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Rodriguez E, Zwart ES, Affandi AA, Verhoeff J, de Kok M, Boyd LNC, Meijer LL, Le Large TYS, Olesek K, Giovannetti E, García-Vallejo JJ, Mebius RE, van Kooyk Y, Kazemier G. In-depth immune profiling of peripheral blood mononuclear cells in patients with pancreatic ductal adenocarcinoma reveals discriminative immune subpopulations. Cancer Sci 2024. [PMID: 38686549 DOI: 10.1111/cas.16147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis with a 5-year survival of less than 10%. More knowledge of the immune response developed in patients with PDAC is pivotal to develop better combination immune therapies to improve clinical outcome. In this study, we used mass cytometry time-of-flight to undertake an in-depth characterization of PBMCs from patients with PDAC and examine the differences with healthy controls and patients with benign diseases of the biliary system or pancreas. Peripheral blood mononuclear cells from patients with PDAC or benign disease are characterized by the increase of pro-inflammatory cells, as CD86+ classical monocytes and memory T cells expressing CCR6+ and CXCR3+, associated with T helper 1 (Th1) and Th17 immune responses, respectively. However, PBMCs from patients with PDAC present also an increase of CD39+ regulatory T cells and CCR4+CCR6-CXCR3- memory T cells, suggesting Th2 and regulatory responses. Concluding, our results show PDAC develops a multifaceted immunity, where a proinflammatory component is accompanied by regulatory responses, which could inhibit potential antitumor mechanisms.
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Affiliation(s)
- Ernesto Rodriguez
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Eline S Zwart
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Alsya A Affandi
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Jan Verhoeff
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Mike de Kok
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Lenka N C Boyd
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Laura L Meijer
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Tessa Y S Le Large
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Katarzyna Olesek
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Elisa Giovannetti
- Department of Medical Oncology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start-Up Unit, Fondazione Pisana per la Scienza, Pisa, Italy
| | - Juan J García-Vallejo
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Reina E Mebius
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Yvette van Kooyk
- Department of Molecular Cell Biology and Immunology, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Cancer Immunology, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Geert Kazemier
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
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Kelbauskas L, Legutki JB, Woodbury NW. Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays. Front Immunol 2024; 15:1335446. [PMID: 38318184 PMCID: PMC10838964 DOI: 10.3389/fimmu.2024.1335446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024] Open
Abstract
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.
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Affiliation(s)
- L Kelbauskas
- Biodesign Institute, Arizona State University, Tempe, AZ, United States
- Biomorph Technologies, Chandler, AZ, United States
| | - J B Legutki
- Biodesign Institute, Arizona State University, Tempe, AZ, United States
- Biomorph Technologies, Chandler, AZ, United States
| | - N W Woodbury
- Biodesign Institute, Arizona State University, Tempe, AZ, United States
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3
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Chang L, Yan M, Zhang J, Liu B, Zhang L, Guo Y, Sun J, Wan Y, Yi M, Lan Y, Cai Y, Ren Y, Zheng H, Zhang A, Li Z, Wang J, Li Y, Zhu X. An investigation of long-term outcome of rabbit anti-thymocyte globulin and cyclosporine therapy for pediatric severe aplastic anemia. BLOOD SCIENCE 2023; 5:180-186. [PMID: 37546712 PMCID: PMC10400069 DOI: 10.1097/bs9.0000000000000157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/20/2023] [Indexed: 08/08/2023] Open
Abstract
Children with severe aplastic anemia (SAA) face heterogeneous prognoses after immunosuppressive therapy (IST). There are few models that can predict the long-term outcomes of IST for these patients. The objective of this paper is to develop a more effective prediction model for SAA prognosis based on clinical electronic medical records from 203 children with newly diagnosed SAA. In the early stage, a novel model for long-term outcomes of SAA patients with IST was developed using machine-learning techniques. Among the indicators related to long-term efficacy, white blood cell count, lymphocyte count, absolute reticulocyte count, lymphocyte ratio in bone-marrow smears, C-reactive protein, and the level of IL-6, IL-8 and vitamin B12 in the early stage are strongly correlated with long-term efficacy (P < .05). Taken together, we analyzed the long-term outcomes of rabbit anti-thymocyte globulin and cyclosporine therapy for children with SAA through machine-learning techniques, which may shorten the observation period of therapeutic effects and reduce treatment costs and time.
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Affiliation(s)
- Lixian Chang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Mingchen Yan
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Jingliao Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Binghang Liu
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Li Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ye Guo
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Jing Sun
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Yang Wan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Meihui Yi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yang Lan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yuli Cai
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yuanyuan Ren
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Haihui Zheng
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Aoli Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Zhenyu Li
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Jian Wang
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Yingrui Li
- Shenzhen Digital Life Institute, Shenzhen, China
| | - Xiaofan Zhu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
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4
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Chowdhury R, Taguchi AT, Kelbauskas L, Stafford P, Diehnelt C, Zhao ZG, Williamson PC, Green V, Woodbury NW. Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease. PLoS Comput Biol 2023; 19:e1010773. [PMID: 37339137 DOI: 10.1371/journal.pcbi.1010773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/15/2023] [Indexed: 06/22/2023] Open
Abstract
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.
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Affiliation(s)
- Robayet Chowdhury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Laimonas Kelbauskas
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Phillip Stafford
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Chris Diehnelt
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | | | - Valerie Green
- Creative Testing Solutions, Tempe, Arizona, United States of America
| | - Neal W Woodbury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
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5
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Abstract
The diversity of the antigen-specific humoral immune response reflects the interaction of the immune system with pathogens and autoantigens. Peptide microarray analysis opens up new perspectives for the use of antibodies as diagnostic biomarkers and provides unique access to a more differentiated view on humoral responses to disease. This review focuses on the latest applications of peptide microarrays for the serologic medical diagnosis of autoimmunity, infectious diseases (including COVID-19), and cancer.
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Affiliation(s)
- Carsten Grötzinger
- Department of Hepatology and Gastroenterology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers. Diagnostics (Basel) 2022; 12:diagnostics12071742. [PMID: 35885645 PMCID: PMC9315466 DOI: 10.3390/diagnostics12071742] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/10/2022] [Accepted: 07/14/2022] [Indexed: 02/07/2023] Open
Abstract
The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.
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Tan M, Ma J, Yang X, You Q, Guo X, Li Y, Wang R, Han G, Chen Y, Qiu X, Wang X, Zhang L. Quantitative proteomics reveals differential immunoglobulin-associated proteome (IgAP) in patients of acute myocardial infarction and chronic coronary syndromes. J Proteomics 2022; 252:104449. [PMID: 34890869 DOI: 10.1016/j.jprot.2021.104449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022]
Abstract
B cells and immunoglobulins are implicated in the pathogenesis of chronic diseases, including coronary artery disease (CAD). However, it remains elusive how the humoral immunity is incriminated in the disease progression of CAD. Using serum samples of chronic coronary syndrome (CCS) and acute myocardial infarction (AMI), we conducted a quantitative profiling of the proteomic landscape recognized by immunoglobulins, which we term immunoglobulin-associated proteome (IgAP). Intriguingly, CCS and AMI patients displayed distinctive IgAP profiles that enriched proteins in the pathways of blood coagulation regulation and lipoprotein transport, suggesting that CCS-AMI transition involves changes of these pathways that are associated with immunoglobulins. Furthermore, we identified immunoglobulin-bound coagulation factor X (F10) as a potential biomarker and validated it with an independent cohort of CCS, AMI and healthy individuals. Our study indicates that IgAP proteins may serve as novel diagnostic biomarkers for CCS and AMI. SIGNIFICANCE: Our work it demonstrates a clear implication of immunoglobulin-associated proteome (IgAP), the proteomic landscape recognized by immunoglobulins, in the pathogenesis of CAD. In addition, it reports for the first time that immunoglobulin-bound F10 is implicated in CAD.
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Affiliation(s)
- Miaomiao Tan
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China; Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Jing Ma
- Department of Cardiology First Medical Center of Chinese PLA General Hospital, Beijing 18 100853, China
| | - Xi Yang
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China
| | - Qi You
- Department of Cardiology First Medical Center of Chinese PLA General Hospital, Beijing 18 100853, China
| | - Xiaoxin Guo
- Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Yiuhei Li
- Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Rui Wang
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China; Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Guiyuan Han
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China; Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Yundai Chen
- Department of Cardiology First Medical Center of Chinese PLA General Hospital, Beijing 18 100853, China
| | - Xiaoyan Qiu
- Department of Immunology, School of Basic Medical Sciences, Peking University, Beijing 100191, China; Peking University Center for Human Disease Genomics, Beijing 100191, China
| | - Xin Wang
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China; Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Liang Zhang
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen 518057, China; Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.
