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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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71 Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 45-46页

作者： Raluca Gordan[a][b][*] Ning Shen[c] Iris Dror[d] Tianyin Zhou[d] John Horton[b] Remo Rohs[d] & Martha L. Bulyk[e][f] [a] Departments of Biostatistics and Bioinformatics Computer Science and Molecular Genetics and Microbiology Duke University Durham NC USA [b] Institute for Genome Sciences and Policy Duke University Durham NC 27708 USA Phone: Phone: +1 (919) 667-8673 Fax: Phone: +1 (919) 667-8673 [c] Department of Pharmacology and Cancer Biology Duke University Durham NC 27708 USA [d] Molecular and Computational Biology Program Departments of Biological Sciences Chemistry and Physics and Astronomy University of Southern California 1050 Childs Way Los Angeles CA 90089 USA [e] Division of Genetics Departments of Medicine and Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA 02115 USA [f] Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School Boston MA 02115 USA

Transcriptional regulation of gene expression is enacted mainly through binding of transcription factors (TFs) to specific, short DNA sites in cis-regulatory regions of genes. Most TFs are members of protein families that share a common DNA-binding domain and thus recognize similar DNA-binding sequences. It is not well understood why paralogous TFs often bind different genomic target sitesin vivoto effect different regulatory programs, despite apparently recognizing the same sequence motifs. Here, we designed custom protein-binding microarrays (PBMs) to analyze the DNA-binding specificities of twoSaccharomyces cerevisiaebasic helix-loop-helix (bHLH) proteins, Tye7 and Cbf1, as a model system. Our data reveal that E-box DNA-binding sequences (CAnnTG), when tested in the context of their native genomic flanking sequences, are bound differently by Cbf1 and Tye7. computational models of the PBM data indicate that DNA sequence features located in the genomic sequences outside the E-box contribute to DNA-binding specificityin vitro. Our analyses suggest that these flanking regions affect DNA-binding specificity indirectly by influencing the three-dimensional structure of the E-box binding sites. Finally, we show that these subtle differences in intrinsic sequence preferences of Cbf1 and Tye7in vitrohelp to explain their differential DNA-binding preferencesin vivo. Our results provide further evidence that the local shape of DNA-binding sites may be an important feature in distinguishing the DNA-binding preferences among paralogous TFs and thus may play a widespread role in determining how transcriptional regulatory specificity within TF families is achieved.

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Proceedings of the 16th Annual UT-KBRIN bioinformatics Summit 2016: bioinformatics

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BMC bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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Developing measures for microbial genome assembly quality control

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BMC bioinformatics 2010年第4期11卷 1-1页

作者： Rachel M Adams Jason B Harris Jeremy J Jay Beth G Johnson Miriam L Land Loren J Hauser Genome Science and Technology Graduate School University of Tennessee Knoxville TN 37996 USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN 37996 USA Department of Mathematics University of Tennessee Knoxville TN 37996 USA Computational Biology and Bioinformatics Group Oak Ridge National Laboratory Oak Ridge TN 37831 USA

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Effect of an Artificial Intelligence-Assisted Antibiotic Susceptibility Test on Reducing the Mortality Rate of and Antibiotics Use in Patients with Bacteremia: A Prospective Observational Cohort Study

SSRN

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SSRN 2023年

作者： Wang, Hsin-Yao Chung, Chia-Ru Tseng, Yi-Ju Huang, Yhu-Chering Lin, Ting-Wei Yu, Jia-Ruei Chen, Chao-Jung Huang, Wan-Ting Lee, Tzong-Yi Horng, Jorng-Tzong Lu, Jang-Jih Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan City Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City Taiwan Division of Pediatric Infectious Diseases Department of Pediatrics Chang Gung Memorial Hospital Taoyuan City Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Proteomics Core Laboratory China Medical University Hospital Taichung Taiwan Department of Laboratory Medicine Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan School of Medicine Chang Gung University Taoyuan City Taiwan Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan City Taiwan

