Protein microarray applications: Autoantibody detection and posttranslational modification

Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire

Phage-displayed immunoprecipitation sequencing (PhIP-seq) has enabled high-throughput profiling of human antibody repertoires. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is lacking. In this study, we investigated the effects of genetic, environmental, and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We detected individual-specificity, temporal consistency, and co-housing similarities in antibody repertoires. Genetic analyses showed the involvement of the HLA, IGHV, and FUT2 gene regions in antibody-bound peptide reactivity. Furthermore, we uncovered associations between phenotypic factors (including age, cell counts, sex, smoking behavior, and allergies, among others) and particular antibody-bound peptides. Our results indicate that human antibody epitope repertoires are shaped by both genetics and environmental exposures and highlight specific signatures of distinct phenotypes and genotypes.

A protein microarray-based serum proteomic investigation reveals distinct autoantibody signature in colorectal cancer

PURPOSE

Colorectal cancer (CRC) has been reported as the second leading cause of cancer death worldwide. The 5-year annual survival is around 50%, mainly due to late diagnosis, striking necessity for early detection. This study aims to identify autoantibody in patients' sera for early screening of cancer.

EXPERIMENTAL DESIGN

The study used a high-density human proteome array with approximately 17,000 recombinant proteins. Screening of sera from healthy individuals, CRC from Indian origin, and CRC from middle-east Asia origin were performed. Bio-statistical analysis was performed to identify significant autoantibodies altered. Pathway analysis was performed to explore the underlying mechanism of the disease.

RESULTS

The comprehensive proteomic analysis revealed dysregulation of 15 panels of proteins including CORO7, KCNAB1, WRAP53, NDUFS6, KRT30, and COLGALT2. Further biological pathway analysis for the top dysregulated autoantigenic proteins revealed perturbation in important biological pathways such as ECM degradation and cytoskeletal remodeling etc. CONCLUSIONS AND CLINICAL RELEVANCE: The generation of an autoimmune response against cancer-linked pathways could be linked to the screening of the disease. The process of immune surveillance can be detected at an early stage of cancer. Moreover, AAbs can be easily extracted from blood serum through the least invasive test for disease screening.

Blood-Based Biomarkers of Repetitive, Subconcussive Blast Overpressure Exposure in the Training Environment: A Pilot Study

Because of their unknown long-term effects, repeated mild traumatic brain injuries (TBIs), including the low, subconcussive ones, represent a specific challenge to healthcare systems. It has been hypothesized that they can have a cumulative effect, and they may cause molecular changes that can lead to chronic degenerative processes. Military personnel are especially vulnerable to consequences of subconcussive TBIs because their training involves repeated exposures to mild explosive blasts. In this pilot study, we collected blood samples at baseline, 6 h, 24 h, 72 h, 2 weeks, and 3 months after heavy weapons training from students and instructors who were exposed to repeated subconcussive blasts. Samples were analyzed using the reverse and forward phase protein microarray platforms. We detected elevated serum levels of glial fibrillary acidic protein, ubiquitin C-terminal hydrolase L1 (UCH-L1), nicotinic alpha 7 subunit (CHRNA7), occludin (OCLN), claudin-5 (CLDN5), matrix metalloprotease 9 (MMP9), and intereukin-6 (IL-6). Importantly, serum levels of most of the tested protein biomarkers were the highest at 3 months after exposures. We also detected elevated autoantibody titers of proteins related to vascular and neuroglia-specific proteins at 3 months after exposures as compared to baseline levels. These findings suggest that repeated exposures to subconcussive blasts can induce molecular changes indicating not only neuron and glia damage, but also vascular changes and inflammation that are detectable for at least 3 months after exposures whereas elevated titers of autoantibodies against vascular and neuroglia-specific proteins can indicate an autoimmune process.