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8
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Koshechkin AA, Romanovich OV, Stamate D, Johnston SA, Zamyatin AV. Technology of Informative Feature Selection for Immunosignature Analysis. Sovrem Tekhnologii Med 2021; 12:19-25. [PMID: 34796001 PMCID: PMC8596259 DOI: 10.17691/stm2020.12.5.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Indexed: 11/30/2022] Open
Abstract
The main difficulty in practical work with data obtained via immunosignature analysis is high dimensionality and the presence of a significant number of uninformative or false-informative features due to the specific character of the technology. To ensure practically relevant quality of data analysis and classification, it is necessary to take due account of this specific character. The aim of the study is to create and test the technology for effective reduction of immunosignature data dimensionality, which provides practically relevant and high quality of classification with due regard for the properties of the data obtained.
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Affiliation(s)
- A A Koshechkin
- Assistant, Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia
| | - O V Romanovich
- Associate Professor, Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia; Leading Engineer, Institute of Applied Mathematics and Computer Science; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia
| | - D Stamate
- Senior Lecturer; Data Science Department of Computing, Goldsmiths, University of London, New Cross, London, SE14 6NW, UK
| | - S A Johnston
- Center Director and Professor; Biodesign Center for Innovations in Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - A V Zamyatin
- Head of the Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia; Director of the Institute of Applied Mathematics and Computer Science National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia
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9
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Paull ML, Bozekowski JD, Daugherty PS. Mapping antibody binding using multiplexed epitope substitution analysis. J Immunol Methods 2021; 499:113178. [PMID: 34757083 DOI: 10.1016/j.jim.2021.113178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/26/2021] [Accepted: 10/26/2021] [Indexed: 01/24/2023]
Abstract
A more complete understanding of antibody epitopes would aid the development of diagnostics, therapeutic antibodies, and vaccines. However, current methods for mapping antibody binding to epitopes require a targeted experimental approach, which limits throughput. To address these limitations, we developed Multiplexed Epitope Substitution Analysis (MESA) which can rapidly characterize various distinct epitopes using millions of antibody-binding peptides. We screened peptides from a random 12-mer library that bound to human serum antibody repertoires and determined their sequences using next-generation sequencing (NGS). Computationally, we divided target epitope sequences into overlapping k-mer subsequences and substituted the positions in each k-mer with all 20 amino acids, mimicking a saturation mutagenesis. We then determined enrichments of the substituted k-mers in the screened peptide dataset and used these enrichments to identify substitutions favored for binding at each position in the target epitope, ultimately revealing the precise binding motif. To validate MESA, we determined binding motifs for monoclonal antibodies spiked into serum, recovering the expected binding positions and amino acid preferences. To characterize epitopes bound by a population, we analyzed 50 serum specimens to determine the binding motifs within various target epitopes from common pathogens. Additionally, by analyzing various HSV-1 glycoprotein epitopes, MESA revealed unique binding signatures for HSV-1 seropositive specimens and demonstrated the variability of binding signatures within a population. These results demonstrate that MESA can rapidly identify and characterize binding motifs for an unlimited number of epitopes from a single experiment, accelerating discoveries and enhancing our understanding of antibody-epitope interactions.
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Affiliation(s)
- Michael L Paull
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Joel D Bozekowski
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.
| | - Patrick S Daugherty
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
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10
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Packer RJ, Iavarone A, Jones DTW, Blakeley JO, Bouffet E, Fisher MJ, Hwang E, Hawkins C, Kilburn L, MacDonald T, Pfister SM, Rood B, Rodriguez FJ, Tabori U, Ramaswamy V, Zhu Y, Fangusaro J, Johnston SA, Gutmann DH. Implications of new understandings of gliomas in children and adults with NF1: report of a consensus conference. Neuro Oncol 2021; 22:773-784. [PMID: 32055852 PMCID: PMC7283027 DOI: 10.1093/neuonc/noaa036] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Gliomas are the most common primary central nervous system tumors occurring in children and adults with neurofibromatosis type 1 (NF1). Over the past decade, discoveries of the molecular basis of low-grade gliomas (LGGs) have led to new approaches for diagnosis and treatments. However, these new understandings have not been fully applied to the management of NF1-associated gliomas. A consensus panel consisting of experts in NF1 and gliomas was convened to review the current molecular knowledge of NF1-associated low-grade “transformed” and high-grade gliomas; insights gained from mouse models of NF1-LGGs; challenges in diagnosing and treating older patients with NF1-associated gliomas; and advances in molecularly targeted treatment and potential immunologic treatment of these tumors. Next steps are recommended to advance the management and outcomes for NF1-associated gliomas.
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Affiliation(s)
- Roger J Packer
- Center for Neuroscience and Behavioral Medicine, Washington, DC, USA.,Gilbert Family Neurofibromatosis Institute, Brain Tumor Institute, and Children's National Hospital, Washington, DC, USA
| | - Antonio Iavarone
- Departments of Neurology and Pathology Institute for Cancer Genetics Columbia University Medical Center, New York, New York, USA
| | - David T W Jones
- Division of Pediatric Neuro-Oncology German Cancer Research Center Hopp Children's Cancer Center Heidelberg, Germany
| | - Jaishri O Blakeley
- Departments of Neurology; Oncology; Neurosurgery, Baltimore, Maryland, USA
| | - Eric Bouffet
- Pediatric Neuro-Oncology Program; Research Institute; and The Arthur and Sonia Labatt; Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Canada
| | - Michael J Fisher
- Department of Pediatric Oncology; Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Eugene Hwang
- Gilbert Family Neurofibromatosis Institute, Brain Tumor Institute, and Children's National Hospital, Washington, DC, USA
| | - Cynthia Hawkins
- Pediatric Neuro-Oncology Program; Research Institute; and The Arthur and Sonia Labatt; Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Canada
| | - Lindsay Kilburn
- Gilbert Family Neurofibromatosis Institute, Brain Tumor Institute, and Children's National Hospital, Washington, DC, USA
| | - Tobey MacDonald
- Department of Pediatrics; Emory University School of Medicine, Atlanta, Georgia, USA
| | - Stefan M Pfister
- Division of Pediatric Neuro-Oncology German Cancer Research Center Hopp Children's Cancer Center Heidelberg, Germany
| | - Brian Rood
- Gilbert Family Neurofibromatosis Institute, Brain Tumor Institute, and Children's National Hospital, Washington, DC, USA
| | - Fausto J Rodriguez
- Pathology; The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Uri Tabori
- Pediatric Neuro-Oncology Program; Research Institute; and The Arthur and Sonia Labatt; Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Canada
| | - Vijay Ramaswamy
- Pediatric Neuro-Oncology Program; Research Institute; and The Arthur and Sonia Labatt; Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Canada
| | - Yuan Zhu
- Gilbert Family Neurofibromatosis Institute, Brain Tumor Institute, and Children's National Hospital, Washington, DC, USA
| | - Jason Fangusaro
- Department of Pediatrics; Emory University School of Medicine, Atlanta, Georgia, USA
| | - Stephen A Johnston
- Center for Innovations in Medicine; Biodesign Institute; Arizona State University, Tempe, Arizona, USA
| | - David H Gutmann
- Department of Neurology; Washington University, St Louis, Missouri, USA
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11
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Heiss K, Heidepriem J, Fischer N, Weber LK, Dahlke C, Jaenisch T, Loeffler FF. Rapid Response to Pandemic Threats: Immunogenic Epitope Detection of Pandemic Pathogens for Diagnostics and Vaccine Development Using Peptide Microarrays. J Proteome Res 2020; 19:4339-4354. [PMID: 32892628 PMCID: PMC7640972 DOI: 10.1021/acs.jproteome.0c00484] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Indexed: 12/18/2022]
Abstract
Emergence and re-emergence of pathogens bearing the risk of becoming a pandemic threat are on the rise. Increased travel and trade, growing population density, changes in urbanization, and climate have a critical impact on infectious disease spread. Currently, the world is confronted with the emergence of a novel coronavirus SARS-CoV-2, responsible for yet more than 800 000 deaths globally. Outbreaks caused by viruses, such as SARS-CoV-2, HIV, Ebola, influenza, and Zika, have increased over the past decade, underlining the need for a rapid development of diagnostics and vaccines. Hence, the rational identification of biomarkers for diagnostic measures on the one hand, and antigenic targets for vaccine development on the other, are of utmost importance. Peptide microarrays can display large numbers of putative target proteins translated into overlapping linear (and cyclic) peptides for a multiplexed, high-throughput antibody analysis. This enabled for example the identification of discriminant/diagnostic epitopes in Zika or influenza and mapping epitope evolution in natural infections versus vaccinations. In this review, we highlight synthesis platforms that facilitate fast and flexible generation of high-density peptide microarrays. We further outline the multifaceted applications of these peptide array platforms for the development of serological tests and vaccines to quickly encounter pandemic threats.
