1
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Serebryany E, Martin RW, Takahashi GR. The Functional Significance of High Cysteine Content in Eye Lens γ-Crystallins. Biomolecules 2024; 14:594. [PMID: 38786000 PMCID: PMC11118217 DOI: 10.3390/biom14050594] [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: 03/28/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
Cataract disease is strongly associated with progressively accumulating oxidative damage to the extremely long-lived crystallin proteins of the lens. Cysteine oxidation affects crystallin folding, interactions, and light-scattering aggregation especially strongly due to the formation of disulfide bridges. Minimizing crystallin aggregation is crucial for lifelong lens transparency, so one might expect the ubiquitous lens crystallin superfamilies (α and βγ) to contain little cysteine. Yet, the Cys content of γ-crystallins is well above the average for human proteins. We review literature relevant to this longstanding puzzle and take advantage of expanding genomic databases and improved machine learning tools for protein structure prediction to investigate it further. We observe remarkably low Cys conservation in the βγ-crystallin superfamily; however, in γ-crystallin, the spatial positioning of Cys residues is clearly fine-tuned by evolution. We propose that the requirements of long-term lens transparency and high lens optical power impose competing evolutionary pressures on lens βγ-crystallins, leading to distinct adaptations: high Cys content in γ-crystallins but low in βB-crystallins. Aquatic species need more powerful lenses than terrestrial ones, which explains the high methionine content of many fish γ- (and even β-) crystallins. Finally, we discuss synergies between sulfur-containing and aromatic residues in crystallins and suggest future experimental directions.
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Affiliation(s)
- Eugene Serebryany
- Department of Physiology & Biophysics, Stony Brook University, SUNY, Stony Brook, NY 11794, USA
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, SUNY, Stony Brook, NY 11794, USA
| | - Rachel W. Martin
- Department of Chemistry, UCI Irvine, Irvine, CA 92697-2025, USA
- Department of Molecular Biology & Biochemistry, UCI Irvine, Irvine, CA 92697-3900, USA
| | - Gemma R. Takahashi
- Department of Molecular Biology & Biochemistry, UCI Irvine, Irvine, CA 92697-3900, USA
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2
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Ko G, Lee JH, Sim YM, Song W, Yoon BH, Byeon I, Lee BH, Kim SO, Choi J, Jang I, Kim H, Yang JO, Jang K, Kim S, Kim JH, Jeon J, Jung J, Hwang S, Park JH, Kim PG, Kim SY, Lee B. KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae017. [PMID: 38862433 DOI: 10.1093/gpbjnl/qzae017] [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: 12/05/2022] [Revised: 11/20/2023] [Accepted: 01/08/2024] [Indexed: 06/13/2024]
Abstract
During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.
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Affiliation(s)
- Gunhwan Ko
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jae Ho Lee
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Young Mi Sim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Wangho Song
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Byung-Ha Yoon
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Iksu Byeon
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Bang Hyuck Lee
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Sang-Ok Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jinhyuk Choi
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Insoo Jang
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Hyerin Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jin Ok Yang
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Kiwon Jang
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Sora Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jong-Hwan Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jongbum Jeon
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Jaeeun Jung
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Seungwoo Hwang
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Ji-Hwan Park
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Pan-Gyu Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Seon-Young Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
| | - Byungwook Lee
- Korea Bioinformation Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon 34141, Republic of Korea
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3
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Basenko EY, Shanmugasundram A, Böhme U, Starns D, Wilkinson PA, Davison HR, Crouch K, Maslen G, Harb OS, Amos B, McDowell MA, Kissinger JC, Roos DS, Jones A. What is new in FungiDB: a web-based bioinformatics platform for omics-scale data analysis for fungal and oomycete species. Genetics 2024; 227:iyae035. [PMID: 38529759 PMCID: PMC11075537 DOI: 10.1093/genetics/iyae035] [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: 12/18/2023] [Accepted: 02/15/2024] [Indexed: 03/27/2024] Open
Abstract
FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species.
