Antiviral antibody profiling by high-density protein arrays

Antibody Profiling of Pan-Cancer Viral Proteome Reveals Biomarkers for Nasopharyngeal Carcinoma Diagnosis and Prognosis

Diagnosing, predicting disease outcome, and identifying effective treatment targets for virus-related cancers are lacking. Protein biomarkers have the potential to bridge the gap between prevention and treatment for these types of cancers. While it has been shown that certain antibodies against EBV proteins could be used to detect nasopharyngeal carcinoma (NPC), antibodies targeting are solely a tiny part of the about 80 proteins expressed by the EBV genome. Furthermore, it remains unclear what role other viruses play in NPC since many diseases are the result of multiple viral infections. For the first time, this study measured both IgA and IgG antibody responses against 646 viral proteins from 23 viruses in patients with NPC and control subjects using nucleic acid programmable protein arrays. Candidate seromarkers were then validated by ELISA using 1665 serum samples from three clinical cohorts. We demonstrated that the levels of five candidate seromarkers (EBV-BLLF3-IgA, EBV-BLRF2-IgA, EBV-BLRF2-IgG, EBV-BDLF1-IgA, EBV-BDLF1-IgG) in NPC patients were significantly elevated than controls. Additional examination revealed that NPC could be successfully diagnosed by combining the clinical biomarker EBNA1-IgA with the five anti-EBV antibodies. The sensitivity of the six-antibody signature at 95% specificity to diagnose NPC was comparable to the current clinically-approved biomarker combination, VCA-IgA, and EBNA1-IgA. However, the recombinant antigens of the five antibodies are easier to produce and standardize compared to the native viral VCA proteins. This suggests the potential replacement of the traditional VCA-IgA assay with the 5-antibodies combination to screen and diagnose NPC. Additionally, we investigated the prognostic significance of these seromarkers titers in NPC. We showed that NPC patients with elevated BLLF3-IgA and BDLF1-IgA titers in their serum exhibited significantly poorer disease-free survival, suggesting the potential of these two seromarkers as prognostic indicators of NPC. These findings will help develop serological tests to detect and treat NPC in the future.

Comparative Analysis of Antimicrobial Antibodies between Mild and Severe COVID-19

Patients with 2019 coronavirus disease (COVID-19) exhibit a broad spectrum of clinical presentations. A person's antimicrobial antibody profile, as partially shaped by past infection or vaccination, can reflect the immune system health that is critical to control and resolve the infection. We performed an explorative immunoproteomics study using microbial protein arrays displaying 318 full-length antigens from 77 viruses and 3 bacteria. We compared antimicrobial antibody profiles between 135 patients with mild COVID-19 disease and 215 patients with severe disease in 3 independent cohorts from Mexico and Italy. Severe disease patients were older with higher prevalence of comorbidities. We confirmed that severe disease patients elicited a stronger anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) response. We showed that antibodies against HCoV-229E and HcoV-NL63 but not against HcoV-HKU1 and HcoV-OC43 were also higher in those who had severe disease. We revealed that for a set of IgG and IgA antibodies targeting coronaviruses, herpesviruses, and other respiratory viruses, a subgroup of patients with the highest reactivity levels had a greater incidence of severe disease compared to those with mild disease across all three cohorts. On the contrary, fewer antibodies showed consistent greater prevalence in mild disease in all 3 cohorts. IMPORTANCE The clinical presentations of COVID-19 range from asymptomatic to critical illness that may lead to intensive care or even death. The health of the immune system, as partially shaped by past infections or vaccinations, is critical to control and resolve the infection. Using an innovative protein array platform, we surveyed antibodies against hundreds of full-length microbial antigens from 80 different viruses and bacteria in COVID-19 patients from different geographic regions with mild or severe disease. We not only confirmed the association of severe COVID-19 disease with higher reactivity of antibody responses to SARS-CoV-2 but also uncovered known and novel associations with antibody responses against herpesviruses and other respiratory viruses. Our study represents a significant step forward in understanding the factors contributing to COVID-19 disease severity. We also demonstrate the power of comprehensive antimicrobial antibody profiling in deciphering risk factors for severe COVID-19. We anticipate that our approach will have broad applications in infectious diseases.