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Affiliation(s)
- Kirsten Heiss
- PEPperPRINT
GmbH, Rischerstrasse
12, 69123 Heidelberg, Germany
| | - Jasmin Heidepriem
- Max
Planck Institute of Colloids and Interfaces, Department of Biomolecular Systems, Am Muehlenberg 1, 14476 Potsdam, Germany
| | - Nico Fischer
- Section
Clinical Tropical Medicine, Department of Infectious Diseases, Heidelberg University Hospital, INF 324, 69120 Heidelberg, Germany
| | - Laura K. Weber
- PEPperPRINT
GmbH, Rischerstrasse
12, 69123 Heidelberg, Germany
- Institute
of Microstructure Technology, Karlsruhe
Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Christine Dahlke
- Division
of Infectious Diseases, First Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- Department
of Clinical Immunology of Infectious Diseases, Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany
- German
Center for Infection Research, Partner Site
Hamburg-Lübeck-Borstel-Riems, 38124 Braunschweig, Germany
| | - Thomas Jaenisch
- Heidelberg
Institute of Global Health (HIGH), Heidelberg
University Hospital, Im Neuenheimer Feld 130, 69120 Heidelberg, Germany
- Center
for Global Health, Colorado School of Public Health, University of Colorado, Aurora, Colorado 80045, United States
- Department
of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, Colorado 80045, United States
| | - Felix F. Loeffler
- Max
Planck Institute of Colloids and Interfaces, Department of Biomolecular Systems, Am Muehlenberg 1, 14476 Potsdam, Germany
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12
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Shen L, Zhao ZG, Lainson JC, Brown JR, Sykes KF, Johnston SA, Diehnelt CW. Production of high-complexity frameshift neoantigen peptide microarrays. RSC Adv 2020; 10:29675-29681. [PMID: 35518269 PMCID: PMC9056171 DOI: 10.1039/d0ra05267a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/02/2020] [Indexed: 12/22/2022] Open
Abstract
Parallel measurement of large numbers of antigen-antibody interactions are increasingly enabled by peptide microarray technologies. Our group has developed an in situ synthesized peptide microarray of >400 000 frameshift neoantigens using mask-based photolithographic peptide synthesis, to profile patient specific neoantigen reactive antibodies in a single assay. The system produces 208 replicate mircoarrays per wafer and is capable of producing multiple wafers per synthetic lot to routinely synthesize over 300 million peptides simultaneously. In this report, we demonstrate the feasibility of the system for detecting peripheral-blood antibody binding to frameshift neoantigens across multiple synthetic lots.
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Affiliation(s)
- Luhui Shen
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University Tempe AZ USA
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University Tempe AZ USA
| | - John C Lainson
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University Tempe AZ USA
| | | | | | - Stephen Albert Johnston
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University Tempe AZ USA .,Calviri, Inc. Tempe AZ USA
| | - Chris W Diehnelt
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University Tempe AZ USA
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13
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Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera. PLoS One 2020; 15:e0226378. [PMID: 31940357 PMCID: PMC6961823 DOI: 10.1371/journal.pone.0226378] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/25/2019] [Indexed: 11/19/2022] Open
Abstract
Detection of protection-associated epitopes via reverse vaccinology is the first step for development of subunit vaccines against microbial pathogens. Mapping subunit vaccine targets requires high throughput methods, which would allow delineation of epitopes recognized by protective antibodies on a large scale. Phage displayed random peptide library coupled to Next Generation Sequencing (PDRPL/NGS) is the universal platform that enables high-yield identification of peptides that mimic epitopes (mimotopes). Despite being unsurpassed as a tool for discovery of polyclonal serum mimotopes, the PDRPL/NGS is far inferior as a quantitative method of immune response. Difficult-to-control fluctuations in amounts of antibody-bound phages after rounds of selection and amplification diminish the quantitative capacity of the PDRPL/NGS. In an attempt to improve the accuracy of the PDRPL/NGS method, we compared the discriminating capacity of two approaches for PDRPL/NGS data analysis. The whole-unique-sequence-based analysis (WUSA) involved generation of 7-mer peptide profiles and comparison of the numbers of sequencing reads for unique peptide sequences between serum samples. The motif-based analysis (MA) included identification of 4-mer consensus motifs unifying unique 7-mer sequences and comparison of motifs between serum samples. The motif comparison was based not on the numbers of sequencing reads, but on the numbers of distinct 7-mers constituting the motifs. Our PDRPL/NGS datasets generated from biopanning of protective and non-protective anti-Borrelia burgdorferi sera of New Zealand rabbits were used to contrast the two approaches. As a result, the principle component analyses (PCA) showed that the discriminating powers of the WUSA and MA were similar. In contrast, the unsupervised hierarchical clustering obtained via the MA classified the preimmune, non-protective, and protective sera better than the WUSA-based clustering. Also, a total number of discriminating motifs was higher than that of discriminating 7-mers. In sum, our results indicate that MA approach improves the accuracy and quantitative capacity of the PDRPL/NGS method.
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14
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Chen L, Pan X, Zeng T, Zhang YH, Zhang Y, Huang T, Cai YD. Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule. Front Bioeng Biotechnol 2019; 7:370. [PMID: 31850330 PMCID: PMC6901955 DOI: 10.3389/fbioe.2019.00370] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022] Open
Abstract
Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the “immunosignature” of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, China.,College of Information Engineering, Shanghai Maritime University, Shanghai, China.,Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice (PMMP), East China Normal University, Shanghai, China
| | - XiaoYong Pan
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.,IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Hang Zhang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - YunHua Zhang
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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15
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Paull ML, Johnston T, Ibsen KN, Bozekowski JD, Daugherty PS. A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes. PLoS One 2019; 14:e0217668. [PMID: 31490930 PMCID: PMC6730857 DOI: 10.1371/journal.pone.0217668] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 08/22/2019] [Indexed: 12/23/2022] Open
Abstract
Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.
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Affiliation(s)
- Michael L. Paull
- Department of Chemical Engineering, University of California Santa Barbara, California, United States of America
- * E-mail: (MLP); (PSD)
| | - Tim Johnston
- Department of Chemical Engineering, University of California Santa Barbara, California, United States of America
| | - Kelly N. Ibsen
- Department of Chemical Engineering, University of California Santa Barbara, California, United States of America
| | - Joel D. Bozekowski
- Department of Chemical Engineering, University of California Santa Barbara, California, United States of America
| | - Patrick S. Daugherty
- Department of Chemical Engineering, University of California Santa Barbara, California, United States of America
- * E-mail: (MLP); (PSD)
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16
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O'Connell GC, Stafford P, Walsh KB, Adeoye O, Barr TL. High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood. Neurotherapeutics 2019; 16:868-877. [PMID: 30783962 PMCID: PMC6694452 DOI: 10.1007/s13311-019-00720-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Accurate stroke recognition during triage can streamline care and afford patients earlier access to life-saving interventions. However, the tools currently available to clinicians for prehospital and early in-hospital identification of stroke are limited. The peripheral immune system is intricately involved in stroke pathology and thus may be targetable for the development of immunodiagnostics. In this preliminary study, we sought to determine whether the circulating antibody pool is altered early in stroke, and whether such alterations could be leveraged for diagnosis. One hundred microliters of peripheral whole blood was sampled from 19 ischemic stroke patients, 17 hemorrhagic stroke patients, and 20 stroke mimics in the acute phase of care. A custom-fabricated high-density peptide array comprising 125,000 unique probes was used to assess the binding characteristics of blood-borne antibodies, and a random forest-based approach was used to select a parsimonious set of probes with an optimal ability to discriminate between groups. The coordinate antibody binding intensities of the top 17 probes identified in our analysis displayed an ability to differentiate the total pool of stroke patients from stroke mimics with 92% sensitivity and 90% specificity, as well as detect hemorrhage with 88% sensitivity and 87% specificity, as determined using a same-set cross-validation. These preliminary findings suggest that stroke-associated alterations in the circulating antibody pool may have clinical utility for diagnosis during triage, and that such a possibility warrants further investigation.
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Affiliation(s)
- Grant C O'Connell
- School of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio, 44106-4904, USA.
| | - Phillip Stafford
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
| | - Kyle B Walsh
- Department of Emergency Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
- Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, Ohio, USA
| | - Opeolu Adeoye
- Department of Emergency Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
- Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, Ohio, USA
| | - Taura L Barr
- Valtari Bio Incorporated, Morgantown, West Virginia, USA
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17
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Antony F, Deantonio C, Cotella D, Soluri MF, Tarasiuk O, Raspagliesi F, Adorni F, Piazza S, Ciani Y, Santoro C, Macor P, Mezzanzanica D, Sblattero D. High-throughput assessment of the antibody profile in ovarian cancer ascitic fluids. Oncoimmunology 2019; 8:e1614856. [PMID: 31428516 DOI: 10.1080/2162402x.2019.1614856] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/25/2019] [Accepted: 04/27/2019] [Indexed: 12/31/2022] Open
Abstract
The identification of effective biomarkers for early diagnosis, prognosis, and response to treatments remains a challenge in ovarian cancer (OC) research. Here, we present an unbiased high-throughput approach to profile ascitic fluid autoantibodies in order to obtain a tumor-specific antigen signature in OC. We first reported the reactivity of immunoglobulins (Igs) purified from OC patient ascites towards two different OC cell lines. Using a discovery set of Igs, we selected tumor-specific antigens from a phage display cDNA library. After biopanning, 700 proteins were expressed as fusion protein and used in protein array to enable large-scale immunoscreening with independent sets of cancer and noncancerous control. Finally, the selected antigens were validated by ELISA. The initial screening identified eight antigenic clones: CREB3, MRPL46, EXOSC10, BCOR, HMGN2, HIP1R, OLFM4, and KIAA1755. These antigens were all validated by ELISA in a study involving ascitic Igs from 153 patients (69 with OC, 34 with other cancers and 50 without cancer), with CREB3 showing the highest sensitivity (86.95%) and specificity (98%). Notably, we were able to identify an association between the tumor-associated (TA) antibody response and the response to a first-line tumor treatment (platinum-based chemotherapy). A stronger association was found by combining three antigens (BCOR, CREB3, and MRLP46) as a single antibody signature. Measurement of an ascitic fluid antibody response to multiple TA antigens may aid in the identification of new prognostic signatures in OC patients and shift attention to new potentially relevant targets.
