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Ma WF, Hodonsky CJ, Turner AW, Wong D, Song Y, Mosquera JV, Ligay AV, Slenders L, Gancayco C, Pan H, Barrientos NB, Mai D, Alencar GF, Owsiany K, Owens GK, Reilly MP, Li M, Pasterkamp G, Mokry M, van der Laan SW, Khomtchouk BB, Miller CL. Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets. Atherosclerosis 2022; 340:12-22. [PMID: 34871816 PMCID: PMC8919504 DOI: 10.1016/j.atherosclerosis.2021.11.025] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND AIMS The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets. METHODS Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets. RESULTS We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge. CONCLUSIONS This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.
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Affiliation(s)
- Wei Feng Ma
- Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA , 22908, USA
| | - Yipei Song
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Computer Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA , 22908, USA
| | - Alexandra V Ligay
- Master of Science in Biomedical Informatics (MScBMI) Program, University of Chicago, Chicago, IL, 60637, USA
| | - Lotte Slenders
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands
| | - Christina Gancayco
- Research Computing, University of Virginia, Charlottesville, VA, 22908, USA
| | - Huize Pan
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, 10032, USA
| | - Nelson B Barrientos
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - David Mai
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Gabriel F Alencar
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA
| | - Katherine Owsiany
- Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA
| | - Gary K Owens
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA; Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, 10032, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands
| | - Michal Mokry
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands; Department of Experimental Cardiology, University Medical Center Utrecht, 3584, CX, Utrecht, the Netherlands
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands
| | - Bohdan B Khomtchouk
- Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL , 60637, USA.
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA , 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA.
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Scott TM, Jensen S, Pickett BE. A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases. F1000Res 2021; 10:330. [PMID: 34868553 PMCID: PMC8607308 DOI: 10.12688/f1000research.52412.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/22/2021] [Indexed: 11/03/2023] Open
Abstract
Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to quickly identify potential prophylactic or therapeutic treatments that can reduce the signs, symptoms, and/or spread of disease when dealing with a novel infectious agent. To combat this problem, we constructed a computational pipeline that uniquely combines existing tools to predict drugs and biologics that could be repurposed to combat an emerging pathogen. Methods: Our workflow analyzes RNA-sequencing data to determine differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells, which can then be used to identify US Food and Drug Administration (FDA)-approved drugs that target human proteins within these pathways. We used this pipeline to perform a meta-analysis of RNA-seq data from cells infected with three Betacoronavirus species including severe acute respiratory syndrome coronavirus (SARS-CoV; SARS), Middle East respiratory syndrome coronavirus (MERS-CoV; MERS), and SARS-CoV-2, as well as respiratory syncytial virus and influenza A virus to identify therapeutics that could be used to treat COVID-19. Results: This analysis identified twelve existing drugs, most of which already have FDA-approval, that are predicted to counter the effects of SARS-CoV-2 infection. These results were cross-referenced with interventional clinical trials and other studies in the literature to identify drugs on our list that had previously been identified or used as treatments for COIVD-19 including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib. Conclusions: While the results reported here are specific to Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can be used to quickly identify candidate therapeutics for future emerging infectious diseases.
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Affiliation(s)
- Tiana M. Scott
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
| | - Sam Jensen
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
| | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
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Scott TM, Jensen S, Pickett BE. A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases. F1000Res 2021; 10:330. [PMID: 34868553 PMCID: PMC8607308 DOI: 10.12688/f1000research.52412.2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/18/2021] [Indexed: 01/08/2023] Open
Abstract
Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to quickly identify potential prophylactic or therapeutic treatments that can reduce the signs, symptoms, and/or spread of disease when dealing with a novel infectious agent. To combat this problem, we constructed a computational pipeline that uniquely combines existing tools to predict drugs and biologics that could be repurposed to combat an emerging pathogen. Methods: Our workflow analyzes RNA-sequencing data to determine differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells, which can then be used to identify US Food and Drug Administration (FDA)-approved drugs that target human proteins within these pathways. We used this pipeline to perform a meta-analysis of RNA-seq data from cells infected with three Betacoronavirus species including severe acute respiratory syndrome coronavirus (SARS-CoV; SARS), Middle East respiratory syndrome coronavirus (MERS-CoV; MERS), and SARS-CoV-2, as well as respiratory syncytial virus and influenza A virus to identify therapeutics that could be used to treat COVID-19. Results: This analysis identified twelve existing drugs, most of which already have FDA-approval, that are predicted to counter the effects of SARS-CoV-2 infection. These results were cross-referenced with interventional clinical trials and other studies in the literature to identify drugs on our list that had previously been identified or used as treatments for COIVD-19 including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib. Conclusions: While the results reported here are specific to Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can be used to quickly identify candidate therapeutics for future emerging infectious diseases.
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Affiliation(s)
- Tiana M. Scott
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
| | - Sam Jensen
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
| | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, 84602, USA
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