Heterogeneous sensing of post-translational modification enzymes by integrating the advantage of homogeneous analysis

Heterogeneous analysis has great application prospects in the detection of post-translational modification (PTM) enzymes with the advantages of signal enhancement, less sample demand, and high sensitivity and selectivity. Nevertheless, once the substrate was fixed on a solid interface, the steric hindrance might limit the approaching of catalytic center to the substrate, thus reducing the efficiency of PTM. Herein, we suggested that the avidin-modified interface could be used to develop heterogeneous sensing platforms with biotin-labeled substrates as the probes, in which the enzymatic PTM was performed in solution and the heterogeneous assay was conducted on a solid surface. The sensing strategy integrates the advantages but overcomes the defects of both homogeneous and heterogeneous assays. Protein kinase A (PKA) and histone acetyltransferase (HAT) were determined as the examples by using sequence-specific peptide substrates. The signal changes were monitored by HRP-based colorimetric assay and antibody-amplified surface plasmon resonance (SPR). The methods were used for analysis of cell lysates and evaluation of inhibition efficiency with satisfactory results. The strategy can be used for the detection of a variety of biological enzymes and provide a new idea for the design of various heterogeneous biosensors. Thus, this work should be of great significance to the popularization and practical application of biosensors.

Detection of leucine-rich alpha-2-glycoprotein 1-containing immunocomplexes in the plasma of lung cancer patients with epitope-specific mAbs

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide. With the expectation of improved survival, tremendous efforts and resources have been invested in the discovery of specific biomarkers for early detection of the disease. Several investigators have reported the presence of cancer-associated autoantibodies in the plasma or serum of lung cancer patients. Previously, we used a monoclonal-antibody proteomics technology platform for the discovery of novel lung cancer-associated proteins.

OBJECTIVE

The identification of specific protein epitopes associated with various cancers is a promising method in biomarker discovery. Here, in a preliminary study, we aimed to detect autoantibody-leucine-rich alpha-2-glycoprotein 1 (LRG1) immunocomplexes using epitope-specific monoclonal antibodies (mAbs).

METHODS

We performed sandwich ELISA assays using the LRG1 epitope-specific capture mAbs, Bsi0352 and Bsi0392, and an IgG-specific polyclonal antibody coupled to a reporter system as the detection reagent. We tested the plasma of lung-cancer patients and apparently healthy controls.

RESULTS

Depending on the epitope specificity of the capture monoclonal mAb, we were either unable to distinguish the control from LC-groups or showed a higher level of LRG1 and IgG autoantibody containing immunocomplexes in the plasma of non-small cell lung cancer and small cell lung cancer subgroups of lung cancer patients than in the plasma of control subjects.

CONCLUSIONS

Our findings underline the importance of protein epitope-specific antibody targeted approaches in biomarker research, as this may increase the accuracy of previously described tests, which will need further validation in large clinical cohorts.

Analysis of the Repertoires of Circulating Autoantibodies' Specificities as a Tool for Identification of the Tumor-Associated Antigens: Current Problems and Solutions

Circulating autoantibodies against tumor-associated autoantigens (TAA) may serve as valuable biomarkers for a wide range of diagnostic purposes. Modern immunology offers a large variety of methods for in-depth comparative analysis of the repertoires of circulating antibodies' antigenic specificities in health and disease. Nevertheless, this research field so far has met somewhat limited clinical success, while numerous data on the repertoires of circulating autoantibodies' specificities in cancer patients are poorly integrated into the contemporary picture of the immunological and molecular landscapes of human tumors. This review is an attempt to identify and systematize the key and essentially universal conceptual and methodological limitations of analyses of the repertoires of circulating antibodies' antigenic specificities in cancer (expression bias, redundancy of TAA repertoires, identification of natural IgG, the absence of the pathogenetically relevant context in the experimental systems used to detect TAA), as well as to discuss potential and already known methodological improvements that may significantly increase the detectability of the pathogenetically relevant and diagnostically significant bona fide TAA.

