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Li H, He F, Lv Z, Yi L, Zhang Z, Li H, Fu S. Tailored wastewater surveillance framework uncovered the epidemics of key pathogens in a Northwestern city of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171833. [PMID: 38522539 DOI: 10.1016/j.scitotenv.2024.171833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/03/2024] [Accepted: 03/18/2024] [Indexed: 03/26/2024]
Abstract
Wastewater surveillance enables rapid pathogen monitoring and community prevalence estimation. However, how to design an integrated and tailored wastewater surveillance framework to monitor major health threats in metropolises remains a major challenge. In this study, we first analyzed the historical clinical data of Xi'an city and designed a wastewater surveillance framework covering five key endemic viruses, namely, SARS-CoV-2, norovirus, influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), and hantavirus. Amplicon sequencing of SARS-CoV-2, norovirus and hantavirus was conducted biweekly to determine the prevalent community genotypes circulating in this region. The results showed that from April 2023 to August 2023, Xi'an experienced two waves of SARS-CoV-2 infection, which peaked in the middle of May-2023 and late August-2023. The sewage concentrations of IAV and RSV peaked in early March and early May 2023, respectively, while the sewage concentrations of norovirus fluctuated throughout the study period and peaked in late August. The dynamics of the sewage concentrations of SARS-CoV-2, norovirus, IAV, RSV, and hantavirus were in line with the trends in the sentinel hospital percent positivity data, indicating the role of wastewater surveillance in enhancing the understanding of epidemic trends. Amplicon sequencing of SARS-CoV-2 revealed a transition in the predominant genotype, which changed from DY.1 and FR.1.4 to the XBB and EG.5 subvariants. Amplicon sequencing also revealed that there was only one predominant hantavirus genotype in the local population, while highly diverse genotypes of norovirus GI and GII were found in the wastewater. In conclusion, this study provided valuable insights into the dynamics of infection trends and predominant genotypes of key pathogens in a city without sufficient clinical surveillance, highlighting the role of a tailored wastewater surveillance framework in addressing public health priorities. More importantly, our study provides the first evidence demonstrating the applicability of wastewater surveillance for hantavirus, which is a major health threat locally.
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Affiliation(s)
- Haifeng Li
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an 710069, China
| | - Fenglan He
- The Collaboration Unit for State Key Laboratory of Infectious Disease Prevention and Control, Jiangxi Provincial Health Commission Key Laboratory of Pathogenic Diagnosis and Genomics of Emerging Infectious Diseases, Nanchang Center for Disease Control and Prevention, Nanchang 330038, China
| | - Ziquan Lv
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Liu Yi
- The Collaboration Unit for State Key Laboratory of Infectious Disease Prevention and Control, Jiangxi Provincial Health Commission Key Laboratory of Pathogenic Diagnosis and Genomics of Emerging Infectious Diseases, Nanchang Center for Disease Control and Prevention, Nanchang 330038, China
| | - Ziqiang Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an 710069, China
| | - Hui Li
- The Collaboration Unit for State Key Laboratory of Infectious Disease Prevention and Control, Jiangxi Provincial Health Commission Key Laboratory of Pathogenic Diagnosis and Genomics of Emerging Infectious Diseases, Nanchang Center for Disease Control and Prevention, Nanchang 330038, China.
| | - Songzhe Fu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an 710069, China; The Collaboration Unit for State Key Laboratory of Infectious Disease Prevention and Control, Jiangxi Provincial Health Commission Key Laboratory of Pathogenic Diagnosis and Genomics of Emerging Infectious Diseases, Nanchang Center for Disease Control and Prevention, Nanchang 330038, China.
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Zhang X, Wu J, Luo Y, Wang Y, Wu Y, Xu X, Zhang Y, Kong R, Chi Y, Sun Y, Chen S, He Q, Zhu F, Zhou Z. CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses. Brief Bioinform 2024; 25:bbae183. [PMID: 38653491 PMCID: PMC11036340 DOI: 10.1093/bib/bbae183] [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: 01/03/2024] [Revised: 02/24/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.
