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Bu C, Zheng X, Zhao X, Xu T, Bai X, Jia Y, Chen M, Hao L, Xiao J, Zhang Z, Zhao W, Tang B, Bao Y. GenBase: A Nucleotide Sequence Database. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae047. [PMID: 38913867 PMCID: PMC11434157 DOI: 10.1093/gpbjnl/qzae047] [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: 09/27/2023] [Revised: 05/28/2024] [Accepted: 06/10/2024] [Indexed: 06/26/2024]
Abstract
The rapid advancement of sequencing technologies poses challenges in managing the large volume and exponential growth of sequence data efficiently and on time. To address this issue, we present GenBase (https://ngdc.cncb.ac.cn/genbase), an open-access data repository that follows the International Nucleotide Sequence Database Collaboration (INSDC) data standards and structures, for efficient nucleotide sequence archiving, searching, and sharing. As a core resource within the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GenBase offers bilingual submission pipeline and services, as well as local submission assistance in China. GenBase also provides a unique Excel format for metadata description and feature annotation of nucleotide sequences, along with a real-time data validation system to streamline sequence submissions. As of April 23, 2024, GenBase received 68,251 nucleotide sequences and 689,574 annotated protein sequences across 414 species from 2319 submissions. Out of these, 63,614 (93%) nucleotide sequences and 620,640 (90%) annotated protein sequences have been released and are publicly accessible through GenBase's web search system, File Transfer Protocol (FTP), and Application Programming Interface (API). Additionally, in collaboration with INSDC, GenBase has constructed an effective data exchange mechanism with GenBank and started sharing released nucleotide sequences. Furthermore, GenBase integrates all sequences from GenBank with daily updates, demonstrating its commitment to actively contributing to global sequence data management and sharing.
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Affiliation(s)
- Congfan Bu
- 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
| | - Xinchang Zheng
- 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
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xuetong 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
| | - Tianyi Xu
- 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
| | - Xue Bai
- 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
| | - Yaokai Jia
- 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
| | - Meili Chen
- 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
| | - Lili Hao
- 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
| | - Jingfa Xiao
- 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
| | - Zhang Zhang
- 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
| | - Wenming 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
| | - Bixia Tang
- 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
| | - 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
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2
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Zhou C, Wheelock ÅM, Zhang C, Ma J, Li Z, Liang W, Gao J, Xu L. Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework. Popul Health Metr 2024; 22:10. [PMID: 38831424 PMCID: PMC11149258 DOI: 10.1186/s12963-024-00330-4] [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: 10/30/2023] [Accepted: 05/27/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND There are significant geographic inequities in COVID-19 case fatality rates (CFRs), and comprehensive understanding its country-level determinants in a global perspective is necessary. This study aims to quantify the country-specific risk of COVID-19 CFR and propose tailored response strategies, including vaccination strategies, in 156 countries. METHODS Cross-temporal and cross-country variations in COVID-19 CFR was identified using extreme gradient boosting (XGBoost) including 35 factors from seven dimensions in 156 countries from 28 January, 2020 to 31 January, 2022. SHapley Additive exPlanations (SHAP) was used to further clarify the clustering of countries by the key factors driving CFR and the effect of concurrent risk factors for each country. Increases in vaccination rates was simulated to illustrate the reduction of CFR in different classes of countries. FINDINGS Overall COVID-19 CFRs varied across countries from 28 Jan 2020 to 31 Jan 31 2022, ranging from 68 to 6373 per 100,000 population. During the COVID-19 pandemic, the determinants of CFRs first changed from health conditions to universal health coverage, and then to a multifactorial mixed effect dominated by vaccination. In the Omicron period, countries were divided into five classes according to risk determinants. Low vaccination-driven class (70 countries) mainly distributed in sub-Saharan Africa and Latin America, and include the majority of low-income countries (95.7%) with many concurrent risk factors. Aging-driven class (26 countries) mainly distributed in high-income European countries. High disease burden-driven class (32 countries) mainly distributed in Asia and North America. Low GDP-driven class (14 countries) are scattered across continents. Simulating a 5% increase in vaccination rate resulted in CFR reductions of 31.2% and 15.0% for the low vaccination-driven class and the high disease burden-driven class, respectively, with greater CFR reductions for countries with high overall risk (SHAP value > 0.1), but only 3.1% for the ageing-driven class. CONCLUSIONS Evidence from this study suggests that geographic inequities in COVID-19 CFR is jointly determined by key and concurrent risks, and achieving a decreasing COVID-19 CFR requires more than increasing vaccination coverage, but rather targeted intervention strategies based on country-specific risks.
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Affiliation(s)
- Cui Zhou
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Åsa M Wheelock
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Karolinska Institutet, Slona, 171 65, Stockholm, Sweden
| | - Chutian Zhang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
- College of Natural Resources and Environment, Northwest A&F University, Yangling, China
| | - Jian Ma
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China.
- Institute for Healthy China, Tsinghua University, Beijing, China.
| | - Jing Gao
- Vanke School of Public Health, Tsinghua University, Beijing, China.
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Karolinska Institutet, Slona, 171 65, Stockholm, Sweden.
- Department of Respiratory Medicine, University of Helsinki, Helsinki, Finland.
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing, China.
- Institute for Healthy China, Tsinghua University, Beijing, China.
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Cao B, Wang X, Yin W, Gao Z, Xia B. The human microbiota is a beneficial reservoir for SARS-CoV-2 mutations. mBio 2024; 15:e0318723. [PMID: 38530031 PMCID: PMC11237538 DOI: 10.1128/mbio.03187-23] [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: 12/17/2023] [Accepted: 02/14/2024] [Indexed: 03/27/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging. In particular, beneficial mutations in the spike (S) protein, which can either make a person more infectious or enable immunological escape, are providing a significant obstacle to the prevention and treatment of pandemics. However, how the virus acquires a high number of beneficial mutations in a short time remains a mystery. We demonstrate here that variations of concern may be mutated due in part to the influence of the human microbiome. We searched the National Center for Biotechnology Information database for homologous fragments (HFs) after finding a mutation and the six neighboring amino acids in a viral mutation fragment. Among the approximate 8,000 HFs obtained, 61 mutations in S and other outer membrane proteins were found in bacteria, accounting for 62% of all mutation sources, which is 12-fold higher than the natural variable proportion. A significant proportion of these bacterial species-roughly 70%-come from the human microbiota, are mainly found in the lung or gut, and share a composition pattern with COVID-19 patients. Importantly, SARS-CoV-2 RNA-dependent RNA polymerase replicates corresponding bacterial mRNAs harboring mutations, producing chimeric RNAs. SARS-CoV-2 may collectively pick up mutations from the human microbiota that change the original virus's binding sites or antigenic determinants. Our study clarifies the evolving mutational mechanisms of SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging, in particular advantageous mutations in the spike (S) protein, which either increase transmissibility or lead to immune escape and are posing a major challenge to pandemic prevention and treatment. However, how the virus acquires a high number of advantageous mutations in a short time remains a mystery. Here, we provide evidence that the human microbiota is a reservoir of advantageous mutations and aids mutational evolution and host adaptation of SARS-CoV-2. Our findings demonstrate a conceptual breakthrough on the mutational evolution mechanisms of SARS-CoV-2 for human adaptation. SARS-CoV-2 may grab advantageous mutations from the widely existing microorganisms in the host, which is undoubtedly an "efficient" manner. Our study might open a new perspective to understand the evolution of virus mutation, which has enormous implications for comprehending the trajectory of the COVID-19 pandemic.
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Affiliation(s)
- Birong Cao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Guangdong Guangya High School, Guangzhou, China
| | - Xiaoxi Wang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wanchao Yin
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China
| | - Zhaobing Gao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China
| | - Bingqing Xia
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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4
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Wu H, Zhou HY, Zheng H, Wu A. Towards Understanding and Identification of Human Viral Co-Infections. Viruses 2024; 16:673. [PMID: 38793555 PMCID: PMC11126107 DOI: 10.3390/v16050673] [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: 04/05/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Viral co-infections, in which a host is infected with multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between the viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus-virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for the identification of viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections and underscores the need for interdisciplinary collaboration to address this complex and important topic.
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Affiliation(s)
- Hui Wu
- 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
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Hang-Yu Zhou
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 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
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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5
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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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6
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Jabeen M, Shoukat S, Shireen H, Bao Y, Khan A, Abbasi AA. Unraveling the genetic variations underlying virulence disparities among SARS-CoV-2 strains across global regions: insights from Pakistan. Virol J 2024; 21:55. [PMID: 38449001 PMCID: PMC10916261 DOI: 10.1186/s12985-024-02328-8] [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: 09/23/2023] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
Over the course of the COVID-19 pandemic, several SARS-CoV-2 variants have emerged that may exhibit different etiological effects such as enhanced transmissibility and infectivity. However, genetic variations that reduce virulence and deteriorate viral fitness have not yet been thoroughly investigated. The present study sought to evaluate the effects of viral genetic makeup on COVID-19 epidemiology in Pakistan, where the infectivity and mortality rate was comparatively lower than other countries during the first pandemic wave. For this purpose, we focused on the comparative analyses of 7096 amino-acid long polyprotein pp1ab. Comparative sequence analysis of 203 SARS-CoV-2 genomes, sampled from Pakistan during the first wave of the pandemic revealed 179 amino acid substitutions in pp1ab. Within this set, 38 substitutions were identified within the Nsp3 region of the pp1ab polyprotein. Structural and biophysical analysis of proteins revealed that amino acid variations within Nsp3's macrodomains induced conformational changes and modified protein-ligand interactions, consequently diminishing the virulence and fitness of SARS-CoV-2. Additionally, the epistatic effects resulting from evolutionary substitutions in SARS-CoV-2 proteins may have unnoticed implications for reducing disease burden. In light of these findings, further characterization of such deleterious SARS-CoV-2 mutations will not only aid in identifying potential therapeutic targets but will also provide a roadmap for maintaining vigilance against the genetic variability of diverse SARS-CoV-2 strains circulating globally. Furthermore, these insights empower us to more effectively manage and respond to potential viral-based pandemic outbreaks of a similar nature in the future.
