1
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Yeo YY, Qiu H, Bai Y, Zhu B, Chang Y, Yeung J, Michel HA, Wright K, Shaban M, Sadigh S, Nkosi D, Shanmugam V, Rock P, Tung Yiu SP, Cramer P, Paczkowska J, Stephan P, Liao G, Huang AY, Wang H, Chen H, Frauenfeld L, Mitra B, Gewurz BE, Schürch CM, Zhao B, Nolan GP, Zhang B, Shalek AK, Angelo M, Mahmood F, Ma Q, Burack WR, Shipp MA, Rodig SJ, Jiang S. Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment. bioRxiv 2024:2024.03.05.583586. [PMID: 38496566 PMCID: PMC10942289 DOI: 10.1101/2024.03.05.583586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. bioRxiv 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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3
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Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull J, Rodig SJ, Barouch DH, Nolan G, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. Res Sq 2024:rs.3.rs-3952048. [PMID: 38410424 PMCID: PMC10896409 DOI: 10.21203/rs.3.rs-3952048/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.
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Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Jordan Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Scott J. Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- William Bosworth Castle Professor of Medicine, Harvard Medical School
- Ragon Institute of MGH, MIT, and Harvard
| | - Garry Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
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4
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Weirather JL, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 2024; 15:28. [PMID: 38167832 PMCID: PMC10761896 DOI: 10.1038/s41467-023-44188-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jason L Weirather
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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5
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: Pathologist-level cell type annotation from tissue images through machine learning. bioRxiv 2023:2023.06.25.546474. [PMID: 37425872 PMCID: PMC10327211 DOI: 10.1101/2023.06.25.546474] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, United States
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
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6
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Ithinji DG, Buchholz DW, Ezzatpour S, Monreal IA, Cong Y, Sahler J, Bangar AS, Imbiakha B, Upadhye V, Liang J, Ma A, Bradel-Tretheway B, Kaza B, Yeo YY, Choi EJ, Johnston GP, Huzella L, Kollins E, Dixit S, Yu S, Postnikova E, Ortega V, August A, Holbrook MR, Aguilar HC. Multivalent viral particles elicit safe and efficient immunoprotection against Nipah Hendra and Ebola viruses. NPJ Vaccines 2022; 7:166. [PMID: 36528644 PMCID: PMC9759047 DOI: 10.1038/s41541-022-00588-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Experimental vaccines for the deadly zoonotic Nipah (NiV), Hendra (HeV), and Ebola (EBOV) viruses have focused on targeting individual viruses, although their geographical and bat reservoir host overlaps warrant creation of multivalent vaccines. Here we explored whether replication-incompetent pseudotyped vesicular stomatitis virus (VSV) virions or NiV-based virus-like particles (VLPs) were suitable multivalent vaccine platforms by co-incorporating multiple surface glycoproteins from NiV, HeV, and EBOV onto these virions. We then enhanced the vaccines' thermotolerance using carbohydrates to enhance applicability in global regions that lack cold-chain infrastructure. Excitingly, in a Syrian hamster model of disease, the VSV multivalent vaccine elicited safe, strong, and protective neutralizing antibody responses against challenge with NiV, HeV, or EBOV. Our study provides proof-of-principle evidence that replication-incompetent multivalent viral particle vaccines are sufficient to provide protection against multiple zoonotic deadly viruses with high pandemic potential.
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Affiliation(s)
- Duncan G Ithinji
- School for Global Animal Health, Washington State University, Pullman, WA, USA.,Kenya Agricultural and Livestock Research Organization, Nairobi, Kenya
| | - David W Buchholz
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Shahrzad Ezzatpour
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - I Abrrey Monreal
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Yu Cong
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Julie Sahler
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | | | - Brian Imbiakha
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Viraj Upadhye
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Janie Liang
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Andrew Ma
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | | | - Benjamin Kaza
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Yao Yu Yeo
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Eun Jin Choi
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Gunner P Johnston
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Louis Huzella
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Erin Kollins
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Saurabh Dixit
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Shuiqing Yu
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Elena Postnikova
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Victoria Ortega
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Avery August
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA
| | - Michael R Holbrook
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Ft Detrick, Frederick, MD, 21702, USA
| | - Hector C Aguilar
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA.
