the SARS-CoV-2 spike protein, membrane glycoprotein and nucleocapsid derived peptides were measured in sera using peptide microarray immunoassay. Fifty-three healthcare workers included in the study were RT-PCR negative for SARS-CoV-2. Using rapid chromatographic immunoassays, 10 were SARS-CoV-2 IgM sero-positive and 7 were SARS-CoV-2 IgG sero-positive. From a total of 10 SARS-CoV-2 peptides contained on the microarray, 3 (QTH34388.1-1-14, QTN64908.1-135-148, and QLL35955.1-22-35) showed reactivity against IgG. Three peptides (QSM17284.1-76-89, QTN64908.1-135-148 and QPK73947.1-8-21) also showed reactivity against IgM. Based on the results we predicted one peptide (QSM17284.1-76-89) that had an acceptable diagnostic performance. Peptide QSM17284.1-76-89 was able to detect IgM antibodies against SARS-CoV-2 with area under the curve (AUC) 0.781 when compared to commercial antibody tests. In conclusion in silico peptide prediction and peptide microarray technology may provide a platform for the development of serological tests for emerging infectious diseases such as COVID-19. However, we recommend using at least three in-silico peptide prediction tools to improve the sensitivity and specificity of B-cell epitope prediction, to predict peptides with excellent diagnostic performances.

IgE and IgG4 Epitope Mapping of Food Allergens with a Peptide Microarray Immunoassay.

Peptide microarrays are a powerful tool to identify linear epitopes of food allergens in a high-throughput manner. The main advantages of the microarray-based immunoassay are as follows: the possibility to assay thousands of targets simultaneously, the requirement of a low volume of serum, the more robust statistical analysis, and the possibility to test simultaneously several immunoglobulin subclasses. Among them, the last one has a special interest in the field of food allergy, because the development of tolerance to food allergens has been associated with a decrease in IgE and an increase in IgG4 levels against linear epitopes. However, the main limitation to the clinical use of microarray is the automated analysis of the data. Recent studies mapping the linear epitopes of food allergens with peptide microarray immunoassays have identified peptide biomarkers that can be used for early diagnosis of food allergies and to predict their severity or the self-development of tolerance. Using this approach, we have worked on epitope mapping of the two most important food allergens in the Spanish population, cow's milk, and chicken eggs. The final aim of these studies is to define subsets of peptides that could be used as biomarkers to improve the diagnosis and prognosis of food allergies. This chapter describes the protocol to produce microarrays using a library of overlapping peptides corresponding to the primary sequences of food allergens and data acquisition and analysis of IgE and IgG4 binding epitopes.

Identification of continuous immunoglobulin G epitopes of Dermatophagoides farinae allergens by peptide microarray immunoassay.

Dermatophagoides farinae, as a common house dust mite species, is one of the main sources of allergens in the world. At present, Dermatophagoides farinae is found to contain more than 30 groups of allergens. These allergens are used for allergen-specific immunotherapy (AIT) of allergic diseases. During the AIT process, immunoglobulin G (IgG) antibodies can block immunoglobulin E (IgE) antibody-induced allergic reactions in the human body. One of the mechanisms may be that IgG and IgE competitively bind to the same allergic protein, so it is necessary to explore the binding sites (epitopes) of IgG antibodies to allergens. In this study, peptide arrays were constructed to react with the serums from patients with allergic asthma to find the IgG epitopes of several allergens including major allergens (Der f 1, 2) and mid-tier allergens (Der f 4, 5, and 7), and then verified by enzyme-linked immunosorbent assay (ELISA) test. Relevant epitopic sequences were located on the tertiary structure of individual allergens, as reconstructed by homology modeling. One IgG epitope of Der f 1 (90-106aa, NVPSELDLRSLRTVTPI), five IgG epitopes of Der f 4 (61-77aa, ERYQPVSYDIHTRSGDE; 193-209aa, FRSDASTHQWPDDLRSI; 226-242aa, HPFIYHETIYYGGNGIN; 271-287aa, LRWLRNFGTEWGLVPSG; 352-368aa, NDWVGPPTDQHGNILSV), and one IgG epitope of Der f 5 (84-101aa, RYNVEIALKSNEILERDL) were identified. IgG epitopes of Der f 2, 7 were not found. There are overlaps between the IgG and IgE epitopes of Der f 1, 4, and 5. These findings not only reflect the practicality of peptide array and ELISA test in the allergen IgG epitope identification, but also provide more information for further understanding of the human immunological changes during AIT and the molecular mechanisms of IgG blocking IgE activity.