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Affiliation(s)
- Momina Jabeen
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad, Pakistan
| | - Shifa Shoukat
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad, Pakistan
| | - Huma Shireen
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad, Pakistan
| | - Yiming Bao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Abbas Khan
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Amir Ali Abbasi
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad, Pakistan.
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7
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Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen X, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo AY, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li CY, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu CJ, Liu D, Liu GH, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao YR, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen WK, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie GY, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Y, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang QY, Yang S, Yang X, Yang X, Yang X, Yang YG, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res 2024; 52:D18-D32. [PMID: 38018256 PMCID: PMC10767964 DOI: 10.1093/nar/gkad1078] [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: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023] Open
Abstract
The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
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8
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Emad R, Naga IS. Comparative genotyping of SARS-CoV-2 among Egyptian patients: near-full length genomic sequences versus selected spike and nucleocapsid regions. Med Microbiol Immunol 2023; 212:437-446. [PMID: 37789185 PMCID: PMC10618331 DOI: 10.1007/s00430-023-00783-8] [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: 07/10/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023]
Abstract
Several tools have been developed for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genotyping based on either whole genome or spike sequencing. We aimed to highlight the molecular epidemiological landscape of SARS-CoV-2 in Egypt since the start of the pandemic, to describe discrepancies between the 3 typing tools: Global Initiative on Sharing Avian Influenza Data (GISAID), Nextclade, and Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN) and to assess the fitness of spike and nucleocapsid regions for lineage assignment compared to the whole genome. A total of 3935 sequences isolated from Egypt (March 2020-2023) were retrieved from the GISAID database. A subset of data (n = 1212) with high coverage whole genome was used for tool discrimination and agreement analyses. Among 1212 sequences, the highest discriminatory power was 0.895 for PANGOLIN, followed by GISAID (0.872) and Nextclade (0.866). There was a statistically significant difference (p = 0.0418) between lineages assigned via spike (30%) and nucleocapsid (46%) compared to their whole genome-assigned lineages. The first 3 pandemic waves were dominated by B.1, followed by C.36 and then C.36.3, while the fourth to sixth waves were dominated by the B.1.617.2, BA, and BA.5.2 lineages, respectively. Current shift in lineage typing to recombinant forms. The 3 typing tools showed comparable discrimination among SARS-CoV-2 lineages. The nucleocapsid region could be used for lineage assignment.
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Affiliation(s)
- Rasha Emad
- Alexandria Main University Hospital, Alexandria, Egypt.
| | - Iman S Naga
- Department of Microbiology, Medical Research Institute, Alexandria University, Alexandria, Egypt
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9
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He S, Gou H, Zhou Y, Wu C, Ren X, Wu X, Guan G, Jin B, Huang J, Jin Z, Zhao T. The SARS-CoV-2 nucleocapsid protein suppresses innate immunity by remodeling stress granules to atypical foci. FASEB J 2023; 37:e23269. [PMID: 37889852 DOI: 10.1096/fj.202201973rr] [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: 11/25/2022] [Revised: 08/10/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023]
Abstract
Viruses deploy multiple strategies to suppress the host innate immune response to facilitate viral replication and pathogenesis. Typical G3BP1+ stress granules (SGs) are usually formed in host cells after virus infection to restrain viral translation and to stimulate innate immunity. Thus, viruses have evolved various mechanisms to inhibit SGs or to repurpose SG components such as G3BP1. Previous studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection inhibited host immunity during the early stage of COVID-19. However, the precise mechanism is not yet well understood. Here we showed that the SARS-CoV-2 nucleocapsid (SARS2-N) protein suppressed the double-stranded RNA (dsRNA)-induced innate immune response, concomitant with inhibition of SGs and the induction of atypical SARS2-N+ /G3BP1+ foci (N+ foci). The SARS2-N protein-induced formation of N+ foci was dependent on the ability of its ITFG motif to hijack G3BP1, which contributed to suppress the innate immune response. Importantly, SARS2-N protein facilitated viral replication by inducing the formation of N+ foci. Viral mutations within SARS2-N protein that impair the formation of N+ foci are associated with the inability of the SARS2-N protein to suppress the immune response. Taken together, our study has revealed a novel mechanism by which SARS-CoV-2 suppresses the innate immune response via induction of atypical N+ foci. We think that this is a critical strategy for viral pathogenesis and has potential therapeutic implications.
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Affiliation(s)
- Su He
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Hongwei Gou
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
- School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Yulin Zhou
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Chunxiu Wu
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Xinxin Ren
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
- School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Xiajunpeng Wu
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
- School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Guanwen Guan
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Boxing Jin
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Jinhua Huang
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
- School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Zhigang Jin
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Tiejun Zhao
- College of Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
- School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
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10
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Dumm W, Barker M, Howard-Snyder W, DeWitt Iii WS, Matsen Iv FA. Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph. J Math Biol 2023; 87:75. [PMID: 37878119 PMCID: PMC10600060 DOI: 10.1007/s00285-023-02006-3] [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: 01/10/2023] [Revised: 09/12/2023] [Accepted: 09/26/2023] [Indexed: 10/26/2023]
Abstract
In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty quantification.
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Affiliation(s)
- Will Dumm
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mary Barker
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - William Howard-Snyder
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - William S DeWitt Iii
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Frederick A Matsen Iv
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
- Department of Statistics, University of Washington, Seattle, Washington, USA.
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11
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Bao Y, Xue Y. From BIG Data Center to China National Center for Bioinformation. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:900-903. [PMID: 37832784 PMCID: PMC10928365 DOI: 10.1016/j.gpb.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/30/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Affiliation(s)
- Yiming Bao
- 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.
| | - Yongbiao Xue
- 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.
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12
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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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13
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Tai C, Li H, Zhang J. BCEDB: a linear B-cell epitopes database for SARS-CoV-2. Database (Oxford) 2023; 2023:baad065. [PMID: 37776561 PMCID: PMC10541793 DOI: 10.1093/database/baad065] [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: 04/20/2023] [Revised: 08/17/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023]
Abstract
The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection. However, there is still no public repository to host the linear B-cell epitopes for facilitating the development vaccines against SARS-CoV-2. Therefore, we developed BCEDB, a linear B-cell epitopes database specifically designed for hosting, exploring and visualizing linear B-cell epitopes and their features. The database provides a comprehensive repository of computationally predicted linear B-cell epitopes from Spike protein; a systematic annotation of epitopes including sequence, antigenicity score, genomic locations of epitopes, mutations in different virus lineages, mutation sites on the 3D structure of Spike protein and a genome browser to visualize them in an interactive manner. It represents a valuable resource for peptide-based vaccine development. Database URL: http://www.oncoimmunobank.cn/bcedbindex.
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Affiliation(s)
- Chengzheng Tai
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Hongjun Li
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No. 8 Youan Gate Outer Xitou Alley, Beijing 100069, China
| | - Jing Zhang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
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14
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Lim HGM, Fann YC, Lee YCG. COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2. Brief Bioinform 2023; 24:bbad280. [PMID: 37738400 PMCID: PMC10516370 DOI: 10.1093/bib/bbad280] [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: 12/30/2022] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 09/24/2023] Open
Abstract
Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.
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Affiliation(s)
- Hendrick Gao-Min Lim
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031
- Department of Medical Research, Tzu Chi Hospital Indonesia, Pantai Indah Kapuk, Greater Jakarta, Indonesia 14470
| | - Yang C Fann
- IT and Bioinformatics Program, Division of Intramural, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA 20892
| | - Yuan-Chii Gladys Lee
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031
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15
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Li L, Xu B, Tian D, Wang A, Zhu J, Li C, Li N, Zhao W, Shi L, Xue Y, Zhang Z, Bao Y, Zhao W, Song S. McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks. Brief Bioinform 2023; 24:bbad174. [PMID: 37170752 PMCID: PMC10199771 DOI: 10.1093/bib/bbad174] [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: 11/21/2022] [Revised: 03/30/2023] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).
