A general method for characterization of humoral immunity induced by a vaccine or infection

Profiling cells with DELs: Small molecule fingerprinting of cell surfaces

DNA-encoded small molecule library technology has recently emerged as a new paradigm for identifying ligands against drug targets. To date, it has been used to identify ligands against targets that are soluble or overexpressed on cell surfaces. Here, we report applying cell-based selection methods to profile surfaces of mouse C2C12 myoblasts and myotube cells in an unbiased, target agnostic manner. A panel of on-DNA compounds were identified and confirmed for cell binding selectivity. We optimized the cell selection protocol and employed a novel data analysis method to identify cell selective ligands against a panel of human B and T lymphocytes. We discuss the generality of using this workflow for DNA encoded small molecule library selection and data analysis against different cell types, and the feasibility of applying this method to profile cell surfaces for biomarker and target identification.

Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

Introduction

Lyme disease (LD), a rapidly growing public health problem in the US, represents a formidable challenge due to the lack of detailed understanding about how the human immune system responds to its pathogen, the Borrelia burgdorferi bacterium. Despite significant advances in gaining deeper insight into mechanisms the pathogen uses to evade immune response, substantial gaps remain. As a result, molecular tools for the disease diagnosis are lacking with the currently available tests showing poor performance. High interpersonal variability in immune response combined with the ability of the pathogen to use a number of immune evasive tactics have been implicated as underlying factors for the limited test performance.

Methods

This study was designed to perform a broad profiling of the entire repertoire of circulating antibodies in human sera at the single-individual level using planar arrays of short linear peptides with random sequences. The peptides sample sparsely, but uniformly the entire combinatorial sequence space of the same length peptides for profiling the humoral immune response to a B.burg. infection and compare them with other diseases with etiology similar to LD and healthy controls.

Results

The study revealed substantial variability in antibody binding profiles between individual LD patients even to the same antigen (VlsE protein) and strong similarity between individuals diagnosed with Lyme disease and healthy controls from the areas endemic to LD suggesting a high prevalence of seropositivity in endemic healthy control.

Discussion

This work demonstrates the utility of the approach as a valuable analytical tool for agnostic profiling of humoral immune response to a pathogen.

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

Evaluation of the immunogenicity of a Crimean-Congo hemorrhagic fever virus vaccine candidate in mice developed based on a baculovirus Zera nanoparticle delivery system

Crimean-Congo hemorrhagic fever (CCHF) is a zoonotic disease caused by Crimean-Congo hemorrhagic fever virus (CCHFV), which can cause severe clinical disease and even death in humans. In recent years, the disease has spread to a wider area, posing a major public health threat to China as well as the Middle East, Europe and Africa, and there is no safe and effective vaccine to prevent the disease. Recently, it has been shown that using the Zera fusion to target proteins can enhance immunogenicity and improve the potential for developing viral vaccines. Based on this finding, in this study, two vaccine candidates, Zera-Gn and Zera-Np, were prepared using an insect baculovirus system expressing CCHFV glycoprotein (Gn) and nucleocapsid protein (Np) fused with Zera tags, and evaluated for immunogenicity in BALB/c mice. The obtainedresults showed that both Zera-Gn and Zera-Np recombinant nanoparticles were successfully expressed, and Zera-Gn had good induction of humoral and cellular immunity in mice, and its immunogenicity was significantly higher than that of Zera-Np. The results indicated that Zera-Gn self-assembled nanoparticles prepared by fusing Zera tags with CCHFV spike-in protein Gn have the potential to be a candidate vaccine for CCHF, and this study provides a reference for the development of Zera self-assembled nanoparticle vaccine for CCHF.

FLU-LISA (fluorescence-linked immunosorbent assay): high-throughput antibody profiling using antigen microarrays

Vaccination and natural infection both elicit potent humoral responses that provide protection from subsequent infections. The immune history of an individual following such exposures is in part encoded by antibodies. While there are multiple immunoassays for measuring antibody responses, the majority of these methods measure responses to a single antigen. A commonly used method for measuring antibody responses is ELISA-a semiquantitative assay that is simple to perform in research and clinical settings. Here, we present FLU-LISA (fluorescence-linked immunosorbent assay)-a novel antigen microarray-based assay for rapid high-throughput antibody profiling. The assay can be used for profiling immunoglobulin (Ig) G, IgA and IgM responses to multiple antigens simultaneously, requiring minimal amounts of sample and antigens. Using several influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen microarrays, we demonstrated the specificity and sensitivity of our novel assay and compared it with the traditional ELISA, using samples from mice, chickens and humans. We also showed that our assay can be readily used with dried blood spots, which can be collected from humans and wild birds. FLU-LISA can be readily used to profile hundreds of samples against dozens of antigens in a single day, and therefore offers an attractive alternative to the traditional ELISA.