Autoimmune Responses in Oncology: Causes and Significance

Specific anti-tumor immune responses have proven to be pivotal in shaping tumorigenesis and tumor progression in solid cancers. These responses can also be of an autoimmune nature, and autoantibodies can sometimes be present even before the onset of clinically overt disease. Autoantibodies can be generated due to mutated gene products, aberrant expression and post-transcriptional modification of proteins, a pro-immunogenic milieu, anti-cancer treatments, cross-reactivity of tumor-specific lymphocytes, epitope spreading, and microbiota-related and genetic factors. Understanding these responses has implications for both basic and clinical immunology. Autoantibodies in solid cancers can be used for early detection of cancer as well as for biomarkers of prognosis and treatment response. High-throughput techniques such as protein microarrays make parallel detection of multiple autoantibodies for increased specificity and sensitivity feasible, affordable, and quick. Cancer immunotherapy has revolutionized cancer treatments and has made a considerable impact on reducing cancer-associated morbidity and mortality. However, immunotherapeutic interventions such as immune checkpoint inhibition can induce immune-related toxicities, which can even be life-threatening. Uncovering the reasons for treatment-induced autoimmunity can lead to fine-tuning of cancer immunotherapy approaches to evade toxic events while inducing an effective anti-tumor immune response.

Detection of Posttranslational Modification Autoantibodies Using Peptide Microarray

Autoantibodies are humoral antibodies against self-proteins and play vital roles in maintaining the homeostasis. Autoantibodies can also target posttranslational modifications (PTMs) of proteins and the identification of new PTM autoantibodies is important to identify biomarkers for the early diagnosis of cancer and autoimmune diseases. In this chapter, we describe a method to detect PTM autoantibodies using citrullinated peptide microarray as an example. This method can be used to screen serum autoantibodies for different human diseases.

Serum Sp17 Autoantibody Serves as a Potential Specific Biomarker in Patients with SAPHO Syndrome

SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) syndrome shows a wide variability in musculoskeletal and cutaneous manifestations, and it is therefore underrecognized and misdiagnosed in the clinic due to a lack of specific markers. In this study, we aimed to identify specific biomarkers by screening serum autoantibodies in SAPHO patients with a 17K human whole-proteome microarray. The serum anti-Sp17 autoantibody was identified and verified to be a specific biomarker in patients with SAPHO syndrome. Indeed, the level of the anti-Sp17 autoantibody was significantly increased in patients with active SAPHO compared to patients with an inactive disease and healthy controls (P < 0.05). Additionally, serum anti-Sp17 autoantibody levels correlated with those of serum hypersensitive C-reactive protein (hsCRP), the erythrocyte sedimentation rate (ESR), and β-crosslaps (β-CTx) in patients with active SAPHO disease. Moreover, anti-Sp17 autoantibody levels were markedly decreased after anti-inflammatory treatment with pamidronate disodium, which downregulated levels of hsCRP and ESR in patients with active SAPHO. Thus, serum levels of the anti-Sp17 autoantibody might serve as a specific biomarker for the diagnosis of SAPHO syndrome or for monitoring the disease status.

Profiling Autoantibody Responses to Devise Novel Diagnostic and Prognostic Markers Using High-Density Protein Microarrays

Protein microarrays are a diverse and high-throughput platform for screening biomolecular interactions, autoantigens, and protein expression profiles across tissues, etc. Autoantibodies produced against aberrant protein expression are often observed in malignancies which makes protein microarrays a powerful platform to elucidate biomarkers of translational interest. Early diagnosis of malignancies is an enduring clinical problem that has a direct impact on disease prognosis. Here, we provide an overview of a method employed to screen autoantibodies using patient sera in brain tumors. In case of brain malignancies, early diagnosis is particularly challenging and often requires highly invasive brain biopsies as a confirmatory test. This chapter summarizes the various considerations for applying a serum-based autoantibody biomarker discovery pipeline that could provide a minimally invasive initial diagnostic screen, potentiating classical diagnostic approaches.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Identification of tumor-associated antigens with diagnostic ability of colorectal cancer by in-depth immunomic and seroproteomic analysis

