Feasibility of an early Alzheimer's disease immunosignature diagnostic test

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.

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.

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection in renal transplantation

BACKGROUND

Acute cellular rejection (ACR) is associated with complications after kidney transplantation, such as graft dysfunction and graft loss. Early risk assessment is therefore critical for the improvement of transplantation outcomes. In this work, we retrospectively analyzed a pre-transplant HLA antigen bead assay data set that was acquired by the e:KID consortium as part of a systems medicine approach.

RESULTS

The data set included single antigen bead (SAB) reactivity profiles of 52 low-risk graft recipients (negative complement dependent cytotoxicity crossmatch, PRA < 30%) who showed detectable pre-transplant anti-HLA 1 antibodies. To assess whether the reactivity profiles provide a means for ACR risk assessment, we established a novel approach which differs from standard approaches in two aspects: the use of quantitative continuous data and the use of a multiparameter classification method. Remarkably, it achieved significant prediction of the 38 graft recipients who experienced ACR with a balanced accuracy of 82.7% (sensitivity = 76.5%, specificity = 88.9%).

CONCLUSIONS

The resultant classifier achieved one of the highest prediction accuracies in the literature for pre-transplant risk assessment of ACR. Importantly, it can facilitate risk assessment in non-sensitized patients who lack donor-specific antibodies. As the classifier is based on continuous data and includes weak signals, our results emphasize that not only strong but also weak binding interactions of antibodies and HLA 1 antigens contain predictive information.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00724022 . Retrospectively registered July 2008.

High-throughput epitope profiling of antibodies in the plasma of Alzheimer's disease patients using random peptide microarrays

The symptoms of Alzheimer's disease (AD), a major cause of dementia in older adults, are linked directly with neuronal cell death, which is thought to be due to aberrant neuronal inflammation. Autoantibodies formed during neuronal inflammation show excellent stability in blood; therefore, they may be convenient blood-based diagnostic markers of AD. Here, we performed microarray analysis of 29,240 unbiased random peptides to be used for comprehensive screening of AD-specific IgG and IgM antibodies in the blood. The results showed that (1) sequence-specific and isotype-specific antibodies are regulated differentially in AD, and combinations of these antibodies showing high area under the receiver operating characteristic curve values (0.862-0.961) can be used to classify AD, (2) AD-specific IgG antibodies arise from IgM antibody-secreting cells that existed before disease onset and (3) target protein profiling of the antibodies identified some AD-related proteins, some of which are involved in AD-related signalling pathways. Therefore, we propose that these epitopes may facilitate the development of biomarkers for AD diagnosis and form the basis for a mechanistic study related to AD progression.

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.

Immunosignature Differentiation of Non-Infectious Meningoencephalomyelitis and Intracranial Neoplasia in Dogs

A variety of inflammatory conditions of unknown cause (meningoencephalomyelitis of unknown etiology-MUE) and neoplastic diseases can affect the central nervous system (CNS) of dogs. MUE can mimic intracranial neoplasia both clinically, radiologically and even in some cases, histologically. Serum immunosignature protein microarray assays have been used in humans to identify CNS diseases such as Alzheimer's and neoplasia, and in dogs, to detect lymphoma and its progression. This study evaluated the effectiveness of immunosignature profiles for distinguishing between three cohorts of dogs: healthy, intracranial neoplasia, and MUE. Using the learned peptide patterns for these three cohorts, classification prediction was evaluated for the same groups using a 10-fold cross validation methodology. Accuracy for classification was 100%, as well as 100% specific and 100% sensitive. This pilot study demonstrates that immunosignature profiles may help serve as a minimally invasive tool to distinguish between MUE and intracranial neoplasia in dogs.

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.

A Simple Platform for the Rapid Development of Antimicrobials

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

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.