Reactivity Graph Yields Interpretable IgM Repertoire Signatures as Potential Tumor Biomarkers

Combining adaptive and innate immunity induction modes, the repertoire of immunoglobulin M (IgM) can reflect changes in the internal environment including malignancies. Previously, it was shown that a mimotope library reflecting the public IgM repertoire of healthy donors (IgM IgOme) can be mined for efficient probes of tumor biomarker antibody reactivities. To better explore the interpretability of this approach for IgM, solid tumor-related profiles of IgM reactivities to linear epitopes of actual tumor antigens and viral epitopes were studied. The probes were designed as oriented planar microarrays of 4526 peptide sequences (as overlapping 15-mers) derived from 24 tumor-associated antigens and 209 cancer-related B cell epitopes from 30 viral antigens. The IgM reactivity in sera from 21 patients with glioblastoma multiforme, brain metastases of other tumors, and non-tumor-bearing neurosurgery patients was thus probed in a proof-of-principle study. A graph representation of the binding data was developed, which mapped the cross-reactivity of the mixture of IgM (poly)specificities, delineating different antibody footprints in the features of the graph-neighborhoods and cliques. The reactivity graph mapped the major features of the IgM repertoire such as the magnitude of the reactivity (titer) and major cross-reactivities, which correlated with blood group reactivity, non-self recognition, and even idiotypic specificities. A correlation between an aspect of this image of the IgM IgOme, namely, small cliques reflecting rare self-reactivities and the capacity of subsets of the epitopes to separate the diagnostic groups studied was found. In this way, the graph representation helped the feature selection in its filtering step and provided reduced feature sets, which, after recursive feature elimination, produced a classifier containing 51 peptide reactivities separating the three diagnostic groups with an unexpected efficiency. Thus, IgM IgOme approaches to repertoire studies is greatly augmented when self/viral antigens are used and the data are represented as a reactivity graph. This approach is most general, and if it is applicable to tumors in immunologically privileged sites, it can be applied to any solid tumors, for instance, breast or lung cancer.

Applications of Peptide Microarrays in Autoantibody, Infection, and Cancer Detection

The diversity of the antigen-specific humoral immune response reflects the interaction of the immune system with pathogens and autoantigens. Peptide microarray analysis opens up new perspectives for the use of antibodies as diagnostic biomarkers and provides unique access to a more differentiated view on humoral responses to disease. This review focuses on the latest applications of peptide microarrays for the serologic medical diagnosis of autoimmunity, infectious diseases (including COVID-19), and cancer.

Multiplex peptide microarray profiling of antibody reactivity against neglected tropical diseases derived B-cell epitopes for serodiagnosis in Zimbabwe

Introduction

Peptides (B-cell epitopes) have broad applications in disease diagnosis and surveillance of pathogen exposure. In this framework, we present a pilot study to design and produce a peptide microarray for the integrated surveillance of neglected tropical diseases. The peptide microarray was evaluated against peptides derived from Ascaris lumbricoides, Necator americanus, Schistosoma haematobium, Schistosoma mansoni, Trichuris trichiura, Bacillus anthracis, Mycobacterium leprae, Wuchereria bancrofti, Rabies lyssavirus, Chlamydia trachomatis and Trypanosoma brucei.

Methods

S. haematobium was diagnosed using the urine filtration technique. S. mansoni, A. lumbricoides, N. americanus and T. trichiura were diagnosed using the Kato Katz and formal ether concentration techniques. Immunogenic peptides were retrieved from the Tackling Infection to Benefit Africa infectious diseases epitope microarray. Further peptides were predicted using ABCpred. IgG and IgM reactivity against the derived peptides were evaluated using peptide microarray multiplex immunoassays. Positive response was defined as fluorescence intensity ≥ 500 fluorescence units. Immunodominant peptides were identified using color-coded heat maps and bar graphs reflecting the obtained fluorescence signal intensities. Receiver Operating Characteristic analysis and Mann-Whitney-U test were performed to determine the diagnostic validity of the peptides.

Results

Species-specific responses with at least one peptide derived from each NTD pathogen were observed. The reactive peptides included; for S. haematobium, XP_035588858.1-206-220 and XP_035588858.1-206-220 immunodominant for IgG and IgM respectively, for S. mansoni, P20287.1-58-72 immunodominant for both antibodies and for T. trichiura, CDW52482.1-326-340 immunodominant for IgG and CDW57769.1-2017-2031 and CDW57769.1-1518-1532 immunodominant for IgM. According to ROC analysis most of the peptides selected were inaccurate; with AUC < 0.5. Some peptides had AUC values ranging from 0.5 to 0.5875 for both IgM and IgG suggesting no discrimination.

Conclusion

Multiplex peptide microarrays are a valuable tool for integrated NTDs surveillance and for screening parasites exposure in endemic areas. Species sero-reactivity observed in the study maybe indicative of exposure to the different NTDs parasites. However, although peptides with the least cross reactivity were selected there is need to validate the sero-reactivity with recombinant antigens and immune-blotting techniques such as western blotting.

Scoping review of the applications of peptide microarrays on the fight against human infections

INTRODUCTION

This scoping review explores the use of peptide microarrays in the fight against infectious diseases. The research domains explored included the use of peptide microarrays in the mapping of linear B-cell and T cell epitopes, antimicrobial peptide discovery, immunosignature characterisation and disease immunodiagnostics. This review also provides a short overview of peptide microarray synthesis.

METHODS

Electronic databases were systematically searched to identify relevant studies. The review was conducted using the Joanna Briggs Institute methodology for scoping reviews and data charting was performed using a predefined form. The results were reported by narrative synthesis in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Ninety-five articles from 103 studies were included in the final data charting process. The majority (92. 0%) of the articles were published during 2010-2020 and were mostly from Europe (44.2%) and North America (34.7%). The findings were from the investigation of viral (45.6%), bacterial (32. 0%), parasitic (23.3%) and fungal (2. 0%) infections. Out of the serological studies, IgG was the most reported antibody type followed by IgM. The largest portion of the studies (77.7%) were related to mapping B-cell linear epitopes, 5.8% were on diagnostics, 5.8% reported on immunosignature characterisation and 8.7% reported on viral and bacterial cell binding assays. Two studies reported on T-cell epitope profiling.