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7
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Ruiz-Aravena M, McKee C, Gamble A, Lunn T, Morris A, Snedden CE, Yinda CK, Port JR, Buchholz DW, Yeo YY, Faust C, Jax E, Dee L, Jones DN, Kessler MK, Falvo C, Crowley D, Bharti N, Brook CE, Aguilar HC, Peel AJ, Restif O, Schountz T, Parrish CR, Gurley ES, Lloyd-Smith JO, Hudson PJ, Munster VJ, Plowright RK. Ecology, evolution and spillover of coronaviruses from bats. Nat Rev Microbiol 2022; 20:299-314. [PMID: 34799704 PMCID: PMC8603903 DOI: 10.1038/s41579-021-00652-2] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2021] [Indexed: 12/24/2022]
Abstract
In the past two decades, three coronaviruses with ancestral origins in bats have emerged and caused widespread outbreaks in humans, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first SARS epidemic in 2002-2003, the appreciation of bats as key hosts of zoonotic coronaviruses has advanced rapidly. More than 4,000 coronavirus sequences from 14 bat families have been identified, yet the true diversity of bat coronaviruses is probably much greater. Given that bats are the likely evolutionary source for several human coronaviruses, including strains that cause mild upper respiratory tract disease, their role in historic and future pandemics requires ongoing investigation. We review and integrate information on bat-coronavirus interactions at the molecular, tissue, host and population levels. We identify critical gaps in knowledge of bat coronaviruses, which relate to spillover and pandemic risk, including the pathways to zoonotic spillover, the infection dynamics within bat reservoir hosts, the role of prior adaptation in intermediate hosts for zoonotic transmission and the viral genotypes or traits that predict zoonotic capacity and pandemic potential. Filling these knowledge gaps may help prevent the next pandemic.
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Affiliation(s)
- Manuel Ruiz-Aravena
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
| | - Clifton McKee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Amandine Gamble
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Tamika Lunn
- Centre for Planetary Health and Food Security, Griffith University, Nathan, QLD, Australia
| | - Aaron Morris
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Celine E Snedden
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Claude Kwe Yinda
- National Institute of Allergy and Infectious Diseases, Hamilton, MT, USA
| | - Julia R Port
- National Institute of Allergy and Infectious Diseases, Hamilton, MT, USA
| | - David W Buchholz
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Yao Yu Yeo
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Christina Faust
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Elinor Jax
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Lauren Dee
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Devin N Jones
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
| | - Maureen K Kessler
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
- Department of Ecology, Montana State University, Bozeman, MT, USA
| | - Caylee Falvo
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
| | - Daniel Crowley
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
| | - Nita Bharti
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Cara E Brook
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Hector C Aguilar
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Alison J Peel
- Centre for Planetary Health and Food Security, Griffith University, Nathan, QLD, Australia
| | - Olivier Restif
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Tony Schountz
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - Colin R Parrish
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Emily S Gurley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - James O Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peter J Hudson
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Vincent J Munster
- National Institute of Allergy and Infectious Diseases, Hamilton, MT, USA
| | - Raina K Plowright
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA.