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Affiliation(s)
- Frank Antony
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | - Cecilia Deantonio
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | - Diego Cotella
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | - Maria Felicia Soluri
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | - Olga Tarasiuk
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | | | - Fulvio Adorni
- Epidemiology Unit, Institute of Biomedical Technologies, National Research Council, Milan, Italy
| | - Silvano Piazza
- Bioinformatics and Functional Genomics Unit, Laboratorio Nazionale del Consorzio Interuniversitario per le Biotecnologie (LNCIB), Area Science Park Trieste, Italy
| | - Yari Ciani
- Bioinformatics and Functional Genomics Unit, Laboratorio Nazionale del Consorzio Interuniversitario per le Biotecnologie (LNCIB), Area Science Park Trieste, Italy
| | - Claudio Santoro
- Department of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
| | - Paolo Macor
- Department of Life Science, University of Trieste, Trieste, Italy
| | - Delia Mezzanzanica
- Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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18
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Behrouzi A, Nafari AH, Siadat SD. The significance of microbiome in personalized medicine. Clin Transl Med 2019; 8:16. [PMID: 31081530 PMCID: PMC6512898 DOI: 10.1186/s40169-019-0232-y] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 04/11/2019] [Indexed: 02/07/2023] Open
Abstract
Considering the important role of microbiome, many of current investigations have focused on its beneficial aspects. Although, research explores new dimensions of the impact of microbiome and examines the differences in patients and healthy individuals for identifying biomarker patterns, but limited information is available, and investigation in this field seems to be of great value. On the other hand, new therapeutic approaches, called personalized medicine, have opened a new window in medical science, and the association between microbiome and personalized medicine seems to be one of the most interesting aspects of the subsequent research, and has a pivotal perspective on the treatment of diseases such as cancer. Accordingly, given the novelty of the relationship between these two axes, there are very few studies in this regard. The presence of specific strains may have the ability to modulate cancer progression and therapeutics; this increases the likelihood of precision medicine in relation to microbiota, in terms of treatment and prognosis, and therefore, microbiota is a next generation medicine and may develop a novel therapeutic action in this field.
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Affiliation(s)
- Ava Behrouzi
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran.,Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
| | - Amir Hossein Nafari
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran.,Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
| | - Seyed Davar Siadat
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran. .,Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran.
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19
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Wittenbrink N, Herrmann S, Blazquez-Navarro A, Bauer C, Lindberg E, Wolk K, Sabat R, Reinke P, Sawitzki B, Thomusch O, Hugo C, Babel N, Seitz H, Or-Guil M. 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. BMC Immunol 2019; 20:11. [PMID: 31029086 PMCID: PMC6486998 DOI: 10.1186/s12865-019-0291-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 04/08/2019] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Nicole Wittenbrink
- Systems Immunology Lab, Department of Biology, Humboldt University Berlin, Berlin, Germany
| | - Sabrina Herrmann
- Fraunhofer Institute for Cell Therapy and Immunology, Bioanalytics und Bioprocesses, Potsdam, Germany
| | - Arturo Blazquez-Navarro
- Systems Immunology Lab, Department of Biology, Humboldt University Berlin, Berlin, Germany
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
| | | | | | - Kerstin Wolk
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
- Psoriasis Research and Treatment Center, Institute of Medical Immunology, Department of Dermatology and Allergy, Charité University Medicine Berlin, Berlin, Germany
| | - Robert Sabat
- Psoriasis Research and Treatment Center, Institute of Medical Immunology, Department of Dermatology and Allergy, Charité University Medicine Berlin, Berlin, Germany
- Interdisciplinary Group of Molecular Immunopathology, Institute of Medical Immunology, Department of Dermatology and Allergy, Charité University Medicine Berlin, Berlin, Germany
| | - Petra Reinke
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
- Department of Nephrology and Internal Intensive Care, Charité University Medicine Berlin, Campus Virchow Clinic, Berlin, Germany
- Berlin Center for Advanced Therapies (BeCAT), Charité University Medicine Berlin, Campus Virchow Clinic, Berlin, Germany
| | - Birgit Sawitzki
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
- Molecular Immune Modulation, Institute for Medical Immunology, Charité University Medicine Berlin, Campus Virchow Clinic, Berlin, Germany
| | - Oliver Thomusch
- Klinik für Allgemein- und Viszeralchirurgie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Christian Hugo
- University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany
| | - Nina Babel
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
- Medical Clinic I, Marien Hospital Herne, Ruhr University Bochum, Herne, Germany
| | - Harald Seitz
- Fraunhofer Institute for Cell Therapy and Immunology, Bioanalytics und Bioprocesses, Potsdam, Germany
| | - Michal Or-Guil
- Systems Immunology Lab, Department of Biology, Humboldt University Berlin, Berlin, Germany
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20
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Bonner ER, Bornhorst M, Packer RJ, Nazarian J. Liquid biopsy for pediatric central nervous system tumors. NPJ Precis Oncol 2018; 2:29. [PMID: 30588509 PMCID: PMC6297139 DOI: 10.1038/s41698-018-0072-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/19/2018] [Indexed: 02/07/2023] Open
Abstract
Central nervous system (CNS) tumors are the most common solid tumors in children, and the leading cause of cancer-related death. Over the past decade, molecular profiling has been incorporated into treatment for pediatric CNS tumors, allowing for a more personalized approach to therapy. Through the identification of tumor-specific changes, it is now possible to diagnose, assign a prognostic subgroup, and develop targeted chemotherapeutic treatment plans for many cancer types. The successful incorporation of informative liquid biopsies, where the liquid biome is interrogated for tumor-associated molecular clues, has the potential to greatly complement the precision-based approach to treatment, and ultimately, to improve clinical outcomes for children with CNS tumors. In this article, the current application of liquid biopsy in cancer therapy will be reviewed, as will its potential for the diagnosis and therapeutic monitoring of pediatric CNS tumors.
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Affiliation(s)
- Erin R Bonner
- 1Center for Genetic Medicine, Children's National Health System, Washington, DC 20010 USA.,2Institute for Biomedical Sciences, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052 USA
| | - Miriam Bornhorst
- 1Center for Genetic Medicine, Children's National Health System, Washington, DC 20010 USA.,3Brain Tumor Institute, Children's National Health System, Washington, DC 20010 USA
| | - Roger J Packer
- 3Brain Tumor Institute, Children's National Health System, Washington, DC 20010 USA
| | - Javad Nazarian
- 1Center for Genetic Medicine, Children's National Health System, Washington, DC 20010 USA.,3Brain Tumor Institute, Children's National Health System, Washington, DC 20010 USA.,4Department of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052 USA
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21
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Günther OP, Gardy JL, Stafford P, Fluge Ø, Mella O, Tang P, Miller RR, Parker SM, Johnston SA, Patrick DM. Immunosignature Analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Mol Neurobiol 2018; 56:4249-4257. [PMID: 30298340 PMCID: PMC6505503 DOI: 10.1007/s12035-018-1354-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 09/14/2018] [Indexed: 12/27/2022]
Abstract
A random-sequence peptide microarray can interrogate serum antibodies in a broad, unbiased fashion to generate disease-specific immunosignatures. This approach has been applied to cancer detection, diagnosis of infections, and interrogation of vaccine response. We hypothesized that there is an immunosignature specific to ME/CFS and that this could aid in the diagnosis. We studied two subject groups meeting the Canadian Consensus Definition of ME/CFS. ME/CFS (n = 25) and matched control (n = 25) sera were obtained from a Canadian study. ME/CFS (n = 25) sera were obtained from phase 1/2 Norwegian trials (NCT01156909). Sera from six healthy controls from the USA were included in the analysis. Canadian cases and controls were tested for a disease immunosignature. By combining results from unsupervised and supervised analyses, a candidate immunosignature with 654 peptides was able to differentiate ME/CFS from controls. The immunosignature was tested and further refined using the Norwegian and USA samples. This resulted in a 256-peptide immunosignature with the ability to separate ME/CFS cases from controls in the international data sets. We were able to identify a 256-peptide signature that separates ME/CFS samples from healthy controls, suggesting that the hit-and-run hypothesis of immune dysfunction merits further investigation. By extending testing of both our signature and one previously reported in the literature to larger cohorts, and further interrogating the specific peptides we and others have identified, we may deepen our understanding of the origins of ME/CFS and work towards a clinically meaningful diagnostic biomarker.