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Affiliation(s)
- Lun Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Bo Xu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Anke Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Junwei Zhu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Cuiping Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Na Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wei Zhao
- 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
| | - Leisheng Shi
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing, China
| | - Yongbiao Xue
- 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
| | - Zhang Zhang
- 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
| | - Wenming 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
| | - 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
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16
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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; 15:1158. [PMID: 37243244 PMCID: PMC10222785 DOI: 10.3390/v15051158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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17
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Guo H, Yang Y, Zhao T, Lu Y, Gao Y, Li T, Xiao H, Chu X, Zheng L, Li W, Cheng H, Huang H, Liu Y, Lou Y, Nguyen HC, Wu C, Chen Y, Yang H, Ji X. Mechanism of a rabbit monoclonal antibody broadly neutralizing SARS-CoV-2 variants. Commun Biol 2023; 6:364. [PMID: 37012333 PMCID: PMC10069731 DOI: 10.1038/s42003-023-04759-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
Due to the continuous evolution of SARS-CoV-2, the Omicron variant has emerged and exhibits severe immune evasion. The high number of mutations at key antigenic sites on the spike protein has made a large number of existing antibodies and vaccines ineffective against this variant. Therefore, it is urgent to develop efficient broad-spectrum neutralizing therapeutic drugs. Here we characterize a rabbit monoclonal antibody (RmAb) 1H1 with broad-spectrum neutralizing potency against Omicron sublineages including BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, BA.3 and BA.4/5. Cryo-electron microscopy (cryo-EM) structure determination of the BA.1 spike-1H1 Fab complexes shows that 1H1 targets a highly conserved region of RBD and avoids most of the circulating Omicron mutations, explaining its broad-spectrum neutralization potency. Our findings indicate 1H1 as a promising RmAb model for designing broad-spectrum neutralizing antibodies and shed light on the development of therapeutic agents as well as effective vaccines against newly emerging variants in the future.
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Affiliation(s)
- Hangtian Guo
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Yixuan Yang
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Tiantian Zhao
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China
| | - Yuchi Lu
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yan Gao
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Tinghan Li
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Hang Xiao
- Yurogen Biosystem LLC, Wuhan, Hubei, 430075, China
| | - Xiaoyu Chu
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Le Zheng
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Wanting Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University, Nanjing, Jiangsu, 210008, China
| | - Hao Cheng
- Yurogen Biosystem LLC, Wuhan, Hubei, 430075, China
| | - Haibin Huang
- Yurogen Biosystem LLC, Wuhan, Hubei, 430075, China
| | - Yang Liu
- Yurogen Biosystem LLC, Wuhan, Hubei, 430075, China
| | - Yang Lou
- Yurogen Biosystem LLC, Wuhan, Hubei, 430075, China
| | - Henry C Nguyen
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Chao Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Yuxin Chen
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu, 210008, China.
| | - Haitao Yang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Xiaoyun Ji
- The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China.
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, 210008, China.
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu, 210008, China.
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Zhu T, Niu G, Zhang Y, Chen M, Li CY, Hao L, Zhang Z. Host-mediated RNA editing in viruses. Biol Direct 2023; 18:12. [PMID: 36978112 PMCID: PMC10043548 DOI: 10.1186/s13062-023-00366-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Viruses rely on hosts for life and reproduction, cause a variety of symptoms from common cold to AIDS to COVID-19 and provoke public health threats claiming millions of lives around the globe. RNA editing, as a crucial co-/post-transcriptional modification inducing nucleotide alterations on both endogenous and exogenous RNA sequences, exerts significant influences on virus replication, protein synthesis, infectivity and toxicity. Hitherto, a number of host-mediated RNA editing sites have been identified in diverse viruses, yet lacking a full picture of RNA editing-associated mechanisms and effects in different classes of viruses. Here we synthesize the current knowledge of host-mediated RNA editing in a variety of viruses by considering two enzyme families, viz., ADARs and APOBECs, thereby presenting a landscape of diverse editing mechanisms and effects between viruses and hosts. In the ongoing pandemic, our study promises to provide potentially valuable insights for better understanding host-mediated RNA editing on ever-reported and newly-emerging viruses.
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Affiliation(s)
- Tongtong Zhu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guangyi Niu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuansheng Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ming Chen
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chuan-Yun Li
- Laboratory of Bioinformatics and Genomic Medicine, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking University, Beijing, 100871, China
| | - Lili Hao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
| | - Zhang Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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Shapira G, Patalon T, Gazit S, Shomron N. Immunosuppression as a Hub for SARS-CoV-2 Mutational Drift. Viruses 2023; 15:v15040855. [PMID: 37112835 PMCID: PMC10145566 DOI: 10.3390/v15040855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/16/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
The clinical course of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is largely determined by host factors, with a wide range of outcomes. Despite an extensive vaccination campaign and high rates of infection worldwide, the pandemic persists, adapting to overcome antiviral immunity acquired through prior exposure. The source of many such major adaptations is variants of concern (VOCs), novel SARS-CoV-2 variants produced by extraordinary evolutionary leaps whose origins remain mostly unknown. In this study, we tested the influence of factors on the evolutionary course of SARS-CoV-2. Electronic health records of individuals infected with SARS-CoV-2 were paired to viral whole-genome sequences to assess the effects of host clinical parameters and immunity on the intra-host evolution of SARS-CoV-2. We found slight, albeit significant, differences in SARS-CoV-2 intra-host diversity, which depended on host parameters such as vaccination status and smoking. Only one viral genome had significant alterations as a result of host parameters; it was found in an immunocompromised, chronically infected woman in her 70s. We highlight the unusual viral genome obtained from this woman, which had an accelerated mutational rate and an excess of rare mutations, including near-complete truncating of the accessory protein ORF3a. Our findings suggest that the evolutionary capacity of SARS-CoV-2 during acute infection is limited and mostly unaffected by host characteristics. Significant viral evolution is seemingly exclusive to a small subset of COVID-19 cases, which typically prolong infections in immunocompromised patients. In these rare cases, SARS-CoV-2 genomes accumulate many impactful and potentially adaptive mutations; however, the transmissibility of such viruses remains unclear.
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Rodrigues GM, Volpato FCZ, Wink PL, Paiva RM, Barth AL, de-Paris F. SARS-CoV-2 Variants of Concern: Presumptive Identification via Sanger Sequencing Analysis of the Receptor Binding Domain (RBD) Region of the S Gene. Diagnostics (Basel) 2023; 13:diagnostics13071256. [PMID: 37046474 PMCID: PMC10093469 DOI: 10.3390/diagnostics13071256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 03/29/2023] Open
Abstract
Variants of concern (VOCs) of SARS-CoV-2 are viral strains that have mutations associated with increased transmissibility and/or increased virulence, and their main mutations are in the receptor binding domain (RBD) region of the viral spike. This study aimed to characterize SARS-CoV-2 VOCs via Sanger sequencing of the RBD region and compare the results with data obtained via whole genome sequencing (WGS). Clinical samples (oro/nasopharyngeal) with positive RT-qPCR results for SARS-CoV-2 were used in this study. The viral RNA from SARS-CoV-2 was extracted and a PCR fragment of 1006 base pairs was submitted for Sanger sequencing. The results of the Sanger sequencing were compared to the lineage assigned by WGS using next-generation sequencing (NGS) techniques. A total of 37 specimens were sequenced via WGS, and classified as: VOC gamma (8); delta (7); omicron (10), with 3 omicron specimens classified as the BQ.1 subvariant and 12 specimens classified as non-VOC variants. The results of the partial Sanger sequencing presented as 100% in agreement with the WGS. The Sanger protocol made it possible to characterize the main SARS-CoV-2 VOCs currently circulating in Brazil through partial Sanger sequencing of the RBD region of the viral spike. Therefore, the sequencing of the RBD region is a fast and cost-effective laboratory tool for clinical and epidemiological use in the genomic surveillance of SARS-CoV-2.
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Affiliation(s)
- Grazielle Motta Rodrigues
- Residência Multiprofissional em Saúde e em Área Profissional da Saúde do Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Serviço de Diagnóstico Laboratorial, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
| | - Fabiana Caroline Zempulski Volpato
- LABRESIS–Laboratório de Pesquisa em Resistência Bacteriana, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Rio Grande do Sul, Porto Alegre 90160-093, Rio Grande do Sul, Brazil
| | - Priscila Lamb Wink
- LABRESIS–Laboratório de Pesquisa em Resistência Bacteriana, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Rio Grande do Sul, Porto Alegre 90160-093, Rio Grande do Sul, Brazil
| | - Rodrigo Minuto Paiva
- Serviço de Diagnóstico Laboratorial, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
| | - Afonso Luís Barth
- LABRESIS–Laboratório de Pesquisa em Resistência Bacteriana, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Rio Grande do Sul, Porto Alegre 90160-093, Rio Grande do Sul, Brazil
| | - Fernanda de-Paris
- Residência Multiprofissional em Saúde e em Área Profissional da Saúde do Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Serviço de Diagnóstico Laboratorial, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- LABRESIS–Laboratório de Pesquisa em Resistência Bacteriana, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Rio Grande do Sul, Brazil
- Correspondence:
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21
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Jian X, Zhang Y, Zhao J, Zhao Z, Lu M, Xie L. CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes. Comput Struct Biotechnol J 2023; 21:1362-1371. [PMID: 36741787 PMCID: PMC9882952 DOI: 10.1016/j.csbj.2023.01.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/08/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.
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Affiliation(s)
- Xingxing Jian
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China,Corresponding author.
| | - Yu Zhang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Zhuoming Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Lu Xie
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China,Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,Corresponding author at: Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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22
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Towards precision medicine: Omics approach for COVID-19. BIOSAFETY AND HEALTH 2023; 5:78-88. [PMID: 36687209 PMCID: PMC9846903 DOI: 10.1016/j.bsheal.2023.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.