Background: In real world setting none of the mass spectrometry-based antibiotic susceptibility test (AST) AI system has been robustly validated and indeed ***: The study extensively validated and deployed an AI-assisted AST prediction system XBugHunter. A total of 90,064 consecutive patients were included in the model’s development. During the deployment, 155 consecutive patients with Staphylococcus aureus bacteremia (SAB) was analyzed, whereas the historical control cohort included 455 ***: The predictive sensitivity and specificity for S. aureus (oxacillin) were 0·95 and 0·97, respectively. The reduction in AST turnaround time was 34·06 h. Death within 28 days was 13·55% for the SAB patients in the XBugHunter intervention group, which was significantly lower than 17·58% in the historical control cohort. A total of 114·77 defined daily doses of inappropriate antibiotics were avoided by deploying ***: This work showed that XBugHunter can prevent inappropriate antibiotic use, and the adjustment in antibiotic treatment can yield a lower mortality ***: This work was supported by Chang Gung Memorial Hospital [CMRPG3M0851], and the Ministry of science and Technology, Taiwan [111-2320-B-182A-002-MY2].Declaration of Interests: The authors have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this *** Approval: This study was approved by the institutional review board of the Chang Gung Medical Foundation (Nos. 201901578B0 and 202101884B1). © 2023, The Authors. All rights reserved.

关键词： Antibiotics

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

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Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

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A web-based, hospital-wide health care-associated bloodstream infection surveillance and classification system: Development and evaluation

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JMIR Medical Informatics 2015年第3期3卷 e31页

作者： Tseng, Yi-Ju Wu, Jung-Hsuan Lin, Hui-Chi Chen, Ming-Yuan Ping, Xiao-Ou Sun, Chun-Chuan Shang, Rung-Ji Sheng, Wang-Huei Chen, Yee-Chun Lai, Feipei Chang, Shan-Chwen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Infection Control National Taiwan University Hospital Taipei Taiwan National Taiwan University Hospital Information Systems Office Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan National Health Research Institutes National Institute of Infectious Diseases and Vaccinology Miaoli Taiwan

Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20;r=.999 P<.001) and by time (n=14;r=.941;P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. © 2020 JMIR Publications Inc. All rights reserved.

关键词： Health care-associated infection Infection control Information systems Surveillance Web-based services

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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SCA-AD, a Novel Method to Uniformly Identify Cells across Single-Cell RNA Sequencing Datasets, Applied to Adult and Pediatric Self-Renewing AML Cells

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Blood 2024年 144卷 2218-2218页

作者： Yoonkyu Lee Wen Wang Timothy K Starr Klara E Noble-Orcutt Chad L Myers Zohar Sachs Masonic Cancer Center University of Minnesota Minneapolis MN Bioinformatics and Computational Biology Program University of Minnesota Minneapolis MN Division of Hematology Oncology and Transplantation Department of Medicine University of Minnesota Minneapolis MN Department of Computer Science and Engineering University of Minnesota Minneapolis MN Department of Obstetrics Gynecology and Women's Health University of Minnesota Minneapolis MN

Introduction Single cell RNA sequencing (scRNAseq) is commonly used to determine cell identity. Several scRNAseq analysis methods are available. Most methods use arbitrary scores to represent the similarity of the query cell to a reference profile and do not assess the statistical significance of those scores. We previously presented a method to identify cells that express a profile of interest (the reference profile) within a scRNAseq dataset. Our method, called Single Cell Correlation Analysis (SCA), identifies cells in a query dataset that are similar to a reference profile. SCA represents a novel advance in the field because it calculates the statistical significance of the correlation coefficient using a permutation-based false discovery rate (FDR) estimation. Here, we advanced SCA to uniformly identify cells across multiple scRNAseq datasets. The new method is called SCA-Across Datasets (SCA-AD). We tested the performance of SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow, benchmarked SCA-AD to other methods, and used SCA-AD to identify and compare self-renewing cells in human AML samples from adult and pediatric patients. Methods and Results SCA-AD uses a universal scoring system to assign cell identity across datasets. Spearman's correlation is used to score the similarity of query cells to a reference profile. SCA-AD integrates a common background dataset into each query dataset which ensures heterogeneity in the data and allows SCA-AD to establish a threshold value for cell identity across the datasets. FDR calculations are used to establish the threshold value. We tested SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow. In both cases, the self-renewing compartment was experimentally-validated using gold-standard, in vivo self-renewal assays. We extracted a reference normal bone marrow self-renewal profile from the in vivo- validated murine scRNAseq dataset (Rodriguez-Fraticelli et al. Nature 2020).

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