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Affiliation(s)
| | - Jennifer L Gardy
- British Columbia Centre for Disease Control, Vancouver, BC, Canada.,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Øystein Fluge
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Olav Mella
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | | | - Ruth R Miller
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Shoshana M Parker
- Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital, Vancouver, BC, Canada
| | | | - David M Patrick
- British Columbia Centre for Disease Control, Vancouver, BC, Canada. .,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
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22
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Sun AC, Alvarez-Fontecilla E, Venkatesh AG, Aronoff-Spencer E, Hall DA. High-Density Redox Amplified Coulostatic Discharge-Based Biosensor Array. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2018; 53:2054-2064. [PMID: 30559530 PMCID: PMC6294472 DOI: 10.1109/jssc.2018.2820705] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
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.
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Affiliation(s)
- Alexander C Sun
- Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA
| | - Enrique Alvarez-Fontecilla
- Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA
| | - A G Venkatesh
- Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA
| | | | - Drew A Hall
- Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA
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23
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Gut immunoglobulin alpha anti-glycan binding profiles as a research tool for local disease detection. Glycoconj J 2018; 35:333-342. [DOI: 10.1007/s10719-018-9828-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 05/22/2018] [Accepted: 05/25/2018] [Indexed: 12/20/2022]
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24
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Bianco A, Malapelle U, Rocco D, Perrotta F, Mazzarella G. Targeting immune checkpoints in non small cell lung cancer. Curr Opin Pharmacol 2018. [DOI: 10.1016/j.coph.2018.02.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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25
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Lake BB, Rossmeisl JH, Cecere J, Stafford P, Zimmerman KL. Immunosignature Differentiation of Non-Infectious Meningoencephalomyelitis and Intracranial Neoplasia in Dogs. Front Vet Sci 2018; 5:97. [PMID: 29868618 PMCID: PMC5958519 DOI: 10.3389/fvets.2018.00097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 04/20/2018] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Bathilda B. Lake
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States
| | - John Henry Rossmeisl
- Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States
| | - Julie Cecere
- Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States
| | - Phillip Stafford
- Biodesign Institute, Center for Innovations in Medicine, Arizona State University, Tempe, AZ, United States
| | - Kurt L. Zimmerman
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States
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26
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27
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Firouzabadi FS, Vard A, Sehhati M, Mohebian M. An Optimized Framework for Cancer Prediction Using Immunosignature. JOURNAL OF MEDICAL SIGNALS & SENSORS 2018; 8:161-169. [PMID: 30181964 PMCID: PMC6116316 DOI: 10.4103/jmss.jmss_2_18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma. Methods: In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers. Results: The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm. Conclusion: The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.
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Affiliation(s)
- Fatemeh Safaei Firouzabadi
- Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammadreza Sehhati
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammadreza Mohebian
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
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28
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Wang L, Whittemore K, Johnston SA, Stafford P. Entropy is a Simple Measure of the Antibody Profile and is an Indicator of Health Status: A Proof of Concept. Sci Rep 2017; 7:18060. [PMID: 29273777 PMCID: PMC5741721 DOI: 10.1038/s41598-017-18469-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 12/12/2017] [Indexed: 01/30/2023] Open
Abstract
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.
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Affiliation(s)
- Lu Wang
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, 85287, United States
| | - Kurt Whittemore
- Centro Nacional de Investigaciones Oncologicas, Madrid, 28029, Spain
| | - Stephen Albert Johnston
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, 85287, United States
| | - Phillip Stafford
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, 85287, United States.
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29
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Peptide Sequencing Directly on Solid Surfaces Using MALDI Mass Spectrometry. Sci Rep 2017; 7:17811. [PMID: 29259225 PMCID: PMC5736625 DOI: 10.1038/s41598-017-18105-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 12/05/2017] [Indexed: 11/08/2022] Open
Abstract
There are an increasing variety of applications in which peptides are both synthesized and used attached to solid surfaces. This has created a need for high throughput sequence analysis directly on surfaces. However, common sequencing approaches that can be adapted to surface bound peptides lack the throughput often needed in library-based applications. Here we describe a simple approach for sequence analysis directly on solid surfaces that is both high speed and high throughput, utilizing equipment available in most protein analysis facilities. In this approach, surface bound peptides, selectively labeled at their N-termini with a positive charge-bearing group, are subjected to controlled degradation in ammonia gas, resulting in a set of fragments differing by a single amino acid that remain spatially confined on the surface they were bound to. These fragments can then be analyzed by MALDI mass spectrometry, and the peptide sequences read directly from the resulting spectra.
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30
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Abstract
Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.
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31
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Maeda DLNF, Batista MT, Pereira LR, de Jesus Cintra M, Amorim JH, Mathias-Santos C, Pereira SA, Boscardin SB, Silva SDR, Faquim-Mauro EL, Silveira VB, Oliveira DBL, Johnston SA, Ferreira LCDS, Rodrigues JF. Adjuvant-Mediated Epitope Specificity and Enhanced Neutralizing Activity of Antibodies Targeting Dengue Virus Envelope Protein. Front Immunol 2017; 8:1175. [PMID: 28993770 PMCID: PMC5622152 DOI: 10.3389/fimmu.2017.01175] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/05/2017] [Indexed: 12/30/2022] Open
Abstract
The heat-labile toxins (LT) produced by enterotoxigenic Escherichia coli display adjuvant effects to coadministered antigens, leading to enhanced production of serum antibodies. Despite extensive knowledge of the adjuvant properties of LT derivatives, including in vitro-generated non-toxic mutant forms, little is known about the capacity of these adjuvants to modulate the epitope specificity of antibodies directed against antigens. This study characterizes the role of LT and its non-toxic B subunit (LTB) in the modulation of antibody responses to a coadministered antigen, the dengue virus (DENV) envelope glycoprotein domain III (EDIII), which binds to surface receptors and mediates virus entry into host cells. In contrast to non-adjuvanted or alum-adjuvanted formulations, antibodies induced in mice immunized with LT or LTB showed enhanced virus-neutralization effects that were not ascribed to a subclass shift or antigen affinity. Nonetheless, immunosignature analyses revealed that purified LT-adjuvanted EDIII-specific antibodies display distinct epitope-binding patterns with regard to antibodies raised in mice immunized with EDIII or the alum-adjuvanted vaccine. Notably, the analyses led to the identification of a specific EDIII epitope located in the EF to FG loop, which is involved in the entry of DENV into eukaryotic cells. The present results demonstrate that LT and LTB modulate the epitope specificity of antibodies generated after immunization with coadministered antigens that, in the case of EDIII, was associated with the induction of neutralizing antibody responses. These results open perspectives for the more rational development of vaccines with enhanced protective effects against DENV infections.
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Affiliation(s)
| | - Milene Tavares Batista
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.,Center for Innovation in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Lennon Ramos Pereira
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Mariana de Jesus Cintra
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jaime Henrique Amorim
- Center of Biological and Health Sciences, Federal University of Western Bahia, Bahia, Brazil
| | - Camila Mathias-Santos
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Sara Araújo Pereira
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Silvia Beatriz Boscardin
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | | | | | - Vanessa Barbosa Silveira
- Clinical and Molecular Virology Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Danielle Bruna Leal Oliveira
- Clinical and Molecular Virology Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Stephen Albert Johnston
- Center for Innovation in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Luís Carlos de Souza Ferreira
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Juliana Falcão Rodrigues
- Vaccine Development Laboratory, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
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32
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Saghapour E, Kermani S, Sehhati M. A novel feature ranking method for prediction of cancer stages using proteomics data. PLoS One 2017; 12:e0184203. [PMID: 28934234 PMCID: PMC5608217 DOI: 10.1371/journal.pone.0184203] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 08/15/2017] [Indexed: 12/21/2022] Open
Abstract
Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.