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Affiliation(s)
- Xue Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - JingCheng Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yuanyuan Luo
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yilin Wang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yujie Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaobin Xu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yufang Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ruiying Kong
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Chi
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- ZJU-UoE Institute, Zhejiang University, Haining 314400, China
| | - Yisheng Sun
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310015, China
| | - Shuqing Chen
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qiaojun He
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
| | - Feng Zhu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
| | - Zhan Zhou
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China
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Shu C, Sun Q, Fan G, Peng K, Yu Z, Luo Y, Gao S, Ma J, Deng T, Hu S, Wu L. VarEPS-Influ:an risk evaluation system of occurred and virtual variations of influenza virus genomes. Nucleic Acids Res 2024; 52:D798-D807. [PMID: 37889020 PMCID: PMC10767863 DOI: 10.1093/nar/gkad912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
Influenza viruses undergo frequent genomic mutations, leading to potential cross-species transmission, phenotypic changes, and challenges in diagnostic reagents and vaccines. Accurately evaluating and predicting the risk of such variations remain significant challenges. To address this, we developed the VarEPS-Influ database, an influenza virus variations risk evaluation system (VarEPS-Influ). This database employs a 'multi-dimensional evaluation of mutations' strategy, utilizing various tools to assess the physical and chemical properties, primary, secondary, and tertiary structures, receptor affinity, antibody binding capacity, antigen epitopes, and other aspects of the variation's impact. Additionally, we consider space-time distribution, host species distribution, pedigree analysis, and frequency of mutations to provide a comprehensive risk evaluation of mutations and viruses. The VarEPS-Influ database evaluates both observed variations and virtual variations (variations that have not yet occurred), thereby addressing the time-lag issue in risk predictions. Our current one-stop evaluation system for influenza virus genomic variation integrates 1065290 sequences from 224 927 Influenza A, B and C isolates retrieved from public resources. Researchers can freely access the data at https://nmdc.cn/influvar/.
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Affiliation(s)
- Chang Shu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qinglan Sun
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Kesheng Peng
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Zhengfei Yu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Yingfeng Luo
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shenghan Gao
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Tao Deng
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Songnian Hu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
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Li C, Ma L, Zou D, Zhang R, Bai X, Li L, Wu G, Huang T, Zhao W, Jin E, Bao Y, Song S. RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1066-1079. [PMID: 37898309 PMCID: PMC10928372 DOI: 10.1016/j.gpb.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023]
Abstract
The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.
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Affiliation(s)
- Cuiping Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Lina Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong Zou
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Rongqin Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xue Bai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Lun Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Gangao Wu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianhao Huang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Enhui Jin
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China.
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Tan M, Xia J, Luo H, Meng G, Zhu Z. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research. Comput Struct Biotechnol J 2023; 21:4697-4705. [PMID: 37841328 PMCID: PMC10568291 DOI: 10.1016/j.csbj.2023.09.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.
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Affiliation(s)
- Meng Tan
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Jiaxin Xia
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Haitao Luo
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Geng Meng
- College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhenglin Zhu
- School of Life Sciences, Chongqing University, Chongqing, China
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Yang L, Ding F, Lin Q, Xie J, Fan W, Dai F, Cui P, Liu W. A tool to automatically design multiplex PCR primer pairs for specific targets using diverse templates. Sci Rep 2023; 13:16451. [PMID: 37777580 PMCID: PMC10542359 DOI: 10.1038/s41598-023-43825-0] [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: 07/21/2023] [Accepted: 09/28/2023] [Indexed: 10/02/2023] Open
Abstract
Multiplex PCR is an increasingly popular method for identifying species, investigating environmental diversity, and conducting phylogenetic analysis. The complexity and increasing availability of diverse templates necessitate a highly automated approach to design degenerate primer pairs for specific targets with multiple sequences. Existing tools for degenerate primer design suffer from poor maintenance, semi-automation, low adaptability, and low tolerance for gaps. We developed PMPrimer, a Python-based tool for automated design and evaluation of multiplex PCR primer pairs for specific targets using diverse templates. PMPrimer automatically designs optimal multiplex PCR primer pairs using a statistical-based template filter; performs multiple sequence alignment, conserved region identification, and primer design; and evaluates the primers based on template coverage, taxon specificity, and target specificity. PMPrimer identifies conserved regions using Shannon's entropy method, tolerates gaps using a haplotype-based method, and evaluates multiplex PCR primer pairs based on template coverage and taxon specificity. We tested PMPrimer using datasets with diverse levels of conservation, sizes, and applications, including tuf genes of Staphylococci, hsp65 genes of Mycobacteriaceae, and 16S ribosomal RNA genes of Archaea. PMPrimer showed outstanding performance compared with existing tools and experimental validated primers. PMPrimer is available as a Python package at https://github.com/AGIScuipeng/PMPrimer .