CONCLUSION

The most important application of peptide microarrays was found to be B-cell epitope mapping or antibody profiling to identify diagnostic and vaccine targets. Immunosignatures identified by random peptide microarrays were found to be applied in the diagnosis of infections and interrogation of vaccine responses. The analysis of the interactions of random peptide microarrays with bacterial and viral cells using binding assays enabled the identification of antimicrobial peptides. Peptide microarray arrays were also used for T-cell linear epitope mapping which may provide more information for the design of peptide-based vaccines and for the development of diagnostic reagents.

EPIphany—A Platform for Analysis and Visualization of Peptide Immunoarray Data

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates. High-resolution analysis of these populations typically requires advanced techniques such as B cell receptor repertoire sequencing, mass spectrometry of isolated immunoglobulins, or phage display libraries that are dependent upon equipment and expertise which are prohibitive for many labs. High-density peptide microarrays representing diverse populations of putative linear epitopes (immunoarrays) are an effective alternative for high-throughput examination of antibody reactivity and diversity. While a promising technology, widespread adoption of immunoarrays has been limited by the need for, and relative absence of, user-friendly tools for consideration and visualization of the emerging data. To address this limitation, we developed EPIphany, a software platform with a simple web-based user interface, aimed at biological users, that provides access to important analysis parameters, data normalization options, and a variety of unique data visualization options. This platform provides researchers the greatest opportunity to extract biologically meaningful information from the immunoarray data, thereby facilitating the discovery and development of novel immuno-therapeutics.

Rapid Response to Pandemic Threats: Immunogenic Epitope Detection of Pandemic Pathogens for Diagnostics and Vaccine Development Using Peptide Microarrays

Emergence and re-emergence of pathogens bearing the risk of becoming a pandemic threat are on the rise. Increased travel and trade, growing population density, changes in urbanization, and climate have a critical impact on infectious disease spread. Currently, the world is confronted with the emergence of a novel coronavirus SARS-CoV-2, responsible for yet more than 800 000 deaths globally. Outbreaks caused by viruses, such as SARS-CoV-2, HIV, Ebola, influenza, and Zika, have increased over the past decade, underlining the need for a rapid development of diagnostics and vaccines. Hence, the rational identification of biomarkers for diagnostic measures on the one hand, and antigenic targets for vaccine development on the other, are of utmost importance. Peptide microarrays can display large numbers of putative target proteins translated into overlapping linear (and cyclic) peptides for a multiplexed, high-throughput antibody analysis. This enabled for example the identification of discriminant/diagnostic epitopes in Zika or influenza and mapping epitope evolution in natural infections versus vaccinations. In this review, we highlight synthesis platforms that facilitate fast and flexible generation of high-density peptide microarrays. We further outline the multifaceted applications of these peptide array platforms for the development of serological tests and vaccines to quickly encounter pandemic threats.

A Complex Dance: Measuring the Multidimensional Worlds of Influenza Virus Evolution and Anti-Influenza Immune Responses

The human antibody response to influenza virus infection or vaccination is as complicated as it is essential for protection against flu. The constant antigenic changes of the virus to escape human herd immunity hinder the yearly selection of vaccine strains since it is hard to predict which virus strains will circulate for the coming flu season. A "universal" influenza vaccine that could induce broad cross-influenza subtype protection would help to address this issue. However, the human antibody response is intricate and often obscure, with factors such as antigenic seniority or original antigenic sin (OAS), and back-boosting ensuring that each person mounts a unique immune response to infection or vaccination with any new influenza virus strain. Notably, the effects of existing antibodies on cross-protective immunity after repeated vaccinations are unclear. More research is needed to characterize the mechanisms at play, but traditional assays such as hemagglutinin inhibition (HAI) and microneutralization (MN) are excessively limited in scope and too resource-intensive to effectively meet this challenge. In the past ten years, new multiple dimensional assays (MDAs) have been developed to help overcome these problems by simultaneously measuring antibodies against a large panel of influenza hemagglutinin (HA) proteins with a minimal amount of sample in a high throughput way. MDAs will likely be a powerful tool for accelerating the study of the humoral immune response to influenza vaccination and the development of a universal influenza vaccine.

Immunosignature Analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

A random-sequence peptide microarray can interrogate serum antibodies in a broad, unbiased fashion to generate disease-specific immunosignatures. This approach has been applied to cancer detection, diagnosis of infections, and interrogation of vaccine response. We hypothesized that there is an immunosignature specific to ME/CFS and that this could aid in the diagnosis. We studied two subject groups meeting the Canadian Consensus Definition of ME/CFS. ME/CFS (n = 25) and matched control (n = 25) sera were obtained from a Canadian study. ME/CFS (n = 25) sera were obtained from phase 1/2 Norwegian trials (NCT01156909). Sera from six healthy controls from the USA were included in the analysis. Canadian cases and controls were tested for a disease immunosignature. By combining results from unsupervised and supervised analyses, a candidate immunosignature with 654 peptides was able to differentiate ME/CFS from controls. The immunosignature was tested and further refined using the Norwegian and USA samples. This resulted in a 256-peptide immunosignature with the ability to separate ME/CFS cases from controls in the international data sets. We were able to identify a 256-peptide signature that separates ME/CFS samples from healthy controls, suggesting that the hit-and-run hypothesis of immune dysfunction merits further investigation. By extending testing of both our signature and one previously reported in the literature to larger cohorts, and further interrogating the specific peptides we and others have identified, we may deepen our understanding of the origins of ME/CFS and work towards a clinically meaningful diagnostic biomarker.