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer related death worldwide. Its diagnosis at early stages would significantly improve the survival of CRC patients. The humoral immune response has been demonstrated useful for cancer diagnosis, predating clinical symptoms up to 3 years. Here, we employed an in-depth seroproteomic approach to identify proteins that elicit a humoral immune response in CRC patients. The seroproteomic approach relied on the immunoprecipitation with patient-derived autoantibodies of proteins from CRC cell lines with different metastatic properties followed by LC-MS/MS. After bioinformatics, we focused on 31 targets of CRC autoantibodies. After WB and IHC validation, ERP44 and TALDO1 showed potential to discriminate disease-free and metastatic CRC patients, and time to recurrence of CRC patients in stage II. Using plasma samples of 30 healthy individuals, 28 premalignant individuals, and 32 CRC patients, nine out of 13 selected targets for seroreactive analysis showed significant diagnostic ability to discriminate either CRC patients or premalignant subjects from controls. Our results suggest that the here defined panel of CRC autoantibodies and their target proteins should be included in CRC blood-based biomarker panels to get a clinically useful blood-based diagnostic signature for CRC detection. SIGNIFICANCE: Colorectal cancer is one of the deadliest cancer types mainly due to its late diagnosis. Its early diagnosis, therefore, is of great importance since it would significantly improve the survival of CRC patients. In our work, the in-depth seroproteomic analysis of colorectal cancer using isolated IgGs from colorectal cancer patients and controls and protein extract of colorectal cancer cells provide the identification of valuable biomarkers with diagnostic and prognostic ability of the disease.

Revisiting immune complexes: Key to understanding immune-related diseases

Immune complexes (ICs) formed by foreign or self-antigens and antibodies in biological fluids affect various tissues and are thought to cause several diseases. Biological and physical properties of IC, abnormal IC amounts, IC deposition and their relationships with disease pathogenesis had been studied. However, the relationship between ICs and each disease is not well understood and little is known of what determined ICs deposition in particular organ and why different organs are affected in different diseases. Recent technological advance enables identification of ICs in particular its antigens in tissues and body fluids, which may provide a key to discover an important trigger for immunological abnormality occurrence. Further identification of their epitopes, that are the exact origin of antigenicity, is developing and may be useful for diagnosis, elucidation of pathogenesis and treatment against IC-induced diseases. Here, we first make an overview of clearance of ICs, IC-induced pathogenesis and biological properties of ICs. Then, we introduce various methods developed to recover ICs from biological fluids or to identify antigens incorporated into ICs. Furthermore, several methods that can be used in epitope mapping for IC antigens are also documented.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Identification of Serum Biomarkers for Systemic Lupus Erythematosus Using a Library of Phage Displayed Random Peptides and Deep Sequencing

Systemic lupus erythematosus (SLE) is one of the most serious autoimmune diseases, characterized by highly diverse clinical manifestations. A biomarker is still needed for accurate diagnostics. SLE serum autoantibodies were discovered and validated using serum samples from independent sample cohorts encompassing 306 participants divided into three groups, i.e. healthy, SLE patients, and other autoimmune-related diseases. To discover biomarkers for SLE, a phage displayed random peptide library (Ph.D. 12) and deep sequencing were applied to screen specific autoantibodies in a total of 100 serum samples from 50 SLE patients and 50 healthy controls. A statistical analysis protocol was set up for the identification of peptides as potential biomarkers. For validation, 10 peptides were analyzed using enzyme-linked immunosorbent assays (ELISA). As a result, four peptides (SLE2018Val001, SLE2018Val002, SLE2018Val006, and SLE2018Val008) were discovered with high diagnostic power to differentiate SLE patients from healthy controls. Among them, two peptides, i.e. SLE2018Val001 and SLE2018Val002, were confirmed between SLE with other autoimmune patients. The procedure we established could be easily adopted for the identification of autoantibodies as biomarkers for many other diseases.