Humoral Immunity Profiling of Subjects with Myalgic Encephalomyelitis Using a Random Peptide Microarray Differentiates Cases from Controls with High Specificity and Sensitivity

Myalgic encephalomyelitis (ME) is a complex, heterogeneous illness of unknown etiology. The search for biomarkers that can delineate cases from controls is one of the most active areas of ME research; however, little progress has been made in achieving this goal. In contrast to identifying biomarkers that are directly involved in the pathological process, an immunosignature identifies antibodies raised to proteins expressed during, and potentially involved in, the pathological process. Although these proteins might be unknown, it is possible to detect antibodies that react to these proteins using random peptide arrays. In the present study, we probe a custom 125,000 random 12-mer peptide microarray with sera from 21 ME cases and 21 controls from the USA and Europe and used these data to develop a diagnostic signature. We further used these peptide sequences to potentially uncover the naturally occurring candidate antigens to which these antibodies may specifically react with in vivo. Our analysis revealed a subset of 25 peptides that distinguished cases and controls with high specificity and sensitivity. Additionally, Basic Local Alignment Search Tool (BLAST) searches suggest that these peptides primarily represent human self-antigens and endogenous retroviral sequences and, to a minor extent, viral and bacterial pathogens.

A General Method to Discover Epitopes from Sera

Antigen-antibody complexes are central players in an effective immune response. However, finding those interactions relevant to a particular disease state can be arduous. Nonetheless many paths to discovery have been explored since deciphering these interactions can greatly facilitate the development of new diagnostics, therapeutics, and vaccines. In silico B cell epitope mapping approaches have been widely pursued, though success has not been consistent. Antibody mixtures in immune sera have been used as handles for biologically relevant antigens, but these and other experimental approaches have proven resource intensive and time consuming. In addition, these methods are often tailored to individual diseases or a specific proteome, rather than providing a universal platform. Most of these methods are not able to identify the specific antibody’s epitopes from unknown antigens, such as un-annotated neo antigens in cancer. Alternatively, a peptide library comprised of sequences unrestricted by naturally-found protein space provides for a universal search for mimotopes of an antibody’s epitope. Here we present the utility of such a non-natural random sequence library of 10,000 peptides physically addressed on a microarray for mimotope discovery without sequence information of the specific antigen. The peptide arrays were probed with serum from an antigen-immunized rabbit, or alternatively probed with serum pre-absorbed with the same immunizing antigen. With this positive and negative screening scheme, we identified the library-peptides as the mimotopes of the antigen. The unique library peptides were successfully used to isolate antigen-specific antibodies from complete immune serum. Sequence analysis of these peptides revealed the epitopes in the immunized antigen. We present this method as an inexpensive, efficient method for identifying mimotopes of any antibody’s targets. These mimotopes should be useful in defining both components of the antigen-antibody complex.

Autoantibodies in Alzheimer's disease: potential biomarkers, pathogenic roles, and therapeutic implications

Alzheimer’s disease (AD) is a prevalent and debilitating neurodegenerative disorder in the elderly. The etiology of AD has not been fully defined and currently there is no cure for this devastating disease. Compelling evidence suggests that the immune system plays a critical role in the pathophysiology of AD. Autoantibodies against a variety of molecules have been associated with AD. The roles of these autoantibodies in AD, however, are not well understood. This review attempts to summarize recent findings on these autoantibodies and explore their potential as diagnostic/ prognostic biomarkers for AD, their roles in the pathogenesis of AD, and their implications in the development of effective immunotherapies for AD.

Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures

One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody–peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors in a clinically useful way.