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23
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Screening of Secondary Metabolite Compounds of Gorontalo Traditional Medicinal Plants Using the In Silico Method as a Candidate for SARS-CoV-2 Antiviral. JURNAL KIMIA SAINS DAN APLIKASI 2023. [DOI: 10.14710/jksa.25.10.382-393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
COVID-19 is a disease that caused a prolonged pandemic in many countries caused by the SARS-CoV-2 virus. This study aims to identify the antiviral potential of secondary metabolites in Gorontalo traditional medicinal plants, which are believed to have the ability to inhibit the main protease protein of this virus. The methods used in this research were molecular docking and molecular dynamic. The main protease proteins for SARS-CoV-2 used based on the homology modeling results were 3V3M and 7TE0. The results of the active compounds in the paxlovid drug were also compared to obtain accurate data comparisons. The validation of the docking method on the 3V3M protein using the natural ligand 0EN revealed an RMSD of 0.75 Å. The RMSD value for validating the 7TE0 protein and natural ligand 4WI was 1.65 Å. The best molecular docking results were obtained using physalin F with a binding affinity of −10.3 kcal/mol for the 3V3M protein and physalin J with a binding affinity of −8.9 kcal/mol for the 7TE0 protein. The outcomes of the molecular dynamic method on the best complexes were determined by examining the value of changes in system energy, changes in system temperature, changes in system pressure, RMSD, RMSF, and bond-free energy (ΔG) of the complex. The standard 0EN ligand had a ΔG of −26.53 kcal/mol, while the standard 4WI ligand had a ΔG of −47.16 kcal/mol. The ΔG of the 3V3M-physalin F and 3V3M-physalin J complexes were respectively −28.22 kcal/mol and −26.62 kcal/mol. The ΔG of the 7TE0-Vitexin 2”-O-gallate and 7TE0-physalin J complexes were found to be −28.08 kcal/mol and −26.62 kcal/mol, respectively. The ΔG produced in paxlovid with complexes 3V3M and 7TE0 was −19.38 kcal/mol and −25.44 kcal/mol, respectively. Physalin F, physalin J, and Vitexin 2”-O-gallate have great potential to become SARS-CoV-2 inhibitor agents. However, in terms of structural stability and binding-active residues, these three compounds do not outperform the active substance in paxlovid.
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Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R, Ma Y, Chen M, Li C, Jiang S, Zou D, Gong Z, Zhao X, Wang Y, Zhu J, Zhang Z, Zhao W, Xue Y, Bao Y, Song S, Zhang G, Ling Y, Wang Y, Yang J, Zhuang X, Duan G, Wu G, Chen X, Tian D, Li Z, Sun Y, Du Z, Hao L, Song S, Gao Y, Xiao J, Zhang Z, Bao Y, Tang B, Zhao W, Zhang Y, Zhang H, Zhang Z, Qian Q, Zhang Z, Xiao J, Kang H, Huang T, Chen X, Xia Z, Zhou X, Chao J, Tang B, Wang Z, Zhu J, Du Z, Zhang S, Xiao J, Tian W, Wang W, Zhao W, Wu S, Huang Y, Zhang M, Gong Z, Wang G, Zheng X, Zong W, Zhao W, Xing P, Li R, Liu Z, Bao Y, Lu M, Zhang Y, Yang F, Mai J, Gao Q, Xu X, Kang H, Hou L, Shang Y, Qain Q, Liu J, Jiang M, Zhang H, Bu C, Wang J, Zhang Z, Zhang Z, Zeng J, Li J, Xiao J, Pan S, Kang H, Liu X, Lin S, Yuan N, Zhang Z, Bao Y, Jia P, Zheng X, Zong W, Li Z, Sun Y, Ma Y, Xiong Z, Wu S, Yang F, Zhao W, Bu C, Du Z, Xiao J, Bao Y, Chen X, Chen T, Zhang S, Sun Y, Yu C, Tang B, Zhu J, Dong L, Zhai S, Sun Y, Chen Q, Yang X, Zhang X, Sang Z, Wang Y, Zhao Y, Chen H, Lan L, Wang Y, Zhao W, Wang A, Yu C, Wang Y, Zhang S, Ma Y, Jia Y, Zhao X, Chen M, Li C, Tian D, Tang B, Pan Y, Dong L, Liu X, Song S, Liu X, Tian D, Li C, Tang B, Wang Z, Zhang R, Pan Y, Wang Y, Zou D, Song S, Li C, Zou D, Ma L, Gong Z, Zhu J, Teng X, Li L, Li N, Cui Y, Duan G, Zhang M, Jin T, Kang H, Wang Z, Wu G, Huang T, Zhao W, Jin E, Zhang T, Zhang Z, Zhao W, Xue Y, Bao Y, Song S, Xu T, Zou D, Chen M, Niu G, Pan R, Zhu T, Chu Y, Hao L, Sang J, Pan R, Zou D, Zhang Y, Wang Z, Chen M, Zhang Y, Xu T, Yao Q, Zhu T, Niu G, Hao L, Xiong Z, Yang F, Wang G, Li R, Zong W, Zhang M, Zou D, Zhao W, Wang G, Yang F, Wu S, Zhang X, Guo X, Ma Y, Xiong Z, Li R, Li Z, Liu L, Feng C, Qin Y, Xiao J, Ma L, Jing W, Luo S, Li Z, Ma L, Jiang S, Qian Q, Zhu T, Zong W, Shang Y, Jin T, Zhang Y, Chen M, Wu Z, Chu Y, Zhang R, Luo S, Jing W, Zou D, Bao Y, Xiao J, Zhang Z, Zou D, Liu L, Qin Y, Luo S, Jing W, Li Q, Liu P, Sun Y, Ma L, Jiang S, Fan Z, Zhao W, Xiao J, Bao Y, Zhang Z, Shen WK, Guo AY, Zuo Z, Ren J, Zhang X, Xiao Y, Li X, Zhang X, Xiao Y, Li X, Liu D, Zhang C, Xue Y, Zhao Z, Jiang T, Wu W, Zhao F, Meng X, Chen M, Gou Y, Chen M, Xue Y, Peng D, Xue Y, Luo H, Gao F, Ning W, Xue Y, Liu W, Ling Y, Cao R, Zhang G, Wei Y, Xue Y, Liu CJ, Guo AY, Xie GY, Guo AY, Yuan H, Su T, Zhang YE, Zhou C, Wang P, Zhang G, Zhou Y, Chen M, Guo G, Zhang Q, Guo AY, Fu S, Tan X, Xue Y, Tang D, Xue Y, Zhang W, Xue Y, Luo M, Guo AY, Xie Y, Ren J, Miao YR, Guo AY, Zhou Y, Chen M, Guo G, Huang X, Feng Z, Xue Y, Liu CJ, Guo AY, Liao X, Gao X, Wang J, Xie G, Guo AY, Yuan C, Chen M, Yang D, Tian F, Gao G, Wu W, Chen M, Han C, Xue Y, Cui Q, Xiao C, Li CY, Luo X, Ren J, Zhang X, Xiao Y, Li X, Tang Q, Guo AY, Luo H, Gao F, Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023. Nucleic Acids Res 2023; 51:D18-D28. [PMID: 36420893 PMCID: PMC9825504 DOI: 10.1093/nar/gkac1073] [Citation(s) in RCA: 97] [Impact Index Per Article: 97.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/14/2022] [Accepted: 10/27/2022] [Indexed: 11/27/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
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Zhou C, Wheelock ÅM, Zhang C, Ma J, Dong K, Pan J, Li Z, Liang W, Gao J, Xu L. The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries. Front Public Health 2023; 11:1150095. [PMID: 37143970 PMCID: PMC10151823 DOI: 10.3389/fpubh.2023.1150095] [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: 01/23/2023] [Accepted: 03/28/2023] [Indexed: 05/06/2023] Open
Abstract
Background The global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR. Method Cross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1-30% in each country. Results Overall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03-0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs. Conclusion Booster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.
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Affiliation(s)
- Cui Zhou
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Åsa M. Wheelock
- Respiratory Medicine Unit, Department of Medicine, Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Chutian Zhang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Jian Ma
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Kaixing Dong
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Jingxiang Pan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
- *Correspondence: Wannian Liang, ; Jing Gao, ; Lei Xu,
| | - Jing Gao
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Respiratory Medicine Unit, Department of Medicine, Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine, University of Helsinki, Helsinki, Finland
- *Correspondence: Wannian Liang, ; Jing Gao, ; Lei Xu,
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
- *Correspondence: Wannian Liang, ; Jing Gao, ; Lei Xu,
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Li C, Yue L, Ju Y, Wang J, Chen M, Lu H, Liu S, Liu T, Wang J, Hu X, Tuohetaerbaike B, Wen H, Zhang W, Xu S, Jiang C, Chen F. Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan. Microbiol Spectr 2022; 10:e0127022. [PMID: 36314975 PMCID: PMC9784772 DOI: 10.1128/spectrum.01270-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/07/2022] [Indexed: 12/24/2022] Open
Abstract
The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.