Hemagglutination Inhibition Antibody Response Following Influenza A(H1N1)pdm09 Virus Natural Infection: A Cross-Sectional Study from Thirthahalli, Karnataka, India

Influenza viruses are major respiratory pathogens that cause seasonal epidemics and occasional pandemics. Immune response to influenza viruses is majorly targeted against the hemagglutinin antigen. A laboratory-based retrospective cross-sectional study was conducted on 50 acute and 50 follow-up samples to assess the immune response to influenza A(H1N1)pdm09 virus after natural infection and detect the presence of pre-existing antibodies against influenza A(H3N2) and influenza B viruses. Two-fourfold rise in hemagglutination-inhibition (HAI) titer was observed in 100% of the follow-up samples for influenza A(H1N1)pdm09 virus. No change in HAI titers for influenza A(H3N2) and influenza B viruses was observed.

Using Frameshift Peptide Arrays for Cancer Neo-Antigens Screening

It has been demonstrated that DNA mutations generating neo-antigens are important for an effective immune response to tumors as evident from recent clinical studies of immune checkpoint inhibitors (ICIs). Further, it was shown that frameshift peptides (FSP) generated in tumors from insertions and deletions (INDELs) of microsatellites (MS) in coding region are a very good correlate of positive response to PD1 treatment. However, these types of DNA-sourced FSPs are infrequent in cancer. We hypothesize that tumors may also generate FSPs in transcription errors through INDELs in MS or by exon mis-splicing. Since there are a finite number of predictable sequences of such possible FSPs in the genome, we propose that peptide arrays with all possible FSPs could be used to analyze antibody reactivity to FSPs in patient sera as a FS neo-antigen screen. If this were the case it would facilitate finding common tumor neoantigens for cancer vaccines. Here we test this proposal using an array of 377 predicted FS antigens. The results of screening 9 types of dog cancer sera indicate that cancer samples had significantly higher antibody responses against FSPs than non-cancer samples. Both common reactive FSPs and cancer-type specific immune responses were detected. In addition, the protection of a common reactive FSP was tested in mouse tumor models, comparing to the non-reactive FSPs. The mouse homologs non-reactive FSPs did not offer protection in either the mouse melanoma or breast cancer models while the reactive FSP did in both models. The tumor protection was positively correlated to antibody response to the FSP. These data suggest that FSP arrays could be used for cancer neo-antigen screening.

High-Density Redox Amplified Coulostatic Discharge-Based Biosensor Array

High-density biosensor arrays are essential for many cutting-edge biomedical applications including point-of-care vaccination screening to detect multiple highly-contagious diseases. Typical electrochemical biosensing techniques are based on the measurement of sub-pA currents for micron-sized sensors requiring highly-sensitive readout circuits. Such circuits are often too complex to scale down for high-density arrays. In this paper, a high-density 4,096-pixel electrochemical biosensor array in 180 nm CMOS is presented. It uses a coulostatic discharge sensing technique and interdigitated electrode geometry to reduce both the complexity and size of the readout circuitry. Each biopixel contains an interdigitated microelectrode with a 13 aA low-leakage readout circuit directly underneath. Compared to standard planar electrodes, the implemented interdigitated electrodes achieve a maximum amplification factor of 10.5× from redox cycling. The array's sensor density is comparable to state-of-the-art arrays, all without augmenting the sensors with complex post-processing. The detection of anti-Rubella and anti-Mumps antibodies in human serum is demonstrated.

Immunological Tolerance, Pregnancy, and Preeclampsia: The Roles of Semen Microbes and the Father

Although it is widely considered, in many cases, to involve two separable stages (poor placentation followed by oxidative stress/inflammation), the precise originating causes of preeclampsia (PE) remain elusive. We have previously brought together some of the considerable evidence that a (dormant) microbial component is commonly a significant part of its etiology. However, apart from recognizing, consistent with this view, that the many inflammatory markers of PE are also increased in infection, we had little to say about immunity, whether innate or adaptive. In addition, we focused on the gut, oral and female urinary tract microbiomes as the main sources of the infection. We here marshall further evidence for an infectious component in PE, focusing on the immunological tolerance characteristic of pregnancy, and the well-established fact that increased exposure to the father's semen assists this immunological tolerance. As well as these benefits, however, semen is not sterile, microbial tolerance mechanisms may exist, and we also review the evidence that semen may be responsible for inoculating the developing conceptus (and maybe the placenta) with microbes, not all of which are benign. It is suggested that when they are not, this may be a significant cause of PE. A variety of epidemiological and other evidence is entirely consistent with this, not least correlations between semen infection, infertility and PE. Our view also leads to a series of other, testable predictions. Overall, we argue for a significant paternal role in the development of PE through microbial infection of the mother via insemination.

Entropy is a Simple Measure of the Antibody Profile and is an Indicator of Health Status: A Proof of Concept

We have previously shown that the diversity of antibodies in an individual can be displayed on chips on which 130,000 peptides chosen from random sequence space have been synthesized. This immunosignature technology is unbiased in displaying antibody diversity relative to natural sequence space, and has been shown to have diagnostic and prognostic potential for a wide variety of diseases and vaccines. Here we show that a global measure such as Shannon's entropy can be calculated for each immunosignature. The immune entropy was measured across a diverse set of 800 people and in 5 individuals over 3 months. The immune entropy is affected by some population characteristics and varies widely across individuals. We find that people with infections or breast cancer, generally have higher entropy values than non-diseased individuals. We propose that the immune entropy as measured from immunosignatures may be a simple method to monitor health in individuals and populations.