Elevated IgG4 in patient circulation is associated with the risk of disease progression in melanoma

Emerging evidence suggests pathological and immunoregulatory functions for IgG4 antibodies and IgG4⁺ B cells in inflammatory diseases and malignancies. We previously reported that IgG4 antibodies restrict activation of immune effector cell functions and impair humoral responses in melanoma. Here, we investigate IgG4 as a predictor of risk for disease progression in a study of human sera (n = 271: 167 melanoma patients; 104 healthy volunteers) and peripheral blood B cells (n = 71: 47 melanoma patients; 24 healthy volunteers). IgG4 (IgG4/IgG_total) serum levels were elevated in melanoma. High relative IgG4 levels negatively correlated with progression-free survival (PFS) and overall survival. In early stage (I-II) disease, serum IgG4 was independently negatively prognostic for progression-free survival, as was elevation of IgG4⁺ circulating B cells (CD45⁺CD22⁺CD19⁺CD3^-CD14^-). In human tissues (n = 256; 108 cutaneous melanomas; 56 involved lymph nodes; 60 distant metastases; 32 normal skin samples) IgG4⁺ cell infiltrates were found in 42.6% of melanomas, 21.4% of involved lymph nodes and 30% of metastases, suggesting inflammatory conditions that favor IgG4 at the peripheral and local levels. Consistent with emerging evidence for an immunosuppressive role for IgG4, these findings indicate association of elevated IgG4 with disease progression and less favorable clinical outcomes. Characterizing immunoglobulin and other humoral immune profiles in melanoma might identify valuable prognostic tools for patient stratification and in the future lead to more effective treatments less prone to tumor-induced blockade mechanisms.

Evaluating biomarkers in melanoma

The incidence of cutaneous melanoma has more than doubled over the last decades making it one of the fastest rising cancers worldwide. Improved awareness and early detection of malignant moles now permit earlier diagnosis aiming to decrease the likelihood of recurrence. However, it is difficult to identify those patients initially diagnosed with localized melanoma who subsequently develop metastatic disease. For this group, prognosis remains poor and clinical outcomes are variable and challenging to predict. Considerable efforts have focused on the search for novel prognostic tools, with numerous markers evaluated in the circulation and in tumor lesions. The most reliable predictors of patient outcome are the clinical and histological features of the primary tumor such as Breslow thickness, ulceration status, and mitotic rate. Elevated serum levels of the enzyme lactate dehydrogenase, likely to indicate active metastatic disease, are also routinely used to monitor patients. The emergence of novel immune and checkpoint antibody treatments for melanoma and increasing appreciation of key roles of the immune system in promoting or halting cancer progression have focused attention to immunological biomarkers. Validation of the most promising of these may have clinical applications in assisting prognosis, assessing endpoints in therapy, and monitoring responses during treatment.

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.

The immunosignature of canine lymphoma: characterization and diagnostic application

Background

Cancer diagnosis in both dogs and humans is complicated by the lack of a non-invasive diagnostic test. To meet this clinical need, we apply the recently developed immunosignature assay to spontaneous canine lymphoma as clinical proof-of-concept. Here we evaluate the immunosignature as a diagnostic for spontaneous canine lymphoma at both at initial diagnosis and evaluating the disease free interval following treatment.

Methods

Sera from dogs with confirmed lymphoma (B cell n = 38, T cell n = 11) and clinically normal dogs (n = 39) were analyzed. Serum antibody responses were characterized by analyzing the binding pattern, or immunosignature, of serum antibodies on a non-natural sequence peptide microarray. Peptides were selected and tested for the ability to distinguish healthy dogs from those with lymphoma and to distinguish lymphoma subtypes based on immunophenotype. The immunosignature of dogs with lymphoma were evaluated for individual signatures. Changes in the immunosignatures were evaluated following treatment and eventual relapse.

Results

Despite being a clonal disease, both an individual immunosignature and a generalized lymphoma immunosignature were observed in each dog. The general lymphoma immunosignature identified in the initial set of dogs (n = 32) was able to predict disease status in an independent set of dogs (n = 42, 97% accuracy). A separate immunosignature was able to distinguish the lymphoma based on immunophenotype (n = 25, 88% accuracy). The individual immunosignature was capable of confirming remission three months following diagnosis. Immunosignature at diagnosis was able to predict which dogs with B cell lymphoma would relapse in less than 120 days (n = 33, 97% accuracy).

