1
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Li C, Shao X, Zhang S, Wang Y, Jin K, Yang P, Lu X, Fan X, Wang Y. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med 2024; 5:101568. [PMID: 38754419 DOI: 10.1016/j.xcrm.2024.101568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/27/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024]
Abstract
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
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Affiliation(s)
- Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| | - Shujing Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Kaiyu Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China; Jinhua Institute of Zhejiang University, Jinhua 321299, China; Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
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2
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Raafat SN, El Wahed SA, Badawi NM, Saber MM, Abdollah MR. Enhancing the anticancer potential of metformin: fabrication of efficient nanospanlastics, in vitro cytotoxic studies on HEP-2 cells and reactome enhanced pathway analysis. Int J Pharm X 2023; 6:100215. [PMID: 38024451 PMCID: PMC10630776 DOI: 10.1016/j.ijpx.2023.100215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 10/17/2023] [Accepted: 10/21/2023] [Indexed: 12/01/2023] Open
Abstract
Metformin (MET), an oral antidiabetic drug, was reported to possess promising anticancer effects. We hypothesized that MET encapsulation in unique nanospanlastics would enhance its anticancer potential against HEP-2 cells. Our results showed the successful fabrication of Nano-MET spanlastics (d = 232.10 ± 0.20 nm; PDI = 0.25 ± 0.11; zeta potential = (-) 44.50 ± 0.96; drug content = 99.90 ± 0.11 and entrapment efficiency = 88.01 ± 2.50%). MTT assay revealed the enhanced Nano-MET cytotoxicity over MET with a calculated IC50 of 50 μg/mL and > 500 μg/mL, respectively. Annexin V/PI apoptosis assay showed that Nano-MET significantly decreased the percentage of live cells from 95.49 to 93.70 compared to MET and increased the percentage of cells arrested in the G0/G1 phase by 8.38%. Moreover, Nano-MET downregulated BCL-2 and upregulated BAX protein levels by 1.57 and 1.88 folds, respectively. RT-qPCR revealed that Nano-MET caused a significant 13.75, 4.15, and 2.23-fold increase in caspase-3, -8, and - 9 levels as well as a 100 and 43.47-fold decrease in cyclin D1 and mTOR levels, respectively. The proliferation marker Ki67 immunofluorescent staining revealed a 3-fold decrease in positive cells in Nano-MET compared to the control. Utilizing the combined Pathway-Enrichment Analysis (PEA) and Reactome analysis indicated high enrichment of certain pathways including nucleotides metabolism, Nudix-type hydrolase enzymes, carbon dioxide hydration, hemostasis, and the innate immune system. In summary, our results confirm MET cytotoxicity enhancement by its encapsulation in nanospanlastics. We also highlight, using PEA, that MET can modulate multiple pathways implicated in carcinogenesis.
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Affiliation(s)
- Shereen Nader Raafat
- Department of Pharmacology, Faculty of Dentistry, The British University in Egypt, Cairo, Egypt
- Stem Cells and Tissue Culture Hub (CIDS), Faculty of Dentistry, The British University in Egypt, Cairo, Egypt
| | - Sara Abd El Wahed
- Department of Oral Pathology, Faculty of Dentistry, The British University in Egypt, Cairo, Egypt
| | - Noha M. Badawi
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt
- Center for Drug Research and Development (CDRD), Faculty of Pharmacy, The British University in Egypt, El Sherouk City, Egypt
| | - Mona M. Saber
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Cairo University, Giza, Egypt
| | - Maha R.A. Abdollah
- Center for Drug Research and Development (CDRD), Faculty of Pharmacy, The British University in Egypt, El Sherouk City, Egypt
- Department of Pharmacology, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt
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3
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Brunson T, Sanati N, Matthews L, Haw R, Beavers D, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminating Dark Proteins using Reactome Pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543335. [PMID: 37333417 PMCID: PMC10274615 DOI: 10.1101/2023.06.05.543335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.
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Affiliation(s)
| | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Patrick Conley
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A1, Canada
| | | | - Guanming Wu
- Oregon Health & Science University, Portland, OR 97239, USA
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4
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Rothfels K, Milacic M, Matthews L, Haw R, Sevilla C, Gillespie M, Stephan R, Gong C, Ragueneau E, May B, Shamovsky V, Wright A, Weiser J, Beavers D, Conley P, Tiwari K, Jassal B, Griss J, Senff-Ribeiro A, Brunson T, Petryszak R, Hermjakob H, D'Eustachio P, Wu G, Stein L. Using the Reactome Database. Curr Protoc 2023; 3:e722. [PMID: 37053306 PMCID: PMC11184634 DOI: 10.1002/cpz1.722] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.
