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Gao C, Wen F, Guan M, Hatuwal B, Li L, Praena B, Tang CY, Zhang J, Luo F, Xie H, Webby R, Tao YJ, Wan XF. MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production. Nat Commun 2024; 15:1128. [PMID: 38321021 PMCID: PMC10847134 DOI: 10.1038/s41467-024-45145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 01/15/2024] [Indexed: 02/08/2024] Open
Abstract
Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.
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Affiliation(s)
- Cheng Gao
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Feng Wen
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA
| | - Minhui Guan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Bijaya Hatuwal
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Lei Li
- Department of Chemistry, Georgia State University, Atlanta, GA, 30303, USA
- Center for Diagnostics & Therapeutics, Georgia State University, Atlanta, GA, 30303, USA
| | - Beatriz Praena
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Cynthia Y Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
| | - Jieze Zhang
- Department of Bioengineering, Rice University, Houston, TX, 77030, USA
| | - Feng Luo
- University School of Computing, Clemson University, Clemson, SC, 29634, USA
| | - Hang Xie
- Laboratory of Respiratory Viral Diseases, Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Richard Webby
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN, 63141, USA
| | - Yizhi Jane Tao
- Department of BioSciences, Rice University, Houston, TX, 77251, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, 65211, USA.
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, 65211, USA.
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, 65211, USA.
- Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
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Li X, Li Y, Shang X, Kong H. A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus. Front Microbiol 2024; 15:1345794. [PMID: 38314434 PMCID: PMC10834737 DOI: 10.3389/fmicb.2024.1345794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Methods Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. Results This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Discussion Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.
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Affiliation(s)
- Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Yanyan Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Huihui Kong
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Harbin, China
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Liu M, Liu J, Song W, Peng Y, Ding X, Deng L, Jiang T. Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses. Virol Sin 2023; 38:541-548. [PMID: 37211247 PMCID: PMC10436056 DOI: 10.1016/j.virs.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/17/2023] [Indexed: 05/23/2023] Open
Abstract
The Influenza A (H1N1) pdm09 virus caused a global pandemic in 2009 and has circulated seasonally ever since. As the continual genetic evolution of hemagglutinin in this virus leads to antigenic drift, rapid identification of antigenic variants and characterization of the antigenic evolution are needed. In this study, we developed PREDAC-H1pdm, a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 strains. Our model performed well in predicting antigenic variants, which was helpful in influenza surveillance. By mapping the antigenic clusters for H1N1pdm, we found that substitutions on the Sa epitope were common for H1N1pdm, whereas for the former seasonal H1N1, substitutions on the Sb epitope were more common in antigenic evolution. Additionally, the localized epidemic pattern of H1N1pdm was more obvious than that of the former seasonal H1N1, which could make vaccine recommendation more sophisticated. Overall, the antigenic relationship prediction model we developed provides a rapid determination method for identifying antigenic variants, and the further analysis of evolutionary and epidemic characteristics can facilitate vaccine recommendations and influenza surveillance for H1N1pdm.
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Affiliation(s)
- Mi Liu
- Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jingze Liu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China; Suzhou Institute of Systems Medicine, Suzhou, 215123, China
| | - Wenjun Song
- Guangzhou Laboratory, Guangzhou, 510005, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China
| | - Xiao Ding
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China; Suzhou Institute of Systems Medicine, Suzhou, 215123, China
| | - Lizong Deng
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China; Suzhou Institute of Systems Medicine, Suzhou, 215123, China
| | - Taijiao Jiang
- Suzhou Institute of Systems Medicine, Suzhou, 215123, China; Guangzhou Laboratory, Guangzhou, 510005, China; State Key Laboratory of Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510120, China.
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Chang W, Nie F, Wang R, Li X. Calibrated multi-task subspace learning via binary group structure constraint. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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Long Z, He J, Shuai Q, Zhang K, Xiang J, Wang H, Xie S, Wang S, Du W, Yao X, Huang J. Influenza vaccination-induced H3 stalk-reactive memory B-cell clone expansion. Vaccine 2023; 41:1132-1141. [PMID: 36621409 DOI: 10.1016/j.vaccine.2022.12.068] [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: 08/09/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023]
Abstract
Current vaccine formulations elicit a recall immune response against viruses by targeting epitopes on the globular head of hemagglutinin (HA), and stalk-reactive antibodies are rarely found. However, stalk-specific memory B-cell expansion after influenza vaccination is poorly understood. In this study, B cells were isolated from individuals immunized with seasonal tetravalent influenza vaccines at days 0 and 28 for H7N9 stimulation in vitro. Plasma and supernatants were collected for the analysis of anti-HA IgG using ELISA and a Luminex assay. Memory B cells were positively enriched, and total RNA was extracted for B cell receptor (BCR) H-CDR3 sequencing. All subjects displayed increased anti-H3 antibody secretion after vaccination, whereas no increase in cH5/3-reactive IgG levels was detected. The number of shared memory B-cell clones among individuals dropped dramatically from 593 to 37. Four out of 5 subjects displayed enhanced frequencies of the VH3-23 and VH3-30 genes, and one exhibited an increase in the frequency of VH1-18, which are associated with the stalk of HA. An increase in H3 stalk-specific antibodies produced by B cells stimulated with H7N9 viruses was detected after vaccination. These results demonstrated that H3 stalk-specific memory B cells can expand and secrete antibodies that bind to the stalk in vitro, although no increase in serum H3 stalk-reactive antibodies was found after vaccination, indicating potential for developing a universal vaccine strategy.