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Affiliation(s)
- Evelina Y Basenko
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - Achchuthan Shanmugasundram
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
- Genomics England Limited, London E14 5AB, UK
| | - Ulrike Böhme
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - David Starns
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - Paul A Wilkinson
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - Helen R Davison
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - Kathryn Crouch
- School of Infection and Immunity, University of Glasgow, Glasgow G12 8QQ, UK
| | | | - Omar S Harb
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | | | | | - David S Roos
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
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4
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Gurwitz D, Shomron N. Artificial intelligence utility for drug development: ChatGPT and beyond. Drug Dev Res 2024; 85:e22121. [PMID: 37815084 DOI: 10.1002/ddr.22121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Affiliation(s)
- David Gurwitz
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv, Israel
| | - Noam Shomron
- Sagol School of Neuroscience, Tel Aviv, Israel
- Department of Cell and Developmental Biology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
- Tel Aviv University Innovation Labs (TILabs), Tel Aviv, Israel
- Djerassi Institute of Oncology, Tel Aviv University, Tel Aviv, Israel
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5
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Yao J, Hua X, Huo W, Xiao L, Wang Y, Tang Q, Valdivia CR, Valdivia HH, Dong W, Xiao L. The Effect of Acidic Residues on the Binding between Opicalcin1 and Ryanodine Receptor from the Structure-Functional Analysis. JOURNAL OF NATURAL PRODUCTS 2024; 87:104-112. [PMID: 38128916 PMCID: PMC10825818 DOI: 10.1021/acs.jnatprod.3c00821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/26/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
Calcin is a group ligand with high affinity and specificity for the ryanodine receptors (RyRs). Little is known about the effect of its acidic residues on the spacial structure as well as the interaction with RyRs. We screened the opicalcin1 acidic mutants and investigated the effect of mutation on activity. The results indicated that all acidic mutants maintained the structural features, but their surface charge distribution underwent significant changes. Molecular docking and dynamics simulations were used to analyze the interaction between opicalcin1 mutants and RyRs, which demonstrated that all opicalcin1 mutants effectively bound to the channel domain of RyR1. This stable binding induced a pronounced asymmetry in the structure of the RyR tetramer, exhibiting a high degree of structural dissimilarity. [3H]Ryanodine binding to RyR1 was enhanced in D2A and D15A, which was similar to opicalcin1, but that effect was suppressed in E12A and E29A and reversed for the DE-4A, thereby inhibiting ryanodine binding. Opicalcin1 and DE-4A also exhibited the ability to form stable docking structures with RyR2. Acidic residues play a crucial role in the structure of calcin and its functional interaction with RyRs that is beneficial for the calcin optimization to develop more active peptide lead compounds for RyR-related diseases.
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Affiliation(s)
- Jinchi Yao
- School
of Life Sciences, Liaoning Normal University, Dalian116081, China
- Department
of Occupational and Environmental Health, Faculty of Naval Medicine, Naval Medical University (Second Military Medical
University), Shanghai 200433, China
| | - Xiaoyu Hua
- Department
of Occupational and Environmental Health, Faculty of Naval Medicine, Naval Medical University (Second Military Medical
University), Shanghai 200433, China
| | - Wenjing Huo
- The
305 Hospital of PLA, Beijing 100017, China
| | - Li Xiao
- Department
of Medicine and Cardiovascular Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin 53188, United States
- Department
of Forensic Toxicological Analysis, West China School of Basic Medical
Sciences and Forensic Medicine, Sichuan
University, Chengdu 610017, China
| | - Yongfang Wang
- Department
of Occupational and Environmental Health, Faculty of Naval Medicine, Naval Medical University (Second Military Medical
University), Shanghai 200433, China
| | - Qinglong Tang
- Central
Medical District of Chinese, PLA General Hospital, Beijing 100120, China
| | - Carmen R. Valdivia
- Department
of Medicine and Cardiovascular Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin 53188, United States
| | - Héctor H. Valdivia
- Department
of Medicine and Cardiovascular Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin 53188, United States
| | - Weibing Dong
- School
of Life Sciences, Liaoning Normal University, Dalian116081, China
| | - Liang Xiao
- Department
of Occupational and Environmental Health, Faculty of Naval Medicine, Naval Medical University (Second Military Medical
University), Shanghai 200433, China
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6
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Deng Y, Lu Y, Li M, Shen J, Qin S, Zhang W, Zhang Q, Shen Z, Li C, Jia T, Chen P, Peng L, Chen Y, Zhang W, Liu H, Zhang L, Rong L, Wang X, Chen D. SCAN: Spatiotemporal Cloud Atlas for Neural cells. Nucleic Acids Res 2024; 52:D998-D1009. [PMID: 37930842 PMCID: PMC10767991 DOI: 10.1093/nar/gkad895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.