Comparative Microbiomics Analysis of Antimicrobial Antibody Response between Patients with Lung Cancer and Control Subjects with Benign Pulmonary Nodules

BACKGROUND

CT screening can detect lung cancer early but suffers a high false-positive rate. There is a need for molecular biomarkers that can distinguish malignant and benign indeterminate pulmonary nodules (IPN) detected by CT scan.

METHODS

We profiled antibodies against 901 individual microbial antigens from 27 bacteria and 29 viruses in sera from 127 lung adenocarcinoma (ADC), 123 smoker controls (SMC), 170 benign nodule controls (BNC) individuals using protein microarrays to identify ADC and BNC specific antimicrobial antibodies.

RESULTS

Analyzing fourth quartile ORs, we found more antibodies with higher prevalence in the three BNC subgroups than in ADC or SMC. We demonstrated that significantly more anti-Helicobacter pylori antibodies showed higher prevalence in ADC relative to SMC. We performed subgroup analysis and found that more antibodies with higher prevalence in light smokers (≤20 pack-years) compared with heavy smokers (>20 pack-years), in BNC with nodule size >1 cm than in those with ≤1 cm nodules, and in stage I ADC than in stage II and III ADC. We performed multivariate analysis and constructed antibody panels that can distinguish ADC versus SMC and ADC versus BNC with area under the ROC curve (AUC) of 0.88 and 0.80, respectively.

CONCLUSIONS

Antimicrobial antibodies have the potential to reduce the false positive rate of CT screening and provide interesting insight in lung cancer development.

IMPACT

Microbial infection plays an important role in lung cancer development and the formation of benign pulmonary nodules.

Applications of Protein Microarrays in Biomarker Discovery for Autoimmune Diseases

Dysregulated autoantibodies and cytokines were deemed to provide important cues for potential illnesses, such as various carcinomas and autoimmune diseases. Increasing biotechnological approaches have been applied to screen and identify the specific alterations of these biomolecules as distinctive biomarkers in diseases, especially autoimmune diseases. As a versatile and robust platform, protein microarray technology allows researchers to easily profile dysregulated autoantibodies and cytokines associated with autoimmune diseases using various biological specimens, mainly serum samples. Here, we summarize the applications of protein microarrays in biomarker discovery for autoimmune diseases. In addition, the key issues in the process of using this approach are presented for improving future studies.

Identification of Antibody Biomarker Using High-Density Nucleic Acid Programmable Protein Array

A novel protein microarray technology, called high-density nucleic acid programmable protein array (HD-NAPPA), enables the serological screening of thousands of proteins at one time. HD-NAPPA extends the capabilities of NAPPA, which produces protein microarrays on a conventional glass microscope slide. By comparison, HD-NAPPA displays proteins in over 10,000 nanowells etched in a silicon slide. Proteins on HD-NAPPA are expressed in the individual isolated nanowells, via in vitro transcription and translation (IVTT), without any diffusion during incubation. Here we describe the method for antibody biomarker identification using HD-NAPPA, including four main steps: (1) HD-NAPPA array protein expression, (2) primary antibodies (serum/plasma) probing, (3) secondary antibody visualization, and (4) image scanning and data processing.

Helicobacter pylori Immunoproteomic Profiles in Gastric Cancer

Chronic Helicobacter pylori infection is the major risk factor for gastric cancer (GC). However, only some infected individuals develop this neoplasia. Previous H. pylori serology studies have been limited by investigating small numbers of candidate antigens. Therefore, we evaluated humoral responses to a nearly complete H. pylori immunoproteome (1527 proteins) among 50 GC cases and 50 controls using Nucleic Acid Programmable Protein Array (NAPPA). Seropositivity was defined as median normalized intensity ≥2 on NAPPA, and 53 anti-H. pylori antibodies had >10% seroprevalence. Anti-GroEL exhibited the greatest seroprevalence (77% overall), which agreed well with ELISA using whole-cell lysates of H. pylori cells. After an initial screen by H. pylori-NAPPA, we discovered and verified that 12 antibodies by ELISA in controls had ≥15% of samples with an optical reading value exceeding the 95th percentile of the GC group. ELISA-verified antibodies were validated blindly in an independent set of 100 case-control pairs. As expected, anti-CagA seropositivity was positively associated with GC (odds ratio, OR = 5.5; p < 0.05). After validation, six anti-H. pylori antibodies showed lower seropositivity in GC, with ORs ranging from 0.44 to 0.12 (p < 0.05): anti-HP1118/Ggt, anti-HP0516/HsIU, anti-HP0243/NapA, anti-HP1293/RpoA, anti-HP0371/FabE, and anti-HP0875/KatA. Among all combinations, a model with anti-Ggt, anti-HslU, anti-NapA, and anti-CagA had an area under the curve of 0.73 for discriminating GC vs. controls. This study represents the first comprehensive assessment of anti-H. pylori humoral profiles in GC. Decreased responses to multiple proteins in GC may reflect mucosal damage and decreased bacterial burden. The higher prevalence of specific anti-H. pylori antibodies in controls may suggest immune protection against GC development.