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Affiliation(s)
- Zhaoyi Long
- Department of Blood Transfusion, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jiang He
- Department of Blood Transfusion, Affiliated Hospital of Zunyi Medical University, Zunyi, China; Department of Blood Transfusion, Suining Central Hospital, Suining, China
| | - Qinglu Shuai
- Department of Blood Transfusion, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Ke Zhang
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jim Xiang
- Cancer Research Cluster, Saskatchewan Cancer Agency, Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Huan Wang
- Key Laboratory of Infectious Disease and Biosafety, Provincial Department of Education, Guizhou, Zunyi Medical University, Zunyi, China
| | - Shuang Xie
- Department of Blood Transfusion, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Shengyu Wang
- Key Laboratory of Infectious Disease and Biosafety, Provincial Department of Education, Guizhou, Zunyi Medical University, Zunyi, China
| | - Wensheng Du
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xinsheng Yao
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Junqiong Huang
- Department of Blood Transfusion, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
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A two-phase filtering of discriminative shapelets learning for time series classification. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04043-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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7
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Li L, Lan LYL, Huang L, Ye C, Andrade J, Wilson PC. Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering. Front Genet 2022; 13:954024. [PMID: 35910222 PMCID: PMC9335369 DOI: 10.3389/fgene.2022.954024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Rapid growth of single-cell sequencing techniques enables researchers to investigate almost millions of cells with diverse properties in a single experiment. Meanwhile, it also presents great challenges for selecting representative samples from massive single-cell populations for further experimental characterization, which requires a robust and compact sampling with balancing diverse properties of different priority levels. The conventional sampling methods fail to generate representative and generalizable subsets from a massive single-cell population or more complicated ensembles. Here, we present a toolkit called Cookie which can efficiently select out the most representative samples from a massive single-cell population with diverse properties. This method quantifies the relationships/similarities among samples using their Manhattan distances by vectorizing all given properties and then determines an appropriate sample size by evaluating the coverage of key properties from multiple candidate sizes, following by a k-medoids clustering to group samples into several clusters and selects centers from each cluster as the most representatives. Comparison of Cookie with conventional sampling methods using a single-cell atlas dataset, epidemiology surveillance data, and a simulated dataset shows the high efficacy, efficiency, and flexibly of Cookie. The Cookie toolkit is implemented in R and is freely available at https://wilsonimmunologylab.github.io/Cookie/.
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Affiliation(s)
- Lei Li
- University of Chicago Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, United States
- Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL, United States
| | - Linda Yu-Ling Lan
- University of Chicago Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, United States
- Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL, United States
| | - Lei Huang
- Center for Research Informatics, University of Chicago, Chicago, IL, United States
| | - Congting Ye
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, China
| | - Jorge Andrade
- Center for Research Informatics, University of Chicago, Chicago, IL, United States
- Department of Pediatrics, University of Chicago, Chicago, IL, United States
| | - Patrick C. Wilson
- University of Chicago Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, United States
- Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL, United States
- *Correspondence: Patrick C. Wilson,
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Wang Y, Tang CY, Wan XF. Antigenic characterization of influenza and SARS-CoV-2 viruses. Anal Bioanal Chem 2022; 414:2841-2881. [PMID: 34905077 PMCID: PMC8669429 DOI: 10.1007/s00216-021-03806-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 12/24/2022]
Abstract
Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.
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Affiliation(s)
- Yang Wang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Cynthia Y Tang
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Xiu-Feng Wan
- MU Center for Influenza and Emerging Infectious Diseases (CIEID), University of Missouri, Columbia, MO, USA.
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA.
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9
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Li L, Changrob S, Fu Y, Stovicek O, Guthmiller JJ, McGrath JJC, Dugan HL, Stamper CT, Zheng NY, Huang M, Wilson PC. Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens. Brief Bioinform 2022; 23:6532539. [PMID: 35183062 PMCID: PMC8921739 DOI: 10.1093/bib/bbac028] [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: 11/18/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial mutagenesis and protein engineering have laid the foundation for antigenic characterization and universal vaccine design for influenza viruses. However, many methods used in this process require manual sequence editing and protein expression, limiting their efficiency and utility in high-throughput applications. More streamlined in silico tools allowing researchers to properly analyze and visualize influenza viral protein sequences with accurate nomenclature are necessary to improve antigen design and productivity. To address this need, we developed Librator, a system for analyzing and designing custom protein sequences of influenza virus hemagglutinin (HA) and neuraminidase (NA) glycoproteins. Within Librator's graphical interface, users can easily interrogate viral sequences and phylogenies, visualize antigen structures and conservation, mutate target residues and design custom antigens. Librator also provides optimized fragment design for Gibson Assembly of HA and NA expression constructs based on peptide conservation of all historical HA and NA sequences, ensuring fragments are reusable and compatible across related subtypes, thereby promoting reagent savings. Finally, the program facilitates single-cell immune profiling, epitope mapping of monoclonal antibodies and mosaic protein design. Using Librator-based antigen construction, we demonstrate that antigenicity can be readily transferred between HA molecules of H3, but not H1, lineage viruses. Altogether, Librator is a valuable tool for analyzing influenza virus HA and NA proteins and provides an efficient resource for optimizing recombinant influenza antigen synthesis.
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Affiliation(s)
| | | | | | - Olivia Stovicek
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jenna J Guthmiller
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Joshua J C McGrath
- Gale and Ira Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Haley L Dugan
- Committee on Immunology, University of Chicago, Chicago, IL 60637, USA
| | | | - Nai-Ying Zheng
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA,Gale and Ira Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Min Huang
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Patrick C Wilson
- Corresponding author: Patrick C. Wilson, Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY 10021, USA. E-mail:
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10
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Chen Y, Cai Z, Shi L, Li W. A fuzzy granular sparse learning model for identifying antigenic variants of influenza viruses. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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