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Affiliation(s)
- Ehsan Saghapour
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeed Kermani
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- * E-mail:
| | - Mohammadreza Sehhati
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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33
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Rowe M, Melnick J, Gerwien R, Legutki JB, Pfeilsticker J, Tarasow TM, Sykes KF. An ImmunoSignature test distinguishes Trypanosoma cruzi, hepatitis B, hepatitis C and West Nile virus seropositivity among asymptomatic blood donors. PLoS Negl Trop Dis 2017; 11:e0005882. [PMID: 28873423 PMCID: PMC5600393 DOI: 10.1371/journal.pntd.0005882] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 09/15/2017] [Accepted: 08/18/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The complexity of the eukaryotic parasite Trypanosoma (T.) cruzi manifests in its highly dynamic genome, multi-host life cycle, progressive morphologies and immune-evasion mechanisms. Accurate determination of infection or Chagas' disease activity and prognosis continues to challenge researchers. We hypothesized that a diagnostic platform with higher ligand complexity than previously employed may hold value. METHODOLOGY We applied the ImmunoSignature Technology (IST) for the detection of T. cruzi-specific antibodies among healthy blood donors. IST is based on capturing the information in an individual's antibody repertoire by exposing their peripheral blood to a library of >100,000 position-addressable, chemically-diverse peptides. PRINCIPAL FINDINGS Initially, samples from two Chagas cohorts declared positive or negative by bank testing were studied. With the first cohort, library-peptides displaying differential binding signals between T. cruzi sero-states were used to train an algorithm. A classifier was fixed and tested against the training-independent second cohort to determine assay performance. Next, samples from a mixed cohort of donors declared positive for Chagas, hepatitis B, hepatitis C or West Nile virus were assayed on the same library. Signals were used to train a single algorithm that distinguished all four disease states. As a binary test, the accuracy of predicting T. cruzi seropositivity by IST was similar, perhaps modestly reduced, relative to conventional ELISAs. However, the results indicate that information beyond determination of seropositivity may have been captured. These include the identification of cohort subclasses, the simultaneous detection and discerning of other diseases, and the discovery of putative new antigens. CONCLUSIONS & SIGNIFICANCE The central outcome of this study established IST as a reliable approach for specific determination of T. cruzi seropositivity versus disease-free individuals or those with other diseases. Its potential contribution for monitoring and controlling Chagas lies in IST's delivery of higher resolution immune-state readouts than obtained with currently-used technologies. Despite the complexity of the ligand presentation and large quantitative readouts, performing an IST test is simple, scalable and reproducible.
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Affiliation(s)
- Michael Rowe
- HealthTell, Inc., San Ramon, CA, United States of America
| | | | - Robert Gerwien
- HealthTell, Inc., San Ramon, CA, United States of America
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34
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Zandian A, Forsström B, Häggmark-Månberg A, Schwenk JM, Uhlén M, Nilsson P, Ayoglu B. Whole-Proteome Peptide Microarrays for Profiling Autoantibody Repertoires within Multiple Sclerosis and Narcolepsy. J Proteome Res 2017; 16:1300-1314. [DOI: 10.1021/acs.jproteome.6b00916] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Arash Zandian
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Björn Forsström
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Anna Häggmark-Månberg
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Jochen M. Schwenk
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Mathias Uhlén
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Peter Nilsson
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
| | - Burcu Ayoglu
- Affinity Proteomics, SciLifeLab,
School of Biotechnology, KTH - Royal Institute of Technology, SE-171 21 Solna, Sweden
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35
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Weber LK, Palermo A, Kügler J, Armant O, Isse A, Rentschler S, Jaenisch T, Hubbuch J, Dübel S, Nesterov-Mueller A, Breitling F, Loeffler FF. Single amino acid fingerprinting of the human antibody repertoire with high density peptide arrays. J Immunol Methods 2017; 443:45-54. [PMID: 28167275 DOI: 10.1016/j.jim.2017.01.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 12/14/2016] [Accepted: 01/03/2017] [Indexed: 11/16/2022]
Abstract
The antibody species that patrol in a patient's blood are an invaluable part of the immune system. While most of them shield us from life-threatening infections, some of them do harm in autoimmune diseases. If we knew exactly all the antigens that elicited all the antibody species within a group of patients, we could learn which ones correlate with immune protection, are irrelevant, or do harm. Here, we demonstrate an approach to this question: First, we use a plethora of phage-displayed peptides to identify many different serum antibody binding peptides. Next, we synthesize identified peptides in the array format and rescreen the serum used for phage panning to validate antibody binding peptides. Finally, we systematically vary the sequence of validated antibody binding peptides to identify those amino acids within the peptides that are crucial for binding "their" antibody species. The resulting immune fingerprints can then be used to trace them back to potential antigens. We investigated the serum of an individual in this pipeline, which led to the identification of 73 antibody fingerprints. Some fingerprints could be traced back to their most likely antigen, for example the immunodominant capsid protein VP1 of enteroviruses, most likely elicited by the ubiquitous poliovirus vaccination. Thus, with our approach, it is possible, to pinpoint those antibody species that correlate with a certain antigen, without any pre-information. This can help to unravel hitherto enigmatic diseases.
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Affiliation(s)
- Laura K Weber
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Andrea Palermo
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Jonas Kügler
- Yumab GmbH, Rebenring 33, 38106 Braunschweig, Germany
| | - Olivier Armant
- Karlsruhe Institute of Technology, Institute of Toxicology and Genetics (ITG), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Awale Isse
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Simone Rentschler
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Thomas Jaenisch
- Heidelberg University Hospital, Department for Infectious Diseases, Parasitology Unit, Im Neuenheimer Feld 324, 69120 Heidelberg, Germany; German Centre for Infection Research (DZIF), partner site Heidelberg, Germany; HEiKA - Heidelberg Karlsruhe Research Partnership, Heidelberg University, Karlsruhe Institute of Technology (KIT), Germany
| | - Jürgen Hubbuch
- Karlsruhe Institute of Technology, Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Engler-Bunte Ring 3, 76131 Karlsruhe, Germany
| | - Stefan Dübel
- Technische Universität Braunschweig, Department of Biotechnology, Institute for Biochemistry and Biotechnology, Spielmannstr. 7, 38106 Braunschweig, Germany
| | - Alexander Nesterov-Mueller
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
| | - Frank Breitling
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
| | - Felix F Loeffler
- Karlsruhe Institute of Technology, Institute of Microstructure Technology (IMT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; HEiKA - Heidelberg Karlsruhe Research Partnership, Heidelberg University, Karlsruhe Institute of Technology (KIT), Germany; Max Planck Institute of Colloids and Interfaces, Department of Biomolecular Systems, Am Mühlenberg 1, 14476 Potsdam, Germany.
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Rastogi A, Ali A, Tan SH, Banerjee S, Chen Y, Cullen J, Xavier CP, Mohamed AA, Ravindranath L, Srivastav J, Young D, Sesterhenn IA, Kagan J, Srivastava S, McLeod DG, Rosner IL, Petrovics G, Dobi A, Srivastava S, Srinivasan A. Autoantibodies against oncogenic ERG protein in prostate cancer: potential use in diagnosis and prognosis in a panel with C-MYC, AMACR and HERV-K Gag. Genes Cancer 2017; 7:394-413. [PMID: 28191285 PMCID: PMC5302040 DOI: 10.18632/genesandcancer.126] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Overdiagnosis and overtreatment of prostate cancer (CaP) is attributable to widespread reliance on PSA screening in the US. This has prompted us and others to search for improved biomarkers for CaP, to facilitate early detection and disease stratification. In this regard, autoantibodies (AAbs) against tumor antigens could serve as potential candidates for diagnosis and prognosis of CaP. Towards this, our goals were: i) To investigate whether AAbs against ERG oncoprotein (overexpressed in 25-50% of Caucasian American and African American CaP) are present in the sera of CaP patients; ii) To evaluate an AAb panel to enhance CaP detection. The results using an enzyme-linked immunosorbent assay (ELISA) showed that anti-ERG AAbs are present in a significantly higher proportion in the sera of CaP patients compared to healthy controls (p = 0.0001). Furthermore, a panel of AAbs against ERG, AMACR and human endogenous retrovirus-K Gag successfully differentiated CaP patient sera from healthy controls (AUC = 0.791). These results demonstrate for the first time that anti-ERG AAbs are present in the sera of CaP patients. In addition, the data also suggest that AAbs against ERG together with AMACR and HERV-K Gag may be a useful panel of biomarkers for diagnosis and prognosis of CaP.
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Affiliation(s)
- Anshu Rastogi
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Amina Ali
- Urology Service, Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Shyh-Han Tan
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Sreedatta Banerjee
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Yongmei Chen
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Jennifer Cullen
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Charles P Xavier
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Ahmed A Mohamed
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Lakshmi Ravindranath
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Jigisha Srivastav
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Denise Young
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | | | - Jacob Kagan
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - David G McLeod
- Urology Service, Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Inger L Rosner
- Urology Service, Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Gyorgy Petrovics
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Albert Dobi
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Shiv Srivastava
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Alagarsamy Srinivasan
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
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Contreras AV, Cocom-Chan B, Hernandez-Montes G, Portillo-Bobadilla T, Resendis-Antonio O. Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine. Front Physiol 2016; 7:606. [PMID: 28018236 PMCID: PMC5145879 DOI: 10.3389/fphys.2016.00606] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 11/21/2016] [Indexed: 12/19/2022] Open
Abstract
It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.