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Affiliation(s)
- Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Marija Milacic
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- College of Pharmacy and Health Sciences, St. John's University, Queens, New York
| | - Ralf Stephan
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bruce May
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Adam Wright
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Joel Weiser
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | | | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Andrea Senff-Ribeiro
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Universidade Federal do Paraná, Curitiba, Brazil
| | | | - Robert Petryszak
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Oregon Health and Science University, Portland, Oregon
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Guanming Wu
- Oregon Health and Science University, Portland, Oregon
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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5
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Pavel A, Saarimäki LA, Möbus L, Federico A, Serra A, Greco D. The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design. Comput Struct Biotechnol J 2022; 20:4837-4849. [PMID: 36147662 PMCID: PMC9464643 DOI: 10.1016/j.csbj.2022.08.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/20/2022] Open
Abstract
Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.
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Affiliation(s)
- Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Laura A Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Lena Möbus
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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6
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Bottomly D, Long N, Schultz AR, Kurtz SE, Tognon CE, Johnson K, Abel M, Agarwal A, Avaylon S, Benton E, Blucher A, Borate U, Braun TP, Brown J, Bryant J, Burke R, Carlos A, Chang BH, Cho HJ, Christy S, Coblentz C, Cohen AM, d'Almeida A, Cook R, Danilov A, Dao KHT, Degnin M, Dibb J, Eide CA, English I, Hagler S, Harrelson H, Henson R, Ho H, Joshi SK, Junio B, Kaempf A, Kosaka Y, Laderas T, Lawhead M, Lee H, Leonard JT, Lin C, Lind EF, Liu SQ, Lo P, Loriaux MM, Luty S, Maxson JE, Macey T, Martinez J, Minnier J, Monteblanco A, Mori M, Morrow Q, Nelson D, Ramsdill J, Rofelty A, Rogers A, Romine KA, Ryabinin P, Saultz JN, Sampson DA, Savage SL, Schuff R, Searles R, Smith RL, Spurgeon SE, Sweeney T, Swords RT, Thapa A, Thiel-Klare K, Traer E, Wagner J, Wilmot B, Wolf J, Wu G, Yates A, Zhang H, Cogle CR, Collins RH, Deininger MW, Hourigan CS, Jordan CT, Lin TL, Martinez ME, Pallapati RR, Pollyea DA, Pomicter AD, Watts JM, Weir SJ, Druker BJ, McWeeney SK, Tyner JW. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell 2022; 40:850-864.e9. [PMID: 35868306 PMCID: PMC9378589 DOI: 10.1016/j.ccell.2022.07.002] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 12/17/2022]
Abstract
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.
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Affiliation(s)
- Daniel Bottomly
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Nicola Long
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Anna Reister Schultz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen E Kurtz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kara Johnson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Melissa Abel
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sammantha Avaylon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Erik Benton
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aurora Blucher
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Uma Borate
- Division of Hematology, Department of Internal Medicine, James Cancer Center, Ohio State University, Columbus, OH 43210, USA
| | - Theodore P Braun
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jordana Brown
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jade Bryant
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Russell Burke
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amy Carlos
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Bill H Chang
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology and Oncology, Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hyun Jun Cho
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen Christy
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Cody Coblentz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aaron M Cohen
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amanda d'Almeida
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rachel Cook
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexey Danilov
- Department of Hematology and Hematopoietic Stem Cell Transplant, City of Hope National Medical Center, Duarte, CA 91010, USA
| | | | - Michie Degnin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - James Dibb
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher A Eide
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Isabel English
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stuart Hagler
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Heath Harrelson
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rachel Henson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hibery Ho
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Brian Junio
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andy Kaempf
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR 97239, USA
| | - Yoko Kosaka
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Matt Lawhead
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hyunjung Lee
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica T Leonard
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Chenwei Lin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Evan F Lind
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Selina Qiuying Liu
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Pierrette Lo
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Marc M Loriaux
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Pathology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Samuel Luty
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julia E Maxson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tara Macey
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jacqueline Martinez
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica Minnier
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR 97239, USA; OHSU-PSU School of Public Health, VA Portland Health Care System, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andrea Monteblanco
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Motomi Mori
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Quinlan Morrow
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Dylan Nelson
- High-Throughput Screening Services Laboratory, Oregon State University, Corvallis, OR 97331, USA
| | - Justin Ramsdill
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Angela Rofelty
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexandra Rogers
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kyle A Romine
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Peter Ryabinin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jennifer N Saultz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - David A Sampson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Samantha L Savage
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robert Searles
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rebecca L Smith
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen E Spurgeon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tyler Sweeney
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ronan T Swords
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aashis Thapa
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Karina Thiel-Klare
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jake Wagner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Beth Wilmot
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joelle Wolf
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amy Yates
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Haijiao Zhang
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher R Cogle
- Department of Medicine, Division of Hematology and Oncology, University of Florida, Gainesville, FL 32610, USA
| | - Robert H Collins
- Department of Internal Medicine/ Hematology Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390-8565, USA
| | - Michael W Deininger
- Division of Hematology & Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84112, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher S Hourigan
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20814-1476, USA
| | - Craig T Jordan
- Division of Hematology, University of Colorado, Denver, CO 80045, USA
| | - Tara L Lin
- Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas, Kansas City, KS 66205, USA
| | - Micaela E Martinez
- Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Rachel R Pallapati
- Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Daniel A Pollyea
- Division of Hematology, University of Colorado, Denver, CO 80045, USA
| | - Anthony D Pomicter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Justin M Watts
- Division of Hematology, Department of Medicine, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Scott J Weir
- Department of Cancer Biology, Division of Medical Oncology, Department of Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA.