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Affiliation(s)
- Cuidan Li
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
| | - Liya Yue
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
| | - Yingjiao Ju
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mengfan Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hao Lu
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Sitong Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tao Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jing Wang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
| | - Xin Hu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
| | - Bahetibieke Tuohetaerbaike
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
| | - Hao Wen
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
| | - Wenbao Zhang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
| | - Sihong Xu
- Division II of In Vitro Diagnostics for Infectious Diseases, Institute for In Vitro Diagnostics Control, National Institutes for Food and Drug Control, Beijing, China
| | - Chunlai Jiang
- National Engineering Laboratory for AIDS Vaccine, School of Life Science, Jilin University, Changchun, China
| | - Fei Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, China
- Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, China
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He L, Xie Z, Long X, Zhang C, He C, Zhao B, Qi F, Zhang N. Charge transport properties of SARS-CoV-2 Delta variant (B.1.617.2). Process Biochem 2022; 121:656-660. [PMID: 35965635 PMCID: PMC9364919 DOI: 10.1016/j.procbio.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 11/21/2022]
Abstract
The B.1.617.2 (Delta) variant of concern is causing a new wave of infections in many countries. In order to better understand the changes of the SARS-CoV-2 mutation at the genetic level, we selected six mutations in the S region of the Delta variant compared with the native SARS-CoV-2 and get the conductance information of these six short RNA oligonucleotides groups by construct RNA: DNA hybrids. The electronic characteristics are investigated by the combination of density functional theory and non-equilibrium Green's function formulation with decoherence. We found that conductance is very sensitive to small changes in virus sequence. Among the 6 mutations in the Delta S region, D950N shows the largest change in relative conductance, reaching a surprising 4104.75%. These results provide new insights into the Delta variant from the perspective of its electrical properties. This may be a new method to distinguish virus variation and possess great research prospects.
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Affiliation(s)
- Lijun He
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Zhiyang Xie
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Xing Long
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Chaopeng Zhang
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Chengyun He
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Boyang Zhao
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Fei Qi
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Nan Zhang
- The School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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28
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Alemrajabi M, Macias Calix K, Assis R. Epistasis-Driven Evolution of the SARS-CoV-2 Secondary Structure. J Mol Evol 2022; 90:429-437. [PMID: 36178491 PMCID: PMC9523185 DOI: 10.1007/s00239-022-10073-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 09/02/2022] [Indexed: 11/25/2022]
Abstract
Epistasis is an evolutionary phenomenon whereby the fitness effect of a mutation depends on the genetic background in which it arises. A key source of epistasis in an RNA molecule is its secondary structure, which contains functionally important topological motifs held together by hydrogen bonds between Watson–Crick (WC) base pairs. Here we study epistasis in the secondary structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by examining properties of derived alleles arising from substitution mutations at ancestral WC base-paired and unpaired (UP) sites in 15 conserved topological motifs across the genome. We uncover fewer derived alleles and lower derived allele frequencies at WC than at UP sites, supporting the hypothesis that modifications to the secondary structure are often deleterious. At WC sites, we also find lower derived allele frequencies for mutations that abolish base pairing than for those that yield G·U “wobbles,” illustrating that weak base pairing can partially preserve the integrity of the secondary structure. Last, we show that WC sites under the strongest epistatic constraint reside in a three-stemmed pseudoknot motif that plays an essential role in programmed ribosomal frameshifting, whereas those under the weakest epistatic constraint are located in 3’ UTR motifs that regulate viral replication and pathogenicity. Our findings demonstrate the importance of epistasis in the evolution of the SARS-CoV-2 secondary structure, as well as highlight putative structural and functional targets of different forms of natural selection.
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Affiliation(s)
- Mahsa Alemrajabi
- Department of Physics, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Ksenia Macias Calix
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Raquel Assis
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
- Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL, 33431, USA.
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29
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Chakraborty C, Sharma AR, Bhattacharya M, Agoramoorthy G, Lee SS. A Paradigm Shift in the Combination Changes of SARS-CoV-2 Variants and Increased Spread of Delta Variant (B.1.617.2) across the World. Aging Dis 2022; 13:927-942. [PMID: 35656100 PMCID: PMC9116911 DOI: 10.14336/ad.2021.1117] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/17/2021] [Indexed: 12/13/2022] Open
Abstract
Since September 2020, the SARS-CoV-2 variants have gained their dominance worldwide, especially in Kenya, Italy, France, the UK, Turkey, Indonesia, India, Finland, Ireland, Singapore, Denmark, Germany, and Portugal. In this study, we developed a model on the frequency of delta variants across 28 countries (R2= 0.1497), displaying the inheritance of mutations during the generation of the delta variants with 123,526 haplotypes. The country-wise haplotype network showed the distribution of haplotypes in USA (10,174), Denmark (5,637), India (4,089), Germany (2,350), Netherlands (1,899), Sweden (1,791), Italy (1,720), France (1,293), Ireland (1,257), Belgium (1,207), Singapore (1,193), Portugal (1,184) and Spain (1,133). Our analysis shows the highest haplotype in Europe with 84% and the lowest in Australia with 0.00001%. A model of scatter plot was generated with a regression line which provided the estimated rate of mutation, including 24.048 substitutions yearly. Our study concluded that the high global prevalence of the delta variants is due to a high frequency of infectivity, supporting the paradigm shift of the viral variants.
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Affiliation(s)
- Chiranjib Chakraborty
- 1Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Ashish Ranjan Sharma
- 2Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Gangwon-do, Korea
| | | | | | - Sang-Soo Lee
- 2Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Gangwon-do, Korea
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30
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Saravanan KA, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Bhushan B, Dutt T. Role of genomics in combating COVID-19 pandemic. Gene 2022; 823:146387. [PMID: 35248659 PMCID: PMC8894692 DOI: 10.1016/j.gene.2022.146387] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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31
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Novel sarbecovirus bispecific neutralizing antibodies with exceptional breadth and potency against currently circulating SARS-CoV-2 variants and sarbecoviruses. Cell Discov 2022; 8:36. [PMID: 35443747 PMCID: PMC9021188 DOI: 10.1038/s41421-022-00401-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/16/2022] [Indexed: 01/20/2023] Open
Abstract
The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) has aroused concerns over their increased infectivity and transmissibility, as well as decreased sensitivity to SARS-CoV-2-neutralizing antibodies (NAbs) and the current coronavirus disease 2019 (COVID-19) vaccines. Such exigencies call for the development of pan-sarbecovirus vaccines or inhibitors to combat the circulating SARS-CoV-2 NAb-escape variants and other sarbecoviruses. In this study, we isolated a broadly NAb against sarbecoviruses named GW01 from a donor who recovered from COVID-19. Cryo-EM structure and competition assay revealed that GW01 targets a highly conserved epitope in a wide spectrum of different sarbecoviruses. However, we found that GW01, the well-known sarbecovirus NAb S309, and the potent SARS-CoV-2 NAbs CC12.1 and REGN10989 only neutralize about 90% of the 56 tested currently circulating variants of SARS-CoV-2 including Omicron. Therefore, to improve efficacy, we engineered an IgG-like bispecific antibody GW01-REGN10989 (G9) consisting of single-chain antibody fragments (scFv) of GW01 and REGN10989. We found that G9 could neutralize 100% of NAb-escape mutants (23 out of 23), including Omicron variant, with a geometric mean (GM) 50% inhibitory concentration of 8.8 ng/mL. G9 showed prophylactic and therapeutic effects against SARS-CoV-2 infection of both the lung and brain in hACE2-transgenic mice. Site-directed mutagenesis analyses revealed that GW01 and REGN10989 bind to the receptor-binding domain in different epitopes and from different directions. Since G9 targets the epitopes for both GW01 and REGN10989, it was effective against variants with resistance to GW01 or REGN10989 alone and other NAb-escape variants. Therefore, this novel bispecific antibody, G9, is a strong candidate for the treatment and prevention of infection by SARS-CoV-2, NAb-escape variants, and other sarbecoviruses that may cause future emerging or re-emerging coronavirus diseases.
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32
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Machado BAS, Hodel KVS, Fonseca LMDS, Pires VC, Mascarenhas LAB, da Silva Andrade LPC, Moret MA, Badaró R. The Importance of Vaccination in the Context of the COVID-19 Pandemic: A Brief Update Regarding the Use of Vaccines. Vaccines (Basel) 2022; 10:591. [PMID: 35455340 PMCID: PMC9027942 DOI: 10.3390/vaccines10040591] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/06/2022] [Accepted: 04/08/2022] [Indexed: 02/06/2023] Open
Abstract
The COVID-19 pandemic has led the world to undertake the largest vaccination campaign in human history. In record time, unprecedented scientific and governmental efforts have resulted in the acquisition of immunizers utilizing different technologies (nucleotide acids, viral vectors, inactivated and protein-based vaccines). Currently, 33 vaccines have already been approved by regulatory agencies in different countries, and more than 10 billion doses have been administered worldwide. Despite the undeniable impact of vaccination on the control of the pandemic, the recurrent emergence of new variants of interest has raised new challenges. The recent viral mutations precede new outbreaks that rapidly spread at global proportions. In addition, reducing protective efficacy rates have been observed among the main authorized vaccines. Besides these issues, several other crucial issues for the appropriate combatting of the pandemic remain uncertain or under investigation. Particularly noteworthy issues include the use of vaccine-boosting strategies to increase protection; concerns related to the long-term safety of vaccines, child immunization reliability and uncommon adverse events; the persistence of the virus in society; and the transition from a pandemic to an endemic state. In this review, we describe the updated scenario regarding SARS-CoV-2 variants and COVID-19 vaccines. In addition, we outline current discussions covering COVID-19 vaccine safety and efficacy, and the future pandemic perspectives.
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Affiliation(s)
- Bruna Aparecida Souza Machado
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Katharine Valéria Saraiva Hodel
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Larissa Moraes dos Santos Fonseca
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Vinícius Couto Pires
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Luis Alberto Brêda Mascarenhas
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Leone Peter Correia da Silva Andrade
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
| | - Marcelo Albano Moret
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
- UNEB, Universidade do Estado da Bahia, Salvador 41150-000, Brazil
| | - Roberto Badaró
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador 41650-010, Brazil; (K.V.S.H.); (L.M.d.S.F.); (V.C.P.); (L.A.B.M.); (L.P.C.d.S.A.); (M.A.M.); (R.B.)