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Affiliation(s)
- Lin Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- State Key Laboratory of Resource Insects, Institute of Sericulture and Systems Biology, Southwest University, Chongqing, 400715, China
| | - Feng Ding
- Shenzhen National Clinical Research Center for Infectious Diseases, No. 29, Bulan Road, Longgang District, Shenzhen, 518112, China
| | - Qiang Lin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Junhua Xie
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- School of Life Sciences, Henan University, Kaifeng, 475004, China
- Shenzhen Research Institute of Henan University, Shenzhen, 518000, China
| | - Wei Fan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Fangyin Dai
- State Key Laboratory of Resource Insects, Institute of Sericulture and Systems Biology, Southwest University, Chongqing, 400715, China.
| | - Peng Cui
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
| | - Wanfei Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
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Song W, Fang Z, Ma F, Li J, Huang Z, Zhang Y, Li J, Chen K. The role of SARS-CoV-2 N protein in diagnosis and vaccination in the context of emerging variants: present status and prospects. Front Microbiol 2023; 14:1217567. [PMID: 37675423 PMCID: PMC10478715 DOI: 10.3389/fmicb.2023.1217567] [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: 05/15/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
Despite many countries rapidly revising their strategies to prevent contagions, the number of people infected with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to surge. The emergent variants that can evade the immune response significantly affect the effectiveness of mainstream vaccines and diagnostic products based on the original spike protein. Therefore, it is essential to focus on the highly conserved nature of the nucleocapsid protein as a potential target in the field of vaccines and diagnostics. In this regard, our review initially discusses the structure, function, and mechanism of action of N protein. Based on this discussion, we summarize the relevant research on the in-depth development and application of diagnostic methods and vaccines based on N protein, such as serology and nucleic acid detection. Such valuable information can aid in designing more efficient diagnostic and vaccine tools that could help end the SARS-CoV-2 pandemic.
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Affiliation(s)
- Wanchen Song
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhongbiao Fang
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
| | - Feike Ma
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
| | - Jiaxuan Li
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
| | - Zhiwei Huang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Yanjun Zhang
- Key Laboratory of Public Health Detection and Etiological Research of Zhejiang Province, Department of Microbiology, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jianhua Li
- Key Laboratory of Public Health Detection and Etiological Research of Zhejiang Province, Department of Microbiology, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Keda Chen
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
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Ren H, Ling Y, Cao R, Wang Z, Li Y, Huang T. Early warning of emerging infectious diseases based on multimodal data. BIOSAFETY AND HEALTH 2023; 5:S2590-0536(23)00074-5. [PMID: 37362865 PMCID: PMC10245235 DOI: 10.1016/j.bsheal.2023.05.006] [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: 02/08/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 06/28/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.
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Affiliation(s)
- Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024 China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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9
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Cheng Y, Ji C, Zhou HY, Zheng H, Wu A. Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking. Viruses 2023; 15:v15051158. [PMID: 37243244 DOI: 10.3390/v15051158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.
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Affiliation(s)
- Yexiao Cheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Chengyang Ji
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Hang-Yu Zhou
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
| | - Aiping Wu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
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10
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Sokhansanj BA, Zhao Z, Rosen GL. Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity. BIOLOGY 2022; 11:1786. [PMID: 36552295 PMCID: PMC9774807 DOI: 10.3390/biology11121786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 11/28/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022]
Abstract
Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.