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Affiliation(s)
- Yushan Deng
- 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
| | - Yubao Lu
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Mengrou Li
- 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
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- 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
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- 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
| | - Wei Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Qiang Zhang
- 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
| | - Zhaoyang Shen
- Life Sciences and Technology College, China Pharmaceutical University, Nanjing 211198, China
| | - Changxiao Li
- 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
| | - Tengfei Jia
- 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
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Peixin Chen
- 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
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Lingmin Peng
- 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
| | - Yangfeng Chen
- 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
| | - Wensheng Zhang
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Liangming Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Limin Rong
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Dongsheng Chen
- 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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7
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Deng Y, Chen P, Xiao J, Li M, Shen J, Qin S, Jia T, Li C, Chang A, Zhang W, Liu H, Xue R, Zhang N, Wang X, Huang L, Chen D. SCAR: Single-cell and Spatially-resolved Cancer Resources. Nucleic Acids Res 2024; 52:D1407-D1417. [PMID: 37739405 PMCID: PMC10767865 DOI: 10.1093/nar/gkad753] [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: 06/08/2023] [Revised: 08/16/2023] [Accepted: 09/05/2023] [Indexed: 09/24/2023] Open
Abstract
Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.
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Affiliation(s)
- Yushan Deng
- 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
| | - Peixin Chen
- 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
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Jiedan Xiao
- 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
| | - Mengrou Li
- 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
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- 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
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- 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
| | - Tengfei Jia
- 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
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Changxiao Li
- 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
| | - Ashley Chang
- 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
| | - Wensheng Zhang
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Ruidong Xue
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ning Zhang
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Li Huang
- The Future Laboratory, Tsinghua University, Beijing 100084, China
| | - Dongsheng Chen
- 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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8
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Rosonovski S, Levchenko M, Bhatnagar R, Chandrasekaran U, Faulk L, Hassan I, Jeffryes M, Mubashar SI, Nassar M, Jayaprabha Palanisamy M, Parkin M, Poluru J, Rogers F, Saha S, Selim M, Shafique Z, Ide-Smith M, Stephenson D, Tirunagari S, Venkatesan A, Xing L, Harrison M. Europe PMC in 2023. Nucleic Acids Res 2024; 52:D1668-D1676. [PMID: 37994696 PMCID: PMC10767826 DOI: 10.1093/nar/gkad1085] [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: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023] Open
Abstract
Europe PMC (https://europepmc.org/) is an open access database of life science journal articles and preprints, which contains over 42 million abstracts and over 9 million full text articles accessible via the website, APIs and bulk download. This publication outlines new developments to the Europe PMC platform since the last database update in 2020 (1) and focuses on five main areas. (i) Improving discoverability, reproducibility and trust in preprints by indexing new preprint content, enriching preprint metadata and identifying withdrawn and removed preprints. (ii) Enhancing support for text and data mining by expanding the types of annotations provided and developing the Europe PMC Annotations Corpus, which can be used to train machine learning models to increase their accuracy and precision. (iii) Developing the Article Status Monitor tool and email alerts, to notify users about new articles and updates to existing records. (iv) Positioning Europe PMC as an open scholarly infrastructure through increasing the portion of open source core software, improving sustainability and accessibility of the service.
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Affiliation(s)
- Summer Rosonovski
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Maria Levchenko
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Rajat Bhatnagar
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | | | - Lynne Faulk
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Islam Hassan
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Matt Jeffryes
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | | | - Maaly Nassar
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | | | - Michael Parkin
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | | | - Frances Rogers
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Shyamasree Saha
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Mohamed Selim
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Zunaira Shafique
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Michele Ide-Smith
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - David Stephenson
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Santosh Tirunagari
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Aravind Venkatesan
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Lijun Xing
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Melissa Harrison
- Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
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9
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Qian FC, Zhou LW, Zhu YB, Li YY, Yu ZM, Feng CC, Fang QL, Zhao Y, Cai FH, Wang QY, Tang HF, Li CQ. scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species. Nucleic Acids Res 2024; 52:D285-D292. [PMID: 37897340 PMCID: PMC10767920 DOI: 10.1093/nar/gkad924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/14/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.