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Chen Z, Azman AS, Chen X, Zou J, Tian Y, Sun R, Xu X, Wu Y, Lu W, Ge S, Zhao Z, Yang J, Leung DT, Domman DB, Yu H. Global landscape of SARS-CoV-2 genomic surveillance and data sharing. Nat Genet 2022; 54:499-507. [PMID: 35347305 PMCID: PMC9005350 DOI: 10.1038/s41588-022-01033-y] [Citation(s) in RCA: 126] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/11/2022] [Indexed: 12/02/2022]
Abstract
Genomic surveillance has shaped our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. We performed a global landscape analysis on SARS-CoV-2 genomic surveillance and genomic data using a collection of country-specific data. Here, we characterize increasing circulation of the Alpha variant in early 2021, subsequently replaced by the Delta variant around May 2021. SARS-CoV-2 genomic surveillance and sequencing availability varied markedly across countries, with 45 countries performing a high level of routine genomic surveillance and 96 countries with a high availability of SARS-CoV-2 sequencing. We also observed a marked heterogeneity of sequencing percentage, sequencing technologies, turnaround time and completeness of released metadata across regions and income groups. A total of 37% of countries with explicit reporting on variants shared less than half of their sequences of variants of concern (VOCs) in public repositories. Our findings indicate an urgent need to increase timely and full sharing of sequences, the standardization of metadata files and support for countries with limited sequencing and bioinformatics capacity.
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Affiliation(s)
- Zhiyuan Chen
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Xinhua Chen
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Junyi Zou
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Yuyang Tian
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Xiangyanyu Xu
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Yani Wu
- School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wanying Lu
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Shijia Ge
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
| | - Zeyao Zhao
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Juan Yang
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Daniel T Leung
- Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, UT, USA
- Division of Microbiology & Immunology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Daryl B Domman
- Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Hongjie Yu
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China.
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
- National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
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34
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Maurya R, Mishra P, Swaminathan A, Ravi V, Saifi S, Kanakan A, Mehta P, Devi P, Praveen S, Budhiraja S, Tarai B, Sharma S, Khyalappa RJ, Joshi MG, Pandey R. SARS-CoV-2 Mutations and COVID-19 Clinical Outcome: Mutation Global Frequency Dynamics and Structural Modulation Hold the Key. Front Cell Infect Microbiol 2022; 12:868414. [PMID: 35386683 PMCID: PMC8978958 DOI: 10.3389/fcimb.2022.868414] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 02/16/2022] [Indexed: 12/23/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an enormous burden on the healthcare system worldwide as a consequence of its new emerging variants of concern (VOCs) since late 2019. Elucidating viral genome characteristics and its influence on disease severity and clinical outcome has been one of the crucial aspects toward pandemic management. Genomic surveillance holds the key to identify the spectrum of mutations vis-à-vis disease outcome. Here, in our study, we performed a comprehensive analysis of the mutation distribution among the coronavirus disease 2019 (COVID-19) recovered and mortality patients. In addition to the clinical data analysis, the significant mutations within the two groups were analyzed for their global presence in an effort to understand the temporal dynamics of the mutations globally in comparison with our cohort. Interestingly, we found that all the mutations within the recovered patients showed significantly low global presence, indicating the possibility of regional pool of mutations and the absence of preferential selection by the virus during the course of the pandemic. In addition, we found the mutation S194L to have the most significant occurrence in the mortality group, suggesting its role toward a severe disease progression. Also, we discovered three mutations within the mortality patients with a high cohort and global distribution, which later became a part of variants of interest (VOIs)/VOCs, suggesting its significant role in enhancing viral characteristics. To understand the possible mechanism, we performed molecular dynamics (MD) simulations of nucleocapsid mutations, S194L and S194*, from the mortality and recovered patients, respectively, to examine its impacts on protein structure and stability. Importantly, we observed the mutation S194* within the recovered to be comparatively unstable, hence showing a low global frequency, as we observed. Thus, our study provides integrative insights about the clinical features, mutations significantly associated with the two different clinical outcomes, its global presence, and its possible effects at the structural level to understand the role of mutations in driving the COVID-19 pandemic.
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Affiliation(s)
- Ranjeet Maurya
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Pallavi Mishra
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Aparna Swaminathan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Varsha Ravi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sheeba Saifi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shaista Praveen
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sandeep Budhiraja
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Bansidhar Tarai
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | | | | | | | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, Council of Scientific & Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- *Correspondence: Rajesh Pandey,
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Kanakan A, Mehta P, Devi P, Saifi S, Swaminathan A, Maurya R, Chattopadhyay P, Tarai B, Das P, Jha V, Budhiraja S, Pandey R. Clinico-Genomic Analysis Reiterates Mild Symptoms Post-vaccination Breakthrough: Should We Focus on Low-Frequency Mutations? Front Microbiol 2022; 13:763169. [PMID: 35308382 PMCID: PMC8927057 DOI: 10.3389/fmicb.2022.763169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 01/19/2022] [Indexed: 12/20/2022] Open
Abstract
Vaccine development against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been of primary importance to contain the ongoing global pandemic. However, studies have demonstrated that vaccine effectiveness is reduced and the immune response is evaded by variants of concern (VOCs), which include Alpha, Beta, Delta, and, the most recent, Omicron. Subsequently, several vaccine breakthrough (VBT) infections have been reported among healthcare workers (HCWs) due to their prolonged exposure to viruses at healthcare facilities. We conducted a clinico-genomic study of ChAdOx1 (Covishield) VBT cases in HCWs after complete vaccination. Based on the clinical data analysis, most of the cases were categorized as mild, with minimal healthcare support requirements. These patients were divided into two sub-phenotypes based on symptoms: mild and mild plus. Statistical analysis showed a significant correlation of specific clinical parameters with VBT sub-phenotypes. Viral genomic sequence analysis of VBT cases revealed a spectrum of high- and low-frequency mutations. More in-depth analysis revealed the presence of low-frequency mutations within the functionally important regions of SARS-CoV-2 genomes. Emphasizing the potential benefits of surveillance, low-frequency mutations, D144H in the N gene and D138Y in the S gene, were observed to potentially alter the protein secondary structure with possible influence on viral characteristics. Substantiated by the literature, our study highlights the importance of integrative analysis of pathogen genomic and clinical data to offer insights into low-frequency mutations that could be a modulator of VBT infections.
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Affiliation(s)
- Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sheeba Saifi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Aparna Swaminathan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Ranjeet Maurya
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Bansidhar Tarai
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Poonam Das
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Vinita Jha
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Sandeep Budhiraja
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
- *Correspondence: Sandeep Budhiraja,
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- Rajesh Pandey,
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Shi W, Fan G, Shen Z, Hu C, Ma J, Zhou Y, Meng Z, Hu S, Bi Y, Wang L, Yu H, Lin S, Sun X, Zhang X, Liu D, Sun Q, Wu L. gcCov: Linked open data for global coronavirus studies. MLIFE 2022; 1:92-95. [PMID: 37731725 PMCID: PMC9088579 DOI: 10.1002/mlf2.12008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 11/25/2021] [Indexed: 09/22/2023]
Abstract
We present a method of mapping data from publicly available genomics and publication resources to the Resource Description Framework (RDF) and implement a server to publish linked open data (LOD). As one of the largest and most comprehensive semantic databases about coronaviruses, the resulted gcCov database demonstrates the capability of using data in the LOD framework to promote correlations between genotypes and phenotypes. These correlations will be helpful for future research on fundamental viral mechanisms and drug and vaccine designs. These LOD with 62,168,127 semantic triplets and their visualizations are freely accessible through gcCov at https://nmdc.cn/gccov/.
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Affiliation(s)
- Wenyu Shi
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Zhihong Shen
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Chuan Hu
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Songnian Hu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Yuhai Bi
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Liang Wang
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Haiying Yu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Siru Lin
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Xiuqiang Sun
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Xinjiao Zhang
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Dongmei Liu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Qinlan Sun
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
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37
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Zhu J, Gouru A, Wu F, Berzofsky JA, Xie Y, Wang T. BepiTBR: T-B reciprocity enhances B cell epitope prediction. iScience 2022; 25:103764. [PMID: 35128358 PMCID: PMC8803616 DOI: 10.1016/j.isci.2022.103764] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/05/2021] [Accepted: 01/10/2022] [Indexed: 02/07/2023] Open
Abstract
The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.
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Affiliation(s)
- James Zhu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Anagha Gouru
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jay A. Berzofsky
- Vaccine Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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38
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Bhat S, Pandey A, Kanakan A, Maurya R, Vasudevan JS, Devi P, Chattopadhyay P, Sharma S, Khyalappa RJ, Joshi MG, Pandey R. Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification. Front Cell Infect Microbiol 2022; 11:783961. [PMID: 35047415 PMCID: PMC8762993 DOI: 10.3389/fcimb.2021.783961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/30/2021] [Indexed: 12/21/2022] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.