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Yeo YY, Buchholz DW, Gamble A, Jager M, Aguilar HC. Headless Henipaviral Receptor Binding Glycoproteins Reveal Fusion Modulation by the Head/Stalk Interface and Post-receptor Binding Contributions of the Head Domain. J Virol 2021; 95:e0066621. [PMID: 34288734 PMCID: PMC8475510 DOI: 10.1128/jvi.00666-21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/13/2021] [Indexed: 11/20/2022] Open
Abstract
Cedar virus (CedV) is a nonpathogenic member of the Henipavirus (HNV) genus of emerging viruses, which includes the deadly Nipah (NiV) and Hendra (HeV) viruses. CedV forms syncytia, a hallmark of henipaviral and paramyxoviral infections and pathogenicity. However, the intrinsic fusogenic capacity of CedV relative to NiV or HeV remains unquantified. HNV entry is mediated by concerted interactions between the attachment (G) and fusion (F) glycoproteins. Upon receptor binding by the HNV G head domain, a fusion-activating G stalk region is exposed and triggers F to undergo a conformational cascade that leads to viral entry or cell-cell fusion. Here, we demonstrate quantitatively that CedV is inherently significantly less fusogenic than NiV at equivalent G and F cell surface expression levels. We then generated and tested six headless CedV G mutants of distinct C-terminal stalk lengths, surprisingly revealing highly hyperfusogenic cell-cell fusion phenotypes 3- to 4-fold greater than wild-type CedV levels. Additionally, similarly to NiV, a headless HeV G mutant yielded a less pronounced hyperfusogenic phenotype compared to wild-type HeV. Further, coimmunoprecipitation and cell-cell fusion assays revealed heterotypic NiV/CedV functional G/F bidentate interactions, as well as evidence of HNV G head domain involvement beyond receptor binding or G stalk exposure. All evidence points to the G head/stalk junction being key to modulating HNV fusogenicity, supporting the notion that head domains play several distinct and central roles in modulating stalk domain fusion promotion. Further, this study exemplifies how CedV may help elucidate important mechanistic underpinnings of HNV entry and pathogenicity. IMPORTANCE The Henipavirus genus in the Paramyxoviridae family includes the zoonotic Nipah (NiV) and Hendra (HeV) viruses. NiV and HeV infections often cause fatal encephalitis and pneumonia, but no vaccines or therapeutics are currently approved for human use. Upon viral entry, Henipavirus infections yield the formation of multinucleated cells (syncytia). Viral entry and cell-cell fusion are mediated by the attachment (G) and fusion (F) glycoproteins. Cedar virus (CedV), a nonpathogenic henipavirus, may be a useful tool to gain knowledge on henipaviral pathogenicity. Here, using homotypic and heterotypic full-length and headless CedV, NiV, and HeV G/F combinations, we discovered that CedV G/F are significantly less fusogenic than NiV or HeV G/F, and that the G head/stalk junction is key to modulating cell-cell fusion, refining the mechanism of henipaviral membrane fusion events. Our study exemplifies how CedV may be a useful tool to elucidate broader mechanistic understanding for the important henipaviruses.
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Affiliation(s)
- Yao Yu Yeo
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - David W. Buchholz
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Amandine Gamble
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, USA
| | - Mason Jager
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Hector C. Aguilar
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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Gamble A, Yeo YY, Butler AA, Tang H, Snedden CE, Mason CT, Buchholz DW, Bingham J, Aguilar HC, Lloyd-Smith JO. Drivers and Distribution of Henipavirus-Induced Syncytia: What Do We Know? Viruses 2021; 13:1755. [PMID: 34578336 PMCID: PMC8472861 DOI: 10.3390/v13091755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/21/2021] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
Syncytium formation, i.e., cell-cell fusion resulting in the formation of multinucleated cells, is a hallmark of infection by paramyxoviruses and other pathogenic viruses. This natural mechanism has historically been a diagnostic marker for paramyxovirus infection in vivo and is now widely used for the study of virus-induced membrane fusion in vitro. However, the role of syncytium formation in within-host dissemination and pathogenicity of viruses remains poorly understood. The diversity of henipaviruses and their wide host range and tissue tropism make them particularly appropriate models with which to characterize the drivers of syncytium formation and the implications for virus fitness and pathogenicity. Based on the henipavirus literature, we summarized current knowledge on the mechanisms driving syncytium formation, mostly acquired from in vitro studies, and on the in vivo distribution of syncytia. While these data suggest that syncytium formation widely occurs across henipaviruses, hosts, and tissues, we identified important data gaps that undermined our understanding of the role of syncytium formation in virus pathogenesis. Based on these observations, we propose solutions of varying complexity to fill these data gaps, from better practices in data archiving and publication for in vivo studies, to experimental approaches in vitro.