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA.
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA.
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7
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Fava VM, Bourgey M, Nawarathna PM, Orlova M, Cassart P, Vinh DC, Cheng MP, Bourque G, Schurr E, Langlais D. A systems biology approach identifies candidate drugs to reduce mortality in severely ill patients with COVID-19. SCIENCE ADVANCES 2022; 8:eabm2510. [PMID: 35648852 PMCID: PMC9159580 DOI: 10.1126/sciadv.abm2510] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Despite the availability of highly efficacious vaccines, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lacks effective drug treatment, which results in a high rate of mortality. To address this therapeutic shortcoming, we applied a systems biology approach to the study of patients hospitalized with severe COVID. We show that, at the time of hospital admission, patients who were equivalent on the clinical ordinal scale displayed significant differential monocyte epigenetic and transcriptomic attributes between those who would survive and those who would succumb to COVID-19. We identified messenger RNA metabolism, RNA splicing, and interferon signaling pathways as key host responses overactivated by patients who would not survive. Those pathways are prime drug targets to reduce mortality of critically ill patients with COVID-19, leading us to identify tacrolimus, zotatifin, and nintedanib as three strong candidates for treatment of severely ill patients at the time of hospital admission.
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Affiliation(s)
- Vinicius M. Fava
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- McGill International TB Centre, McGill University, Montreal, QC, Canada
| | - Mathieu Bourgey
- Canadian Centre for Computational Genomics, McGill University, Montréal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
| | - Pubudu M. Nawarathna
- Canadian Centre for Computational Genomics, McGill University, Montréal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
| | - Marianna Orlova
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- McGill International TB Centre, McGill University, Montreal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
| | - Pauline Cassart
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- McGill International TB Centre, McGill University, Montreal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
| | - Donald C. Vinh
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Division of Infectious Diseases and Division of Medical Microbiology, McGill University Health Center, McGill University, Montreal, QC, Canada
| | - Matthew Pellan Cheng
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Division of Infectious Diseases and Division of Medical Microbiology, McGill University Health Center, McGill University, Montreal, QC, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Guillaume Bourque
- Canadian Centre for Computational Genomics, McGill University, Montréal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- McGill University Research Centre on Complex Traits, Montreal, QC, Canada
| | - Erwin Schurr
- Infectious Diseases and Immunity in Global Health Program, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- McGill International TB Centre, McGill University, Montreal, QC, Canada
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- McGill University Research Centre on Complex Traits, Montreal, QC, Canada
- Corresponding author. (D.L.); (E.S.)
| | - David Langlais
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
- McGill University Research Centre on Complex Traits, Montreal, QC, Canada
- Corresponding author. (D.L.); (E.S.)