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Affiliation(s)
- Shikha Bhat
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Birla Institute of Technology and Science, Pilani, India
| | - Anuradha Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Birla Institute of Technology and Science, Pilani, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Ranjeet Maurya
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Janani Srinivasa Vasudevan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shimpa Sharma
- D. Y. Patil Medical College Kolhapur, Kasaba Bawada, Kolhapur, India
| | | | - Meghnad G Joshi
- D. Y. Patil Medical College Kolhapur, Kasaba Bawada, Kolhapur, India
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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39
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Zhang Y, Jiang N, Qi W, Li T, Zhang Y, Zhang H, Wu J, Zhu Z, Ai J, Qiu C, Zhang W. Intra-host SARS-CoV-2 Single-Nucleotide Variants Emerged During the Early Stage of COVID-19 Pandemic Forecast Population Fixing Mutations. J Infect 2022; 84:722-746. [PMID: 35041922 PMCID: PMC8760650 DOI: 10.1016/j.jinf.2022.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/11/2022] [Indexed: 12/02/2022]
Affiliation(s)
- Yi Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China
| | - Ning Jiang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Weiqiang Qi
- Shanghai Public Health Clinical Center, Shanghai, China
| | - Tao Li
- Shanghai Public Health Clinical Center, Shanghai, China
| | - Yumeng Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China
| | - Haocheng Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wu
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhaoqin Zhu
- Shanghai Public Health Clinical Center, Shanghai, China.
| | - Jingwen Ai
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chao Qiu
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China; Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Wenhong Zhang
- Department of Infectious Diseases, National Clinical Research Center for Aging and Medicine, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai, China; Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China; Key Laboratory of Medical Molecular Virology (MOE/MOH), Shanghai Medical College, Fudan University, Shanghai, China.
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40
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Ma W, Yang J, Fu H, Su C, Yu C, Wang Q, de Vasconcelos ATR, Bazykin GA, Bao Y, Li M. Genomic perspectives on the emerging SARS-CoV-2 omicron variant. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:60-69. [PMID: 35033679 PMCID: PMC8791331 DOI: 10.1016/j.gpb.2022.01.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 01/06/2023]
Abstract
A new variant of concern for SARS-CoV-2, Omicron (B.1.1.529), was designated by the World Health Organization on November 26, 2021. This study analyzed the viral genome sequencing data of 108 samples collected from patients infected with Omicron. First, we found that the enrichment efficiency of viral nucleic acids was reduced due to mutations in the region where the primers anneal to. Second, the Omicron variant possesses an excessive number of mutations compared to other variants circulating at the same time (median: 62 vs. 45), especially in the Spike gene. Mutations in the Spike gene confer alterations in 32 amino acid residues, more than those observed in other SARS-CoV-2 variants. Moreover, a large number of nonsynonymous mutations occur in the codons for the amino acid residues located on the surface of the Spike protein, which could potentially affect the replication, infectivity, and antigenicity of SARS-CoV-2. Third, there are 53 mutations between the Omicron variant and its closest sequences available in public databases. Many of these mutations were rarely observed in public databases and had a low mutation rate. In addition, the linkage disequilibrium between these mutations was low, with a limited number of mutations concurrently observed in the same genome, suggesting that the Omicron variant would be in a different evolutionary branch from the currently prevalent variants. To improve our ability to detect and track the source of new variants rapidly, it is imperative to further strengthen genomic surveillance and data sharing globally in a timely manner.
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Affiliation(s)
- Wentai Ma
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haoyi Fu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Su
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Caixia Yu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
| | - Qihui Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | | | - Georgii A Bazykin
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia; Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow 127051, Russia
| | - Yiming Bao
- University of Chinese Academy of Sciences, Beijing 100049, China; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
| | - Mingkun Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China.
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41
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Li J, Du P, Yang L, Zhang J, Song C, Chen D, Song Y, Ding N, Hua M, Han K, Song R, Xie W, Chen Z, Wang X, Liu J, Xu Y, Gao G, Wang Q, Pu L, Di L, Li J, Yue J, Han J, Zhao X, Yan Y, Yu F, Wu AR, Zhang F, Gao YQ, Huang Y, Wang J, Zeng H, Chen C. Two-step fitness selection for intra-host variations in SARS-CoV-2. Cell Rep 2022; 38:110205. [PMID: 34982968 PMCID: PMC8674508 DOI: 10.1016/j.celrep.2021.110205] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/10/2021] [Accepted: 12/13/2021] [Indexed: 12/30/2022] Open
Abstract
Spontaneous mutations introduce uncertainty into coronavirus disease 2019 (COVID-19) control procedures and vaccine development. Here, we perform a spatiotemporal analysis on intra-host single-nucleotide variants (iSNVs) in 402 clinical samples from 170 affected individuals, which reveals an increase in genetic diversity over time after symptom onset in individuals. Nonsynonymous mutations are overrepresented in the pool of iSNVs but underrepresented at the single-nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated in the host (positive selection), and these substitutions tend to be unfixed as SNPs in the population (negative selection). Dynamic iSNV changes in subpopulations with different gender, age, illness severity, and viral shedding time displayed a varied fitness selection process among populations. Our study highlights that iSNVs provide a mutational pool shaping the rapid global evolution of the virus.
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Affiliation(s)
- Jiarui Li
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Pengcheng Du
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, Peking University, Beijing 100871, China
| | - Ju Zhang
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Chuan Song
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Danying Chen
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Yangzi Song
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Nan Ding
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Mingxi Hua
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Kai Han
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Rui Song
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Wen Xie
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Zhihai Chen
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Xianbo Wang
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Jingyuan Liu
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Yanli Xu
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Guiju Gao
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Qi Wang
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Lin Pu
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Lin Di
- Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, Peking University, Beijing 100871, China; School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Institute for Cell Analysis, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Jie Li
- School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Jinglin Yue
- Peking University Ditan Teaching Hospital, Beijing 100015, China
| | - Junyan Han
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Xuesen Zhao
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Yonghong Yan
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, P. R. China
| | - Fengting Yu
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China
| | - Angela R Wu
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, P.R. China; Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, P.R. China
| | - Fujie Zhang
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P. R. China.
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, Peking University, Beijing 100871, China; Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.
| | - Yanyi Huang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, Peking University, Beijing 100871, China; School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Institute for Cell Analysis, Shenzhen Bay Laboratory, Shenzhen 518055, China; Chinese Institute for Brain Research, Beijing 102206, China.
| | - Jianbin Wang
- School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China; Chinese Institute for Brain Research, Beijing 102206, China.
| | - Hui Zeng
- Biomedical Innovation Center, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
| | - Chen Chen
- Biomedical Innovation Center, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
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Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R, Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Du Z, Xiao J, Wang Y, Kang H, Xu T, Hao L, Bao Y, Jia P, Jiang S, Qian Q, Zhu T, Shang Y, Zong W, Jin T, Zhang Y, Zou D, Bao Y, Xiao J, Zhang Z, Jiang S, Du Q, Feng C, Ma L, Zhang S, Wang A, Dong L, Wang Y, Zou D, Zhang Z, Liu W, Yan X, Ling Y, Zhao G, Zhou Z, Zhang G, Kang W, Jin T, Zhang T, Ma S, Yan H, Liu Z, Ji Z, Cai Y, Wang S, Song M, Ren J, Zhou Q, Qu J, Zhang W, Bao Y, Liu G, Chen X, Chen T, Zhang S, Sun Y, Yu C, Tang B, Zhu J, Dong L, Zhai S, Sun Y, Chen Q, Yang X, Zhang X, Sang Z, Wang Y, Zhao Y, Chen H, Lan L, Wang Y, Zhao W, Ma Y, Jia Y, Zheng X, Chen M, Zhang Y, Zou D, Zhu T, Xu T, Chen M, Niu G, Zong W, Pan R, Jing W, Sang J, Liu C, Xiong Y, Sun Y, Zhai S, Chen H, Zhao W, Xiao J, Bao Y, Hao L, Zhang M, Wang G, Zou D, Yi L, Zhao W, Zong W, Wu S, Xiong Z, Li R, Zong W, Kang H, Xiong Z, Ma Y, Jin T, Gong Z, Yi L, Zhang M, Wu S, Wang G, Li R, Liu L, Li Z, Liu C, Zou D, Li Q, Feng C, Jing W, Luo S, Ma L, Wang J, Shi Y, Zhou H, Zhang P, Song T, Li Y, He S, Xiong Z, Yang F, Li M, Zhao W, Wang G, Li Z, Ma Y, Zou D, Zong W, Kang H, Jia Y, Zheng X, Li R, Tian D, Liu X, Li C, Teng X, Song S, Liu L, Zhang Y, Niu G, Li Q, Li Z, Zhu T, Feng C, Liu X, Zhang Y, Xu T, Chen R, Teng X, Zhang R, Zou D, Ma L, Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, He Y, Zhang G, Yang Z, Song S, Ma L, Zou D, Tian D, Li C, Zhu J, Li L, Li N, Gong Z, Chen M, Wang A, Ma Y, Teng X, Cui Y, Duan G, Zhang M, Jin T, Wu G, Huang T, Jin E, Zhao W, Kang H, Wang Z, Du Z, Zhang Y, Li R, Zeng J, Hao L, Jiang S, Chen H, Li M, Xiao J, Zhang Z, Zhao W, Xue Y, Bao Y, Ning W, Xue Y, Tang B, Liu Y, Sun Y, Duan G, Cui Y, Zhou Q, Dong L, Jin E, Liu X, Zhang L, Mao B, Zhang S, Zhang Y, Wang G, Zhao W, Wang Z, Zhu Q, Li X, Zhu J, Tian D, Kang H, Li C, Zhang S, Song S, Li M, Zhao W, Liu Y, Wang Z, Luo H, Zhu J, Wu X, Tian D, Li C, Zhao W, Jing H, Zhu J, Tang B, Zou D, Liu L, Pan Y, Liu C, Chen M, Liu X, Zhang Y, Li Z, Feng C, Du Q, Chen R, Zhu T, Ma L, Zou D, Jiang S, Zhang Z, Gong Z, Zhu J, Li C, Jiang S, Ma L, Tang B, Zou D, Chen M, Sun Y, Shi L, Song S, Zhang Z, Li M, Xiao J, Xue Y, Bao Y, Du Z, Zhao W, Li Z, Du Q, Jiang S, Ma L, Zhang Z, Xiong Z, Li M, Zou D, Zong W, Li R, Chen M, Du Z, Zhao W, Bao Y, Ma Y, Zhang X, Lan L, Xue Y, Bao Y, Jiang S, Feng C, Zhao W, Xiao J, Bao Y, Zhang Z, Zuo Z, Ren J, Zhang X, Xiao Y, Li X, Zhang X, Xiao Y, Li X, Liu D, Zhang C, Xue Y, Zhao Z, Jiang T, Wu W, Zhao F, Meng X, Chen M, Peng D, Xue Y, Luo H, Gao F, Ning W, Xue Y, Lin S, Xue Y, Liu C, Guo A, Yuan H, Su T, Zhang YE, Zhou Y, Chen M, Guo G, Fu S, Tan X, Xue Y, Zhang W, Xue Y, Luo M, Guo A, Xie Y, Ren J, Zhou Y, Chen M, Guo G, Wang C, Xue Y, Liao X, Gao X, Wang J, Xie G, Guo A, Yuan C, Chen M, Tian F, Yang D, Gao G, Tang D, Xue Y, Wu W, Chen M, Gou Y, Han C, Xue Y, Cui Q, Li X, Li CY, Luo X, Ren J, Zhang X, Xiao Y, Li X. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 2022; 50:D27-D38. [PMID: 34718731 PMCID: PMC8728233 DOI: 10.1093/nar/gkab951] [Citation(s) in RCA: 375] [Impact Index Per Article: 187.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/29/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