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Affiliation(s)
- Bahrad A. Sokhansanj
- Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Drexel University, Philadelphia, PA 19104, USA
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11
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Chakraborty C, Bhattacharya M, Sharma AR, Dhama K, Lee SS. The rapid emergence of multiple sublineages of Omicron (B.1.1.529) variant: Dynamic profiling via molecular phylogenetics and mutational landscape studies. J Infect Public Health 2022; 15:1234-1258. [PMID: 36270226 DOI: 10.1016/j.jiph.2022.10.004] [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: 08/29/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 11/28/2022] Open
Abstract
PURPOSE The recent Omicron (B.1.1.529) variant poses a significant threat to global health. This variant has spread worldwide, and several sublineages have rapidly emerged. Study tried to analyze the microevolution of this variant. METHODS We studied the molecular phylogenetics, divergence, geographical distributions, frequencies, risk mutations for antibody affinity, and mutational landscape for Omicron sublineages using in silico analysis and statistical models. The risk mutation of spike for nAb affinity was analyzed and illustrated by statistical plots. Finally, the mutational properties of the spike mutations and their stability were predicted and demonstrated. RESULTS First, we studied the microevolutionary Omicron sublineages using molecular phylogenetics. Simultaneously, we revealed divergence events of the Omicron sublineages and observed the lowest minimum divergence of 51 in clade 21K and the highest maximum divergence of 90 in clade 21L. We have demonstrated cluster analyses, geographical distributions, frequencies of Omicron and its sublineages. Finally, we evaluated the mutational landscape of the Omicron sublineages. In this mutational study, we performed a genome-wide analysis of general mutations, mutations in the non-spike genome, and spike mutations of Omicron sublineages. The risk mutation of S-glycoprotein for nAb affinity has been analyzed for Omicron sublineages. Here, we found that some sublineages have all four significant highly destabilizing mutations. Such sublineages are BA.1 (G446S, E484A, T95I, and D614G), BA.2 (H655Y, Q493R, G493S, and D614G), BA.4 (N501Y, Y505H, N969K, and D614G), and BA.2.75 (Q454H, T547K, N764K, D614G and G446S). Finally, from the mutation stability prediction through ΔΔG, we observed that BA.1 and BA.4 had two destabilizing and two stabilizing mutations. Similarly, BA.2, BA.5, and BA.2.12.1 have one destabilizing and three stabilizing mutations. However, all four mutations in BA.2.75 are stabilizing mutations. CONCLUSIONS Our molecular phylogenetic studies provided a deeper understanding of the microevolution of sublineages and the creation of Omicron. Similarly, this study might help scientists develop pan-coronavirus vaccines that consider their mutational properties.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India.
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging &Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, Uttar Pradesh, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging &Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea.
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12
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Chakraborty C, Bhattacharya M, Sharma AR, Dhama K, Agoramoorthy G. A comprehensive analysis of the mutational landscape of the newly emerging Omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs. GeroScience 2022; 44:2393-2425. [PMID: 35989365 PMCID: PMC9393103 DOI: 10.1007/s11357-022-00631-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 07/20/2022] [Indexed: 01/18/2023] Open
Abstract
The Omicron variant is spreading rapidly throughout several countries. Thus, we comprehensively analyzed Omicron's mutational landscape and compared mutations with VOC/VOI. We analyzed SNVs throughout the genome, and AA variants (NSP and SP) in VOC/VOI, including Omicron. We generated heat maps to illustrate the AA variants with high mutation prevalence (> 75% frequency) of Omicron, which demonstrated eight mutations with > 90% prevalence in ORF1a and 29 mutations with > 75% prevalence in S-glycoprotein. A scatter plot for Omicron and VOC/VOI's cluster evaluation was computed. We performed a risk analysis of the antibody-binding risk among four mutations (L452, F490, P681, D614) and observed three mutations (L452R, F490S, D614G) destabilized antibody interactions. Our comparative study evaluated the properties of 28 emerging mutations of the S-glycoprotein of Omicron, and the ΔΔG values. Our results showed K417N with minimum and Q954H with maximum ΔΔG value. Furthermore, six important RBD mutations (G339D, S371L, N440K, G446S, T478K, Q498R) were chosen for comprehensive analysis for stabilizing/destabilizing properties and molecular flexibility. The G339D, S371L, N440K, and T478K were noted as stable mutations with 0.019 kcal/mol, 0.127 kcal/mol, 0.064 kcal/mol, and 1.009 kcal/mol. While, G446S and Q498R mutations showed destabilizing results. Simultaneously, among six RBD mutations, G339D, G446S, and Q498R mutations increased the molecular flexibility of S-glycoprotein. This study depicts the comparative mutational pattern of Omicron and other VOC/VOI, which will help researchers to design and deploy novel vaccines and therapeutic antibodies to fight against VOC/VOI, including Omicron.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Barasat-Barrackpore Rd, Kolkata, West Bengal, 700126, India.