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8
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Wright AJ, Orlic-Milacic M, Rothfels K, Weiser J, Trinh QM, Jassal B, Haw RA, Stein LD. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase. Database (Oxford) 2022; 2022:6555052. [PMID: 35348650 PMCID: PMC9216552 DOI: 10.1093/database/baac009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/04/2022] [Accepted: 02/15/2022] [Indexed: 11/14/2022]
Abstract
Abstract Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. Database URL www.reactome.org
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Affiliation(s)
- Adam J Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Marija Orlic-Milacic
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Quang M Trinh
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Robin A Haw
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Lincoln D Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, Room 4396, Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A1, Canada
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Exploring Toxins for Hunting SARS-CoV-2 Main Protease Inhibitors: Molecular Docking, Molecular Dynamics, Pharmacokinetic Properties, and Reactome Study. Pharmaceuticals (Basel) 2022; 15:ph15020153. [PMID: 35215266 PMCID: PMC8875976 DOI: 10.3390/ph15020153] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/20/2022] [Accepted: 01/23/2022] [Indexed: 12/13/2022] Open
Abstract
The main protease (Mpro) is a potential druggable target in SARS-CoV-2 replication. Herein, an in silico study was conducted to mine for Mpro inhibitors from toxin sources. A toxin and toxin-target database (T3DB) was virtually screened for inhibitor activity towards the Mpro enzyme utilizing molecular docking calculations. Promising toxins were subsequently characterized using a combination of molecular dynamics (MD) simulations and molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations. According to the MM-GBSA binding energies over 200 ns MD simulations, three toxins—namely philanthotoxin (T3D2489), azaspiracid (T3D2672), and taziprinone (T3D2378)—demonstrated higher binding affinities against SARS-CoV-2 Mpro than the co-crystalized inhibitor XF7 with MM-GBSA binding energies of −58.9, −55.9, −50.1, and −43.7 kcal/mol, respectively. The molecular network analyses showed that philanthotoxin provides a ligand lead using the STRING database, which includes the biochemical top 20 signaling genes CTSB, CTSL, and CTSK. Ultimately, pathway enrichment analysis (PEA) and Reactome mining results revealed that philanthotoxin could prevent severe lung injury in COVID-19 patients through the remodeling of interleukins (IL-4 and IL-13) and the matrix metalloproteinases (MMPs). These findings have identified that philanthotoxin—a venom of the Egyptian solitary wasp—holds promise as a potential Mpro inhibitor and warrants further in vitro/in vivo validation.
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10
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/27/2021] [Indexed: 07/26/2023] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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11
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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12
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2022] [Indexed: 07/26/2023] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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13
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Wong RLY, Wong MRE, Kuick CH, Saffari SE, Wong MK, Tan SH, Merchant K, Chang KTE, Thangavelu M, Periyasamy G, Chen ZX, Iyer P, Tan EEK, Soh SY, Iyer NG, Fan Q, Loh AHP. Integrated Genomic Profiling and Drug Screening of Patient-Derived Cultures Identifies Individualized Copy Number-Dependent Susceptibilities Involving PI3K Pathway and 17q Genes in Neuroblastoma. Front Oncol 2021; 11:709525. [PMID: 34722256 PMCID: PMC8551924 DOI: 10.3389/fonc.2021.709525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/28/2021] [Indexed: 11/18/2022] Open
Abstract
Neuroblastoma is the commonest extracranial pediatric malignancy. With few recurrent single nucleotide variations (SNVs), mutation-based precision oncology approaches have limited utility, but its frequent and heterogenous copy number variations (CNVs) could represent genomic dependencies that may be exploited for personalized therapy. Patient-derived cell culture (PDC) models can facilitate rapid testing of multiple agents to determine such individualized drug-responses. Thus, to study the relationship between individual genomic aberrations and therapeutic susceptibilities, we integrated comprehensive genomic profiling of neuroblastoma tumors with drug screening of corresponding PDCs against 418 targeted inhibitors. We quantified the strength of association between copy number and cytotoxicity, and validated significantly correlated gene-drug pairs in public data and using machine learning models. Somatic mutations were infrequent (3.1 per case), but copy number losses in 1p (31%) and 11q (38%), and gains in 17q (69%) were prevalent. Critically, in-vitro cytotoxicity significantly correlated only with CNVs, but not SNVs. Among 1278 significantly correlated gene-drug pairs, copy number of GNA13 and DNA damage response genes CBL, DNMT3A, and PPM1D were most significantly correlated with cytotoxicity; the drugs most commonly associated with these genes were PI3K/mTOR inhibitor PIK-75, and CDK inhibitors P276-00, SNS-032, AT7519, flavopiridol and dinaciclib. Predictive Markov random field models constructed from CNVs alone recapitulated the true z-score-weighted associations, with the strongest gene-drug functional interactions in subnetworks involving PI3K and JAK-STAT pathways. Together, our data defined individualized dose-dependent relationships between copy number gains of PI3K and STAT family genes particularly on 17q and susceptibility to PI3K and cell cycle agents in neuroblastoma. Integration of genomic profiling and drug screening of patient-derived models of neuroblastoma can quantitatively define copy number-dependent sensitivities to targeted inhibitors, which can guide personalized therapy for such mutationally quiet cancers.