Peptide Sequencing Directly on Solid Surfaces Using MALDI Mass Spectrometry

There are an increasing variety of applications in which peptides are both synthesized and used attached to solid surfaces. This has created a need for high throughput sequence analysis directly on surfaces. However, common sequencing approaches that can be adapted to surface bound peptides lack the throughput often needed in library-based applications. Here we describe a simple approach for sequence analysis directly on solid surfaces that is both high speed and high throughput, utilizing equipment available in most protein analysis facilities. In this approach, surface bound peptides, selectively labeled at their N-termini with a positive charge-bearing group, are subjected to controlled degradation in ammonia gas, resulting in a set of fragments differing by a single amino acid that remain spatially confined on the surface they were bound to. These fragments can then be analyzed by MALDI mass spectrometry, and the peptide sequences read directly from the resulting spectra.

LT adjuvant modulates epitope specificity and improves the efficacy of murine antibodies elicited by sublingual vaccination with the N-terminal domain of Streptococcus mutans P1

In this study, we evaluated the immunogenicity, protective efficacy and peptide-based immune signatures of antibodies raised in mice after sublingual immunization with a recombinant form of the P1 (aka AgI/II, PAc) adhesin (P1_39-512) of Streptococcus mutans, a major etiological agent of dental caries. Sublingual administration of P1_39-512 in combination with the mucosal adjuvant LTK4R (a derivative of heat-labile LT toxin) induced strong and long-lasting systemic and mucosal immune responses. Incorporation of the adjuvant resulted in an enhancement of the anti-adhesive and anti-colonization activity against S. mutans as evaluated both under in vitro and in vivo conditions. Incorporation of the adjuvant to the vaccine formulation also changed the epitope specificity of the induced antibodies as determined by immunological signatures of sera collected from vaccinated mice. Use of a peptide microarray library led to the identification of peptide targets recognized by antibodies in serum samples with enhanced anti-adhesive effects. Altogether, the results presented herein showed that the sublingual administration of a P1-based subunit vaccine represents a promising approach for the prevention of dental caries caused by S. mutans. In addition, the present study disclosed the role of adjuvants on the epitope specificity and functionality of antibodies raised by subunit vaccines.

Adjuvant-Mediated Epitope Specificity and Enhanced Neutralizing Activity of Antibodies Targeting Dengue Virus Envelope Protein

The heat-labile toxins (LT) produced by enterotoxigenic Escherichia coli display adjuvant effects to coadministered antigens, leading to enhanced production of serum antibodies. Despite extensive knowledge of the adjuvant properties of LT derivatives, including in vitro-generated non-toxic mutant forms, little is known about the capacity of these adjuvants to modulate the epitope specificity of antibodies directed against antigens. This study characterizes the role of LT and its non-toxic B subunit (LTB) in the modulation of antibody responses to a coadministered antigen, the dengue virus (DENV) envelope glycoprotein domain III (EDIII), which binds to surface receptors and mediates virus entry into host cells. In contrast to non-adjuvanted or alum-adjuvanted formulations, antibodies induced in mice immunized with LT or LTB showed enhanced virus-neutralization effects that were not ascribed to a subclass shift or antigen affinity. Nonetheless, immunosignature analyses revealed that purified LT-adjuvanted EDIII-specific antibodies display distinct epitope-binding patterns with regard to antibodies raised in mice immunized with EDIII or the alum-adjuvanted vaccine. Notably, the analyses led to the identification of a specific EDIII epitope located in the EF to FG loop, which is involved in the entry of DENV into eukaryotic cells. The present results demonstrate that LT and LTB modulate the epitope specificity of antibodies generated after immunization with coadministered antigens that, in the case of EDIII, was associated with the induction of neutralizing antibody responses. These results open perspectives for the more rational development of vaccines with enhanced protective effects against DENV infections.

An ImmunoSignature test distinguishes Trypanosoma cruzi, hepatitis B, hepatitis C and West Nile virus seropositivity among asymptomatic blood donors

BACKGROUND

The complexity of the eukaryotic parasite Trypanosoma (T.) cruzi manifests in its highly dynamic genome, multi-host life cycle, progressive morphologies and immune-evasion mechanisms. Accurate determination of infection or Chagas' disease activity and prognosis continues to challenge researchers. We hypothesized that a diagnostic platform with higher ligand complexity than previously employed may hold value.

METHODOLOGY

We applied the ImmunoSignature Technology (IST) for the detection of T. cruzi-specific antibodies among healthy blood donors. IST is based on capturing the information in an individual's antibody repertoire by exposing their peripheral blood to a library of >100,000 position-addressable, chemically-diverse peptides.

PRINCIPAL FINDINGS

Initially, samples from two Chagas cohorts declared positive or negative by bank testing were studied. With the first cohort, library-peptides displaying differential binding signals between T. cruzi sero-states were used to train an algorithm. A classifier was fixed and tested against the training-independent second cohort to determine assay performance. Next, samples from a mixed cohort of donors declared positive for Chagas, hepatitis B, hepatitis C or West Nile virus were assayed on the same library. Signals were used to train a single algorithm that distinguished all four disease states. As a binary test, the accuracy of predicting T. cruzi seropositivity by IST was similar, perhaps modestly reduced, relative to conventional ELISAs. However, the results indicate that information beyond determination of seropositivity may have been captured. These include the identification of cohort subclasses, the simultaneous detection and discerning of other diseases, and the discovery of putative new antigens.

CONCLUSIONS & SIGNIFICANCE

The central outcome of this study established IST as a reliable approach for specific determination of T. cruzi seropositivity versus disease-free individuals or those with other diseases. Its potential contribution for monitoring and controlling Chagas lies in IST's delivery of higher resolution immune-state readouts than obtained with currently-used technologies. Despite the complexity of the ligand presentation and large quantitative readouts, performing an IST test is simple, scalable and reproducible.