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Affiliation(s)
- Amandine Gamble
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.A.B.); (H.T.); (C.E.S.); (J.O.L.-S.)
| | - Yao Yu Yeo
- Department of Microbiology & Immunology, Cornell University, Ithaca, NY 14850, USA; (Y.Y.Y.); (D.W.B.); (H.C.A.)
| | - Aubrey A. Butler
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.A.B.); (H.T.); (C.E.S.); (J.O.L.-S.)
| | - Hubert Tang
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.A.B.); (H.T.); (C.E.S.); (J.O.L.-S.)
| | - Celine E. Snedden
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.A.B.); (H.T.); (C.E.S.); (J.O.L.-S.)
| | - Christian T. Mason
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - David W. Buchholz
- Department of Microbiology & Immunology, Cornell University, Ithaca, NY 14850, USA; (Y.Y.Y.); (D.W.B.); (H.C.A.)
| | - John Bingham
- CSIRO Australian Centre for Disease Preparedness, Geelong, VIC 3220, Australia;
| | - Hector C. Aguilar
- Department of Microbiology & Immunology, Cornell University, Ithaca, NY 14850, USA; (Y.Y.Y.); (D.W.B.); (H.C.A.)
| | - James O. Lloyd-Smith
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.A.B.); (H.T.); (C.E.S.); (J.O.L.-S.)
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Abstract
A recently designed single-crystal surface calorimeter has been deployed to measure the energy difference between two solid surface structures. The clean Pt{100} surface is reconstructed to a stable phase in which the surface layer of platinum atoms has a quasi-hexagonal structure. By comparison of the heats of adsorption of CO and of C(2)H(4) on this stable Pt{100}-hex phase with those on a metastable Pt{100}-(1x1) surface, the energy difference between the two clean phases was measured as 20 +/- 3 and 25 +/- 3 kilojoules per mole of surface platinum atoms.
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Hsiung M, Yeo YY, Itiaba K, Crawhall JC. Cysteamine, penicillamine, glutathione, and their derivatives analyzed by automated ion exchange column chromatography. Biochem Med 1978; 19:305-17. [PMID: 678299 DOI: 10.1016/0006-2944(78)90032-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Widmer K, Moake JL, Kent CJ, Yeo YY, Reynolds JP. Clot retraction: evaluation in dilute suspensions of platelet-rich plasma and gel-separated platelets. J Lab Clin Med 1976; 87:49-57. [PMID: 1245783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Suspensions of platelet-rich plasma (PRP) or gel-separated platelets (GSP) can be used to evaluate clot retraction subsequent to platelet aggregation and fibrin formation. PRP (200,000 per cubic millimeter) or GSP (200,000 or 100,000 per cubic millimeter) are diluted 1:10 (PRP) or 1:8 (GSP) in phosphate buffer, pH 7.4, and clotted with a high concentration (2.5 U. per milliliter) of thrombin. Human fibrinogen (25 mg. per cent) is added to GSP prior to dilution. Clot retraction is 91 to 100 per cent completed in 1 hour and is quantified by measurement of residual fluid volume. Test conditions are unfavorable for fibrinolysis. Very low concentrations of fibrin/fibrinogen degradation products D and E are detected in residual fluid, and no erythrocyte fall-out occurs. Furthermore, the extent of retraction in the dilute systems is related only to platelet numbers and platelet function. The dilute PRP and GSP methods allow evaluation of clot retraction in the presence of PGE1, the most potent inhibitor of platelet aggregation induced by conventional concentrations of collagen, ADP, epinephrine, and thrombin (0.1 to 0.5 U. per milliliter). High concentrations of PGE1 (to 6 x 10(-6) M) do not inhibit aggregation of GSP, fibrin formation, or platelet-fibrin interaction induced by 2.5 U. per milliliter of thrombin. In contrast, PGE1 concentrations as low as 1.5 to 3.0 x 10(-8) M inhibit clot retraction in both the dilute PRP and GSP systems. Thus, using dilute PRP or GSP the effects of platelet aggregation inhibitors on clot retraction can be determined independently of effects on platelet aggregation.
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