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Chen J, Zhang Y, Shen B. Bioinformatics for the Origin and Evolution of Viruses. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1368:53-71. [DOI: 10.1007/978-981-16-8969-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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44
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Donzelli S, Spinella F, di Domenico EG, Pontone M, Cavallo I, Orlandi G, Iannazzo S, Ricciuto GM, Team ISGVC, Pellini R, Muti P, Strano S, Ciliberto G, Ensoli F, Zapperi S, La Porta CA, Blandino G, Morrone A, Pimpinelli F. Evidence of a SARS-CoV-2 double Spike mutation D614G/S939F potentially affecting immune response of infected subjects. Comput Struct Biotechnol J 2022; 20:733-744. [PMID: 35096288 PMCID: PMC8780065 DOI: 10.1016/j.csbj.2022.01.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 01/18/2022] [Accepted: 01/18/2022] [Indexed: 12/16/2022] Open
Abstract
Objectives Despite extensive efforts to monitor the diffusion of COVID-19, the actual wave of infection is worldwide characterized by the presence of emerging SARS-CoV-2 variants. The present study aims to describe the presence of yet undiscovered SARS-CoV-2 variants in Italy. Methods Next Generation Sequencing was performed on 16 respiratory samples from occasionally employed within the Bangladeshi community present in Ostia and Fiumicino towns. Computational strategy was used to identify all potential epitopes for reference and mutated Spike proteins. A simulation of proteasome activity and the identification of possible cleavage sites along the protein guided to a combined score involving binding affinity, peptide stability and T-cell propensity. Results Retrospective sequencing analysis revealed a double Spike D614G/S939F mutation in COVID-19 positive subjects present in Ostia while D614G mutation was evidenced in those based in Fiumicino. Unlike D614G, S939F mutation affects immune response by the slight but significant modulation of T-cell propensity and the selective enrichment of potential binding epitopes for some HLA alleles. Conclusion Collectively, our findings mirror further the importance of deep sequencing of SARS-CoV-2 genome as a unique approach to monitor the appearance of specific mutations as for those herein reported for Spike protein. This might have implications on both the type of immune response triggered by the viral infection and the severity of the related illness.
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45
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Cheng Y, Peng X. In silico study on the effects of disulfide bonds in ORF8 of SARS-CoV-2. Phys Chem Chem Phys 2022; 24:16876-16883. [DOI: 10.1039/d2cp01724e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The COVID-19 epidemic, caused by virus SARS-CoV-2, has been a pandemic and threatening everyone's health in the past two years. In SARS-CoV-2, ORF8 is one of the most important accessory...
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46
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Prabakaran R, Jemimah S, Rawat P, Sharma D, Gromiha MM. A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19. Sci Rep 2021; 11:24073. [PMID: 34912038 PMCID: PMC8674241 DOI: 10.1038/s41598-021-03436-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.
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Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Sherlyn Jemimah
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Puneet Rawat
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.
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47
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Liu WJ, Liu S. A Tale of Two Cities: From Influenza HxNy to SARS-CoV-z. China CDC Wkly 2021; 3:1052-1056. [PMID: 34934515 PMCID: PMC8668405 DOI: 10.46234/ccdcw2021.256] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/30/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- William J Liu
- Chinese National Influenza Center (CNIC), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shelan Liu
- Department of Infectious Diseases, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Hangzhou, Zhejiang, China
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48
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Ma L, Li H, Lan J, Hao X, Liu H, Wang X, Huang Y. Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic. Comput Biol Chem 2021; 95:107599. [PMID: 34773807 PMCID: PMC8560182 DOI: 10.1016/j.compbiolchem.2021.107599] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/24/2021] [Accepted: 10/29/2021] [Indexed: 02/07/2023]
Abstract
Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.
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Affiliation(s)
- Lifei Ma
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding author at: State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Huiyang Li
- Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin 300192, China
| | - Jinping Lan
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiuqing Hao
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Huiying Liu
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiaoman Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,Corresponding authors
| | - Yong Huang
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding authors
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49
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Gao R, Zu W, Liu Y, Li J, Li Z, Wen Y, Wang H, Yuan J, Cheng L, Zhang S, Zhang Y, Zhang S, Liu W, Lan X, Liu L, Li F, Zhang Z. Quasispecies of SARS-CoV-2 revealed by single nucleotide polymorphisms (SNPs) analysis. Virulence 2021; 12:1209-1226. [PMID: 34030593 PMCID: PMC8158041 DOI: 10.1080/21505594.2021.1911477] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/28/2021] [Accepted: 03/23/2021] [Indexed: 12/12/2022] Open
Abstract
New SARS-CoV-2 mutants have been continuously indentified with enhanced transmission ever since its outbreak in early 2020. As an RNA virus, SARS-CoV-2 has a high mutation rate due to the low fidelity of RNA polymerase. To study the single nucleotide polymorphisms (SNPs) dynamics of SARS-CoV-2, 158 SNPs with high confidence were identified by deep meta-transcriptomic sequencing, and the most common SNP type was C > T. Analyses of intra-host population diversity revealed that intra-host quasispecies' composition varies with time during the early onset of symptoms, which implicates viral evolution during infection. Network analysis of co-occurring SNPs revealed the most abundant non-synonymous SNP 22,638 in the S glycoprotein RBD region and 28,144 in the ORF8 region. Furthermore, SARS-CoV-2 variations differ in an individual's respiratory tissue (nose, throat, BALF, or sputum), suggesting independent compartmentalization of SARS-CoV-2 populations in patients. The positive selection analysis of the SARS-CoV-2 genome uncovered the positive selected amino acid G251V on ORF3a. Alternative allele frequency spectrum (AAFS) of all variants revealed that ORF8 could bear alternate alleles with high frequency. Overall, the results show the quasispecies' profile of SARS-CoV-2 in the respiratory tract in the first two months after the outbreak.
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Affiliation(s)
- Rongsui Gao
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Wenhong Zu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Yang Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Junhua Li
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, China
| | - Zeyao Li
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Yanling Wen
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Haiyan Wang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Jing Yuan
- Department of Infectious Diseases, Shenzhen Third People’s Hospital, National Clinical Research Center for Infectious Disease, Shenzhen, Guangdong Province, China
| | - Lin Cheng
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Shengyuan Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Yu Zhang
- National Supercomputing Center in Shenzhen (Shenzhen Cloud Computing Center), Shenzhen, China
| | - Shuye Zhang
- Shanghai Public Health Clinical Center, Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Weilong Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Xun Lan
- Department of Basic Medical Sciences at School of Medicine, Tsinghua University, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Lei Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Feng Li
- Guangzhou Eighth People’s Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zheng Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
- Shenzhen Research Center for Communicable Disease Diagnosis and Treatment of Chinese Academy of Medical Science, Shenzhen, Guangdong Province, China
- Guangdong Key Laboratory for Anti-infection Drug Quality Evaluation, Shenzhen, Guangdong Province, China
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50
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Li J, Lai S, Gao GF, Shi W. The emergence, genomic diversity and global spread of SARS-CoV-2. Nature 2021; 600:408-418. [PMID: 34880490 DOI: 10.1038/s41586-021-04188-6] [Citation(s) in RCA: 218] [Impact Index Per Article: 72.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.
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Affiliation(s)
- Juan Li
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - George F Gao
- National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.,CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology,, Chinese Academy of Sciences, Beijing, China.,Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing, China
| | - Weifeng Shi
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China. .,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.
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