Single amino acid fingerprinting of the human antibody repertoire with high density peptide arrays

The antibody species that patrol in a patient's blood are an invaluable part of the immune system. While most of them shield us from life-threatening infections, some of them do harm in autoimmune diseases. If we knew exactly all the antigens that elicited all the antibody species within a group of patients, we could learn which ones correlate with immune protection, are irrelevant, or do harm. Here, we demonstrate an approach to this question: First, we use a plethora of phage-displayed peptides to identify many different serum antibody binding peptides. Next, we synthesize identified peptides in the array format and rescreen the serum used for phage panning to validate antibody binding peptides. Finally, we systematically vary the sequence of validated antibody binding peptides to identify those amino acids within the peptides that are crucial for binding "their" antibody species. The resulting immune fingerprints can then be used to trace them back to potential antigens. We investigated the serum of an individual in this pipeline, which led to the identification of 73 antibody fingerprints. Some fingerprints could be traced back to their most likely antigen, for example the immunodominant capsid protein VP1 of enteroviruses, most likely elicited by the ubiquitous poliovirus vaccination. Thus, with our approach, it is possible, to pinpoint those antibody species that correlate with a certain antigen, without any pre-information. This can help to unravel hitherto enigmatic diseases.

Interaction of Serum Antibodies from Breast Cancer Patients with Synthetic Peptides

The blood serum of tumor patients contains antibodies recognizing tumor-associated antigens and other molecular products of tumor growth. We studied the interaction of blood antibodies from breast cancer patients with synthetic peptides that were applied on the microchip surface. The serum from healthy volunteers and breast cancer patients was shown to contain antibodies that interact with various peptides. Statistically significant between-group differences were observed in the level of binding with 122 informative peptides (0.01% of the total number of peptides on a microchip). Analysis of antibodies that interact with the peptide panel holds much promise for the diagnostics of breast cancer.

General Assessment of Humoral Activity in Healthy Humans

The humoral immune system is network of biological molecules designed to maintain a healthy homeostatic equilibrium. Because antibodies are an abundant and highly specific effector of immunological action, they are also an important reservoir of previous host exposures. Antibodies may play a major role in early detection of host challenge. Unfortunately, few practical methods exist for interpreting the information stored in antibody variable regions. Immunosignatures use a microarray of thousands of random sequence peptides to interrogate antibodies in a broad and unbiased fashion. The pattern of binding between antibody and peptide is reproducible. Once the system has been trained on a disease cohort, blinded samples can be reliably predicted. Although immunosignatures of both chronic and infectious disease have been extensively tested, less has been done to demonstrate how healthy immunosignatures change over time or between individuals. Here, we report the results of a study of immunosignatures of healthy persons over brief (12 h sampled once per hour), intermediate (32 days sampled once per day), and long (5 years sampled once every year) time spans. Using this information, we were also able to detect intentional and unintentional immunological perturbations in the form of a vaccine and an infection, respectively. Our findings suggest that, even with the variability inherent in healthy immunosignatures, a single person's immunosignature will remain constant over time. Over this healthy signature, vaccines and infections create subsignatures that are common across multiple people, even subsuming healthy fluctuations. These findings have implications for disease monitoring and early diagnosis.

Peptide Microarrays for Medical Applications in Autoimmunity, Infection, and Cancer

The diversity of the antigen-specific humoral immune response reflects the interaction of the immune system with pathogens and autoantigens. Peptide microarray analysis opens up new perspectives for the use of antibodies as diagnostic biomarkers and provides unique access to a more differentiated serological diagnosis. This review focusses on latest applications of peptide microarrays for the serologic medical diagnosis of autoimmunity, infectious diseases, and cancer.

Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets

Motivation: Proteins often recognize their interaction partners on the basis of short linear motifs located in disordered regions on proteins’ surface. Experimental techniques that study such motifs use short peptides to mimic the structural properties of interacting proteins. Continued development of these methods allows for large-scale screening, resulting in vast amounts of peptide sequences, potentially containing information on multiple protein-protein interactions. Processing of such datasets is a complex but essential task for large-scale studies investigating protein-protein interactions.

Results: The software tool presented in this article is able to rapidly identify multiple clusters of sequences carrying shared specificity motifs in massive datasets from various sources and generate multiple sequence alignments of identified clusters. The method was applied on a previously published smaller dataset containing distinct classes of ligands for SH3 domains, as well as on a new, an order of magnitude larger dataset containing epitopes for several monoclonal antibodies. The software successfully identified clusters of sequences mimicking epitopes of antibody targets, as well as secondary clusters revealing that the antibodies accept some deviations from original epitope sequences. Another test indicates that processing of even much larger datasets is computationally feasible.

Availability and implementation: Hammock is published under GNU GPL v. 3 license and is freely available as a standalone program (from http://www.recamo.cz/en/software/hammock-cluster-peptides/) or as a tool for the Galaxy toolbox (from https://toolshed.g2.bx.psu.edu/view/hammock/hammock). The source code can be downloaded from https://github.com/hammock-dev/hammock/releases.

Contact:muller@mou.cz

Supplementaryinformation:Supplementary data are available at Bioinformatics online.

A Multiplex Label-Free Approach to Avian Influenza Surveillance and Serology

Influenza serology has traditionally relied on techniques such as hemagglutination inhibition, microneutralization, and ELISA. These assays are complex, challenging to implement in a format allowing detection of several types of antibody-analyte interactions at once (multiplex), and troublesome to implement in the field. As an alternative, we have developed a hemagglutinin microarray on the Arrayed Imaging Reflectometry (AIR) platform. AIR provides sensitive, rapid, and label-free multiplex detection of targets in complex analyte samples such as serum. In preliminary work, we demonstrated the application of this array to the testing of human samples from a vaccine trial. Here, we report the application of an expanded label-free hemagglutinin microarray to the analysis of avian serum samples. Samples from influenza virus challenge experiments in mallards yielded strong, selective detection of antibodies to the challenge antigen in most cases. Samples acquired in the field from mallards were also analyzed, and compared with viral hemagglutinin inhibition and microneutralization assays. We find that the AIR hemagglutinin microarray can provide a simple and robust alternative to standard methods, offering substantially greater information density from a simple workflow.