IgE Epitope Mapping Using Peptide Microarray Immunoassay.

IgE epitope mapping has the potential to become an additional tool for food allergy diagnosis/prognosis and to lead to a better understanding of the pathogenesis and tolerance induction of food allergy. Due to its ability to screen thousands of targets in parallel using small volumes of sample, peptide microarray has greatly facilitated large-scale IgE epitope mapping. In the past 10 years, we have developed and optimized a reliable and sensitive peptide microarray immunoassay, which has been successfully applied for IgE epitope mapping of many food allergens in our lab. Here, we describe the method of performing the peptide microarray immunoassay for IgE epitope mapping. In addition, we have upgraded the microarray platform to measure antibody affinity by adding one additional competition step, which is also described in this chapter.

Sequential epitopes of Dermatophagoides farinae allergens identified using peptide microarray-based immunoassay.

House dust mites produce over 30 proteins proposed to induce immunoglobulin E (IgE) antibody production in patients. Continued identification of IgE-binding epitopes of these allergens is critical to advancing diagnosis and treatment of allergic disease. To identify possible sequential IgE-binding epitopes of the major- and mid-potency allergens from the house dust mite Dermatophagoides farinae by peptide microarray-based immunoassay, nucleotide sequences of D. farinae allergens (Der f) 1, 2, 4, 5, and 7 were used to generate overlapping peptides covering the full protein sequences minus signal peptides. Short peptides were printed onto microarray chips. Because asthma occurs as a symptom of mite allergy more commonly among children than adults, the peptide chips were exposed to sera pooled from six serum-positive pediatric patients with D. farinae hypersensitivity and six serum-negative control children for screening sequential IgE-binding epitopes by IgE immunolabeling. Higher-than-average immunolabel signal intensity was observed for 21 short peptides in the serum-positive group (P < 0.01). Due to sequence overlap, these 21 signals represented four fragments of Der f 1 (amino acid positions 46-53, 71-78, 99-110, 179-186), three fragments of Der f 2 (15-22, 80-89, 106-113), six fragments of Der f 4 (69-82, 107-116, 225-232, 261-268, 355-365, 483-496), one fragment of Der f 5 (102-109), and three fragments of Der f 7 (32-39, 52-64, 100-107). These findings not only demonstrate the utility of a peptide microarray immunoassay in identifying epitopes for these allergens, but also provide a foundation for future exploration of specific immunotherapies. © 2016 IUBMB Life, 68(10):792-798, 2016.

Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays.

The peptide microarray immunoassay simultaneously screens sample serum against thousands of peptides, determining the presence of antibodies bound to array probes. Peptide microarrays tiling immunogenic regions of pathogens (e.g., envelope proteins of a virus) are an important high throughput tool for querying and mapping antibody binding. Because of the assay's many steps, from probe synthesis to incubation, peptide microarray data can be noisy with extreme outliers. In addition, subjects may produce different antibody profiles in response to an identical vaccine stimulus or infection, due to variability among subjects' immune systems. We present a robust Bayesian hierarchical model for peptide microarray experiments, pepBayes, to estimate the probability of antibody response for each subject/peptide combination. Heavy-tailed error distributions accommodate outliers and extreme responses, and tailored random effect terms automatically incorporate technical effects prevalent in the assay. We apply our model to two vaccine trial data sets to demonstrate model performance. Our approach enjoys high sensitivity and specificity when detecting vaccine induced antibody responses. A simulation study shows an adaptive thresholding classification method has appropriate false discovery rate control with high sensitivity, and receiver operating characteristics generated on vaccine trial data suggest that pepBayes clearly separates responses from non-responses.