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Affiliation(s)
| | - Benjamin Cocom-Chan
- Instituto Nacional de Medicina GenómicaMexico City, Mexico; Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMexico City, Mexico
| | - Georgina Hernandez-Montes
- Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM) Mexico City, Mexico
| | - Tobias Portillo-Bobadilla
- Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM) Mexico City, Mexico
| | - Osbaldo Resendis-Antonio
- Instituto Nacional de Medicina GenómicaMexico City, Mexico; Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMexico City, Mexico; Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM)Mexico City, Mexico
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38
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Han Y, Li H, Guan Y, Huang J. Immune repertoire: A potential biomarker and therapeutic for hepatocellular carcinoma. Cancer Lett 2016; 379:206-12. [DOI: 10.1016/j.canlet.2015.06.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 06/29/2015] [Accepted: 06/30/2015] [Indexed: 12/27/2022]
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Gumireddy K, Li A, Chang DH, Liu Q, Kossenkov AV, Yan J, Korst RJ, Nam BT, Xu H, Zhang L, Ganepola GAP, Showe LC, Huang Q. AKAP4 is a circulating biomarker for non-small cell lung cancer. Oncotarget 2016; 6:17637-47. [PMID: 26160834 PMCID: PMC4627334 DOI: 10.18632/oncotarget.3946] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 05/01/2015] [Indexed: 12/18/2022] Open
Abstract
Cancer testis antigens (CTAs) are widely expressed in tumor tissues, circulating tumor cells (CTCs) and in cancer derived exosomes that are frequently engulfed by lymphoid cells. To determine whether tumor derived CTA mRNAs could be detected in RNA from purified peripheral blood mononuclear cells (PBMC) of non-small cell lung cancer (NSCLC) patients, we assayed for the expression of 116 CTAs in PBMC RNA in a discovery set and identified AKAP4 as a potential NSCLC biomarker. We validated AKAP4 as a highly accurate biomarker in a cohort of 264 NSCLCs and 135 controls from 2 different sites including a subset of controls with high risk lung nodules. When all (264) lung cancers were compared with all (135) controls the area under the ROC curve (AUC) was 0.9714. When 136 stage I NSCLC lung cancers are compared with all controls the AUC is 0.9795 and when all lung cancer patients were compared to 27 controls with histologically confirmed benign lung nodules, a comparison of significant clinical importance, the AUC was 0.9825. AKAP4 expression increases significantly with tumor stage, but independent of age, gender, smoking history or cancer subtype. Follow-up studies in a small number of resected NSCLC patients revealed a decrease of AKAP4 expression post-surgical resection that remained low in patients in remission and increased with tumor recurrence. AKAP4 is a highly accurate biomarker for the detection of early stage lung cancer.
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Affiliation(s)
| | - Anping Li
- The Wistar Institute Cancer Center, Philadelphia, PA 19104, USA
| | - David H Chang
- Center for Cancer Research and Genomic Medicine, The Valley Hospital, Paramus, NJ 07652, USA
| | - Qin Liu
- The Wistar Institute Cancer Center, Philadelphia, PA 19104, USA
| | | | - Jinchun Yan
- University of Washington Medical Center, Seattle, WA 98195, USA
| | - Robert J Korst
- Department of Surgery, The Valley Hospital, Ridgewood, NJ 07450, USA
| | - Brian T Nam
- Helen F. Graham Cancer Center & Research Institute, Christiana Care Health System, Newark, DE 19713, USA
| | - Hua Xu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan 430030, China
| | - Lin Zhang
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ganepola A P Ganepola
- Center for Cancer Research and Genomic Medicine, The Valley Hospital, Paramus, NJ 07652, USA.,Department of Surgery, The Valley Hospital, Ridgewood, NJ 07450, USA
| | - Louise C Showe
- The Wistar Institute Cancer Center, Philadelphia, PA 19104, USA
| | - Qihong Huang
- The Wistar Institute Cancer Center, Philadelphia, PA 19104, USA
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40
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Abstract
Autoantibodies are a key component for the diagnosis, prognosis and monitoring of various diseases. In order to discover novel autoantibody targets, highly multiplexed assays based on antigen arrays hold a great potential and provide possibilities to analyze hundreds of body fluid samples for their reactivity pattern against thousands of antigens in parallel. Here, we provide an overview of the available technologies for producing antigen arrays, highlight some of the technical and methodological considerations and discuss their applications as discovery tools. Together with recent studies utilizing antigen arrays, we give an overview on how the different types of antigen arrays have and will continue to deliver novel insights into autoimmune diseases among several others.
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41
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Kuznetsov IB. Identification of non-random sequence properties in groups of signature peptides obtained in random sequence peptide microarray experiments. Biopolymers 2016; 106:318-29. [PMID: 27037995 DOI: 10.1002/bip.22845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2015] [Revised: 02/16/2016] [Accepted: 03/28/2016] [Indexed: 11/09/2022]
Abstract
Immunosignaturing is an emerging experimental technique that uses random sequence peptide microarrays to detect antibodies produced by the immune system in response to a particular disease. Two important questions regarding immunosignaturing are "Do microarray peptides that exhibit a strong affinity to a given type of antibodies share common sequence properties?" and "If so, what are those properties?" In this work, three statistical tests designed to detect non-random patterns in the amino acid makeup of a group of microarray peptides are presented. One test detects patterns of significantly biased amino acid usage, whereas the other two detect patterns of significant bias in the biochemical properties. These tests do not require a large number of peptides per group. The tests were applied to analyze 19 groups of peptides identified in immunosignaturing experiments as being specific for antibodies produced in response to various types of cancer and other diseases. The positional distribution of the biochemical properties of the amino acids in these 19 peptide groups was also studied. Remarkably, despite the random nature of the sequence libraries used to design the microarrays, a unique group-specific non-random pattern was identified in the majority of the peptide groups studied. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 318-329, 2016.
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Affiliation(s)
- Igor B Kuznetsov
- Cancer Research Center and Department of Epidemiology and Biostatistics, University at Albany, State University of New York, One Discovery Drive, Rensselaer, NY, 12144
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42
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Yu X, Petritis B, LaBaer J. Advancing translational research with next-generation protein microarrays. Proteomics 2016; 16:1238-50. [PMID: 26749402 PMCID: PMC7167888 DOI: 10.1002/pmic.201500374] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 11/23/2015] [Accepted: 01/04/2016] [Indexed: 01/14/2023]
Abstract
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.
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Affiliation(s)
- Xiaobo Yu
- State Key Laboratory of ProteomicsBeijing Proteome Research CenterNational Center for Protein Sciences (The PHOENIX Center, Beijing)BeijingP. R. China
- The Virginia G. Piper Center for Personalized DiagnosticsBiodesign InstituteArizona State UniversityTempeAZUSA
| | - Brianne Petritis
- The Virginia G. Piper Center for Personalized DiagnosticsBiodesign InstituteArizona State UniversityTempeAZUSA
| | - Joshua LaBaer
- The Virginia G. Piper Center for Personalized DiagnosticsBiodesign InstituteArizona State UniversityTempeAZUSA
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43
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Stafford P, Wrapp D, Johnston SA. General Assessment of Humoral Activity in Healthy Humans. Mol Cell Proteomics 2016; 15:1610-21. [PMID: 26902205 DOI: 10.1074/mcp.m115.054601] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Indexed: 11/06/2022] Open
Abstract
The humoral immune system is network of biological molecules designed to maintain a healthy homeostatic equilibrium. Because antibodies are an abundant and highly specific effector of immunological action, they are also an important reservoir of previous host exposures. Antibodies may play a major role in early detection of host challenge. Unfortunately, few practical methods exist for interpreting the information stored in antibody variable regions. Immunosignatures use a microarray of thousands of random sequence peptides to interrogate antibodies in a broad and unbiased fashion. The pattern of binding between antibody and peptide is reproducible. Once the system has been trained on a disease cohort, blinded samples can be reliably predicted. Although immunosignatures of both chronic and infectious disease have been extensively tested, less has been done to demonstrate how healthy immunosignatures change over time or between individuals. Here, we report the results of a study of immunosignatures of healthy persons over brief (12 h sampled once per hour), intermediate (32 days sampled once per day), and long (5 years sampled once every year) time spans. Using this information, we were also able to detect intentional and unintentional immunological perturbations in the form of a vaccine and an infection, respectively. Our findings suggest that, even with the variability inherent in healthy immunosignatures, a single person's immunosignature will remain constant over time. Over this healthy signature, vaccines and infections create subsignatures that are common across multiple people, even subsuming healthy fluctuations. These findings have implications for disease monitoring and early diagnosis.
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Affiliation(s)
- Phillip Stafford
- From the ‡Biodesign Institute, Center for Innovations in Medicine, Arizona State University, Tempe, AZ
| | - Daniel Wrapp
- §Department of Biochemistry, Geisel School of Medicine at Dartmouth, Hanover, NH
| | - Stephen Albert Johnston
- From the ‡Biodesign Institute, Center for Innovations in Medicine, Arizona State University, Tempe, AZ
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44
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Abstract
The diversity of the antigen-specific humoral immune response reflects the interaction of the immune system with pathogens and autoantigens. Peptide microarray analysis opens up new perspectives for the use of antibodies as diagnostic biomarkers and provides unique access to a more differentiated serological diagnosis. This review focusses on latest applications of peptide microarrays for the serologic medical diagnosis of autoimmunity, infectious diseases, and cancer.