Proteome microarray technology and application: higher, wider, and deeper

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Multi-platform Approach for Microbial Biomarker Identification Using Borrelia burgdorferi as a Model

The identification of microbial biomarkers is critical for the diagnosis of a disease early during infection. However, the identification of reliable biomarkers is often hampered by a low concentration of microbes or biomarkers within host fluids or tissues. We have outlined a multi-platform strategy to assess microbial biomarkers that can be consistently detected in host samples, using Borrelia burgdorferi, the causative agent of Lyme disease, as an example. Key aspects of the strategy include the selection of a macaque model of human disease, in vivo Microbial Antigen Discovery (InMAD), and proteomic methods that include microbial biomarker enrichment within samples to identify secreted proteins circulating during infection. Using the described strategy, we have identified 6 biomarkers from multiple samples. In addition, the temporal antibody response to select bacterial antigens was mapped. By integrating biomarkers identified from early infection with temporal patterns of expression, the described platform allows for the data driven selection of diagnostic targets.

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.

Advances in cell-free protein array methods

INTRODUCTION

Cell-free protein microarrays represent a special form of protein microarray which display proteins made fresh at the time of the experiment, avoiding storage and denaturation. They have been used increasingly in basic and translational research over the past decade to study protein-protein interactions, the pathogen-host relationship, post-translational modifications, and antibody biomarkers of different human diseases. Their role in the first blood-based diagnostic test for early stage breast cancer highlights their value in managing human health. Cell-free protein microarrays will continue to evolve to become widespread tools for research and clinical management. Areas covered: We review the advantages and disadvantages of different cell-free protein arrays, with an emphasis on the methods that have been studied in the last five years. We also discuss the applications of each microarray method. Expert commentary: Given the growing roles and impact of cell-free protein microarrays in research and medicine, we discuss: 1) the current technical and practical limitations of cell-free protein microarrays; 2) the biomarker discovery and verification pipeline using protein microarrays; and 3) how cell-free protein microarrays will advance over the next five years, both in their technology and applications.

Multiplexed Nucleic Acid Programmable Protein Arrays

Rationale: Cell-free protein microarrays display naturally-folded proteins based on just-in-time in situ synthesis, and have made important contributions to basic and translational research. However, the risk of spot-to-spot cross-talk from protein diffusion during expression has limited the feature density of these arrays. Methods: In this work, we developed the Multiplexed Nucleic Acid Programmable Protein Array (M-NAPPA), which significantly increases the number of displayed proteins by multiplexing as many as five different gene plasmids within a printed spot. Results: Even when proteins of different sizes were displayed within the same feature, they were readily detected using protein-specific antibodies. Protein-protein interactions and serological antibody assays using human viral proteome microarrays demonstrated that comparable hits were detected by M-NAPPA and non-multiplexed NAPPA arrays. An ultra-high density proteome microarray displaying > 16k proteins on a single microscope slide was produced by combining M-NAPPA with a photolithography-based silicon nano-well platform. Finally, four new tuberculosis-related antigens in guinea pigs vaccinated with Bacillus Calmette-Guerin (BCG) were identified with M-NAPPA and validated with ELISA. Conclusion: All data demonstrate that multiplexing features on a protein microarray offer a cost-effective fabrication approach and have the potential to facilitate high throughput translational research.

Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases

Organisms are constantly exposed to microbial pathogens in their environments. When a pathogen meets its host, a series of intricate intracellular interactions shape the outcome of the infection. The understanding of these host–pathogen interactions is crucial for the development of treatments and preventive measures against infectious diseases. Over the past decade, proteomic approaches have become prime contributors to the discovery and understanding of host–pathogen interactions that represent anti‐ and pro‐pathogenic cellular responses. Here, we review these proteomic methods and their application to studying viral and bacterial intracellular pathogens. We examine approaches for defining spatial and temporal host–pathogen protein interactions upon infection of a host cell. Further expanding the understanding of proteome organization during an infection, we discuss methods that characterize the regulation of host and pathogen proteomes through alterations in protein abundance, localization, and post‐translational modifications. Finally, we highlight bioinformatic tools available for analyzing such proteomic datasets, as well as novel strategies for integrating proteomics with other omic tools, such as genomics, transcriptomics, and metabolomics, to obtain a systems‐level understanding of infectious diseases.

Identification of Antibody Targets for Tuberculosis Serology using High-Density Nucleic Acid Programmable Protein Arrays

Better and more diverse biomarkers for the development of simple point-of-care tests for active tuberculosis (TB), a clinically heterogeneous disease, are urgently needed. We generated a proteomic Mycobacterium tuberculosis (Mtb) High-Density Nucleic Acid Programmable Protein Array (HD-NAPPA) that used a novel multiplexed strategy for expedited high-throughput screening for antibody responses to the Mtb proteome. We screened sera from HIV uninfected and coinfected TB patients and controls (n = 120) from the US and South Africa (SA) using the multiplex HD-NAPPA for discovery, followed by deconvolution and validation through single protein HD-NAPPA with biologically independent samples (n = 124). We verified the top proteins with enzyme-linked immunosorbent assays (ELISA) using the original screening and validation samples (n = 244) and heretofore untested samples (n = 41). We identified 8 proteins with TB biomarker value; four (Rv0054, Rv0831c, Rv2031c and Rv0222) of these were previously identified in serology studies, and four (Rv0948c, Rv2853, Rv3405c, Rv3544c) were not known to elicit antibody responses. Using ELISA data, we created classifiers that could discriminate patients' TB status according to geography (US or SA) and HIV (HIV- or HIV+) status. With ROC curve analysis under cross validation, the classifiers performed with an AUC for US/HIV- at 0.807; US/HIV+ at 0.782; SA/HIV- at 0.868; and SA/HIV+ at 0.723. With this study we demonstrate a new platform for biomarker/antibody screening and delineate its utility to identify previously unknown immunoreactive proteins.

Proteomics Tracing the Footsteps of Infectious Disease

Every year, a major cause of human disease and death worldwide is infection with the various pathogens-viruses, bacteria, fungi, and protozoa-that are intrinsic to our ecosystem. In efforts to control the prevalence of infectious disease and develop improved therapies, the scientific community has focused on building a molecular picture of pathogen infection and spread. These studies have been aimed at defining the cellular mechanisms that allow pathogen entry into hosts cells, their replication and transmission, as well as the core mechanisms of host defense against pathogens. The past two decades have demonstrated the valuable implementation of proteomic methods in all these areas of infectious disease research. Here, we provide a perspective on the contributions of mass spectrometry and other proteomics approaches to understanding the molecular details of pathogen infection. Specifically, we highlight methods used for defining the composition of viral and bacterial pathogens and the dynamic interaction with their hosts in space and time. We discuss the promise of MS-based proteomics in supporting the development of diagnostics and therapies, and the growing need for multiomics strategies for gaining a systems view of pathogen infection.

Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays

We performed an unbiased proteome-scale profiling of humoral autoimmunity in recent-onset type 1 diabetes (T1D) patients and nondiabetic controls against ∼10 000 human proteins using a Nucleic Acid Programmable Protein Array (NAPPA) platform, complemented by a knowledge-based selection of proteins from genes enriched in human pancreas. Although the global response was similar between cases and controls, we identified and then validated six specific novel T1D-associated autoantibodies (AAbs) with sensitivities that ranged from 16 to 27% at 95% specificity. These included AAbs against PTPRN2, MLH1, MTIF3, PPIL2, NUP50 (from NAPPA screening), and QRFPR (by targeted ELISA). Immunohistochemistry demonstrated that NUP50 protein behaved differently in islet cells, where it stained both nucleus and cytoplasm, compared with only nuclear staining in exocrine pancreas. Conversely, PPIL2 staining was absent in islet cells, despite its presence in exocrine cells. The combination of anti-PTPRN2, -MLH1, -PPIL2, and -QRFPR had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. These data indicate that these markers behave independently and support the use of unbiased screening to find biomarkers because the majority was not predicted based on predicted abundance. Our study enriches the knowledge of the "autoantibody-ome" in unprecedented breadth and width.