IgE and IgG4 Epitope Mapping of Food Allergens with a Peptide Microarray Immunoassay.

Peptide microarrays are a powerful tool to identify linear epitopes of food allergens in a high-throughput manner. The main advantages of the microarray-based immunoassay are the possibility to assay thousands of targets simultaneously, the requirement of a low volume of serum, the more robust statistical analysis, and the possibility to test simultaneously several immunoglobulin subclasses. Among them, the last one has a special interest in the field of food allergy, because the development of tolerance to food allergens has been associated with a decrease in IgE and an increase in IgG4 levels against linear epitopes. However, the main limitation to the clinical use of microarray is the automated analysis of the data. Recent studies mapping the linear epitopes of food allergens with peptide microarray immunoassays have identified peptide biomarkers that can be used for early diagnosis of food allergies and to predict their severity or the self-development of tolerance. Using this approach, we have worked on epitope mapping of the two most important food allergens in the Spanish population, cow's milk and chicken eggs. The final aim of these studies is to define subsets of peptides that could be used as biomarkers to improve the diagnosis and prognosis of food allergies. This chapter describes the protocol to produce microarrays using a library of overlapping peptides corresponding to the primary sequences of food allergens and data acquisition and analysis of IgE- and IgG4-binding epitopes.

Mapping of Epitopes Occurring in Bovine α(s1)-Casein Variants by Peptide Microarray Immunoassay.

Immunoglobulin E epitope mapping of milk proteins reveals important information about their immunologic properties. Genetic variants of αS1-casein, one of the major allergens in bovine milk, are until now not considered when discussing the allergenic potential. Here we describe the complete procedure to assess the allergenicity of αS1-casein variants B and C, which are frequent in most breeds, starting from milk with identification and purification of casein variants by isoelectric focusing (IEF) and anion-exchange chromatography, followed by in vitro gastrointestinal digestion of the casein variants, identification of the resulting peptides by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), in silico analysis of the variant-specific peptides as allergenic epitopes, and determination of their IgE-binding properties by microarray immunoassay with cow's milk allergic human sera.

Identification of novel peptide biomarkers to predict safety and efficacy of cow's milk oral immunotherapy by peptide microarray.

Cow's milk oral immunotherapy (CM-OIT) is still an experimental treatment. The development of novel biomarkers to predict the safety and efficacy of CM-OIT is crucial to translate this treatment to common clinical practice. To analyse long-term changes in IgE and IgG4 epitope binding profile induced by CM-OIT to identify safety and efficacy biomarkers. We studied 25 CM-allergic children who underwent CM-OIT and seven non-treated CM-allergic children as controls. CM-OIT patients were classified as low, moderate, and high risk according to the number of allergic reactions (safety), time required to achieve desensitization (efficacy) and need of premedication. IgE and IgG4 peptide microarray immunoassay was performed using a library of overlapping peptides of CM proteins at baseline, after oral desensitization, and 6, 12, and 24months of follow-up. Cow's milk oral immunotherapy induced a rapid increase of IgG4-binding epitopes and a slow decrease in IgE-binding epitopes. High-risk patients recognized a statistically significant higher number of IgE peptides in caseins at all the times studied. Similar but less pronounced changes were observed for IgG4-positive peptides. Clustering analysis grouped together the high-risk patients, and we identified 13 regions of caseins significantly differed between groups of patients. Bioinformatics analysis selected two sets of 16 IgE-binding peptides at baseline that predicted safety (R(2) =0.858) and efficacy (R(2) =0.732), respectively, of CM-OIT. We found two sets of IgE-binding peptides that can be used as novel biomarkers to predict the safety and efficacy of CM-OIT before starting treatment.