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Affiliation(s)
| | - Megan R E Wong
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore
| | - Chik Hong Kuick
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Seyed Ehsan Saffari
- Centre for Quantitative Medicine, Duke NUS Medical School, Singapore, Singapore
| | - Meng Kang Wong
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore
| | - Sheng Hui Tan
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore
| | - Khurshid Merchant
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Kenneth T E Chang
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Matan Thangavelu
- Centre for High Throughput Phenomics (CHiP-GIS), Genome Institute of Singapore, Singapore, Singapore
| | - Giridharan Periyasamy
- Centre for High Throughput Phenomics (CHiP-GIS), Genome Institute of Singapore, Singapore, Singapore
| | - Zhi Xiong Chen
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Physiology, National University of Singapore, Singapore, Singapore
| | - Prasad Iyer
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Paediatric Subspecialties Haematology Oncology Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - Enrica E K Tan
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Paediatric Subspecialties Haematology Oncology Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - Shui Yen Soh
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Paediatric Subspecialties Haematology Oncology Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - N Gopalakrishna Iyer
- Duke NUS Medical School, Singapore, Singapore.,Division of Medical Sciences, National Cancer Centre, Singapore, Singapore
| | - Qiao Fan
- Centre for Quantitative Medicine, Duke NUS Medical School, Singapore, Singapore
| | - Amos H P Loh
- Duke NUS Medical School, Singapore, Singapore.,VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.,Department of Paediatric Surgery, KK Women's and Children's Hospital, Singapore, Singapore
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14
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Mohamed TA, Elshamy AI, Ibrahim MAA, Atia MAM, Ahmed RF, Ali SK, Mahdy KA, Alshammari SO, Al-Abd AM, Moustafa MF, Farrag ARH, Hegazy MEF. Gastroprotection against Rat Ulcers by Nephthea Sterol Derivative. Biomolecules 2021; 11:1247. [PMID: 34439913 PMCID: PMC8393318 DOI: 10.3390/biom11081247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/16/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Different species belonging to the genus Nephthea (Acyonaceae) are a rich resource for bioactive secondary metabolites. The literature reveals that the gastroprotective effects of marine secondary metabolites have not been comprehensively studied in vivo. Hence, the present investigation aimed to examine and determine the anti-ulcer activity of 4α,24-dimethyl-5α-cholest-8β,18-dihydroxy,22E-en-3β-ol (ST-1) isolated from samples of a Nephthea species. This in vivo study was supported by in silico molecular docking and protein-protein interaction techniques. Oral administration of ST-1 reduced rat stomach ulcers with a concurrent increase in gastric mucosa. Molecular docking calculations against the H+/K+-ATPase transporter showed a higher binding affinity of ST-1, with a docking score value of -9.9 kcal/mol and a pKi value of 59.7 nM, compared to ranitidine (a commercial proton pump inhibitor, which gave values of -6.2 kcal/mol and 27.9 µM, respectively). The combined PEA-reactome analysis results revealed promising evidence of ST-1 potency as an anti-ulcer compound through significant modulation of the gene set controlling the PI3K signaling pathway, which subsequently plays a crucial role in signaling regarding epithelialization and tissue regeneration, tissue repairing and tissue remodeling. These results indicate a probable protective role for ST-1 against ethanol-induced gastric ulcers.
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Affiliation(s)
- Tarik A. Mohamed
- National Research Centre, Chemistry of Medicinal Plants Department, 33 El−Bohouth St., Dokki, Giza 12622, Egypt; (T.A.M.); (S.K.A.); (M.-E.F.H.)
| | - Abdelsamed I. Elshamy
- National Research Centre, Chemistry of Natural Compounds Department, Dokki, Giza 12622, Egypt; (A.I.E.); (R.F.A.)
| | - Mahmoud A. A. Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt;
| | - Mohamed A. M. Atia
- Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department, Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza 12619, Egypt;
| | - Rania F. Ahmed
- National Research Centre, Chemistry of Natural Compounds Department, Dokki, Giza 12622, Egypt; (A.I.E.); (R.F.A.)
| | - Sherin K. Ali
- National Research Centre, Chemistry of Medicinal Plants Department, 33 El−Bohouth St., Dokki, Giza 12622, Egypt; (T.A.M.); (S.K.A.); (M.-E.F.H.)
| | - Karam A. Mahdy
- National Research Centre, Medical Biochemistry Department, 33 El Bohouth St., Dokki, Giza 12622, Egypt;
| | - Shifaa O. Alshammari
- Department of Biology, College of Science, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi Arabia;
| | - Ahmed M. Al-Abd
- Department of Pharmaceutical Sciences, College of Pharmacy & Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman 4184, United Arab Emirates
- Pharmacology Department, Medical Division, National Research Centre, Cairo 12622, Egypt
| | - Mahmoud F. Moustafa
- Department of Biology, College of Science, King Khalid University, Abha 9004, Saudi Arabia;
- Department of Botany & Microbiology, Faculty of Science, South Valley University, Qena 83523, Egypt
| | - Abdel Razik H. Farrag
- National Research Centre, Pathology Department, 33 El Bohouth St., Dokki, Giza 12622, Egypt;
| | - Mohamed-Elamir F. Hegazy
- National Research Centre, Chemistry of Medicinal Plants Department, 33 El−Bohouth St., Dokki, Giza 12622, Egypt; (T.A.M.); (S.K.A.); (M.-E.F.H.)