PhIP-Seq characterization of serum antibodies using oligonucleotide-encoded peptidomes

The binding specificities of an individual's antibody repertoire contain a wealth of biological information. They harbor evidence of environmental exposures, allergies, ongoing or emerging autoimmune disease processes, and responses to immunomodulatory therapies, for example. Highly multiplexed methods to comprehensively interrogate antibody-binding specificities have therefore emerged in recent years as important molecular tools. Here, we provide a detailed protocol for performing 'phage immunoprecipitation sequencing' (PhIP-Seq), which is a powerful method for analyzing antibody-repertoire binding specificities with high throughput and at low cost. The methodology uses oligonucleotide library synthesis (OLS) to encode proteomic-scale peptide libraries for display on bacteriophage. These libraries are then immunoprecipitated, using an individual's antibodies, for subsequent analysis by high-throughput DNA sequencing. We have used PhIP-Seq to identify novel self-antigens associated with autoimmune disease, to characterize the self-reactivity of broadly neutralizing HIV antibodies, and in a large international cross-sectional study of exposure to hundreds of human viruses. Compared with alternative array-based techniques, PhIP-Seq is far more scalable in terms of sample throughput and cost per analysis. Cloning and expression of recombinant proteins are not required (versus protein microarrays), and peptide lengths are limited only by DNA synthesis chemistry (up to 90-aa (amino acid) peptides versus the typical 8- to 12-aa length limit of synthetic peptide arrays). Compared with protein microarrays, however, PhIP-Seq libraries lack discontinuous epitopes and post-translational modifications. To increase the accessibility of PhIP-Seq, we provide detailed instructions for the design of phage-displayed peptidome libraries, their immunoprecipitation using serum antibodies, deep sequencing-based measurement of peptide abundances, and statistical determination of peptide enrichments that reflect antibody-peptide interactions. Once a library has been constructed, PhIP-Seq data can be obtained for analysis within a week.

Recent Advances on the Molecular Mechanism of Cervical Carcinogenesis Based on Systems Biology Technologies

Cervical cancer is one of the common malignancies in women worldwide. Exploration of pathogenesis and molecular mechanism of cervical cancer is pivotal for development of effective treatment for this disease. Recently, systems biology approaches based on high-throughput technologies have been carried out to investigate the expression of some genes and proteins in genomics, transcriptomics, proteomics, and metabonomics of cervical cancer. Compared with traditional methods，systems biology technology has been shown to provide large of information regarding prognostic biomarkers and therapeutic targets for cervical cancer. These molecular signatures from system biology technology could be useful to understand the molecular mechanisms of cervical cancer development and progression, and help physicians to design targeted therapeutic strategies for patients with cervical cancer.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.

Anti-Drug Antibodies: Emerging Approaches to Predict, Reduce or Reverse Biotherapeutic Immunogenicity

The development of anti-drug antibodies (ADAs) following administration of biotherapeutics to patients is a vexing problem that is attracting increasing attention from pharmaceutical and biotechnology companies. This serious clinical problem is also spawning creative research into novel approaches to predict, avoid, and in some cases even reverse such deleterious immune responses. CD4⁺ T cells are essential players in the development of most ADAs, while memory B-cell and long-lived plasma cells amplify and maintain these responses. This review summarizes methods to predict and experimentally identify T-cell and B-cell epitopes in therapeutic proteins, with a particular focus on blood coagulation factor VIII (FVIII), whose immunogenicity is clinically significant and is the subject of intensive current research. Methods to phenotype ADA responses in humans are described, including T-cell stimulation assays, and both established and novel approaches to determine the titers, epitopes and isotypes of the ADAs themselves. Although rational protein engineering can reduce the immunogenicity of many biotherapeutics, complementary, novel approaches to induce specific tolerance, especially during initial exposures, are expected to play significant roles in future efforts to reduce or reverse these unwanted immune responses.

Tissue and plasma proteomics for early stage cancer detection

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.

Mining the fecal proteome: from biomarkers to personalised medicine

INTRODUCTION

Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance. Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current 'Proteomics Toolbox', explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform. Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.