Proteomic approaches to uncovering virus-host protein interactions during the progression of viral infection

The integration of proteomic methods to virology has facilitated a significant breadth of biological insight into mechanisms of virus replication, antiviral host responses and viral subversion of host defenses. Throughout the course of infection, these cellular mechanisms rely heavily on the formation of temporally and spatially regulated virus–host protein–protein interactions. Reviewed here are proteomic-based approaches that have been used to characterize this dynamic virus–host interplay. Specifically discussed are the contribution of integrative mass spectrometry, antibody-based affinity purification of protein complexes, cross-linking and protein array techniques for elucidating complex networks of virus–host protein associations during infection with a diverse range of RNA and DNA viruses. The benefits and limitations of applying proteomic methods to virology are explored, and the contribution of these approaches to important biological discoveries and to inspiring new tractable avenues for the design of antiviral therapeutics is highlighted.

A Contra Capture Protein Array Platform for Studying Post-translationally Modified (PTM) Auto-antigenomes

Aberrant modifications of proteins occur during disease development and elicit disease-specific antibody responses. We have developed a protein array platform that enables the modification of many proteins in parallel and assesses their immunogenicity without the need to express, purify, and modify proteins individually. We used anticitrullinated protein antibodies (ACPAs) in rheumatoid arthritis (RA) as a model modification and profiled antibody responses to ∼190 citrullinated proteins in 20 RA patients. We observed unique antibody reactivity patterns in both clinical anticyclic citrullinated peptide assay positive (CCP+) and CCP- RA patients. At individual antigen levels, we detected antibodies against known citrullinated autoantigens and discovered and validated five novel antibodies against specific citrullinated antigens (osteopontin (SPP1), flap endonuclease (FEN1), insulin like growth factor binding protein 6 (IGFBP6), insulin like growth factor I (IGF1) and stanniocalcin-2 (STC2)) in RA patients. We also demonstrated the utility of our innovative array platform in the identification of immune-dominant epitope(s) for citrullinated antigens. We believe our platform will promote the study of post-translationally modified antigens at a breadth that has not been achieved before, by both identifying novel autoantigens and investigating their roles in disease development. The developed platforms can potentially be used to study many autoimmune disease-relevant modifications and their immunogenicity.

Advancing translational research with next-generation protein microarrays

Protein microarrays are a high-throughput technology used increasingly in translational research, seeking to apply basic science findings to enhance human health. In addition to assessing protein levels, posttranslational modifications, and signaling pathways in patient samples, protein microarrays have aided in the identification of potential protein biomarkers of disease and infection. In this perspective, the different types of full-length protein microarrays that are used in translational research are reviewed. Specific studies employing these microarrays are presented to highlight their potential in finding solutions to real clinical problems. Finally, the criteria that should be considered when developing next-generation protein microarrays are provided.

Variability in Humoral Immunity to Measles Vaccine: New Developments

Despite the existence of an effective measles vaccine, resurgence in measles cases in the USA and across Europe has occurred, including in individuals vaccinated with two doses of the vaccine. Host genetic factors result in inter-individual variation in measles vaccine-induced antibodies, and play a role in vaccine failure. Studies have identified HLA (human leukocyte antigen) and non-HLA genetic influences that individually or jointly contribute to the observed variability in the humoral response to vaccination among healthy individuals. In this exciting era, new high-dimensional approaches and techniques including vaccinomics, systems biology, GWAS, epitope prediction and sophisticated bioinformatics/statistical algorithms provide powerful tools to investigate immune response mechanisms to the measles vaccine. These might predict, on an individual basis, outcomes of acquired immunity post measles vaccination.