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, 24252, Gangwon-do, Republic of Korea
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India
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Cheng Y, Ji C, Han N, Li J, Xu L, Chen Z, Yang R, Zhou HY, Wu A. covSampler: A Subsampling Method with Balanced Genetic Diversity for Large-Scale SARS-CoV-2 Genome Data Sets. Virus Evol 2022; 8:veac071. [PMCID: PMC9384632 DOI: 10.1093/ve/veac071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 07/28/2022] [Accepted: 08/04/2022] [Indexed: 11/14/2022] Open
Abstract
Phylogenetic analysis has been widely used to describe, display and infer the evolutionary patterns of viruses. The unprecedented accumulation of SARS-CoV-2 genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to postsubsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users’ requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.
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Affiliation(s)
- Yexiao Cheng
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
- School of Life Science and Technology, China Pharmaceutical University , Nanjing, Jiangsu 211100, China
| | - Chengyang Ji
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Na Han
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Jiaying Li
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Lin Xu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
- School of Life Science and Technology, China Pharmaceutical University , Nanjing, Jiangsu 211100, China
| | - Ziyi Chen
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Rong Yang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Hang-Yu Zhou
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
| | - Aiping Wu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005, China
- Suzhou Institute of Systems Medicine , Suzhou, Jiangsu 215123, China
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14
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Bhattacharya M, Sharma AR, Dhama K, Agoramoorthy G, Chakraborty C. Omicron variant (B.1.1.529) of SARS-CoV-2: understanding mutations in the genome, S-glycoprotein, and antibody-binding regions. GeroScience 2022; 44:619-637. [PMID: 35258772 PMCID: PMC8902853 DOI: 10.1007/s11357-022-00532-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/16/2022] [Indexed: 01/18/2023] Open
Abstract
The Omicron variant has been detected in nearly 150 countries. We analyzed the mutational landscape of Omicron throughout the genome, focusing the S-glycoprotein. We also evaluated mutations in the antibody-binding regions and observed some important mutations overlapping those of previous variants including N501Y, D614G, H655Y, N679K, and P681H. Various new receptor-binding domain mutations were detected, including Q493K, G496S, Q498R, S477N, G466S, N440K, and Y505H. New mutations were found in the NTD (Δ143-145, A67V, T95I, L212I, and Δ211) including one new mutation in fusion peptide (D796Y). There are several mutations in the antibody-binding region including K417N, E484A, Q493K, Q498R, N501Y, and Y505H and several near the antibody-binding region (S477N, T478K, G496S, G446S, and N440K). The impact of mutations in regions important for the affinity between spike proteins and neutralizing antibodies was evaluated. Furthermore, we examined the effect of significant antibody-binding mutations (K417N, T478K, E484A, and N501Y) on antibody affinity, stability to ACE2 interaction, and possibility of amino acid substitution. All the four mutations destabilize the antibody-binding affinity. This study reveals future directions for developing neutralizing antibodies against the Omicron variant.
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Affiliation(s)
- Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, 24252, Gangwon-do, Republic of Korea
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India
| | | | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Barasat-Barrackpore Rd, Kolkata, West Bengal, 700126, India.
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15
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Rigden DJ, Fernández XM. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2022; 50:D1-D10. [PMID: 34986604 PMCID: PMC8728296 DOI: 10.1093/nar/gkab1195] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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