New approaches to understanding the immune response to vaccination and infection

The immune system is a network of specialized cell types and tissues that communicates via cytokines and direct contact, to orchestrate specific types of defensive responses. Until recently, we could only study immune responses in a piecemeal, highly focused fashion, on major components like antibodies to the pathogen. But recent advances in technology and in our understanding of the many components of the system, innate and adaptive, have made possible a broader approach, where both the multiple responding cells and cytokines in the blood are measured. This systems immunology approach to a vaccine response or an infection gives us a more holistic picture of the different parts of the immune system that are mobilized and should allow us a much better understanding of the pathways and mechanisms of such responses, as well as to predict vaccine efficacy in different populations well in advance of efficacy studies. Here we summarize the different technologies and methods and discuss how they can inform us about the differences between diseases and vaccines, and how they can greatly accelerate vaccine development.

Epitope identification from fixed-complexity random-sequence peptide microarrays

Antibodies play an important role in modern science and medicine. They are essential in many biological assays and have emerged as an important class of therapeutics. Unfortunately, current methods for mapping antibody epitopes require costly synthesis or enrichment steps, and no low-cost universal platform exists. In order to address this, we tested a random-sequence peptide microarray consisting of over 330,000 unique peptide sequences sampling 83% of all possible tetramers and 27% of pentamers. It is a single, unbiased platform that can be used in many different types of tests, it does not rely on informatic selection of peptides for a particular proteome, and it does not require iterative rounds of selection. In order to optimize the platform, we developed an algorithm that considers the significance of k-length peptide subsequences (k-mers) within selected peptides that come from the microarray. We tested eight monoclonal antibodies and seven infectious disease cohorts. The method correctly identified five of the eight monoclonal epitopes and identified both reported and unreported epitope candidates in the infectious disease cohorts. This algorithm could greatly enhance the utility of random-sequence peptide microarrays by enabling rapid epitope mapping and antigen identification.

Scalable high-density peptide arrays for comprehensive health monitoring

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

Exploiting fluorescence for multiplex immunoassays on protein microarrays

Protein microarray technology is becoming the method of choice for identifying protein interaction partners, detecting specific proteins, carbohydrates and lipids, or for characterizing protein interactions and serum antibodies in a massively parallel manner. Availability of the well-established instrumentation of DNA arrays and development of new fluorescent detection instruments promoted the spread of this technique. Fluorescent detection has the advantage of high sensitivity, specificity, simplicity and wide dynamic range required by most measurements. Fluorescence through specifically designed probes and an increasing variety of detection modes offers an excellent tool for such microarray platforms. Measuring for example the level of antibodies, their isotypes and/or antigen specificity simultaneously can offer more complex and comprehensive information about the investigated biological phenomenon, especially if we take into consideration that hundreds of samples can be measured in a single assay. Not only body fluids, but also cell lysates, extracted cellular components, and intact living cells can be analyzed on protein arrays for monitoring functional responses to printed samples on the surface. As a rapidly evolving area, protein microarray technology offers a great bulk of information and new depth of knowledge. These are the features that endow protein arrays with wide applicability and robust sample analyzing capability. On the whole, protein arrays are emerging new tools not just in proteomics, but glycomics, lipidomics, and are also important for immunological research. In this review we attempt to summarize the technical aspects of planar fluorescent microarray technology along with the description of its main immunological applications.

Application of immunosignatures for diagnosis of valley fever

Valley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays

Background

An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans.

Results

In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets.

Conclusions

These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

Could immunosignatures technology enable the development of a preventative cancer vaccine?

The exciting prospect of developing a universal prophylactic cancer vaccine now seems more possible due to advances in technology and basic knowledge. However, the problem of testing the efficacy of such a vaccine in a clinical trial seems daunting. The low incidence and long lead-time to diagnosis of cancer would make a standard clinical trial long and expensive. Recently, we demonstrated that the immunosignatures diagnostic technology could be useful in evaluating vaccines. The technology is based on profiling the antibody diversity in an individual on a peptide chip platform. Here we propose that this technology may also enable a clinical trial of a preventative vaccine. Preliminary evidence supports the prospect of immunosignatures detecting cancer at very early stages, well before conventional diagnosis. Because the technology is simple and inexpensive, it could be used to monitor the occurrence of cancer in participants and shorten the clinical trial.

Immunosignatures can predict vaccine efficacy

The development of new vaccines would be greatly facilitated by having effective methods to predict vaccine performance. Such methods could also be helpful in monitoring individual vaccine responses to existing vaccines. We have developed "immunosignaturing" as a simple, comprehensive, chip-based method to display the antibody diversity in an individual on peptide arrays. Here we examined whether this technology could be used to develop correlates for predicting vaccine effectiveness. By using a mouse influenza infection, we show that the immunosignaturing of a natural infection can be used to discriminate a protective from nonprotective vaccine. Further, we demonstrate that an immunosignature can determine which mice receiving the same vaccine will survive. Finally, we show that the peptides comprising the correlate signatures of protection can be used to identify possible epitopes in the influenza virus proteome that are correlates of protection.