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15
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Ibrahim MAA, Abdelrahman AHM, Atia MAM, Mohamed TA, Moustafa MF, Hakami AR, Khalifa SAM, Alhumaydhi FA, Alrumaihi F, Abidi SH, Allemailem KS, Efferth T, Soliman ME, Paré PW, El-Seedi HR, Hegazy MEF. Blue Biotechnology: Computational Screening of Sarcophyton Cembranoid Diterpenes for SARS-CoV-2 Main Protease Inhibition. Mar Drugs 2021; 19:391. [PMID: 34356816 PMCID: PMC8308023 DOI: 10.3390/md19070391] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 12/29/2022] Open
Abstract
The coronavirus pandemic has affected more than 150 million people, while over 3.25 million people have died from the coronavirus disease 2019 (COVID-19). As there are no established therapies for COVID-19 treatment, drugs that inhibit viral replication are a promising target; specifically, the main protease (Mpro) that process CoV-encoded polyproteins serves as an Achilles heel for assembly of replication-transcription machinery as well as down-stream viral replication. In the search for potential antiviral drugs that target Mpro, a series of cembranoid diterpenes from the biologically active soft-coral genus Sarcophyton have been examined as SARS-CoV-2 Mpro inhibitors. Over 360 metabolites from the genus were screened using molecular docking calculations. Promising diterpenes were further characterized by molecular dynamics (MD) simulations based on molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. According to in silico calculations, five cembranoid diterpenes manifested adequate binding affinities as Mpro inhibitors with ΔGbinding < -33.0 kcal/mol. Binding energy and structural analyses of the most potent Sarcophyton inhibitor, bislatumlide A (340), was compared to darunavir, an HIV protease inhibitor that has been recently subjected to clinical-trial as an anti-COVID-19 drug. In silico analysis indicates that 340 has a higher binding affinity against Mpro than darunavir with ΔGbinding values of -43.8 and -34.8 kcal/mol, respectively throughout 100 ns MD simulations. Drug-likeness calculations revealed robust bioavailability and protein-protein interactions were identified for 340; biochemical signaling genes included ACE, MAPK14 and ESR1 as identified based on a STRING database. Pathway enrichment analysis combined with reactome mining revealed that 340 has the capability to re-modulate the p38 MAPK pathway hijacked by SARS-CoV-2 and antagonize injurious effects. These findings justify further in vivo and in vitro testing of 340 as an antiviral agent against SARS-CoV-2.
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Affiliation(s)
- Mahmoud A. A. Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt;
| | - Alaa H. M. Abdelrahman
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt;
| | - Mohamed A. M. Atia
- Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department, Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza 12619, Egypt;
| | - Tarik A. Mohamed
- Chemistry of Medicinal Plants Department, National Research Centre, 33 El-Bohouth St., Dokki, Giza 12622, Egypt;
| | - Mahmoud F. Moustafa
- Department of Biology, College of Science, King Khalid University, Abha 9004, Saudi Arabia; or
- Department of Botany & Microbiology, Faculty of Science, South Valley University, Qena 83523, Egypt
| | - Abdulrahim R. Hakami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61481, Saudi Arabia;
| | - Shaden A. M. Khalifa
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, S-106 91 Stockholm, Sweden;
| | - Fahad A. Alhumaydhi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.A.); (F.A.); (K.S.A.)
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.A.); (F.A.); (K.S.A.)
| | - Syed Hani Abidi
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi 74800, Pakistan;
| | - Khaled S. Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.A.); (F.A.); (K.S.A.)