Conclusion

We conclude that the immunosignature can serve as a multilevel diagnostic for canine, and potentially human, lymphoma.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-657) contains supplementary material, which is available to authorized users.

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.

Immunosignature system for diagnosis of cancer

Although the search for disease biomarkers continues, the clinical return has thus far been disappointing. The complexity of the body's response to disease makes it difficult to represent this response with only a few biomarkers, particularly when many are present at low levels. An alternative to the typical reductionist biomarker paradigm is an assay we call an "immunosignature." This approach leverages the response of antibodies to disease-related changes, as well as the inherent signal amplification associated with antigen-stimulated B-cell proliferation. To perform an immunosignature assay, the antibodies in diluted blood are incubated with a microarray of thousands of random sequence peptides. The pattern of binding to these peptides is the immunosignature. Because the peptide sequences are completely random, the assay is effectively disease-agnostic, potentially providing a comprehensive diagnostic on multiple diseases simultaneously. To explore the ability of an immunosignature to detect and identify multiple diseases simultaneously, 20 samples from each of five cancer cohorts collected from multiple sites and 20 noncancer samples (120 total) were used as a training set to develop a reference immunosignature. A blinded evaluation of 120 blinded samples covering the same diseases gave 95% classification accuracy. To investigate the breadth of the approach and test sensitivity to biological diversity further, immunosignatures of >1,500 historical samples comprising 14 different diseases were examined by training with 75% of the samples and testing the remaining 25%. The average accuracy was >98%. These results demonstrate the potential power of the immunosignature approach in the accurate, simultaneous classification of disease.

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.

Autoimmune profiling with protein microarrays in clinical applications

In recent years, knowledge about immune-related disorders has substantially increased, especially in the field of central nervous system (CNS) disorders. Recent innovations in protein-related microarray technology have enabled the analysis of interactions between numerous samples and up to 20,000 targets. Antibodies directed against ion channels, receptors and other synaptic proteins have been identified, and their causative roles in different disorders have been identified. Knowledge about immunological disorders is likely to expand further as more antibody targets are discovered. Therefore, protein microarrays may become an established tool for routine diagnostic procedures in the future. The identification of relevant target proteins requires the development of new strategies to handle and process vast quantities of data so that these data can be evaluated and correlated with relevant clinical issues, such as disease progression, clinical manifestations and prognostic factors. This review will mainly focus on new protein array technologies, which allow the processing of a large number of samples, and their various applications with a deeper insight into their potential use as diagnostic tools in neurodegenerative diseases and other diseases. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Antibody biomarker discovery through in vitro directed evolution of consensus recognition epitopes

To enable discovery of serum antibodies indicative of disease and simultaneously develop reagents suitable for diagnosis, in vitro directed evolution was applied to identify consensus peptides recognized by patients' serum antibodies. Bacterial cell-displayed peptide libraries were quantitatively screened for binders to serum antibodies from patients with celiac disease (CD), using cell-sorting instrumentation to identify two distinct consensus epitope families specific to CD patients (PEQ and (E)/DxFV(Y)/FQ). Evolution of the (E)/DxFV(Y)/FQ consensus epitope identified a celiac-specific epitope, distinct from the two CD hallmark antigens tissue transglutaminase-2 and deamidated gliadin, exhibiting 71% sensitivity and 99% specificity (n = 231). Expansion of the first-generation PEQ consensus epitope via in vitro evolution yielded octapeptides QPEQAFPE and PFPEQxFP that identified ω- and γ-gliadins, and their deamidated forms, as immunodominant B-cell epitopes in wheat and related cereal proteins. The evolved octapeptides, but not first-generation peptides, discriminated one-way blinded CD and non-CD sera (n = 78) with exceptional accuracy, yielding 100% sensitivity and 98% specificity. Because this method, termed antibody diagnostics via evolution of peptides, does not require prior knowledge of pathobiology, it may be broadly useful for de novo discovery of antibody biomarkers and reagents for their detection.