Identification of biomarkers to predict safety and efficacy of cow's milk oral immunotherapy by peptide microarray

Methods We studied 25 CM-allergic children at baseline, after oral desensitization, and at 6, 12 and 24 months of follow-up. Seven patients who refused CM-OIT were used as controls. Patients were classified as low, moderate, and high risk according to the number of allergic reactions (safety), time required to achieve tolerance (efficacy), and requirement of premedication. IgE and IgG4 peptide microarray immunoassay was performed using a library of overlapping peptides of CM proteins.

Relationship of IgE to basophil phenotypes in peanut-sensitized adults

Relationship of IgE to basophil phenotypes in peanut-sensitized adults

Mapping of IgE epitopes in in vitro gastroduodenal digests of β-lactoglobulin produced with human and simulated fluids

This work aimed to identify the IgE epitopes of the allergenic whey protein β-lactoglobulin (β-LG) following in vitro gastrointestinal digestion using human (HF) or simulated digestive fluids (SF). The IgE-binding of the digests was evaluated by inhibition ELISA with sera from milk allergic patients and the low molecular weight digestion products were identified by RP-HPLC–MS/MS. These peptides were then chemically synthesized and analyzed by a peptide microarray immunoassay. β-LG resisted digestion during the gastric phase, while no residual β-LG was observed at the end of the duodenal phase. The immunoreactivity of the digests was consistent with the levels of intact β-LG present, showing almost negligible IgE binding after gastrointestinal digestion with both systems. There were similarities in the peptide patterns produced, showing, respectively, 21 and 18 identical peptides in the gastric and duodenal digests. Digestion products related to five high-frequency IgE-binding synthetic peptides: β-LG (43–60), β-LG (47–62), β-LG (86–99), β-LG (86–100) and β-LG (135–147), which were released upon in vitro digestion, particularly with SF. These matched two very immunoreactive areas of the protein: 43–68 and 122–146, as mapped using a library of 36 20-amino acid peptides corresponding to the primary sequence of β-LG. However, no IgE-binding peptides comprising the residues 86–100 were detected by analyzing the 36-peptide library by microarray immunoassay.

Epitope mapping of the major allergen from Atlantic cod in Spanish population reveals different IgE‐binding patterns

IgE-epitope mapping of allergens reveal important information about antigen components involved in allergic reactions. The peptide-based microarray immunoassay has been used to map epitopes of some food allergens. We developed a peptide microarray immunoassay to map allergenic epitopes in parvalbumin from Atlantic cod (Gad m 1), the most consumed cod species in Spain. Sera from 13 fish-allergic patients with specific IgE to cod parvalbumin were used. A library of overlapping peptides was synthesized, representing the primary sequence of Gad m 1. Peptides were used to analyze allergen-specific IgE antibodies in patient sera. 100% of the patients recognized one antigenic region of 15 amino acids in length in Gad m 1. This region only partially correlated with one of the three antigenic determinants of Gad c 1 (Allergen M), parvalbumin from Baltic cod (Gadus callarias). In the 3D model of the protein, this region was located on the surface of the protein. We have identified a relevant antigenic region in Gad m 1. This epitope could be considered as a severity marker and provides additional information to improve fish allergy diagnosis and the design of safe immunotherapeutic tools.

Mapping of the IgE and IgG4 Sequential Epitopes of Ovomucoid with a Peptide Microarray Immunoassay