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45
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Stewart BW, Bray F, Forman D, Ohgaki H, Straif K, Ullrich A, Wild CP. Cancer prevention as part of precision medicine: 'plenty to be done'. Carcinogenesis 2016; 37:2-9. [PMID: 26590901 PMCID: PMC4700936 DOI: 10.1093/carcin/bgv166] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 10/30/2015] [Accepted: 11/12/2015] [Indexed: 02/06/2023] Open
Abstract
Cancer burden worldwide is projected to rise from 14 million new cases in 2012 to 24 million in 2035. Although the greatest increases will be in developing countries, where cancer services are already hard pressed, even the richest nations will struggle to meet demands of increasing patient numbers and spiralling treatment costs. No country can treat its way out of the cancer problem. Consequently, cancer control must combine improvements in treatment with greater emphasis on prevention and early detection. Cancer prevention is founded on describing the burden of cancer, identifying the causes and evaluating and implementing preventive interventions. Around 40-50% of cancers could be prevented if current knowledge about risk factors was translated into effective public health strategies. The benefits of prevention are attested to by major successes, for example, in tobacco control, vaccination against oncogenic viruses, reduced exposure to environmental and occupational carcinogens, and screening. Progress is still needed in areas such as weight control and physical activity. Fresh impetus for prevention and early detection will come through interdisciplinary approaches, encompassing knowledge and tools from advances in cancer biology. Examples include mutation profiles giving clues about aetiology and biomarkers for early detection, to stratify individuals for screening or for prognosis. However, cancer prevention requires a broad perspective stretching from the submicroscopic to the macropolitical, recognizing the importance of molecular profiling and multisectoral engagement across urban planning, transport, environment, agriculture, economics, etc., and applying interventions that may just as easily rely on a legislative measure as on a molecule.
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Affiliation(s)
| | - Freddie Bray
- International Agency for Research on Cancer, 69008 Lyon, France and
| | - David Forman
- International Agency for Research on Cancer, 69008 Lyon, France and
| | - Hiroko Ohgaki
- International Agency for Research on Cancer, 69008 Lyon, France and
| | - Kurt Straif
- International Agency for Research on Cancer, 69008 Lyon, France and
| | - Andreas Ullrich
- Noncommunicable Diseases and Mental Health, World Health Organization, 1121 Geneva 27, Switzerland
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46
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Chapoval AI, Legutki JB, Stafford P, Trebukhov AV, Johnston SA, Shoikhet YN, Lazarev AF. Immunosignature: Serum Antibody Profiling for Cancer Diagnostics. Asian Pac J Cancer Prev 2015; 16:4833-7. [DOI: 10.7314/apjcp.2015.16.12.4833] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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47
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Wine Y, Horton AP, Ippolito GC, Georgiou G. Serology in the 21st century: the molecular-level analysis of the serum antibody repertoire. Curr Opin Immunol 2015; 35:89-97. [PMID: 26172290 DOI: 10.1016/j.coi.2015.06.009] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 06/22/2015] [Accepted: 06/22/2015] [Indexed: 12/11/2022]
Abstract
The ensemble of antibodies found in serum and secretions represents the key adaptive component of B-cell mediated humoral immunity. The antibody repertoire is shaped by the historical record of exposure to exogenous factors such as pathogens and vaccines, as well as by endogenous host-intrinsic factors such as genetics, self-antigens, and age. Thanks to very recent technology advancements it is now becoming possible to identify and quantify the individual antibodies comprising the serological repertoire. In parallel, the advent of high throughput methods for antigen and immunosignature discovery opens up unprecedented opportunities to transform our understanding of numerous key questions in adaptive humoral immunity, including the nature and dynamics of serological memory, the role of polyspecific antibodies in health and disease and how protective responses to infections or vaccine challenge arise. Additionally, these technologies also hold great promise for therapeutic antibody and biomarker discovery in a variety of settings.
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Affiliation(s)
- Yariv Wine
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Andrew P Horton
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA; Center for Systems and Synthetic Biology, University of Texas at Austin, Austin, TX, USA
| | - Gregory C Ippolito
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - George Georgiou
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA; Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA; Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA; Institute for Cell and Molecular Biology, University of Texas at Austin, Austin, TX, USA.
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48
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O'Donnell B, Maurer A, Papandreou-Suppappola A, Stafford P. Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures. Cancer Inform 2015; 14:219-33. [PMID: 26157331 PMCID: PMC4476374 DOI: 10.4137/cin.s17285] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Revised: 03/02/2015] [Accepted: 03/06/2015] [Indexed: 12/21/2022] Open
Abstract
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.
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Affiliation(s)
- Brian O'Donnell
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
| | - Alexander Maurer
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Phillip Stafford
- Center for Innovations in Medicine, The Biodesign Institute, Arizona State University, Tempe, AZ, USA
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49
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Breen V, Kasabov N, Kamat AM, Jacobson E, Suttie JM, O'Sullivan PJ, Kavalieris L, Darling DG. A holistic comparative analysis of diagnostic tests for urothelial carcinoma: a study of Cxbladder Detect, UroVysion® FISH, NMP22® and cytology based on imputation of multiple datasets. BMC Med Res Methodol 2015; 15:45. [PMID: 25962444 PMCID: PMC4494166 DOI: 10.1186/s12874-015-0036-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 05/07/2015] [Indexed: 01/30/2023] Open
Abstract
Background Comparing the relative utility of diagnostic tests is challenging when available datasets are small, partial or incomplete. The analytical leverage associated with a large sample size can be gained by integrating several small datasets to enable effective and accurate across-dataset comparisons. Accordingly, we propose a methodology for a holistic comparative analysis and ranking of cancer diagnostic tests through dataset integration and imputation of missing values, using urothelial carcinoma (UC) as a case study. Methods Five datasets comprising samples from 939 subjects, including 89 with UC, where up to four diagnostic tests (cytology, NMP22®, UroVysion® Fluorescence In-Situ Hybridization (FISH) and Cxbladder Detect) were integrated into a single dataset containing all measured records and missing values. The tests were firstly ranked using three criteria: sensitivity, specificity and a standard variable (feature) ranking method popularly known as signal-to-noise ratio (SNR) index derived from the mean values for all subjects clinically known to have UC versus healthy subjects. Secondly, step-wise unsupervised and supervised imputation (the latter accounting for the ‘clinical truth’ as determined by cystoscopy) was performed using personalized modelling, k-nearest-neighbour methods, multiple logistic regression and multilayer perceptron neural networks. All imputation models were cross-validated by comparing their post-imputation predictive accuracy for UC with their pre-imputation accuracy. Finally, the post-imputation tests were re-ranked using the same three criteria. Results In both measured and imputed data sets, Cxbladder Detect ranked higher for sensitivity, and urine cytology a higher specificity, when compared with other UC tests. Cxbladder Detect consistently ranked higher than FISH and all other tests when SNR analyses were performed on measured, unsupervised and supervised imputed datasets. Supervised imputation resulted in a smaller cross-validation error. Cxbladder Detect was robust to imputation showing a 2 % difference in its predictive versus clinical accuracy, outperforming FISH, NMP22 and cytology. Conclusion All data analysed, pre- and post-imputation showed that Cxbladder Detect had higher SNR and outperformed all other comparator tests, including FISH. The methodology developed and validated for comparative ranking of the diagnostic tests for detecting UC, may be further applied to other cancer diagnostic datasets across population groups and multiple datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12874-015-0036-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Vivienne Breen
- Auckland University of Technology, Auckland, New Zealand.
| | - Nikola Kasabov
- Auckland University of Technology, Auckland, New Zealand.
| | - Ashish M Kamat
- M. D. Anderson Cancer Center, University of Texas, Houston, TX, USA.
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50
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Carmona SJ, Nielsen M, Schafer-Nielsen C, Mucci J, Altcheh J, Balouz V, Tekiel V, Frasch AC, Campetella O, Buscaglia CA, Agüero F. Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants. Mol Cell Proteomics 2015; 14:1871-84. [PMID: 25922409 PMCID: PMC4587317 DOI: 10.1074/mcp.m114.045906] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Indexed: 01/09/2023] Open
Abstract
Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens.
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Affiliation(s)
- Santiago J Carmona
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Morten Nielsen
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina; §Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Lyngby, Denmark
| | | | - Juan Mucci
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Jaime Altcheh
- ‖Servicio de Parasitología y Chagas, Hospital de Niños Ricardo Gutiérrez, Ciudad de Buenos Aires, Argentina
| | - Virginia Balouz
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Valeria Tekiel
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Alberto C Frasch
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Oscar Campetella
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Carlos A Buscaglia
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina
| | - Fernán Agüero
- From the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
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