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Affiliation(s)
- Feng-Cui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Li-Wei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Yan-Bing Zhu
- Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yan-Yu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Zheng-Min Yu
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Chen-Chen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiao-Li Fang
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Fu-Hong Cai
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qiu-Yu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hui-Fang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chun-Quan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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10
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Mohr SE, Kim AR, Hu Y, Perrimon N. Finding information about uncharacterized Drosophila melanogaster genes. Genetics 2023; 225:iyad187. [PMID: 37933691 PMCID: PMC10697813 DOI: 10.1093/genetics/iyad187] [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: 07/31/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023] Open
Abstract
Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.
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Affiliation(s)
- Stephanie E Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Ah-Ram Kim
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02115, USA
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11
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Chao H, Zhang S, Hu Y, Ni Q, Xin S, Zhao L, Ivanisenko VA, Orlov YL, Chen M. Integrating omics databases for enhanced crop breeding. J Integr Bioinform 2023; 20:jib-2023-0012. [PMID: 37486120 PMCID: PMC10777369 DOI: 10.1515/jib-2023-0012] [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: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.
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Affiliation(s)
- Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Shilong Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Yueming Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Qingyang Ni
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Saige Xin
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Liang Zhao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
| | - Yuriy L. Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow117198, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow119991, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
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12
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Zhao K, Farrell K, Mashiku M, Abay D, Tang K, Oberste MS, Burns CC. A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health. Front Public Health 2023; 11:1254976. [PMID: 38035280 PMCID: PMC10683794 DOI: 10.3389/fpubh.2023.1254976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023] Open
Abstract
Background The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health. Methods To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them. Results SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% for geographic coordinates. Conclusion SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors.
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Affiliation(s)
- Kun Zhao
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Katie Farrell
- Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, Centers for Disease Control and Prevention, Catoosa, OK, United States
| | - Melchizedek Mashiku
- Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, Centers for Disease Control and Prevention, Catoosa, OK, United States
| | - Dawit Abay
- Cherokee Nation Businesses, Contracting Agency to the Division of Viral Diseases, Centers for Disease Control and Prevention, Catoosa, OK, United States
| | - Kevin Tang
- Division of Scientific Resources, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - M Steven Oberste
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Cara C Burns
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
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13
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Zhan C, Tang T, Wu E, Zhang Y, He M, Wu R, Bi C, Wang J, Zhang Y, Shen B. From multi-omics approaches to personalized medicine in myocardial infarction. Front Cardiovasc Med 2023; 10:1250340. [PMID: 37965091 PMCID: PMC10642346 DOI: 10.3389/fcvm.2023.1250340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.
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Affiliation(s)
- Chaoying Zhan
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Erman Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yuxin Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Mengqiao He
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Rongrong Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Cheng Bi
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiao Wang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yingbo Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Bairong Shen
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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14
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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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15
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Hartley M, Iudin A, Padwardhan A, Sarkans U, Yoldaş AK, Kleywegt GJ. Providing open imaging data at scale: An EMBL-EBI perspective. Histochem Cell Biol 2023; 160:211-221. [PMID: 37537341 PMCID: PMC10492673 DOI: 10.1007/s00418-023-02216-2] [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] [Accepted: 05/30/2023] [Indexed: 08/05/2023]
Abstract
Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.
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Affiliation(s)
- Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ardan Padwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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16
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Lannelongue L, Aronson HEG, Bateman A, Birney E, Caplan T, Juckes M, McEntyre J, Morris AD, Reilly G, Inouye M. GREENER principles for environmentally sustainable computational science. NATURE COMPUTATIONAL SCIENCE 2023; 3:514-521. [PMID: 38177425 DOI: 10.1038/s43588-023-00461-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 05/09/2023] [Indexed: 01/06/2024]
Abstract
The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.
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Affiliation(s)
- Loïc Lannelongue
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
| | | | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | | | - Martin Juckes
- RAL Space, Science and Technology Facilities Council, Harwell Campus, Didcot, UK
| | - Johanna McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | | | | | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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17
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Iudin A, Hartley M, Kleywegt GJ, Patwardhan A. Public archiving of volume EM data. Methods Cell Biol 2023; 177:389-399. [PMID: 37451775 DOI: 10.1016/bs.mcb.2023.02.002] [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] [Indexed: 03/29/2023]
Abstract
Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future. In this chapter we discuss these benefits, explain how EMBL-EBI's image data services support archival of both vEM and correlative imaging data, and discuss how future developments will unlock more value from these vEM datasets.
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Affiliation(s)
- Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom.
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
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