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
| | - Mahmoud E. Soliman
- Molecular Modelling and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4000, South Africa;
| | - Paul W. Paré
- Department of Chemistry & Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
| | - Hesham R. El-Seedi
- Department of Chemistry, Faculty of Science, El-Menoufia University, Shebin El-Kom 32512, Egypt
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, Uppsala University, Biomedical Centre, Box 574, 751 23 Uppsala, Sweden
| | - Mohamed-Elamir F. Hegazy
- Chemistry of Medicinal Plants Department, National Research Centre, 33 El-Bohouth St., Dokki, Giza 12622, Egypt;
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
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16
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Elshamy AI, Mohamed TA, Ibrahim MAA, Atia MAM, Yoneyama T, Umeyama A, Hegazy MEF. Two novel oxetane containing lignans and a new megastigmane from Paronychia arabica and in silico analysis of them as prospective SARS-CoV-2 inhibitors. RSC Adv 2021; 11:20151-20163. [PMID: 35479905 PMCID: PMC9033657 DOI: 10.1039/d1ra02486h] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/15/2021] [Indexed: 12/22/2022] Open
Abstract
The chemical characterization of the extract of the aerial parts of Paronychia arabica afforded two oxetane containing lignans, paronychiarabicine A (1) and B (2), and one new megastigmane, paronychiarabicastigmane A (3), alongside a known lignan (4), eight known phenolic compounds (5–12), one known elemene sesquiterpene (13) and one steroid glycoside (14). The chemical structures of the isolated compounds were constructed based upon the HRMS, 1D, and 2D-NMR results. The absolute configurations were established via NOESY experiments as well as experimental and TDDFT-calculated electronic circular dichroism (ECD). Utilizing molecular docking, the binding scores and modes of compounds 1–3 towards the SARS-CoV-2 main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp) were revealed. Compound 3 exhibited a promising docking score (−9.8 kcal mol−1) against SARS-CoV-2 Mpro by forming seven hydrogen bonds inside the active site with the key amino acids. The reactome pathway enrichment analysis revealed a correlation between the inhibition of GSK3 and GSK3B genes (identified as the main targets of megastigmane treatment) and significant inhibition of SARS-CoV-1 viral replication in infected Vero E6 cells. Our results manifest a novel understanding of genes, proteins and corresponding pathways against SARS-CoV-2 infection and could facilitate the identification and characterization of novel therapeutic targets as treatments of SARS-CoV-2 infection. The hydromethanolic extract of Paronychia arabica aerial parts afforded two oxetane containing lignans, paronychiarabicine A (1) and B (2), and one new megastigmane, paronychiarabicastigmane A (3), alongside a known secondary metabolites (4–14).![]()
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Affiliation(s)
- Abdelsamed I Elshamy
- Faculty of Pharmaceutical Sciences, Tokushima Bunri University Yamashiro-cho Tokushima 770-8514 Japan.,Chemistry of Natural Compounds Department, National Research Centre Dokki Giza 12622 Egypt
| | - Tarik A Mohamed
- Chemistry of Medicinal Plants Department, National Research Centre 33 El-Bohouth St., Dokki Giza 12622 Egypt +20-233370931 +20-233371635
| | - Mahmoud A A Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University Minia 61519 Egypt
| | - Mohamed A M Atia
- Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department, Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC) Giza 12619 Egypt
| | - Tatsuro Yoneyama
- Faculty of Pharmaceutical Sciences, Tokushima Bunri University Yamashiro-cho Tokushima 770-8514 Japan
| | - Akemi Umeyama
- Faculty of Pharmaceutical Sciences, Tokushima Bunri University Yamashiro-cho Tokushima 770-8514 Japan
| | - Mohamed-Elamir F Hegazy
- Chemistry of Medicinal Plants Department, National Research Centre 33 El-Bohouth St., Dokki Giza 12622 Egypt +20-233370931 +20-233371635.,Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University Staudinger Weg 5 55128 Mainz Germany
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17
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Ibrahim MAA, Abdelrahman AHM, Mohamed TA, Atia MAM, Al-Hammady MAM, Abdeljawaad KAA, Elkady EM, Moustafa MF, Alrumaihi F, Allemailem KS, El-Seedi HR, Paré PW, Efferth T, Hegazy MEF. In Silico Mining of Terpenes from Red-Sea Invertebrates for SARS-CoV-2 Main Protease (M pro) Inhibitors. Molecules 2021; 26:2082. [PMID: 33916461 PMCID: PMC8038614 DOI: 10.3390/molecules26072082] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 02/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19 pandemic, which generated more than 1.82 million deaths in 2020 alone, in addition to 83.8 million infections. Currently, there is no antiviral medication to treat COVID-19. In the search for drug leads, marine-derived metabolites are reported here as prospective SARS-CoV-2 inhibitors. Two hundred and twenty-seven terpene natural products isolated from the biodiverse Red-Sea ecosystem were screened for inhibitor activity against the SARS-CoV-2 main protease (Mpro) using molecular docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area binding energy calculations. On the basis of in silico analyses, six terpenes demonstrated high potency as Mpro inhibitors with ΔGbinding ≤ -40.0 kcal/mol. The stability and binding affinity of the most potent metabolite, erylosides B, were compared to the human immunodeficiency virus protease inhibitor, lopinavir. Erylosides B showed greater binding affinity towards SARS-CoV-2 Mpro than lopinavir over 100 ns with ΔGbinding values of -51.9 vs. -33.6 kcal/mol, respectively. Protein-protein interactions indicate that erylosides B biochemical signaling shares gene components that mediate severe acute respiratory syndrome diseases, including the cytokine- and immune-signaling components BCL2L1, IL2, and PRKC. Pathway enrichment analysis and Boolean network modeling were performed towards a deep dissection and mining of the erylosides B target-function interactions. The current study identifies erylosides B as a promising anti-COVID-19 drug lead that warrants further in vitro and in vivo testing.