Characterization of influenza vaccine immunogenicity using influenza antigen microarrays

BACKGROUND

Existing methods to measure influenza vaccine immunogenicity prohibit detailed analysis of epitope determinants recognized by immunoglobulins. The development of highly multiplex proteomics platforms capable of capturing a high level of antibody binding information will enable researchers and clinicians to generate rapid and meaningful readouts of influenza-specific antibody reactivity.

METHODS

We developed influenza hemagglutinin (HA) whole-protein and peptide microarrays and validated that the arrays allow detection of specific antibody reactivity across a broad dynamic range using commercially available antibodies targeted to linear and conformational HA epitopes. We derived serum from blood draws taken from 76 young and elderly subjects immediately before and 28±7 days post-vaccination with the 2008/2009 trivalent influenza vaccine and determined the antibody reactivity of these sera to influenza array antigens.

RESULTS

Using linear regression and correcting for multiple hypothesis testing by the Benjamini and Hochberg method of permutations over 1000 resamplings, we identified antibody reactivity to influenza whole-protein and peptide array features that correlated significantly with age, H1N1, and B-strain post-vaccine titer as assessed through a standard microneutralization assay (p<0.05, q <0.2). Notably, we identified several peptide epitopes that were inversely correlated with regard to age and seasonal H1N1 and B-strain neutralization titer (p<0.05, q <0.2), implicating reactivity to these epitopes in age-related defects in response to H1N1 influenza. We also employed multivariate linear regression with cross-validation to build models based on age and pre-vaccine peptide reactivity that predicted vaccine-induced neutralization of seasonal H1N1 and H3N2 influenza strains with a high level of accuracy (84.7% and 74.0%, respectively).

CONCLUSION

Our methods provide powerful tools for rapid and accurate measurement of broad antibody-based immune responses to influenza, and may be useful in measuring response to other vaccines and infectious agents.

A technology for developing synbodies with antibacterial activity

The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and in vitro tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding Staphylococcus aureus to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block S. aureus induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.

Feasibility of an early Alzheimer's disease immunosignature diagnostic test

A practical diagnostic test is needed for early Alzheimer's disease (AD) detection. Immunosignaturing, a technology that employs antibody binding to a random-sequence peptide microarray, generates profiles that distinguish transgenic mice engineered with familial AD mutations (APPswe/PSEN1-dE9) from non-transgenic littermates. It can also detect an AD-like signature in humans. Here, we assess the changes in the immunosignature at different time points of the disease in mice and humans. We also evaluate the accuracy of the late-stage signature as a test to discriminate between young mice with familial AD mutations from non-transgenic littermates. Plasma samples from AD patients were assayed 3-12 months apart, while APPswe/PSEN1-dE9 and non-transgenic controls supplied plasma at monthly intervals until they reached 15 months of age. Microarrays with 10,000 random-sequence peptides were used to compare antibody binding patterns. These patterns gradually changed over the life-span of mice. Strong, characteristic signatures were observed in transgenic mice at early, mid and late stages, but these profiles had minimal overlap. The signature of young transgenic mice had an error rate of 18% at classifying plasma samples from late-stage transgenic mice. Conversely, the late-stage transgenic mice signature discriminated between young transgenic mice and littermates with an error rate of 21%. Less distinctive profiles were recognizable throughout the transgenic mice lifespan, being detectable as early as 2 months. The human signature had minimal change on short-term follow-up. Our results call for a reappraisal of the way incipient AD is studied, as biomarkers seen in late-stages of the disease may not be relevant in earlier stages.

Immunosignaturing can detect products from molecular markers in brain cancer

Immunosignaturing shows promise as a general approach to diagnosis. It has been shown to detect immunological signs of infection early during the course of disease and to distinguish Alzheimer’s disease from healthy controls. Here we test whether immunosignatures correspond to clinical classifications of disease using samples from people with brain tumors. Blood samples from patients undergoing craniotomies for therapeutically naïve brain tumors with diagnoses of astrocytoma (23 samples), Glioblastoma multiforme (22 samples), mixed oligodendroglioma/astrocytoma (16 samples), oligodendroglioma (18 samples), and 34 otherwise healthy controls were tested by immunosignature. Because samples were taken prior to adjuvant therapy, they are unlikely to be perturbed by non-cancer related affects. The immunosignaturing platform distinguished not only brain cancer from controls, but also pathologically important features about the tumor including type, grade, and the presence or absence of O⁶-methyl-guanine-DNA methyltransferase methylation promoter (MGMT), an important biomarker that predicts response to temozolomide in Glioblastoma multiformae patients.

Comparative study of classification algorithms for immunosignaturing data

BACKGROUND

High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

RESULTS

We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

CONCLUSIONS

'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Hepatitis C virus antigenic convergence

Vaccine development against hepatitis C virus (HCV) is hindered by poor understanding of factors defining cross-immunoreactivity among heterogeneous epitopes. Using synthetic peptides and mouse immunization as a model, we conducted a quantitative analysis of cross-immunoreactivity among variants of the HCV hypervariable region 1 (HVR1). Analysis of 26,883 immunological reactions among pairs of peptides showed that the distribution of cross-immunoreactivity among HVR1 variants was skewed, with antibodies against a few variants reacting with all tested peptides. The HVR1 cross-immunoreactivity was accurately modeled based on amino acid sequence alone. The tested peptides were mapped in the HVR1 sequence space, which was visualized as a network of 11,319 sequences. The HVR1 variants with a greater network centrality showed a broader cross-immunoreactivity. The entire sequence space is explored by each HCV genotype and subtype. These findings indicate that HVR1 antigenic diversity is extensively convergent and effectively limited, suggesting significant implications for vaccine development.