Background: Ovomucoid (Gal d 1) has been demonstrated to be the most important allergen in IgE-mediated egg allergy. Peptide microarray analysis is a novel method that can provide useful information on the nature of specific allergens. Methods: A peptide microarray immunoassay was performed using a 15- and 20-amino acid (aa) library of overlapping peptides (3-offset) of the primary sequence of ovomucoid. Sera from 50 patients with IgE-mediated egg allergy and reactivity to ovomucoid, with more than 1 year of follow-up, and sera from 10 controls were tested. Peptides were considered major epitopes when the average weighted Z-score was greater than 3 and recognized by at least 20% of the patient’s sera. Specific IgE epitopes were established on the basis of the IgE/IgG4 Z-score ratio. Results: The IgE and IgG4 recognition pattern was similar in both sets of peptides, but the signal intensity was generally higher in the 20-aa set. Thirty-four percent of the patients did not recognize any IgE sequential peptide and 20% of the patients recognized more than 10 sequential peptides. We identified 3 major IgE B-cell epitopes in domains I and II of ovomucoid. IgE/IgG4 ratio analysis showed that peptides 1–2 (aa 4–20) and peptides 29–31 (aa 91–104) were specific IgE epitopes. Conclusion: By using peptide microarray immunoassay in egg-allergic patients, we established that 34% of the patients do not have any linear epitope recognized by IgE. Further studies are needed to determine the clinical relevance of this finding.

A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay

Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy-related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice. We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods. Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG(4) binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis. Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG(4) binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers. In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice.

Ovalbumin and Ovomucoid IgE/IgG4 Epitopes in Patients Ingesting Baked Egg Products by Peptide Microarray Immunoassay

Ovalbumin and Ovomucoid IgE/IgG4 Epitopes in Patients Ingesting Baked Egg Products by Peptide Microarray Immunoassay

Epitope Mapping of Atlantic Salmon Major Allergen by Peptide Microarray Immunoassay

Background: IgE epitope mapping of allergens reveals important information about antigen elicitors involved in allergic reactions. The peptide-based microarray immunoassay offers an advantage of scale and parallel design over previous methods of epitope mapping. It has been used to map epitopes of some food allergens but has never been used with fish allergens. Objective: We sought to develop a peptide microarray immunoassay to map allergenic fish epitopes of two isoforms of Atlantic salmon (Salmo salar) parvalbumin, Sal s 1 beta 1 and Sal s 1 beta 2. Methods: Sera from 16 fish-allergic patients with specific IgE to salmon parvalbumin were used. Twelve healthy volunteers were used as negative controls. A library of overlapping peptides was synthesized commercially, representing the primary sequence of Sal s 1 beta 1 and Sal s 1 beta 2. Peptides were used to analyze allergen-specific IgE antibodies by immunolabeling with patient sera. Results: Three antigenic regions, not previously described, were identified in Sal s 1 beta 1. Two of them correlated with those previously reported in Gad c 1, parvalbumin from Baltic cod (Gadus callarias). No allergenic regions were found in Sal s 1 beta 2. This could be explained by crucial amino acid substitutions between isoforms. Conclusions: We have identified three antigenic regions in Sal s 1 beta 1 using a peptide microarray immunoassay. These three sequential epitopes formed a unique antigenic determinant in the three-dimensional model of the protein. In addition, we proved that isoforms from the same protein might have a different allergenic behavior.

A Bayesian hierarchical model for identifying epitopes in peptide microarray data

Peptide Microarray Immunoassay (PMI for brevity) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. PMI studies aim at recognizing antigen-specific antibodies from serum samples and at detecting epitope regions of the protein antigen. PMI data present new challenges for statistical analysis mainly due to the structural dependence among peptides. A PMI is made of a complete library of consecutive peptides. They are synthesized by systematically shifting a window of a fixed number of amino acids through the finite sequence of amino acids of the antigen protein as ordered in the primary structure of the protein. This implies that consecutive peptides have a certain number of amino acids in common and hence are structurally dependent. We propose a new flexible Bayesian hierarchical model framework, which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the structural dependence between peptides through a suitable latent Markov structure. The proposed model is illustrated using PMI data from a recent study about egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.

IgE and IgG4 Epitope Mapping by Peptide Microarray Immunoassay for the Identification of Patients with Symptomatic Peanut Allergy

IgE and IgG4 Epitope Mapping by Peptide Microarray Immunoassay for the Identification of Patients with Symptomatic Peanut Allergy