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Affiliation(s)
- Mahmoud A. A. Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt; (A.H.M.A.); (K.A.A.A.)
| | - Alaa H. M. Abdelrahman
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt; (A.H.M.A.); (K.A.A.A.)
| | - Tarik A. Mohamed
- Chemistry of Medicinal Plants Department, National Research Centre, 33 El-Bohouth St., Dokki, Giza 12622, Egypt;
| | - Mohamed A. M. Atia
- Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department, Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza 12619, Egypt;
| | | | - Khlood A. A. Abdeljawaad
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt; (A.H.M.A.); (K.A.A.A.)
| | - Eman M. Elkady
- National Institute of Oceanography & Fisheries, NIOF, Cairo 11516, Egypt; (M.A.M.A.-H.); (E.M.E.)
| | - Mahmoud F. Moustafa
- Department of Biology, College of Science, King Khalid University, Abha 9004, Saudi Arabia;
- Department of Botany & Microbiology, Faculty of Science, South Valley University, Qena 83523, Egypt
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (K.S.A.)
| | - Khaled S. Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (K.S.A.)
| | - Hesham R. El-Seedi
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, S-106 91 Stockholm, Sweden
- Department of Chemistry, Faculty of Science, El-Menoufia University, Shebin El-Kom 32512, Egypt
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Paul W. Paré
- Department of Chemistry & Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
| | - Mohamed-Elamir F. Hegazy
- Chemistry of Medicinal Plants Department, National Research Centre, 33 El-Bohouth St., Dokki, Giza 12622, Egypt;
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
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18
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MINERVA, A Platform for the Exploration of Disease Maps. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11685-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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19
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Biological characteristics of aging in human acute myeloid leukemia cells: the possible importance of aldehyde dehydrogenase, the cytoskeleton and altered transcriptional regulation. Aging (Albany NY) 2020; 12:24734-24777. [PMID: 33349623 PMCID: PMC7803495 DOI: 10.18632/aging.202361] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Patients with acute myeloid leukemia (AML) have a median age of 65-70 years at diagnosis. Elderly patients have more chemoresistant disease, and this is partly due to decreased frequencies of favorable and increased frequencies of adverse genetic abnormalities. However, aging-dependent differences may also contribute. We therefore compared AML cell proteomic and phosphoproteomic profiles for (i) elderly low-risk and younger low-risk patients with favorable genetic abnormalities; and (ii) high-risk patients with adverse genetic abnormalities and a higher median age against all low-risk patients with lower median age. Elderly low-risk and younger low-risk patients showed mainly phosphoproteomic differences especially involving transcriptional regulators and cytoskeleton. When comparing high-risk and low-risk patients both proteomic and phosphoproteomic studies showed differences involving cytoskeleton and immunoregulation but also transcriptional regulation and cell division. The age-associated prognostic impact of cyclin-dependent kinases was dependent on the cellular context. The protein level of the adverse prognostic biomarker mitochondrial aldehyde dehydrogenase (ALDH2) showed a similar significant upregulation both in elderly low-risk and elderly high-risk patients. Our results suggest that molecular mechanisms associated with cellular aging influence chemoresistance of AML cells, and especially the cytoskeleton function may then influence cellular hallmarks of aging, e.g. mitosis, polarity, intracellular transport and adhesion.
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20
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Choonoo G, Blucher AS, Higgins S, Boardman M, Jeng S, Zheng C, Jacobs J, Anderson A, Chamberlin S, Evans N, Vigoda M, Cordier B, Tyner JW, Kulesz-Martin M, McWeeney SK, Laderas T. Illuminating biological pathways for drug targeting in head and neck squamous cell carcinoma. PLoS One 2019; 14:e0223639. [PMID: 31596908 PMCID: PMC6785123 DOI: 10.1371/journal.pone.0223639] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/25/2019] [Indexed: 11/18/2022] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either “light” or “dark” to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations (or copy number alterations) and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations (or copy number alterations) but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35–38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development.
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Affiliation(s)
- Gabrielle Choonoo
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Aurora S. Blucher
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, United States of America
- * E-mail:
| | - Samuel Higgins
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Mitzi Boardman
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Sophia Jeng
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Christina Zheng
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - James Jacobs
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
- Pediatric Hematology and Oncology, OHSU Doernbecher Children’s Hospital, Portland, Oregon, United States of America
| | - Ashley Anderson
- Department of Dermatology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Steven Chamberlin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Nathaniel Evans
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Myles Vigoda
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Dermatology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Benjamin Cordier
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Jeffrey W. Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Hematology and Medical Oncology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Molly Kulesz-Martin
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Dermatology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Shannon K. McWeeney
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Ted Laderas
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
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